File size: 366,879 Bytes
cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 546b9c6 cd5d5b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499 4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628 4629 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683 4684 4685 4686 4687 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697 4698 4699 4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713 4714 4715 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727 4728 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741 4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755 4756 4757 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769 4770 4771 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783 4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797 4798 4799 4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 4812 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853 4854 4855 4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867 4868 4869 4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881 4882 4883 4884 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 4896 4897 4898 4899 4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913 4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925 4926 4927 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939 4940 4941 4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 4952 4953 4954 4955 4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967 4968 4969 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 4980 4981 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 4994 4995 4996 4997 4998 4999 5000 5001 5002 5003 5004 5005 5006 5007 5008 5009 5010 5011 5012 5013 5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 5070 5071 5072 5073 5074 5075 5076 5077 | <title>copernicus-rag — architecture</title>
<style>
:root{
--bg:#FFFFFF; --ink:#16222C; --sub:#7A8791; --line:#E4E6E9;
--acc:#0E8A99; --accD:#0A6570; --accBg:#F0F7F8;
--warn:#B67F1E; --warnBg:#FBF4E4;
--mono:ui-monospace,"SF Mono",Menlo,Consolas,monospace;
--sans:system-ui,-apple-system,"Segoe UI",Roboto,sans-serif;
}
*{box-sizing:border-box}
body{margin:0;background:var(--bg);color:var(--ink);font-family:var(--sans)}
.wrap{max-width:940px;margin:0 auto;padding:44px 24px 64px;display:flex;flex-direction:column;align-items:center;gap:0}
.num{font-family:var(--mono);font-variant-numeric:tabular-nums}
header{width:100%;display:flex;justify-content:space-between;align-items:baseline;gap:12px;flex-wrap:wrap;margin-bottom:34px}
h1{font-family:var(--mono);font-size:19px;font-weight:700;margin:0}
.meta{font-family:var(--mono);font-size:11.5px;color:var(--sub)}
.lbl{font-family:var(--mono);font-size:10px;font-weight:700;letter-spacing:.16em;text-transform:uppercase;color:var(--sub)}
.arrow{color:#C3C9CE;font-size:15px;line-height:1;padding:10px 0;text-align:center}
/* stores */
.stores{display:flex;gap:10px;flex-wrap:wrap;justify-content:center}
.store{font-family:var(--mono);font-size:12.5px;font-weight:700;border:1.5px solid var(--ink);
border-radius:6px;padding:7px 16px;background:#fff}
.store span{color:var(--sub);font-weight:400;font-size:11px;margin-left:6px}
/* pipeline strip */
.pipe{font-family:var(--mono);font-size:11px;color:var(--sub);border:1px dashed var(--line);
border-radius:999px;padding:7px 16px;text-align:center}
.pipe b{color:var(--ink);font-weight:600}
/* tiers */
.tiers{display:grid;grid-template-columns:1fr 1.25fr 1fr;gap:14px;width:100%;align-items:stretch}
@media(max-width:720px){.tiers{grid-template-columns:1fr}}
.tier{border:1.5px solid var(--ink);border-radius:10px;padding:14px;display:flex;flex-direction:column;gap:8px;background:#fff}
.tier .tl{font-family:var(--mono);font-size:10.5px;font-weight:700;letter-spacing:.14em;color:var(--accD)}
.tier .q{font-size:12.5px;color:var(--sub);margin:0 0 2px}
.coll{display:flex;justify-content:space-between;align-items:baseline;gap:10px;background:var(--accBg);
border-radius:6px;padding:7px 11px}
.coll b{font-family:var(--mono);font-size:12px;font-weight:600}
.coll .num{font-size:12px;color:var(--accD);white-space:nowrap}
.rider{border:1px dashed var(--acc);background:#fff}
.build{background:var(--warnBg)}
.build .num{color:var(--warn)}
.bar{height:6px;border-radius:999px;background:#EDE4CE;overflow:hidden;margin-top:2px}
.bar i{display:block;height:100%;width:97.8%;background:var(--warn)}
.bnote{font-family:var(--mono);font-size:10.5px;color:var(--warn)}
/* server */
.server{width:100%;border:1.5px solid var(--ink);border-radius:10px;padding:14px 16px;
display:flex;flex-direction:column;gap:10px;background:#fff}
.server .head{display:flex;justify-content:space-between;align-items:baseline;gap:10px;flex-wrap:wrap}
.server .head b{font-family:var(--mono);font-size:13px}
.tools{display:flex;flex-wrap:wrap;gap:6px}
.t{font-family:var(--mono);font-size:11px;border:1px solid var(--line);border-radius:999px;
padding:3px 10px;color:var(--ink);background:#fff}
.t.l1{border-color:var(--acc);color:var(--accD)}
.t.l3{border-color:var(--warn);color:var(--warn)}
.agent{font-family:var(--mono);font-size:12.5px;font-weight:700;border:1.5px solid var(--ink);
border-radius:999px;padding:8px 22px;background:var(--ink);color:#fff}
footer{margin-top:34px;font-family:var(--mono);font-size:10.5px;color:var(--sub);text-align:center;line-height:1.7}
/* test section */
.qa-sec{width:100%;margin-top:44px;border-top:1.5px solid var(--ink);padding-top:26px;display:flex;flex-direction:column;gap:12px}
.qa-head{display:flex;justify-content:space-between;align-items:baseline;gap:10px;flex-wrap:wrap}
.qa-pass{font-family:var(--mono);font-size:12px;color:#2E7D46;font-weight:700}
.qa-note{font-size:12.5px;color:var(--sub);margin:0;max-width:66ch}
.qa-grp{display:flex;flex-direction:column;gap:4px;margin-top:10px;width:100%}
.qa-gt{display:flex;gap:10px;align-items:baseline;font-family:var(--mono);margin-bottom:3px}
.qa-gt b{font-size:11px;letter-spacing:.1em;text-transform:uppercase;color:var(--accD)}
.qa-gt span{font-size:11px;color:var(--sub)}
details.qa{border:1px solid var(--line);border-radius:8px;background:#fff}
details.qa[open]{border-color:var(--acc)}
details.qa summary{display:flex;gap:10px;align-items:baseline;padding:8px 12px;cursor:pointer;list-style:none;flex-wrap:wrap}
details.qa summary::-webkit-details-marker{display:none}
details.qa summary:focus-visible{outline:2px solid var(--acc);outline-offset:2px;border-radius:8px}
.qa-tag{font-family:var(--mono);font-size:10.5px;font-weight:700;color:var(--sub);min-width:28px}
.qa-tool{font-family:var(--mono);font-size:11px;color:var(--accD)}
.qa-q{font-size:12.5px;flex:1;min-width:220px}
.qa-x{font-family:var(--mono);font-size:10.5px;color:var(--warn)}
.qa-dt{font-family:var(--mono);font-size:10.5px;color:var(--sub)}
.qa-ok{font-family:var(--mono);font-size:10px;font-weight:700;color:#2E7D46;background:#EAF5EC;border-radius:999px;padding:2px 8px}
details.qa pre{margin:0;border-top:1px dashed var(--line);padding:10px 12px;font-family:var(--mono);font-size:10.5px;line-height:1.55;white-space:pre-wrap;word-break:break-word;background:var(--accBg);border-radius:0 0 8px 8px;max-height:340px;overflow:auto}
.bnote.done{color:#2E7D46}
</style>
<div class="wrap">
<header>
<h1>copernicus-rag</h1>
<span class="meta">12 tools · 5 collections · hybrid dense+BM25 · 2026-07-23</span>
</header>
<div class="stores">
<div class="store">CMEMS<span>marine</span></div>
<div class="store">CDS<span>climate</span></div>
<div class="store">ADS<span>atmosphere</span></div>
<div class="store">EWDS<span>early-warning</span></div>
</div>
<div class="arrow">▼</div>
<div class="pipe">parse <b>MinerU·A100</b> → chunk → embed <b>gemini 768d</b> → <b>qdrant ×4</b></div>
<div class="arrow">▼</div>
<div class="tiers">
<div class="tier">
<span class="tl">L1 · DISCOVER</span>
<p class="q">which dataset?</p>
<div class="coll"><b>copernicus_docs</b><span class="num">1,418</span></div>
</div>
<div class="tier">
<span class="tl">L2 · ANALYZE</span>
<p class="q">what is it, how to read it?</p>
<div class="coll"><b>marine_docs</b><span class="num">29,249</span></div>
<div class="coll"><b>cds_docs</b><span class="num">23,341</span></div>
<div class="coll"><b>eqc_qa</b><span class="num">1,274</span></div>
<div class="coll"><b>metadata</b><span class="num">1,436</span></div>
<div class="coll rider"><b>notebooks ⤳ rider</b><span class="num">194</span></div>
</div>
<div class="tier">
<span class="tl">L3 · METHOD</span>
<p class="q">how is it used in science?</p>
<div class="coll"><b>publications</b><span class="num">430,066</span></div>
<span class="bnote done">✓ done · 12,411 papers · 31,190 links relinked</span>
</div>
</div>
<div class="arrow">▼</div>
<div class="server">
<div class="head"><b>MCP server · rag_server.py</b><span class="meta">RRF fusion · BM25 fallback · opt. rerank</span></div>
<div class="tools">
<span class="t l1">search_datasets</span>
<span class="t">get_dataset_docs</span>
<span class="t">search_docs</span>
<span class="t">search_deep_docs</span>
<span class="t">list_dataset_documents</span>
<span class="t">read_document</span>
<span class="t">dataset_metadata</span>
<span class="t">get_eqc_quality_report</span>
<span class="t">get_dataset_code</span>
<span class="t l3">search_publications</span>
<span class="t l3">get_dataset_publications</span>
<span class="t l3">read_publication</span>
</div>
</div>
<div class="arrow">▼</div>
<div class="agent">agent</div>
<div class="qa-sec">
<div class="qa-head"><span class="lbl">test · 50 queries</span><span class="qa-pass num">50/50 pass · 2026-07-23</span></div>
<p class="qa-note">Every row is a real MCP tool call (direct import of the same functions the server exposes). Click a row to expand the complete raw server response — untruncated.</p>
<div class="qa-grp"><div class="qa-gt"><b>L1 · discover</b><span>search_datasets — which dataset?</span></div>
<details class="qa"><summary><span class="qa-tag num">A1</span><span class="qa-tool">search_datasets</span><span class="qa-q">global daily gap-free sea surface temperature</span><span class="qa-dt num">6.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "global daily gap-free sea surface temperature",
"store": "ALL",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CMEMS",
"dataset_id": "METOFFICE-GLO-SST-L4-REP-OBS-SST",
"product_id": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011",
"title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed",
"description": "Dataset: \"Global SST & Sea Ice Analysis, L4 OSTIA, 0.05 deg daily (METOFFICE-GLO-SST-L4-REP-OBS-SST-V2)\" [METOFFICE-GLO-SST-L4-REP-OBS-SST]\nProduct: Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed [SST_GLO_SST_L4_REP_OBSERVATIONS_010_011]\nCoverage: global | 1981-10 to 2025-12\nVariables: analysed_sst; analysis_error; mask; sea_ice_fraction\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; sea-surface temperature fields and SST gradients; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nThe OSTIA (Worsfold et al. 2024) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00168\n\n**References:**\n\n* Worsfold, M.;...",
"has_eqc_docs": true,
"notebooks": [],
"score": 1.0
},
{
"store": "CMEMS",
"dataset_id": "C3S-GLO-SST-L4-REP-OBS-SST",
"product_id": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024",
"title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses",
"description": "Dataset: \"Global Sea and Ice Surface Temperature, L4, 5km daily reprocessed SST and IST\" [C3S-GLO-SST-L4-REP-OBS-SST]\nProduct: ESA SST CCI and C3S reprocessed sea surface temperature analyses [SST_GLO_SST_L4_REP_OBSERVATIONS_010_024]\nCoverage: global | 1982-01 to 2024-12\nVariables: analysed_sst; analysed_st; analysis_error_sst; analysis_error_st; mask; sea_ice_fraction; sea_ice_fraction_flag\nService types: arco-geo-series; arco-time-series; wmts; original-files\nBest for: data assimilation inputs; ground-truth for model assessment; sea-surface temperature fields and SST gradients; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nThe C3S global Sea Surface and Sea Ice Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth and IST skin at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the ESA SST_cci v3.0 L3U data from (A)ATSRs, SLSTR and AVHRR, L2P data from the AMSRE and AMSR2 Passive Microwave Instruments (Embury et al., 2024) and L2P data from the AASTI and C3S IST CDR/ICDR v.1. The C3S level 4 SST/IST analyses were produced by running the DMI Optimal...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.41025641025641024
},
{
"store": "CMEMS",
"dataset_id": "SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d",
"product_id": "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004",
"title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis",
"description": "Dataset: \"Mediterranean SST Anomaly, L4, 1km daily (SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d)\" [SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d]\nProduct: Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis [SST_MED_SST_L4_NRT_OBSERVATIONS_010_004]\nCoverage: lon -18.12 to 36.25, lat 30.25 to 46.00 | 2008-01 to present\nVariables: sst_anomaly\nService types: wmts; original-files; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; sea-surface temperature fields and SST gradients; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nThis product provides daily (nighttime), gap-free (Level-4, L4) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Mediterranean Sea, at high (HR, 1/16°) and ultra-high (UHR, 1/100°) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values (centered at 00:00 UTC) and is produced by the Italian National Research Council – Institute of Marine Sciences (CNR-ISMAR).\nL4 maps are generated by...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.3333333333333333
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A2</span><span class="qa-tool">search_datasets</span><span class="qa-q">river discharge forecast for European rivers</span><span class="qa-dt num">0.6s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "river discharge forecast for European rivers",
"store": "ALL",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_id": "sis-hydrology-variables-derived-seasonal-forecast",
"product_id": "sis-hydrology-variables-derived-seasonal-forecast",
"title": "Multi-model seasonal forecasts of river discharge for Europe from January 2021 to present",
"description": "Dataset: \"Multi-model seasonal forecasts of river discharge for Europe from January 2021 to present\" [sis-hydrology-variables-derived-seasonal-forecast]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Europe; Product type: Seasonal forecasts; Variable domain: Land (hydrology); Temporal coverage: Present; Provider: Copernicus C3S; Temporal coverage: Future; Sector: Water management\nCoverage: bbox -22.0,27.0 → 45.0,72.0 | time 2020-12-01 → 2026-05-01\nVariables/options: brier_skill_score_above_normal_conditions; brier_skill_score_below_normal_conditions; continuous_ranked_probability_skill_score; fair_ranked_probability_skill_score; reference_river_discharge_lower_tercile; reference_river_discharge_upper_tercile; river_discharge\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.52f45864\nUpdate frequency: Not planned\n---\nThis dataset provides hydrological seasonal forecasts of monthly mean river discharge across Europe. Two hydrological model ensembles are provided. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and EFAS (LISFLOOD) hydrological models at a 5km gridded resolution.\nThe initialisation of the hydrological seasonal forecast uses the European Flood Awareness System (EFAS) daily gridded meteorological observations (EFAS-Meteo) up until the start of the forecast, and the subsequent integration of the meteorological seasonal forecasts using all 51 members of the ECMWF seasonal forecast system 5 (SEAS5) meteorological for",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.8333333333333333
},
{
"store": "EWDS",
"dataset_id": "cems-glofas-seasonal-reforecast",
"product_id": "cems-glofas-seasonal-reforecast",
"title": "Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System",
"description": "Dataset: \"Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System\" [cems-glofas-seasonal-reforecast]\nStore: EWDS (Early Warning Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Global; Provider: Copernicus CEMS; Variable domain: Land (hydrology); Product type: Seasonal reforecasts\nCoverage: bbox 0.0,-60.0 → 360.0,90.0 | time 1981-01-01 → 2023-07-01\nVariables/options: river_discharge_in_the_last_24_hours\nProvider: ECMWF\nLicense: other\nDOI: 10.24381/cds.0aa9b9dd\n---\nThis dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing the open-source LISFLOOD hydrological model with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are ini",
"has_eqc_docs": false,
"notebooks": [
{
"notebook_id": "cems-global-flood-meeting-session-2-part-2-best-practices",
"title": "Part 2 : Best practices to download GloFAS data from CEMS Early Warning Data Store",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_lines": 130,
"source_repo": "ecmwf-training/2025-glofas-annual-meeting"
}
],
"score": 0.5833333333333334
},
{
"store": "CDS",
"dataset_id": "sis-hydrology-variables-derived-seasonal-reforecast",
"product_id": "sis-hydrology-variables-derived-seasonal-reforecast",
"title": "Multi-model seasonal reforecasts of river discharge for Europe",
"description": "Dataset: \"Multi-model seasonal reforecasts of river discharge for Europe\" [sis-hydrology-variables-derived-seasonal-reforecast]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Europe; Product type: Seasonal forecasts; Variable domain: Land (hydrology); Provider: Copernicus C3S; Sector: Water management\nCoverage: bbox -22.0,27.0 → 45.0,72.0 | time 2021-01-01 → \nVariables/options: brier_skill_score_above_normal_conditions; brier_skill_score_below_normal_conditions; continuous_ranked_probability_skill_score; fair_ranked_probability_skill_score; reference_river_discharge_lower_tercile; reference_river_discharge_upper_tercile; river_discharge\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.13c18212\nUpdate frequency: Monthly\n---\nThis dataset provides hydrological seasonal reforecasts of monthly mean river discharge across Europe for the period 1993 to 2016. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and LISFLOOD-EFAS hydrological models at a 5km gridded resolution.\nThe initialisation of the hydrological seasonal forecast uses the European Flood Awareness System (EFAS) daily gridded meteorological observations (EFAS-Meteo) up until the start of the reforecast, and the subsequent integration of the meteorological seasonal reforecasts using all 25 members of the ECMWF seasonal forecast system 5 (SEAS5) meteorological reforecasts for the period January 1993 to December 2016. Seasonal reforecasts are produced to past dates but us",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.5
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A3</span><span class="qa-tool">search_datasets</span><span class="qa-q">satellite observations of atmospheric CO2 concentration</span><span class="qa-x">store=CDS</span><span class="qa-dt num">0.6s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "satellite observations of atmospheric CO2 concentration",
"store": "CDS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_id": "satellite-carbon-dioxide",
"product_id": "satellite-carbon-dioxide",
"title": "Carbon dioxide data from 2002 to present derived from satellite observations",
"description": "Dataset: \"Carbon dioxide data from 2002 to present derived from satellite observations\" [satellite-carbon-dioxide]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Variable domain: Atmosphere (composition); Spatial coverage: Global; Product type: Satellite observations\nCoverage: bbox 0.0,-70.0 → 360.0,70.0 | time 2002-10-01 → 2024-12-31\nVariables/options: co2; xco2\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.f74805c8\nUpdate frequency: Semiannual\n---\nThis dataset provides atmospheric CO₂ observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CO₂ mixing ratio of the entire atmosphere from the Earth’s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as “XCO2” products. (ii) Mid-tropospheric (MT) CO₂ mixing ratio products. These products are referred to as \"MTCO2\" products. These products can be used to monitor the spatial distribution and temporal evolution of CO₂. This is important as CO₂ is the most important anthropogenic so-called greenhouse gas driving global warming and associated climate change. CO₂ time series show an annual cycle primarily due to uptake and release of CO₂ by the terrestrial biosphere (but also due to ocean uptake and release) and a general increase due to burning of fossil fuels (oil, gas, coal). The annual increase of CO₂ shows significant inter-annual variations, due to variations of the (e.g., weather related) uptake and release of CO₂ by the natural CO₂ sources and sinks. The ",
"has_eqc_docs": false,
"notebooks": [
{
"notebook_id": "satellite_satellite-carbon-dioxide_consistency_q04",
"title": "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models.",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 226,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-carbon-dioxide_resolution_q03",
"title": "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 82,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-carbon-dioxide_trend-assessment_q01",
"title": "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 137,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-carbon-dioxide_uncertainty-quality-flags_q02",
"title": "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 180,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "ecv-notebooks__greenhouse-gases-level2-products",
"title": "How to access and use a satellite-derived GHG Level 2 data product using XCO2_EMMA as an example?",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 150,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 1.0
},
{
"store": "CDS",
"dataset_id": "satellite-methane",
"product_id": "satellite-methane",
"title": "Methane data from 2002 to present derived from satellite observations",
"description": "Dataset: \"Methane data from 2002 to present derived from satellite observations\" [satellite-methane]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Variable domain: Atmosphere (composition); Spatial coverage: Global; Product type: Satellite observations\nCoverage: bbox 0.0,-70.0 → 360.0,70.0 | time 2002-10-01 → 2024-12-31\nVariables/options: ch4; xch4\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.b25419f8\nUpdate frequency: Not planned\n---\nThis dataset provides atmospheric CH₄ observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CH₄ mixing ratio of the entire atmosphere from the Earth’s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as “XCH4” products. (ii) Mid-tropospheric (MT) CH₄ mixing ratio products. These products are referred to as \"MTCH4\" products. These products can be used to monitor the spatial distribution and temporal evolution of CH₄. This is important as CH₄ is after CO₂ the most important anthropogenic so-called greenhouse gas enhancing global warming and associated climate change. Methane has many natural and anthropogenic sources. CH₄ time series show an annual cycle, e.g., due seasonal wetland emissions. Currently atmospheric methane also shows and increasing trend. The dataset can be used for several climate related applications: It can be used in combination with appropriate modelling (e.g., inverse modelling) to obtain information on CH₄ emission sources, to assess the quality of ",
"has_eqc_docs": false,
"notebooks": [
{
"notebook_id": "satellite_satellite-methane_completeness_q03",
"title": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 225,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_completeness_q04",
"title": "Methane satellite observations completeness assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 155,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_extremes-detection_q02",
"title": "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 151,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.47619047619047616
},
{
"store": "CDS",
"dataset_id": "satellite-earth-radiation-budget",
"product_id": "satellite-earth-radiation-budget",
"title": "Earth's radiation budget from 1979 to present derived from satellite observations",
"description": "Dataset: \"Earth's radiation budget from 1979 to present derived from satellite observations\" [satellite-earth-radiation-budget]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Global; Temporal coverage: Present; Product type: Satellite observations; Variable domain: Atmosphere (upper air); Provider: Copernicus C3S; Provider: ESA CCI; Provider: EUMETSAT SAF\nCoverage: bbox -180.0,-90.0 → 180.0,90.0 | time 1979-01-01 → 2026-02-01\nVariables/options: all_variables; incoming_shortwave_radiation; outgoing_longwave_radiation; outgoing_shortwave_radiation; spectral_solar_irradiance; total_solar_irradiance\nProvider: ECMWF\nLicense: other\nDOI: 10.24381/cds.85a8f66e\nUpdate frequency: Monthly\n---\nThe Earth’s Radiation Budget (ERB) represents the overall balance between incoming and outgoing radiant energy at the Earth’s top-of-the-atmosphere. This Essential Climate Variable is the primary forcing of the climate system and is therefore a fundamental quantity to be monitored to understand the Earth’s climate and its variability. The Earth’s Radiation Budget comprises of the quantification of the incoming radiation from the Sun and the outgoing reflected shortwave and emitted longwave radiation. \nThe Earth’s Radiation Budget represents the balance between incoming, predominantly solar, radiation and outgoing radiation as either reflected solar radiation or thermal radiation emitted by the Earth system. The Earth is in a state of dynamic radiative balance, energy arriving from the Sun is balanced by outgoing radiation from the top-of-the-atmosphere. Chang",
"has_eqc_docs": false,
"notebooks": [
{
"notebook_id": "datasets_satellite_earth_radiation_budget_arco_access",
"title": "Access the analysis ready satellite earth radiation budget data",
"recipe_kinds": [
"analyze",
"download"
],
"n_code_lines": 58,
"source_repo": "ecmwf-training/dss-notebooks"
},
{
"notebook_id": "ecv-notebooks__erb-ceres-climatology",
"title": "Analysis of the Earth's Radiation Budget using the CERES product",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 419,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
},
{
"notebook_id": "ecv-notebooks__erb-clara-climatology",
"title": "Calculation of global climatology of Earth Radiation Budget from EUMETSAT's CM SAF CLARA-A3 dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 72,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
},
{
"notebook_id": "ecv-notebooks__erb-outgoing-longwave-radiation",
"title": "Calculation of global distribution and timeseries of Outgoing Longwave Radiation (OLR) using NOAA/NCEI HIRS data",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 115,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.367816091954023
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A4</span><span class="qa-tool">search_datasets</span><span class="qa-q">high resolution regional reanalysis Europe</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "high resolution regional reanalysis Europe",
"store": "ALL",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_id": "reanalysis-uerra-europe-complete",
"product_id": "reanalysis-uerra-europe-complete",
"title": "Complete UERRA regional reanalysis for Europe from 1961 to 2019",
"description": "Dataset: \"Complete UERRA regional reanalysis for Europe from 1961 to 2019\" [reanalysis-uerra-europe-complete]\nStore: CDS (Climate Data Store)\nKeywords: Product type: Reanalysis; Temporal coverage: Past; Spatial coverage: Europe; Variable domain: Atmosphere (upper air); Provider: Copernicus C3S\nCoverage: bbox -180,-90 → 180,90 | time 2018-10-18 → 2019-07-31\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.dd7c6d66\nUpdate frequency: Not planned\n---\nThe UERRA datasets contain reanalysis data of the atmosphere, the surface and near-surface as well as for the soil covering Europe. Essential climate variables are generated with the UERRA-HARMONIE and the MESCAN-SURFEX systems. UERRA-HARMONIE is a 3-dimensional variational data assimilation system, while MESCAN-SURFEX is a complementary surface analysis system. Using the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation and six-hourly air temperature and relative humidity at 2 meters above the model topography.\nThe land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data assimilation cycles. Using SURFEX offline allows taking full benefit of precipitation analysis and to use the more advanced physics options to better represent surface variables such as surface temperature and surface fluxes, and soil processes related to water and heat transfer in the soil and snow.\nIn general, reanalysis combines model",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.6111111111111112
},
{
"store": "CDS",
"dataset_id": "sis-tourism-snow-indicators",
"product_id": "sis-tourism-snow-indicators",
"title": "Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections",
"description": "Dataset: \"Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections\" [sis-tourism-snow-indicators]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Europe; Variable domain: Land (hydrology); Product type: Climate projections; Temporal coverage: Future; Sector: Tourism\nCoverage: bbox -27.3,27.7 → 44.0,70.0 | time 1950-01-01 → 2100-01-01\nVariables/options: annual_amount_of_machine_made_snow_produced; end_of_the_longest_period_with_groomed_snow; end_of_the_longest_period_with_managed_snow; end_of_the_longest_period_with_natural_snow; mean_winter_air_temperature; monthly_mean_air_temperature_for_april; monthly_mean_air_temperature_for_december; monthly_mean_air_temperature_for_february; monthly_mean_air_temperature_for_january; monthly_mean_air_temperature_for_march; monthly_mean_air_temperature_for_november; period_with_high_amount_of_groomed_snow; period_with_high_amount_of_managed_snow; period_with_high_amount_of_natural_snow; period_with_high_height_of_groomed_snow; period_with_high_height_of_managed_snow; period_with_high_height_of_natural_snow; period_with_low_height_of_groomed_snow; period_with_low_height_of_managed_snow; period_with_low_height_of_natural_snow; period_with_medium_amount_of_groomed_snow; period_with_medium_amount_of_managed_snow; period_with_medium_amount_of_natural_snow; period_with_medium_height_of_groomed_snow; period_with_medium_height_of_groomed_snow_between_fourth_and_tenth_december; period_with_medium_height_of_groomed_snow_between_twenty_seco",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.5
},
{
"store": "CDS",
"dataset_id": "reanalysis-uerra-europe-pressure-levels",
"product_id": "reanalysis-uerra-europe-pressure-levels",
"title": "UERRA regional reanalysis for Europe on pressure levels from 1961 to 2019",
"description": "Dataset: \"UERRA regional reanalysis for Europe on pressure levels from 1961 to 2019\" [reanalysis-uerra-europe-pressure-levels]\nStore: CDS (Climate Data Store)\nKeywords: Product type: Reanalysis; Temporal coverage: Past; Spatial coverage: Europe; Variable domain: Atmosphere (upper air); Provider: Copernicus C3S\nCoverage: bbox -180,-90 → 180,90 | time 2018-10-18 → 2019-07-31\nVariables/options: geopotential; geopotential_height; relative_humidity; temperature; u_component_of_wind; v_component_of_wind\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.92221551\nUpdate frequency: Not planned\n---\nThe present UERRA dataset contains analyses of atmospheric variables on pressure levels, from 1961 to 2019. \nIt has been generated using the UERRA-HARMONIE system by combining model data with observations into a complete and consistent dataset using the laws of physics.\nThis principle is called data assimilation. UERRA-HARMONIE employs a 3-dimensional variational data assimilation method. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has evolved drastically over time, and although the assimilation system can resolve data holes, the quality of analyses varies throughout the period, with less accurate estimates in 19",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.47619047619047616
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A5</span><span class="qa-tool">search_datasets</span><span class="qa-q">chlorophyll concentration Baltic Sea</span><span class="qa-x">store=CMEMS</span><span class="qa-dt num">0.5s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "chlorophyll concentration Baltic Sea",
"store": "CMEMS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CMEMS",
"dataset_id": "cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m",
"product_id": "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202",
"title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation",
"description": "Dataset: \"CMEMS HR-OC Baltic Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic\" [cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m]\nProduct: Baltic Sea, Bio-Geo-Chemical, L3, daily observation [OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202]\nCoverage: lon 9.00 to 31.00, lat 53.00 to 66.00 | 2020-01 to present\nVariables: CHL; SPM; TUR\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; ocean colour and chlorophyll-a / productivity proxies from satellite; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.6666666666666666
},
{
"store": "CMEMS",
"dataset_id": "omi_health_chl_baltic_oceancolour_area_averaged_mean",
"product_id": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean",
"title": "Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing",
"description": "Dataset: \"Baltic Sea Chlorophyll-a time series\" [omi_health_chl_baltic_oceancolour_area_averaged_mean]\nProduct: Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing [OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean]\nCoverage: | 1997-06 to 2024-12\nVariables: chlor_a; chlor_deseasonalized\nService types: original-files; omi-arco\nBest for: concise ocean-state diagnostics; ocean carbon, nutrients, oxygen, and chlorophyll budgets; policy and reporting summaries; trend and anomaly monitoring\nNot good for: high-resolution spatial or temporal analysis; primary data ingestion (indicators are summaries)\nQuality flags: no_spatial_extent\n---\n**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_BAL_BGC_L3_MY_009_133) which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA,...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.5
},
{
"store": "CMEMS",
"dataset_id": "cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M",
"product_id": "OCEANCOLOUR_BAL_BGC_L4_MY_009_134",
"title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations",
"description": "Dataset: \"cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M\" [cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M]\nProduct: Baltic Sea Multiyear Ocean Colour Plankton monthly observations [OCEANCOLOUR_BAL_BGC_L4_MY_009_134]\nCoverage: lon 9.26 to 30.24, lat 53.26 to 65.84 | 1997-09 to 2025-12\nVariables: CHL; CHL_count; CHL_error\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; ocean colour and chlorophyll-a / productivity proxies from satellite; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nFor the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.41025641025641024
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A6</span><span class="qa-tool">search_datasets</span><span class="qa-q">soil moisture climate data record</span><span class="qa-dt num">0.5s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "soil moisture climate data record",
"store": "ALL",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_id": "satellite-soil-moisture",
"product_id": "satellite-soil-moisture",
"title": "Soil moisture gridded data from 1978 to present",
"description": "Dataset: \"Soil moisture gridded data from 1978 to present\" [satellite-soil-moisture]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Global; Variable domain: Land (hydrology); Product type: Satellite observations; Provider: Copernicus C3S\nCoverage: bbox 0.0,-89.0 → 360.0,89.0 | time 1978-11-01 → 2026-04-20\nVariables/options: freeze_thaw_classification; root_zone_soil_moisture_volumetric; surface_soil_moisture_saturation; surface_soil_moisture_volumetric\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/cds.d7782f18\nUpdate frequency: Not planned\n---\nThe C3S satellite soil moisture (SM) service provides a range of global soil moisture variables derived from numerous satellite sensors, including scatterometers and radiometers. The products are designed primarily for applications related to climate variability and change, land–atmosphere interactions, global biogeochemical cycles, ecology, hydrological and land surface modelling, drought monitoring, and meteorology. \nAll C3S SM data are based on algorithms and scientific methods developed within the ESA Climate Change Initiative for Soil Moisture (ESA CCI SM) programme. They represent the current state of the art in the production of satellite-based soil moisture climate data records and comply with the Systematic Observation Requirements for Satellite-Based Products for Climate defined by the Global Climate Observing System (GCOS). The data are provided on a regular latitude–longitude grid, with potential gaps in space and time due to missing satellite overpasses. \nIn satellite-based climate",
"has_eqc_docs": false,
"notebooks": [
{
"notebook_id": "satellite_satellite-soil-moisture_completeness_q02",
"title": "Satellite soil moisture for drought monitoring in Europe (2023 case study)",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 650,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "datasets_satellite_soil_moisture_arco_access",
"title": "Access the analysis ready satellite soil moisture data",
"recipe_kinds": [
"analyze",
"download"
],
"n_code_lines": 64,
"source_repo": "ecmwf-training/dss-notebooks"
}
],
"score": 1.0
},
{
"store": "EWDS",
"dataset_id": "efas-historical",
"product_id": "efas-historical",
"title": "River discharge and related historical data from the European Flood Awareness System",
"description": "Dataset: \"River discharge and related historical data from the European Flood Awareness System\" [efas-historical]\nStore: EWDS (Early Warning Data Store)\nKeywords: Product type: Reanalysis; Temporal coverage: Past; Spatial coverage: Europe; Provider: Copernicus CEMS; Variable domain: Land (hydrology)\nCoverage: bbox 0.0,-70.0 → 360.0,70.0 | time 1991-01-01 → 2026-05-13\nVariables/options: elevation; field_capacity; river_discharge_in_the_last_24_hours; river_discharge_in_the_last_6_hours; runoff_water_equivalent; snow_depth_water_equivalent; soil_depth; soil_wetness_index; upstream_area; volumetric_soil_moisture; wilting_point\nProvider: ECMWF\nLicense: other\nDOI: 10.24381/cds.e3458969\n---\nThis dataset provides gridded modelled sub-daily and daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:\n\nRiver discharge\nVolumetric soil moisture\nSnow water equivalent\nSoil wetness index (root zone)\nRunoff water equivalent (surface plus subsurface)\n\nAlso provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD.\nThis dataset was produced by forcing the open-source LISFLOOD hydrological model with g",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.41666666666666663
},
{
"store": "CDS",
"dataset_id": "insitu-observations-near-surface-temperature-us-climate-reference-network",
"product_id": "insitu-observations-near-surface-temperature-us-climate-reference-network",
"title": "In situ observations of meteorological and soil variables from the US Climate Reference Network near the surface from 2006 to present",
"description": "Dataset: \"In situ observations of meteorological and soil variables from the US Climate Reference Network near the surface from 2006 to present\" [insitu-observations-near-surface-temperature-us-climate-reference-network]\nStore: CDS (Climate Data Store)\nKeywords: Temporal coverage: Past; Spatial coverage: Global; Product type: In-situ observations; Variable domain: Atmosphere (surface)\nCoverage: bbox 0.0,-89.0 → 360.0,89.0 | time 2006-01-01 → \nVariables/options: accumulated_precipitation; air_temperature; daily_global_solar_radiation; daily_maximum_air_temperature; daily_maximum_relative_humidity; daily_mean_air_temperature; daily_minimum_air_temperature; daily_minimum_relative_humidity; maximum_soil_temperature; maximum_solar_irradiance; minimum_soil_temperature; minimum_solar_irradiance; monthly_global_solar_radiation; relative_humidity; soil_moisture_100cm_from_earth_surface; soil_moisture_10cm_from_earth_surface; soil_moisture_20cm_from_earth_surface; soil_moisture_50cm_from_earth_surface; soil_moisture_5cm_from_earth_surface; soil_temperature; soil_temperature_100cm_from_earth_surface; soil_temperature_10cm_from_earth_surface; soil_temperature_20cm_from_earth_surface; soil_temperature_50cm_from_earth_surface; soil_temperature_5cm_from_earth_surface; solar_irradiance; wetness; wind_speed_2_meters_from_earth_surface\nProvider: ECMWF\nLicense: other\nDOI: 10.24381/cds.d1f4864d\nUpdate frequency: Irregular\n---\nThis catalogue entry provides access to a continuous series of near-surface climate observations collected in-situ at \nUnited States Climate Reference Network (USCRN) sta",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.375
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A7</span><span class="qa-tool">search_datasets</span><span class="qa-q">global fire emissions for atmosphere modelling</span><span class="qa-x">store=ADS</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "global fire emissions for atmosphere modelling",
"store": "ADS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "ADS",
"dataset_id": "cams-global-fire-emissions-gfas",
"product_id": "cams-global-fire-emissions-gfas",
"title": "CAMS global biomass burning emissions based on fire radiative power (GFAS)",
"description": "Dataset: \"CAMS global biomass burning emissions based on fire radiative power (GFAS)\" [cams-global-fire-emissions-gfas]\nStore: ADS (Atmosphere Data Store)\nKeywords: Temporal coverage: Past; Parameter family: Aerosol; Parameter family: Reactive gas; Spatial coverage: Global; Variable domain: Emissions and surface fluxes; Product type: Analysis; Temporal coverage: Present\nCoverage: bbox -180.0,-90.0 → 180.0,90.0 | time 2003-01-01 → 2025-12-03\nVariables/options: altitude_of_plume_bottom; altitude_of_plume_top; injection_height; mean_altitude_of_maximum_injection; wildfire_combustion_rate; wildfire_flux_of_acetaldehyde; wildfire_flux_of_acetone; wildfire_flux_of_ammonia; wildfire_flux_of_benzene; wildfire_flux_of_black_carbon; wildfire_flux_of_butanes; wildfire_flux_of_butenes; wildfire_flux_of_carbon_dioxide; wildfire_flux_of_carbon_monoxide; wildfire_flux_of_dimethyl_sulfide; wildfire_flux_of_ethane; wildfire_flux_of_ethanol; wildfire_flux_of_ethene; wildfire_flux_of_formaldehyde; wildfire_flux_of_heptane; wildfire_flux_of_hexanes; wildfire_flux_of_hexene; wildfire_flux_of_higher_alkanes; wildfire_flux_of_higher_alkenes; wildfire_flux_of_hydrogen; wildfire_flux_of_isoprene; wildfire_flux_of_methane; wildfire_flux_of_methanol; wildfire_flux_of_nitrogen_oxides; wildfire_flux_of_nitrous_oxide; wildfire_flux_of_non_methane_hydrocarbons; wildfire_flux_of_octene; wildfire_flux_of_organic_carbon; wildfire_flux_of_particulate_matter_d_2_5_µm; wildfire_flux_of_pentanes; wildfire_flux_of_pentenes; wildfire_flux_of_propane; wildfire_flux_of_propene; wildfire_flux_of_sulphur_dioxide; wil",
"has_eqc_docs": false,
"notebooks": [],
"score": 1.0
},
{
"store": "ADS",
"dataset_id": "cams-global-emission-inventories",
"product_id": "cams-global-emission-inventories",
"title": "CAMS global emission inventories",
"description": "Dataset: \"CAMS global emission inventories\" [cams-global-emission-inventories]\nStore: ADS (Atmosphere Data Store)\nKeywords: Temporal coverage: Past; Parameter family: Aerosol; Parameter family: Reactive gas; Product type: Emission inventory; Spatial coverage: Global; Variable domain: Emissions and surface fluxes\nCoverage: bbox -180.0,-90.0 → 180.0,90.0 | time 2000-01-01 → 2020-12-31\nVariables/options: acetaldehyde; acetic_acid; acetone; acetylene; acids; alcohols; alpha_pinene; ammonia; ash; benzene; beta_pinene; black_carbon; bromoform; butanes; butanes_and_higher_alkanes; butenes_and_higher_alkenes; carbon_dioxide; carbon_dioxide_excl_short_cycle; carbon_dioxide_short_cycle; carbon_monoxide; chlorinated_hydrocarbons; dibromomethane; dimethyl_sulphide; elemental_carbon; esters; ethane; ethanol; ethene; ethers; formaldehyde; formic_acid; hexanes; hydrogen_cyanide; iodomethane; isoprene; ketones; methane; methanol; methyl_bromide; methyl_chloride (+24 more)\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/1d158bec\n---\nThis data set contains gridded distributions of global anthropogenic and natural emissions.\nNatural and anthropogenic emissions of atmospheric pollutants and greenhouse gases are key drivers of the evolution of the composition of the atmosphere, so an accurate representation of them in forecast models of atmospheric composition is essential. CAMS compiles inventories of emission data that serve as input to its own forecast models, but which can also be used by other atmospheric chemical transport models. These inventories are based on a combination of existing ",
"has_eqc_docs": false,
"notebooks": [
{
"notebook_id": "cams-emissions",
"title": "Emissions",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 162,
"source_repo": "ecmwf-training/cams-training"
}
],
"score": 0.6666666666666666
},
{
"store": "ADS",
"dataset_id": "cams-global-greenhouse-gas-inversion",
"product_id": "cams-global-greenhouse-gas-inversion",
"title": "CAMS global inversion-optimised greenhouse gas fluxes and concentrations",
"description": "Dataset: \"CAMS global inversion-optimised greenhouse gas fluxes and concentrations\" [cams-global-greenhouse-gas-inversion]\nStore: ADS (Atmosphere Data Store)\nKeywords: Product type: Reanalysis; Temporal coverage: Past; Variable domain: Atmosphere (composition); Spatial coverage: Global; Variable domain: Emissions and surface fluxes; Parameter family: Greenhouse gas\nCoverage: bbox -180.0,-90.0 → 180.0,90.0 | time 1979-01-01 → 2025-12-31\nVariables/options: carbon_dioxide; methane; nitrous_oxide\nProvider: ECMWF\nLicense: CC-BY-4.0\nDOI: 10.24381/ed2851d2\n---\nThis data set contains net fluxes at the surface, atmospheric mixing ratios at model levels, and column-mean atmospheric mixing ratios for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20).\nNatural and anthropogenic surface fluxes of greenhouse gases are key drivers of the evolution of Earth’s climate, so their monitoring is essential. Such information has been used in particular as part of the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Ground-based and satellite remote-sensing observations provide a means to quantifying the net fluxes between the land and ocean on the one hand and the atmosphere on the other hand. This is done through a process called atmospheric inversion, which uses transport models of the atmosphere to link the observed concentrations of CO2, CH4 and N2O to the net fluxes at the Earth's surface. By correctly modelling the winds, vertical diffusion, and convection in the global atmosphere, the observed concentrations of the greenhouse gases are used to infer the",
"has_eqc_docs": false,
"notebooks": [],
"score": 0.5
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">A8</span><span class="qa-tool">search_datasets</span><span class="qa-q">Arctic sea ice concentration long time series</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "Arctic sea ice concentration long time series",
"store": "ALL",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 4,
"results": [
{
"store": "CMEMS",
"dataset_id": "cmems_obs-si_arc_phy_nrt_1km-svb_P1D-irr",
"product_id": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002",
"title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland",
"description": "Dataset: \"Arctic Sea Ice Concentration From Norwegian Meteorological Institute\" [cmems_obs-si_arc_phy_nrt_1km-svb_P1D-irr]\nProduct: Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland [SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002]\nCoverage: lon -90.00 to 90.00, lat 50.00 to 90.00 | 2023-01 to present\nVariables: ice_concentration; satellite_availability\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; sea-ice extent, concentration, thickness, and drift; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nFor the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.5588235294117647
},
{
"store": "CMEMS",
"dataset_id": "cmems_obs-si_arc_phy-siage_my_P1D-m",
"product_id": "SEAICE_ARC_PHY_AUTO_L4_MY_011_025",
"title": "Arctic Sea Ice Age",
"description": "Dataset: \"Arctic Sea Ice Age Climate Data Record v2.1\" [cmems_obs-si_arc_phy-siage_my_P1D-m]\nProduct: Arctic Sea Ice Age [SEAICE_ARC_PHY_AUTO_L4_MY_011_025]\nCoverage: lon -180.00 to 179.80, lat 17.00 to 89.53 | 1995-09 to present\nVariables: conc_1yi; conc_2yi; conc_3yi; conc_4yi; conc_5yi; conc_6yi; siage; status_flag; latitude; longitude\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; sea-ice extent, concentration, thickness, and drift; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nDaily sea ice age and sea ice age fractions with uncertainties in the period 1991 - 2025. Coverage: Arctic Ocean. Resolution: 25 km.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00371",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.527027027027027
},
{
"store": "CMEMS",
"dataset_id": "cmems_obs-si_arc_physic_nrt_1km-grl_P1D-irr",
"product_id": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002",
"title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland",
"description": "Dataset: \"Arctic Sea Ice Greenland\" [cmems_obs-si_arc_physic_nrt_1km-grl_P1D-irr]\nProduct: Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland [SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002]\nCoverage: lon -110.00 to 25.00, lat 50.00 to 90.00 | 2020-09 to present\nVariables: concentration_range; ice_concentration; ice_poly_id_grid\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; sea-ice extent, concentration, thickness, and drift; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nFor the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview...",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.37037037037037035
},
{
"store": "CMEMS",
"dataset_id": "cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m",
"product_id": "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021",
"title": "Arctic Ocean - Sea and Ice Surface Temperature L3S REPROCESSED",
"description": "Dataset: \"DMI Arctic SST/IST, L3S , (SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021)\" [cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m]\nProduct: Arctic Ocean - Sea and Ice Surface Temperature L3S REPROCESSED [SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021]\nCoverage: arctic, 58 to 90 latitude | 1982-01 to 2024-12\nVariables: analysed_st; mask; or_number_of_st_pixels; quality_level; sea_ice_fraction; source_of_st; sses_bias; sses_standard_deviation; sum_square_st; sum_st; st_dtime\nService types: original-files; wmts; arco-geo-series; arco-time-series\nBest for: data assimilation inputs; ground-truth for model assessment; sea-ice extent, concentration, thickness, and drift; validation against in-situ or satellite truth\nNot good for: spatially-complete fields (observations are sparse in space and time); uniform global coverage at every time step\n---\nArctic Sea and Ice surface temperature\n**Detailed description:** Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00315",
"has_eqc_docs": true,
"notebooks": [],
"score": 0.36666666666666664
}
],
"next_step": "for CMEMS hits call get_dataset_docs(product_id) to read quality docs; where a hit has notebooks[], call get_dataset_code(dataset_id) for runnable code"
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>L2 · CMEMS deep docs</b><span>PUM/QUID/SQO — what it is and how to read it</span></div>
<details class="qa"><summary><span class="qa-tag num">B9</span><span class="qa-tool">get_dataset_docs</span><span class="qa-q">What is the SST accuracy?</span><span class="qa-x">dataset_or_product_id=GLOBAL_ANALYSISFORECAST_PHY_001_024</span><span class="qa-dt num">1.3s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"product_id": "GLOBAL_ANALYSISFORECAST_PHY_001_024",
"product_title": "Global Ocean Physics Analysis and Forecast",
"matched_by": "product_id",
"doc_types_available": [
"PUM",
"QUID",
"SQO"
],
"dataset_ids": [
"cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m",
"cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m",
"cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i",
"cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m",
"cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m",
"cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i",
"cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m",
"cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m",
"cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i",
"cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m",
"cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m",
"cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m",
"cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m",
"cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m",
"cmems_mod_glo_phy_anfc_0.083deg_P1D-m",
"cmems_mod_glo_phy_anfc_0.083deg_P1M-m",
"cmems_mod_glo_phy_anfc_0.083deg_PT1H-m",
"cmems_mod_glo_phy_anfc_0.083deg_static",
"cmems_mod_glo_phy_anfc_merged-sl_PT1H-i",
"cmems_mod_glo_phy_anfc_merged-uv_PT1H-i"
],
"query": "What is the SST accuracy?",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doc_type": "QUID",
"doc_id": "CMEMS-GLO-QUID-001-024",
"section": "I.3 Estimated Accuracy Numbers",
"text": "The following 3D temperature and salinity (T/S), SLA, and SST accuracy numbers are estimated using statistical metrics performed with the model counterpart in time and space of the available observations. In all cases these numbers are an estimate of the mean error and not a maximum error on the domain. Here we present only the accuracy numbers for the global area. They are also computed for each region but are not shown in this document. Accuracy values are represented by RMSD and Mean bias for SLA, SST, and surface currents. They are given also for 3D temperature and salinity in the following vertical layers: 0-5 m, 5-100 m, 100-300 m, 300-800 m, 800-2000 m, and 2000-5000 m.\n\nIn order to calculate these accuracy numbers, we defined a one-year qualification (or calibration) period in 2019. Results are presented for each variable in the set of tables below.",
"score": 0.7
},
{
"doc_type": "QUID",
"doc_id": "CMEMS-GLO-QUID-001-024",
"section": "I.3.3 SST",
"text": "[TABLE in section: I.3.3 SST]\nCaption: Table 3. Globally averaged RMSD and mean SST misfits in °C (observation-model) with respect to observations (SST TAC) and computed in 2019.\n\n| SST (K) vsSatellite data | Hindcast | Forecast day 3 |\n| GLO | -0.06 | 0.4 | 0.1 | 0.6 |",
"score": 0.6666666666666666
},
{
"doc_type": "QUID",
"doc_id": "CMEMS-GLO-QUID-001-024",
"section": "I.2.2 Sea Surface Temperature (SST)",
"text": "The GLO12 sea-surface-temperature analysis is very close to in situ observations. It displays a weak global average bias with respect to Operational Sea Surface Temperature and Ice Analysis (OSTIA) SST product (warm bias of 0.1 °C), as well as a comparable bias with Level 3 along Swath (L3S) ODYSSEA SST product (https://doi.org/10.48670/moi-00164) in terms of amplitude but of opposite sign (cold bias of 0.1 °C). Warm biases up to 1 °C can be observed in upwelling areas, where the thermocline is too deep in GLO12 with respect to observations. The estimated uncertainty is close to 0.6 °C; which is in the range of satellite observations' uncertainty.",
"score": 0.4242424242424242
}
],
"next_step": "read_document(doc_id) pulls a full document; then subset data via the copernicus MCP server"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B10</span><span class="qa-tool">get_dataset_docs</span><span class="qa-q">Which in-situ observations are assimilated?</span><span class="qa-x">dataset_or_product_id=GLOBAL_MULTIYEAR_PHY_001_030</span><span class="qa-dt num">1.1s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"product_id": "GLOBAL_MULTIYEAR_PHY_001_030",
"product_title": "Global Ocean Physics Reanalysis",
"matched_by": "product_id",
"doc_types_available": [
"PUM",
"QUID",
"SQO"
],
"dataset_ids": [
"cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m",
"cmems_mod_glo_phy_my_0.083deg_P1D-m",
"cmems_mod_glo_phy_my_0.083deg_P1M-m",
"cmems_mod_glo_phy_my_0.083deg_static"
],
"query": "Which in-situ observations are assimilated?",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doc_type": "QUID",
"doc_id": "CMEMS-GLO-QUID-001-030",
"section": "II.1 GLORYS12V1 reanalysis (001_030)",
"text": "[TABLE in section: II.1 GLORYS12V1 reanalysis (001_030)]\nCaption: Table 2 (cont): Comparing data assimilation configurations of GLORYS12V1, PSY4V3R1, and GLORYS2V4.\n\n| PSY4V3R1GLOBAL_ANALYSIS_FORECAST_001_024 | G2V4GLOBAL_REANALYSIS_PHY_001_025 | G12V1GLOBAL_REANALYSIS_PHY_001_030 |\n| Relative wind with 50% ocean currents in momentum flux | identical to PSY4V3R1 | identical to PSY4V3R1 |\n| Outputs: High Frequency (1h) + daily mean + coarsened files at $\\frac{1}{4}^{\\circ}$ for biogeochemistry | Outputs : daily means | identical to PSY4V3R1 |\n| Assimilation of CORA4.1 in situ dataset until 2003 (early version of INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b). From 2014 to present, the near-real time CMEMS product INSITU_GLO_NRT_OBSERVATIONS_013_030 is assimilated. | identical to PSY4V3R1 | Assimilation of CORA4.1 until 2003. From 2014 to 2015, CORA5.0 product is assimilated. For 2016, CORA5.1 product is assimilated. |\n| Assimilation of sea ice concentration from OSISAFSEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009 | Assimilation of sea ice concentration from CERSAT | identical to GLORYS2V4 + OSISAF only as verification |\n| Assimilation of SST OSTIASST_GLO_SST_L4_NRT_OBSERVATIONS_010_001 | Assimilation of NOAA SST analyses at $\\frac{1}{4}^{\\circ}$ (AVHRR) | identical to GLORYS2V4 |\n| Hybrid Mean Dynamic Topography (MDT) based on CNES-CLS13 + new glacial isostatic adjustment file | Hybrid MDT based on CNES-CLS13 | identical to PSY4V3R1 |\n| Error covariance (ocean + sea ice state anomalies) from twin simulation of PSY4V3R1 (only corrected from large scale T and S biases ) + spati",
"score": 0.8333333333333333
},
{
"doc_type": "QUID",
"doc_id": "CMEMS-GLO-QUID-001-030",
"section": "II.1 GLORYS12V1 reanalysis (001_030)",
"text": "[TABLE in section: II.1 GLORYS12V1 reanalysis (001_030)]\nCaption: Table 2 (cont.): Comparing data assimilation configurations of GLORYS12V1, PSY4V3R1, and GLORYS2V4.\n\n| PSY4V3R1GLOBAL_ANALYSIS_FORECAST_001_024 | GLORYS2V4GLOBAL_REANALYSIS_PHY_001_025 | GLORYS12V1GLOBAL_REANALYSIS_PHY_001_030 |\n| 50 z-levels | 75 z-levels | identical to PSY4V3R1 |\n| ECMWF operational atmospheric analyses with 3H frequencyNo snowfall | ECMWF atmospheric reanalysisERAinterim 3H/24H frequency + diurnal cycleSnowfallERA5 after January 2019 | identical to GLORYS2V4 |\n| Large scale correction of ECMWF operational atmospheric analyses precipitations towards PMWC (Hilburn, K. A., (2009)) | Large scale correction of ERAiprecipitations (towards GPCPV2.2 observations:https://climatedataguide.ucar.edu/climate-data/gpcp-monthly-global-precipitation-climatology-project) and radiative fluxes (towards GEWEX SRB3.0 &amp; 3.1 observations:https://www.gewex.org/resources/data-sets/)ERA5 without correction after 2019. | Large scale correction of ERAiprecipitations (towards PMWC observations) and radiative fluxes (towards GEWEX SRB3.0 &amp; 3.1 observations)ERA5 without correction after 2019. |\n| Initial Conditions (December 2006) estimated from EN4.2.0 monthly gridded fields for December 2006 using a regression technique | Initial Conditions (December 1991) estimated from EN4.0.2 monthly gridded fields for December 1991 using a regression technique | Initial conditions (December 1991) estimated from EN4.2.0 monthly gridded fields for December 1991 using a regression technique |\n| Surface mass Budget EMP (Evapor",
"score": 0.5454545454545454
},
{
"doc_type": "PUM",
"doc_id": "CMEMS-GLO-PUM-001-030",
"section": "b) Production System Description",
"text": "[TABLE in section: b) Production System Description]\n\n| Domain Resolution and grid Geographic coverage | GLOBAL (180°W-180°E; 80°S - 90°N) 1/12°; regular grid; 4320 x 2041 This product is global with dedicated projection and spatial resolution. It is defined on a standard collocated grid at 1/12 degree (approx. 8 km). The parameters are interpolated from the native grid model, the 1/12 degree and 50 vertical levels Arakawa C native grid. |\n| --- | --- |\n| Model Version | LIM2 EVP NEMO 3.1 |\n| Atmospheric forcing | 3-h and 24-h atmospheric forcing from ERA-Interim, including precipitation and radiative fluxes (SW+LW) corrections Hourly ERA5 forcing from January 1st 2019 |\n| Assimilation scheme | SAM2 (SEEK Kernel) + FGAT + IAU and 3D-VAR T/S bias correction |\n| Assimilated observations | Reynolds 0.25° AVHRR-only SST, Delayed Time SLA from all altimetric satellites, in situ T/S profiles from Copernicus Marine CORAv4.1 database, CERSAT Sea Ice Concentration |\n| Initial conditions | January 1993 T/S regressed from EN.4.2.0 |\n| Tidal constituents | Not taken into account |\n| Bathymetry | ETOPO1 for deep ocean and GEBCO8 on coast and continental shelf. |",
"score": 0.45
}
],
"next_step": "read_document(doc_id) pulls a full document; then subset data via the copernicus MCP server"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B11</span><span class="qa-tool">search_docs</span><span class="qa-q">known biases Mediterranean wave model</span><span class="qa-x">doc_type=QUID</span><span class="qa-dt num">1.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "known biases Mediterranean wave model",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"product_id": "MEDSEA_MULTIYEAR_WAV_006_012",
"product_title": "Mediterranean Sea Waves Reanalysis",
"doc_type": "QUID",
"doc_id": "CMEMS-MED-QUID-006-012",
"section": "IV.1.1 Comparison with in-situ observations",
"text": "In the first case, deterioration of wave model performance is because the resolution of the forcing wind model is not capable of well reproducing the fine interaction between the prevailing north-northwesterly winds in the northern Mediterranean Sea and the complex orography sheltering the northern Mediterranean coastline. In the second case, when the buoys are located only a few kilometers from the coastline and the wind is blowing from the coast (e.g., 61188, Algeciras, Malaga, Tarragona) the approximation of the wave model grid size can lead to non-negligible fetch differences (e.g., Ardhuin et al., 2007; Cavaleri and Sclavo, 2006). Finally, in the latter case, it is also the spatial resolution of the wave model that is not adequate to resolve the fine bathymetric features. In general, the more close the location to the coastline (e.g., Malaga) and/or the more complex the surrounding topography (e.g., SARON), the poorer the model performance expected (e.g., Cavaleri and Sclavo, 2006; Bertotti et al., 2013; Zacharioudaki et al., 2015). The correlation coefficient (CORR) largely follows the pattern of variation of the SI. It ranges from 0.86 at SARON in the Aegean Sea to 0.98 at the deep water buoy 6100002, offshore from France, and at buoy 61213 west of Sardinia. The latter two buoys are well exposed to the prevailing north-westerly winds in the region. bias varies from -0.29 m at buoy 6100294 west of Corsica to 0.17 m at buoy SANTO in the Aegean Sea. bias is mainly negative indicating an underestimation of the observed wave height by the model. Positive bias is mostly fo",
"score": 0.7
},
{
"product_id": "MEDSEA_ANALYSISFORECAST_WAV_006_017",
"product_title": "Mediterranean Sea Waves Analysis and Forecast",
"doc_type": "QUID",
"doc_id": "CMEMS-MED-QUID-006-017",
"section": "I.2 Summary of the results",
"text": "The quality of the MED-MFC-waves analysis and forecast system is assessed over a 1-year period (Jan - Dec 2021) by compaon with in-situ and satellite observations. The main results of the MEDSEA_ANALYSISFORECAST_WAV_006_017 product quality assessment are summarized below:\n\nSpectral Significant Wave Height (Hm0): Overall, the significant wave height is accurately simulated by the model. Considering the Mediterranean Sea as a whole, the typical root mean square differences (RMSDs) with respect to in-situ and satellite observations are 0.21 m and 0.22 m, respectively, with corresponding biases of 0 and -0.01 m (1%) and scatter indices (SI) of 0.25 and 0.18. In general, and depending on the reference dataset, the model either slightly underestimates or converges to the observations for wave heights smaller than about 3-4 m and mostly overestimates or converges to the observations for higher waves. Its performance is better in winter when wave conditions are well-defined. Spatially, the model performs optimally at offshore wave buoy locations and well-exposed Mediterranean sub-regions. Within enclosed basins and near the coast, topography unresolved by the wind and wave models and fetch limitations can cause the wave model performance to deteriorate.\n\nSpectral moments (0,2) wave period (Tm02): The mean wave period (MWP) is reasonably well simulated by the model. The typical RMSD w.r.t. in situ observations is 0.70 s and is mainly caused by model bias which has a value of -0.48 s (12%). In general, the model underestimates the observed mean wave period and exhibits greater variab",
"score": 0.5
},
{
"product_id": "MEDSEA_MULTIYEAR_WAV_006_012",
"product_title": "Mediterranean Sea Waves Reanalysis",
"doc_type": "QUID",
"doc_id": "CMEMS-MED-QUID-006-012",
"section": "IV.1.2 Comparison with satellite observations",
"text": "This sub-section starts with the compaon of the ERA5 forcing wind speeds, U10, and Med-MFC first-guess SWH with satellite observations of U10 and SWH respectively, separately for each satellite. This is done for the reanalysis period 1993-2016 for U10 and 1993-2018 for SWH. Respective metrics are given for the full Mediterranean Sea and are computed as single values for the entire period considered (Figure 10 and Tables A2, A3 in the Appendix) and as yearly values (Figure 11). It is noted that altimeter observations over periods smaller than a full year have been omitted from the analysis. In addition, years with significantly reduced Mediterranean coverage have been removed (ERS2, 2004-2007). Finally, measurements exhibiting known inconsistencies have not been considered (Jason-2 U10 negative drift over years 2014-2016). An apparent feature in Figure 10 is a differentiation in model-satellite Relative bias found for ERS2 with respects to U10 and for Cryosat-2 with respects to SWH. In particular, U10 model-ERS2 and SWH model-Cryosat-2 Relative bias is found to be different by at least 3% and 5%, respectively, compared to the values computed for the other model-satellite pairs. SWH model-ERS1 and model-GEOSAT FO Relative bias is also different (about 3%) compared to the majority of model-satellite compaons. Another observation is that SWH model-ERS1 and model-ERS2 SI is 2-4% greater compared to the other model-satellite SI values. Otherwise, the model-satellite compaon behaves similarly for the different satellites with the\n\nMed-MFC reanalysis wave model exhibiting its best ",
"score": 0.5
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B12</span><span class="qa-tool">get_dataset_docs</span><span class="qa-q">grid resolution and projection</span><span class="qa-x">dataset_or_product_id=SEALEVEL_GLO_PHY_L4_MY_008_047</span><span class="qa-dt num">1.1s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"product_id": "SEALEVEL_GLO_PHY_L4_MY_008_047",
"product_title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)",
"matched_by": "product_id",
"doc_types_available": [
"PUM",
"QUID",
"SQO"
],
"dataset_ids": [
"cmems_obs-sl_glo_phy-ssh_my_allsat-demo-l4-duacs-0.125deg_P1D-i",
"cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D",
"cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m"
],
"query": "grid resolution and projection",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doc_type": "QUID",
"doc_id": "CMEMS-SL-QUID-008-033-068",
"section": "Observable wavelengths:",
"text": "The along-track (L3) products are delivered with a 1 Hz (\\~7 km) and 5 Hz (\\~1 km) along-track sampling. Gridded (L4) products are delivered on a 0.125° x 0.125° regular grid for global and 0.0625° x 0.0625° for regional products. Nevertheless, this spatial sampling is not representative of the effective spatial resolution of the products. The along-track product is affected by measurement noise that limit the observation of the small scales as discussed in section IV.1.1.2.1. The gridded products resolution capability is directly linked to the altimeter constellation state and mapping methodology as discussed in section IV.1.2.2. The effective resolution capability of the products is summarized in Table 16, Table 17 and Table 18, and is fully discussed in Ballarotta et al., (2019).",
"score": 0.8333333333333333
},
{
"doc_type": "QUID",
"doc_id": "CMEMS-SL-QUID-008-033-068",
"section": "IV.1.2.2 Global gridded products",
"text": "[TABLE in section: IV.1.2.2 Global gridded products]\nCaption: Figure 13: (a) Effective spatial resolution (wavelength in km) of the DUACS-DT2024 global maps and (b) Gain(-)/loss(+) of effective resolution between DUACS-DT2024 and DUACS-DT2021 gridded products. Negative values (i.e., blue colour) mean finer resolution in DUACS-DT2024 than in DUACS-DT2021.\n\n| Latitude | Longitude | ΔEffective resolution [%] |\n| 30°N | 120°W | ~0 |\n| 0° | 60°W | ~5 |\n| 30°S | 0° | ~0 |",
"score": 0.6111111111111112
},
{
"doc_type": "QUID",
"doc_id": "CMEMS-SL-QUID-008-033-068",
"section": "I.1.2.2 Gridded products",
"text": "[TABLE in section: I.1.2.2 Gridded products]\nCaption: Table 8: Description of the product SEALEVEL_GLO_PHY_L4_MY_008_047\n\n| Product | SEALEVEL_GLO_PHY_L4_MY_008_047 |\n| --- | --- |\n| Spatial resolution | 1/8°x1/8° Cartesian grid |",
"score": 0.47619047619047616
}
],
"next_step": "read_document(doc_id) pulls a full document; then subset data via the copernicus MCP server"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B13</span><span class="qa-tool">search_docs</span><span class="qa-q">ocean colour chlorophyll validation against in-situ matchups</span><span class="qa-dt num">0.8s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "ocean colour chlorophyll validation against in-situ matchups",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"product_id": "OCEANCOLOUR_GLO_BGC_L3_MY_009_107",
"product_title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations",
"doc_type": "QUID",
"doc_id": "CMEMS-OC-QUID-009-107to108",
"section": "Method",
"text": "An abundance-based method (Brewin et al., 2010) was re-tuned to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size, from satellite data in the global oceans (Brewin et al. 2015b).\n\nData collected in sixteen in situ sampling campaigns (see Figure) was used to re-tune, adapt and validate the model of Brewin et al. (2010), compute the root mean square error ( $\\Psi$ ) and bias ( $\\delta$ ) map phytoplankton functional type products and associated errors using ocean-colour data. Samples were also matched to daily, level 3 (4 km sinusoidal projected) satellite chlorophyll data, from version 1.0 of the Ocean Colour Climate Change Initiative (OC-CCI, a merged ME, MODIS-Aqua and SeaWiFS product available at http://www.oceancolour.org/), between 1997 and 2012. Each in situ sample was matched in time (daily temporal match-up) and space (latitude and longitude) with the satellite data. Validation using the latest version of the Ocean Colour Climate Change Initiative, currently v6, was completed using the original datasets in the TOSCA project (Brewin et al. 2015).",
"score": 0.5454545454545454
},
{
"product_id": "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205",
"product_title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation",
"doc_type": "QUID",
"doc_id": "CMEMS-OC-QUID-009-201to212",
"section": "I.2 Summary of the results",
"text": "Estimated Accuracy Numbers (EANs) provide a quick overview of the product performances with respect to in situ observations (Table 1). The EAN retrieval is sometimes hampered by the limited availability of in situ observations, such as SPM and BBP observations. Additionally, the 5-day Sentinel-2/MSI revisit time (average) drastically reduces the retained number of matchups. The products presented in this document show an acceptable quality with respect to their in situ counterparts. An intercompaon with existing CMEMS ocean colour products was performed for different parameters and in the case of unavailability of in situ data suitable for ocean colour validation for traditional matchup analysis.\n\nRemote Sensing Reflectance: The visible bands show a strong correlation with a relatively higher spread in the blue bands (i.e. 443 nm and 490 nm) for low values compared to the green (i.e. 560 nm) and red (665 nm) bands. The mismatch between satellite and in situ observations increases slightly for the near-infrared bands (704 nm, 740 nm, 783 nm), especially in the 865 nm band showing a general underestimation. The spread increases in the red and near-infrared bands compared to the green band as most in situ stations are located in clear waters with very low RRS values where sensor noise plays a bigger role.\n\nTurbidity/Suspended Particulate Matter: The Turbidity products follow the 1:1 line (slope = S=0.94) with a slight overestimation in values below 10 FNU. SPM products are expected to have similar uncertainty charactetics as the TUR product. Still, additional bias can be expec",
"score": 0.5
},
{
"product_id": "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010",
"product_title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient, Particulate Organic Carbon, Downwelling Photosynthetic Available Radiation and downwelling irradiance at three different wavelengths (ED380, ED412 and ED490)",
"doc_type": "QUID",
"doc_id": "CMEMS-MOB-QUID-015-010",
"section": "IV.2 Chlorophyll-a concentration",
"text": "The accuracy of the derived Chl product is evaluated here against multiple high-quality in situ measurements to ensure robustness. Validation is performed against both the BGC-Argo database (20% subset and 6 independent floats) and highly accurate HPLC measurements.",
"score": 0.3333333333333333
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B14</span><span class="qa-tool">search_docs</span><span class="qa-q">Baltic sea ice product update frequency timeliness</span><span class="qa-dt num">0.8s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "Baltic sea ice product update frequency timeliness",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"product_id": "SEAICE_BAL_PHY_L4_MY_011_019",
"product_title": "Baltic Sea ice concentration, extent, and classification time series",
"doc_type": "PUM",
"doc_id": "CMEMS-SEAICE-PUM-011-019",
"section": "a) General Information",
"text": "[TABLE in section: a) General Information]\nCaption: Figure 1. The coverage of the data: Baltic Sea area is indicated by the light blue color.\n\n| Update Frequency | Annually after ice season (early June) |\n| --- | --- |\n| Target delivery time | 15 June |\n| Resolution | 1 km |\n| Number of vertical levels | 1 |\n| Format | NetCDF4 |",
"score": 0.6428571428571428
},
{
"product_id": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004",
"product_title": "Baltic Sea - Sea Ice Concentration and Thickness Charts",
"doc_type": "PUM",
"doc_id": "CMEMS-SEAICE-PUM-011-004",
"section": "a) General Information",
"text": "[TABLE in section: a) General Information]\nCaption: Figure 1. The coverage of the data: Baltic Sea area is indicated by the light blue color.\n\n| Product | Baltic Sea IceSEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004 |\n| --- | --- |\n| Geographical coverage | Baltic Sea : 9°E - 31°E; 53°N – 66°N |\n| Variables | Sea Ice ConcentrationSea Ice ThicknessSea Ice Extent |\n| Product Type | Near Real Time - Observations |\n| Available time series | -2Y to -16h |\n| Temporal resolution | Daily |\n| Update Frequency | Daily |\n| Target delivery time | daily at 14 UTC |\n| Resolution | 1 km |\n| Number of vertical levels | 1 |\n| Format | NetCDF-4 |",
"score": 0.5714285714285714
},
{
"product_id": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011",
"product_title": "Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration",
"doc_type": "PUM",
"doc_id": "CMEMS-SEAICE-PUM-011-011",
"section": "a) General Information",
"text": "[TABLE in section: a) General Information]\nCaption: Figure 1. The Baltic Sea area (light blue color) is approximately between 53 and 66 degrees of Northern latitude and 9 and 31 degrees of Eastern longitude.\n\n| Product | Baltic Sea Ice :SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011 |\n| --- | --- |\n| Geographical coverage | Baltic Sea : 9° E - 31° E; 53° N - 66° N, coverage of the SAR image area for single NRT data, whole area for the mosaic productsSea Ice ConcentrationSea Ice ThicknessSea Ice DriftSea Ice Extent |\n| Variables | |\n| Product Type | Near Real Time - Observations |\n| Available time series | -2Y |\n| Temporal resolution | Varying (for observations derived from satellite image scenes soon after acquisition) / Daily mosaics |\n| Update Frequency | Varying (for observations derived from satellite image scenes soon after acquisition) / Daily mosaics |\n| Target delivery time | 4 hours after SAR acquisition or daily (mosaics) |\n| Resolution | 0.5 (SIC, SIT) / 0.8 km (SID) |\n| Format | NetCDF v. 3 or 4 CF1.4 |",
"score": 0.3627450980392157
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B15</span><span class="qa-tool">search_docs</span><span class="qa-q">Class 4 forecast verification metrics against observations</span><span class="qa-dt num">0.8s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "Class 4 forecast verification metrics against observations",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"product_id": "IBI_ANALYSISFORECAST_BGC_005_004",
"product_title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast",
"doc_type": "QUID",
"doc_id": "CMEMS-IBI-QUID-005-004",
"section": "III VALIDATION FRAMEWORK",
"text": "Prior to the release of any new operational, the IBI-MFC conducts a qualification of the proposed future IBI system. The qualification phase is based on a scientific assessment of the IBI products, (i) measuring the quality of any new updated version of the IBI forecast system, (ii) evaluating the proposed new operational products against the previous IBI operational ones in order to quantify potential added value associated with the novelties, and (iii) verifying the absence of any regression in product quality with respect to the previous IBI solutions. This qualification phase is the objective of this document. Once the proposed future system has been qualified, it will enter into operational mode and replace the previous system. The operational system is then monitored on a regular basis to evaluate the near-real time product and the forecast accuracy. The IBI MFC employs using an operational validation tool called NARVAL, which is also used to validate the physical products (Lorente et al., 2012; Sotillo et al., 2015). Operational forecast skill scores are available on the product quality dashboard of the Copernicus Marine Service (https://pqd.mercator-ocean.fr/). The validation methodology is built upon different classes of metrics, defined by the GODAE/OceanPredict community (Hernandez et al., 2009, 2015). These metrics provide a comprehensive set of statistics and compaons in both spatial and temporal domains, enabling a thorough evaluation of the consistency, representativeness, accuracy, performance, and robustness of ocean model outputs. More specifically, the bi",
"score": 0.6428571428571428
},
{
"product_id": "NWSHELF_REANALYSIS_WAV_004_015",
"product_title": "Atlantic- European North West Shelf- Wave Physics Reanalysis",
"doc_type": "QUID",
"doc_id": "CMEMS-NWS-QUID-004-015",
"section": "III.4 Observation datasets",
"text": "[TABLE in section: III.4 Observation datasets]\nCaption: Table 5 identifies the parameters and observation sources that were used in the quality assessment. The de-facto standard for wave measurement is in-situ data, using various instruments mounted on floating buoys and fixed marine platforms (e.g. oil installations). For open waters, a global dataset of these measurements is collated and quality controlled each month under the World Meteorological Organisation – International Oceanographic Commission (WMO-IOC) Joint Commission On Marine Meteorology’s operational Wave Forecast Verification Scheme (Bidlot et al. 2007, hereafter referred to as the JCOMM-WFVS). Over 400 measurement sites are registered in the system, although these are\n\n| Parameter | Coverage | Supporting Observations | Metric Class |\n| --- | --- | --- | --- |\n| Significant wave height | Full domain | In-situ platforms | 2 |\n| Significant wave height | Full domain | Satellite altimeter | 4 |\n| Wave peak period | Full domain (predominantly coastal, none along French coasts) | In-situ platforms | 2 |\n| Average zero crossing wave period | Full domain | In-situ platforms | 2 |\n| Average period of highest 1/3 of waves | Bay of Biscay | In-situ buoys | 2 |\n| Mean wave direction | UK coastal | In-situ buoys | 2 |",
"score": 0.5
},
{
"product_id": "NWSHELF_ANALYSISFORECAST_WAV_004_014",
"product_title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast",
"doc_type": "QUID",
"doc_id": "CMEMS-NWS-QUID-004-014",
"section": "III.3 Summary of tested metrics",
"text": "To summae the tested metrics, two graphical tests are applied:\n\n1) model-observation scatter data are rendered using a 'hexbin' plot which identifies the frequency of data falling within sub-ranges of model-observation coordinate space 2) quantile-quantile (QQ) data are used to test whether the model is able to replicate the observed sample climate (effectively this relaxes the temporal and location constraints placed on data within the scatter plot).\n\nIn both cases, performance is considered good when data collapse along the 1:1 relationship between model and observation.\n\nFor all parameters, the following standard CMEMS metrics are provided:\n\n• Mean (MEAN) of observations. • Standard deviation (STD) of observations. • Mean of model minus observation differences (bias). - Standard deviation (STD) of model and observation differences. - Root mean square difference of model and observations (RMSD). - Pearson Correlation coefficient (CORR) between model and observations.\n\nIn addition, two further derived statistics have been provided in order to enable compaon between data from this QuID and metrics provided in support of WMO wave forecast systems (verification charts $^{4}$ for example); these include :\n\n\\- Scatter Index (SI): describing a compaon between model-observation difference, standard deviation and an observed background value. For WMO wave forecast verification, the background is defined as the observed mean. In this QuID however, the observed standard deviation has been applied as the background value. The reason to adopt this convention is twofold: i) using stand",
"score": 0.43333333333333335
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">B16</span><span class="qa-tool">search_docs</span><span class="qa-q">estimated accuracy numbers temperature salinity</span><span class="qa-dt num">0.7s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "estimated accuracy numbers temperature salinity",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"product_id": "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030",
"product_title": "Global Ocean- In-Situ Near-Real-Time Observations",
"doc_type": "QUID",
"doc_id": "CMEMS-INS-QUID-013-030-036",
"section": "I.3 Estimated Accuracy Numbers",
"text": "[TABLE in section: I.3 Estimated Accuracy Numbers]\nCaption: Table 2: Accuracy numbers (i.e., accuracy level of measurements or technical specs of the sensor) for temperature and salinity observations in the different platforms of the In Situ TAC.\n\n| Data-type | Temperature [°C] | Salinity [PSU] |\n| CTD (Conductivity Temperature Depth) | 0.005 - 0.001 | 0.02-0.003 |\n| XBT (Expendable Bathythermograph) | 0.1 | |\n| XCTD (Expendable Conductivity Temperature Depth) | 0.02 | 0.003 |\n| PF (profiling floats) - Argo Floats | 0.01 | 0.01 |\n| Moored buoy data: Tropical Moored Buoy (TRITON/TAO PIRATA/RAM Tropical Moored Buoy Array https://www.pmel.noaa.gov/gtmba/) | 0.002 | 0.003 |\n| Surface | 0.01 - 0.3 | |\n| Subsurface | 0.01 - 0.09 | |\n| Drifting buoy data | 0.01 | 0.01 |\n| Marine mammals | 0.005 | 0.01 |\n| Glider | 0.005 | 0.02 |\n| Underway (Ferrybox, Research vessel TSG) (Depending on sensor type ) | 0.001 – 0.1 | 0.003-0.2 |",
"score": 0.75
},
{
"product_id": "GLOBAL_ANALYSISFORECAST_PHY_001_024",
"product_title": "Global Ocean Physics Analysis and Forecast",
"doc_type": "QUID",
"doc_id": "CMEMS-GLO-QUID-001-024",
"section": "I.3 Estimated Accuracy Numbers",
"text": "The following 3D temperature and salinity (T/S), SLA, and SST accuracy numbers are estimated using statistical metrics performed with the model counterpart in time and space of the available observations. In all cases these numbers are an estimate of the mean error and not a maximum error on the domain. Here we present only the accuracy numbers for the global area. They are also computed for each region but are not shown in this document. Accuracy values are represented by RMSD and Mean bias for SLA, SST, and surface currents. They are given also for 3D temperature and salinity in the following vertical layers: 0-5 m, 5-100 m, 100-300 m, 300-800 m, 800-2000 m, and 2000-5000 m.\n\nIn order to calculate these accuracy numbers, we defined a one-year qualification (or calibration) period in 2019. Results are presented for each variable in the set of tables below.",
"score": 0.5263157894736842
},
{
"product_id": "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032",
"product_title": "Baltic Sea- In Situ Near Real Time Observations",
"doc_type": "QUID",
"doc_id": "CMEMS-INS-QUID-013-030-036",
"section": "I.3 Estimated Accuracy Numbers",
"text": "[TABLE in section: I.3 Estimated Accuracy Numbers]\nCaption: Table 2: Accuracy numbers (i.e., accuracy level of measurements or technical specs of the sensor) for temperature and salinity observations in the different platforms of the In Situ TAC.\n\n| Data-type | Temperature [°C] | Salinity [PSU] |\n| CTD (Conductivity Temperature Depth) | 0.005 - 0.001 | 0.02-0.003 |\n| XBT (Expendable Bathythermograph) | 0.1 | |\n| XCTD (Expendable Conductivity Temperature Depth) | 0.02 | 0.003 |\n| PF (profiling floats) - Argo Floats | 0.01 | 0.01 |\n| Moored buoy data: Tropical Moored Buoy (TRITON/TAO PIRATA/RAM Tropical Moored Buoy Array https://www.pmel.noaa.gov/gtmba/) | 0.002 | 0.003 |\n| Surface | 0.01 - 0.3 | |\n| Subsurface | 0.01 - 0.09 | |\n| Drifting buoy data | 0.01 | 0.01 |\n| Marine mammals | 0.005 | 0.01 |\n| Glider | 0.005 | 0.02 |\n| Underway (Ferrybox, Research vessel TSG) (Depending on sensor type ) | 0.001 – 0.1 | 0.003-0.2 |",
"score": 0.45
}
]
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>L2 · CDS/ADS/EWDS deep docs</b><span>PUG/ATBD for climate · atmosphere · early-warning</span></div>
<details class="qa"><summary><span class="qa-tag num">C17</span><span class="qa-tool">search_deep_docs</span><span class="qa-q">ERA5 uncertainty estimation ensemble spread</span><span class="qa-x">store=CDS</span><span class="qa-dt num">3.6s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "ERA5 uncertainty estimation ensemble spread",
"store": "CDS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_ids": [
"derived-era5-pressure-levels-daily-statistics",
"derived-era5-single-levels-daily-statistics",
"reanalysis-era5-complete",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels"
],
"doc_title": "ERA5 data documentation",
"doc_url": "https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation",
"section": "ERA5 data documentation > Accuracy and uncertainty",
"text": "ERA5 is produced using 4D-Var data assimilation and model forecasts in CY41R2 of the IFS. The 4D-Var in ERA5 utilises 12 hour assimilation windows from 9-21 UTC and 21-9 UTC, where the background forecast and all the observations falling within a time window are used to specify all the analyses during that window. However, the accuracy of the analyses is not uniform throughout each window. If the model and observations are unbiased and their errors follow Gaussian distributions and if the observations are homogeneous in space and time, then the analysis error will be smallest in the middle of the assimilation window. However, because none of these assumptions are actually true in the IFS, the particular parameter and location of interest are important, too. Knowing that, a careful study should show at which points during the assimilation windows the analysis is most accurate.\n\nThe 10 member ensemble is required for the data assimilation procedure. However, as a useful by-product, this ensemble also provides an estimate of the **relative, random uncertainty**. The \"spread\" of the 10 member ensemble, encapsulated by the standard deviation, provides a measure of this uncertainty and is larger for time periods and spatial locations where the uncertainty is relatively large and is smaller when and where there is more certainty in the analysed/forecast values. The spread is a measure of the relative uncertainty, so the numbers do not provide the absolute uncertainty. On the whole, the uncertainty becomes larger as you go back in time, when the observing system was not as good as ",
"score": 1.0
},
{
"store": "CDS",
"dataset_ids": [
"reanalysis-pan-carra",
"reanalysis-pan-carra-means"
],
"doc_title": "Uncertainty estimation methodology",
"doc_url": "https://confluence.ecmwf.int/x/J6oTJg",
"section": "Uncertainty estimation methodology > 2. Data Set > 2.3. Choice of parameters",
"text": "20 January 2022 for all four analysis cycles.\n\n**Table 1:** List of the input near surface parameters for uncertainty estimation in model space using the machine learning method for both ERA5 and CARRA2 ensembles.\n\n| | | | |\n| --- | --- | --- | --- |\n| **Variables** | **Level** | **CARRA2** **(2.5 km)** | **ERA5** **(ensemble)** |\n| 2m Temperature (in Kelvin) | Near Surface | Y =2869; X = 2869 | Y = 114; X = 130 |\n| Zonal Wind (10 m, u- in m/sec) | Near Surface | Y =2869; X = 2869 | Y = 114; X = 130 |\n| Meridional Wind (10 m, v- in m/sec) | Near Surface | Y =2869; X = 2869 | Y = 114; X = 130 |\n| Surface Pressure (Pa) | Surface | Y =2869; X = 2869 | Y = 114; X = 130 |\n\nTable 1 displays the input parameters near the surface for uncertainty estimation in model space using the ML approach for both the ERA5 ensemble and CARRA2 ensemble. Precipitation is excluded as a variable in the diffusion-based ML method because the approach requires gridded spread data among ensemble members. When there is no precipitation or a null value for any ensemble member, the spread becomes excessively high and unrealistic. As a result, the ML model is likely to perform poorly in most cases within the CARRA2 domain.\n\n**Figure 5:** Same as Figure 4, but for the ensemble mean of 2m-temperature (in Kelvin). The mean temperature flow between the ERA5-EDA and CARRA2 ensemble datasets is very similar, strongly supporting predictability and the underlying correlation between the lower resolution (ERA5-EDA) and higher-resolution (CARRA2) datasets.\n\nWe calculated ensemble spread (standard deviation, SD",
"score": 0.39999999999999997
},
{
"store": "CDS",
"dataset_ids": [
"reanalysis-oras5",
"reanalysis-oras5-timeseries"
],
"doc_title": "The ECMWF operational ensemble reanalysis-analysis system for ocean and sea ice: a description of the system and assessment",
"doc_url": "https://os.copernicus.org/articles/15/779/2019/os-15-779-2019.pdf",
"section": "The ECMWF operational ensemble reanalysis-analysis system for ocean and sea ice: a description of the system and assessment",
"text": "2019\nwww.ocean-sci.net/15/779/2019/\n\nH. Zuo et al.: The ECMWF operational OCEAN5 system\n799\nFigure 19. Ensemble spread of ORAS5 SST (K) estimated using\nfive ensemble members of ORAS5, computed using the monthly-\nmean SST anomaly in 2010.\nof these ECVs, which are generally derived from a combi-\nnation of satellite and in situ observations. Here, the latest\nversions of these ESA CCI climate data records for SST,\nSLA and SIC were chosen as reference climate data sets\nto verify ORAS5 and some relevant sensitivity experiments.\nThese observation-only analyses are produced with different\nproduction systems (e.g. different satellite missions) and/or\nprocessing chains (e.g. bias correction method) compared to\nthe observational data sets that were assimilated in ORAS5.\nAll statistics are computed using monthly-mean fields from\nORAS5 and ESA CCI observation data sets interpolated to a\ncommon 1◦× 1◦latitude–longitude grid.\n5.1\nSea surface temperature\nThe ESA SST CCI (SST_cci) long-term analysis provides\ndaily surface temperature of the global ocean over the period\n1992–2010. Unlike the HadISST2 and OSTIA SST analy-\nses, both of which are bias-corrected against in situ observa-\ntions (e.g. drifting buoys), ESA SST_cci only uses satellite\nobservations (AVHRR and ATSR). Therefore, it provides a\nreference SST data set of a quality that is suitable for climate\nresearch. The latest version 1.1 of the ESA SST_cci (Mer-\nchant et al., 2016) data set (referred to as SST_cci1.1 here-\nafter) has been used here for verification of the performance\nof ORAS5 at the sea surface. The SST_cci1.1 data set is",
"score": 0.3333333333333333
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">C18</span><span class="qa-tool">search_deep_docs</span><span class="qa-q">EAC4 aerosol optical depth validation AERONET</span><span class="qa-x">store=ADS</span><span class="qa-dt num">1.2s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "EAC4 aerosol optical depth validation AERONET",
"store": "ADS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "ADS",
"dataset_ids": [
"cams-global-reanalysis-eac4",
"cams-global-reanalysis-eac4-monthly"
],
"doc_title": "Data citation",
"doc_url": "http://www.atmos-chem-phys.net/19/3515/2019/",
"section": "The CAMS reanalysis of atmospheric composition > 4.4 Aerosols",
"text": ") | [Download XLSX](https://acp.copernicus.org/articles/19/3515/2019/acp-19-3515-2019-t08.xlsx)\n\nFigure 22 shows time series of monthly mean biases from the three reanalyses\nagainst AERONET for several areas, and Table 8 shows the corresponding mean\nbiases and their standard deviations. Figure 23 shows global correlation\ncoefficients with AERONET. As explained above, Mauna Loa and Mexico City were\nexcluded from these statistics. As already seen in Fig. 21, CAMSRA has\nthe smallest bias with respect to the AERONET data in most areas (see also\nTable 8) and has the largest correlation coefficient (Fig. 23). It shows a\ngood consistency throughout the time period from 2003 to 2016, while\nMACCRA shows an increasing positive bias with time in Europe and North\nAmerica. CIRA also shows increasing positive biases with time in North\nAmerica, particularly from 2013 onwards, and a change in biases in Europe,\nfrom negative at the beginning of the time series to positive at the end. It\nstill has to be assessed if this improvement is due to model changes or a\nbetter representation of the emissions in CAMSRA. There is a change to\nslightly higher AOD in CAMSRA (biases more positive in the global mean\nand in particular over Europe and North America) that seems to coincide with\nthe loss of AATSR data in April 2012. Over Southeast Asia all reanalyses have\na negative bias, with CAMSRA having the smallest and CIRA the largest\nbias (see Table 8). In the NH, the bias changes with season and is largest\n(positive) during the summer months.\n\n**Figure 23**Time series of global correlation coeff",
"score": 0.5769230769230769
},
{
"store": "ADS",
"dataset_ids": [
"cams-global-atmospheric-composition-forecasts"
],
"doc_title": "CAMS Global atmospheric composition forecast data documentation",
"doc_url": "https://confluence.ecmwf.int/x/5cqpD",
"section": "CAMS Global atmospheric composition forecast data documentation > Parameter listings > Table 2: Single level Slow access parameters (last reviewed on 02 Aug 2023 )",
"text": "ical\\_depth\\_1640nm | aodabs1640 | 215113 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 2130 nm |\n\n dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_2130nm | aodabs2130 | 215176 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 340 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_340nm | aodabs340 | 215096 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 355 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_355nm | aodabs355 | 215097 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 380 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_380nm | aodabs380 | 215098 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 400 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_400nm | aodabs400 | 215099 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 440 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_440nm | aodabs440 | 215100 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 469 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_469nm | aodabs469 | 215101 | | X | X | Only available from 26 June 2018 12UTC |\n| Total absorption aerosol optical depth at 500 nm | dimensionless | total\\_absorption\\_aerosol\\_optical\\_depth\\_500nm | aodabs500 | 215102 |",
"score": 0.5
},
{
"store": "ADS",
"dataset_ids": [
"cams-global-reanalysis-eac4",
"cams-global-reanalysis-eac4-monthly"
],
"doc_title": "Data citation",
"doc_url": "http://www.atmos-chem-phys.net/19/3515/2019/",
"section": "The CAMS reanalysis of atmospheric composition > 4.4 Aerosols",
"text": "https://acp.copernicus.org/articles/19/3515/2019/acp-19-3515-2019-f21-high-res.pdf)\n\nThe AOD at 550ânm from the reanalyses is evaluated with observations of the\nAErosol RObotic NETwork (AERONET; Figs. 21â23) Version 3 Level\n2.0 data. AERONET is a\nnetwork of about 400 stations measuring spectral AOD with ground-based sun\nphotometers (Holben et al., 1998). The stations are mostly located over land,\nwith a high number of stations situated in North America and Europe. The\nglobal number of stations contributing observations to the evaluation\nincreased from about 60 in 2003 to about 300 in 2016. Figure 21 shows maps of\nthe mean biases from the three reanalyses against AERONET. CAMSRA has\nthe smallest mean bias at most locations, while CIRA shows larger positive\nbiases over North America, Australia and desert areas (North Africa, Middle\nEast and Gobi) and a larger underestimation in India and Southeast Asia.\nMACCRA has even larger positive biases in North America and larger\npositive biases in Europe and the Mediterranean. Figure 21 shows that in\nCAMSRA there are some hotspots around outgassing volcanoes (in\nparticular Mauna Loa and Mexico City) with high analysis AOD values that\ndegrade the global average bias. These hotspots are dominated by sulfate and\nare a side effect of possibly erroneous model treatment of diffuse volcanic\nemissions, i.e. the model-resolution orography not resolving the height of\nthe volcanoes and therefore not being representative of the measurement sites\nwith respect to the volcanic plumes. It is also possible that there are\nerrors in how quickly ae",
"score": 0.3655913978494624
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">C19</span><span class="qa-tool">search_deep_docs</span><span class="qa-q">GloFAS forecast skill evaluation</span><span class="qa-x">store=EWDS</span><span class="qa-dt num">1.2s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "GloFAS forecast skill evaluation",
"store": "EWDS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "EWDS",
"dataset_ids": [
"cems-glofas-reforecast"
],
"doc_title": "GloFAS v2.2 metadata and skill scores",
"doc_url": "https://dast.copernicus-climate.eu/documents/cems-flood/GloFAS_v2.2_metadata_and_medium-range_skill_scores.pdf",
"section": "GloFAS v2.2 metadata and skill scores",
"text": "-532, in review, 2020.\n\nCopernicus Emergency Management Service\n\nGloFAS v2.2 Metadata and Skill Scores\n\n3\n\nTable of Contents\nGloFAS Diagnostic River Points ............................................................................................................... 4\nTable 1: GloFAS diagnostic river points for Africa. ............................................................................. 4\nTable 2: GloFAS diagnostic river points for Asia. .............................................................................. 33\nTable 3: GloFAS diagnostic river points for Australia-Oceania. ........................................................ 89\nTable 4: GloFAS diagnostic river points for Europe. ....................................................................... 104\nTable 5: GloFAS diagnostic river points for North America. ........................................................... 120\nTable 6: GloFAS diagnostic river points for South America. ........................................................... 152\nForecast Skill Scores against Climatology ........................................................................................... 176\nTable 7: GloFAS forecast skill scores against climatology for Africa. .............................................. 176\nTable 8: GloFAS forecast skill scores against climatology for Asia. ................................................. 206\nTable 9: GloFAS forecast skill scores against climatology for Australia-Oceania. ........................... 262\nTable 10: GloFAS forecast skill scores against climatology for Europe. ....",
"score": 0.5212765957446809
},
{
"store": "EWDS",
"dataset_ids": [
"cems-glofas-reforecast"
],
"doc_title": "GloFAS v2.2 metadata and skill scores",
"doc_url": "https://dast.copernicus-climate.eu/documents/cems-flood/GloFAS_v2.2_metadata_and_medium-range_skill_scores.pdf",
"section": "GloFAS v2.2 metadata and skill scores",
"text": ".93\n0.89\n0.84\n0.80\n0.74\n0.59\n0.44\n0.31\n0.19\n0.08\n-0.04\n-0.09\n\nCopernicus Emergency Management Service\n\nGloFAS v2.2 Metadata and Skill Scores| GloFAS Forecast Skill Scores against Climatology\n\n190",
"score": 0.5
},
{
"store": "EWDS",
"dataset_ids": [
"cems-glofas-reforecast"
],
"doc_title": "GloFAS v2.2 metadata and skill scores",
"doc_url": "https://dast.copernicus-climate.eu/documents/cems-flood/GloFAS_v2.2_metadata_and_medium-range_skill_scores.pdf",
"section": "GloFAS v2.2 metadata and skill scores",
"text": ".46\n0.38\n0.31\n0.27\n0.22\n0.12\n0.06\n0.00\n-0.04\n-0.06\n-0.15\n-0.22\n\nCopernicus Emergency Management Service\n\nGloFAS v2.2 Metadata and Skill Scores| GloFAS Forecast Skill Scores against Persistence",
"score": 0.3333333333333333
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">C20</span><span class="qa-tool">search_deep_docs</span><span class="qa-q">ERA5-Land soil layer depths definition</span><span class="qa-dt num">1.1s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "ERA5-Land soil layer depths definition",
"store": "CDS/ADS/EWDS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_ids": [
"satellite-soil-moisture"
],
"doc_title": "Product quality assessment report",
"doc_url": "https://confluence.ecmwf.int/x/8t6nHg",
"section": "Executive summary > 1. Product validation methodology > 1.2. Validation Reference Data > 1.2.2. ERA5-Land",
"text": "ERA5-Land (Muñoz-Sabater et al. 2021), produced by ECMWF, is a global reanalysis available from 1950 to present (with a few days delay). It provides surface variables with an increased spatial resolution compared to ERA5 (Hersbach et al. 2020). Soil Moisture in ERA5-Land is available in 1-hour intervals on a ~9 km grid and without temporal gaps. ERA5-Land provides various (land) variables, such as soil moisture (\"*swvlX*\") or soil temperature (\"*stlX*\"), where X stands for one of four soil layers (1 at 0-7 cm depth, 2 at 7-28 cm, 3 at 28-100 cm, and 4 at 100-289 cm).\n\nHere we use ERA5-Land data from 1981 to 2024 extracted at hours 0, 6, 12, and 18 of each day. Data from ERA5-Land layer 1 is used for comparison to the C3S SSM products. For the validation of the RZSM products, we also use layers 2 and 3 of ERA5-Land accordingly. Soil temperature information of each layer is used to mask observations before validation (when *stlX* is below 0°C) to eliminate the potential impact of remaining outliers in the satellite data at the transition from liquid to frozen soil moisture. ERA5-Land *stl1* data with a 0 °C threshold is also used to assess the accuracy of the C3S satellite soil moisture Freeze/Thaw product, assuming that soil moisture is correctly classified as \"frozen\" when the daily average soil temperature is below 0 °C.\n\nERA5-Land can be downloaded directly through [the Copernicus Climate Data Store](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land). Documentation available [online](https://confluence.ecmwf.int/x/shteC). (Resources validated 3rd September 2",
"score": 0.537037037037037
},
{
"store": "CDS",
"dataset_ids": [
"reanalysis-cerra-land"
],
"doc_title": "CERRA-Land User Guide",
"doc_url": "https://confluence.ecmwf.int/x/t06aE",
"section": "4. Details about the data fields > 4.2. Overview of variables available for the natural land fraction > 4.2.3. Soil variables",
"text": "The prognostic variables of soil temperature and soil moisture are represented in the model by a diffusive approach. Such a method proposes a discretisation of the soil into 14 layers, resulting in a total depth of 12 m, with a fine description of the subsurface layers to capture the diurnal cycle. The vertical discretisation (bottom depth of each layer in metres) is as follows: 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. Heat transfer is resolved over the total depth, while moisture transfer is resolved only over the depth of the roots, which depends on the type of vegetation and its geographical location.",
"score": 0.5217391304347826
},
{
"store": "CDS",
"dataset_ids": [
"derived-era5-pressure-levels-daily-statistics",
"derived-era5-single-levels-daily-statistics",
"reanalysis-era5-complete",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels"
],
"doc_title": "ERA5 data documentation",
"doc_url": "https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation",
"section": "ERA5 data documentation > Parameter listings > Table 2: surface and single level parameters: [instantaneous](https://confluence.ecmwf.int/display/CKB/Parameters+valid+at+the+specified+time)",
"text": "123 | | x |\n\n1 Soil layers\n\n| Layer | Range |\n| --- | --- |\n| Layer 1 | 0 - 7 cm |\n| Layer 2 | 7 - 28 cm |\n| Layer 3 | 28 - 100 cm |\n| Layer 4 | 100 - 289 cm |\n\nPlease note that in GRIB1, the largest value which can be stored in 1 octet is 255, so the layer 4 bottom value is set to \"missing\" (rather than 289). Some software can therefore give incorrect values for the lower boundary of this layer (e.g. CDO reports the value as 255). Please see <https://confluence.ecmwf.int/x/uqOGC> for more details.\n\n2GRIB2 format\n\n3**Leaf Area Index (LAI)** parameters are based on a monthly climatology. Users will only see monthly variability, but not inter-annual variability.",
"score": 0.3333333333333333
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">C21</span><span class="qa-tool">search_deep_docs</span><span class="qa-q">CERRA domain extent horizontal resolution</span><span class="qa-dt num">0.9s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "CERRA domain extent horizontal resolution",
"store": "CDS/ADS/EWDS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_ids": [
"reanalysis-cerra-height-levels",
"reanalysis-cerra-model-levels",
"reanalysis-cerra-pressure-levels",
"reanalysis-cerra-single-levels"
],
"doc_title": "CERRA User Guide.",
"doc_url": "https://confluence.ecmwf.int/x/WFQ7E",
"section": "3. Introduction > 3.3. The CERRA system components > 3.3.1. The CERRA system",
"text": "The CERRA system is based on the HARMONIE-ALADIN data assimilation system which has been developed and used within the ACCORD consortium. It is implemented and optimized for the entire European area with surrounding sea areas (see Fig. 2) with a horizontal resolution of 5.5 km and 106 vertical levels. The system uses lateral boundaries conditions obtained from the ERA5 global reanalysis (Fig. 5). Also, the large scales in the regional system are constrained by data from the global reanalysis. The increase of resolution from the global reanalysis (RA) as well as the precursor regional reanalysis (RRA) is depicted in Figure 6.\n\n**Figure 5:** Three different stages of RA: the global reanalyses from ERA5 are used as lateral boundary conditions for CERRA and CERRA-EDA reanalyses. Subsequently, short-forecast data from CERRA are used as background fields for the CERRA-Land surface reanalysis. As indicated by the vertical arrows, the amount of assimilated observations per area unit increases, in principle, from the global to the regional reanalysis as indicated by the arrows.\n\nThe CERRA system employs the 3D variational analysis (3D-VAR) method depicted schematically in Figure 4. At fixed points in time the model state is adjusted based on the observed state, taking into account the error statistics of both model and observations. The CERRA high-resolution system has been running with eight assimilation cycles per day performing analyses at 00, 03, 06, 09, 12, 15, 18 and 21 UTC. The forecasts lengths vary between 6 and 30 hours (see section 4.1.3 for more information) depending on",
"score": 0.5625
},
{
"store": "CDS",
"dataset_ids": [
"reanalysis-cerra-height-levels",
"reanalysis-cerra-model-levels",
"reanalysis-cerra-pressure-levels",
"reanalysis-cerra-single-levels"
],
"doc_title": "CERRA User Guide.",
"doc_url": "https://confluence.ecmwf.int/x/WFQ7E",
"section": "5. Detailed data description and availability > 5.2. CERRA-EDA",
"text": "CERRA-EDA comprises the same domain as CERRA and has exactly the same set of parameters as the high-resolution CERRA dataset. It differs only in the horizontal resolution, which is 11km as well as in the number of available time steps. CERRA-EDA has four analyses per day, at 00, 06, 12 and 18 UTC. Starting from the analyses, forecasts are run for six hours. Forecast fields are saved with hourly resolution.\n\n| | |\n| --- | --- |\n| **Metadata for CERRA-EDA parameters** | |\n| **Horizontal coverage** | Same as CERRA. See section 3.1 as well as Figure 2 for an overview of the model domain. |\n| **Horizontal resolution** | 11 km x 11 km |\n| **Vertical coverage** | Same as for CERRA parameters. |\n| **Vertical resolution** | Same as for CERRA-parameters. |\n| **Temporal coverage** | Same as for CERRA. |\n| **Temporal resolution** | Analyses are available at 00, 06, 12, and 18 UTC. The forecast length is six hours for all cycles and forecast data are saved with hourly resolution. |\n| **Data type and format** | Gridded data in GRIB2 |\n| **Grid** | Lambert conformal conic grid with 565x565 grid points |",
"score": 0.5238095238095238
},
{
"store": "CDS",
"dataset_ids": [
"reanalysis-cerra-height-levels",
"reanalysis-cerra-model-levels",
"reanalysis-cerra-pressure-levels",
"reanalysis-cerra-single-levels"
],
"doc_title": "CERRA User Guide.",
"doc_url": "https://confluence.ecmwf.int/x/WFQ7E",
"section": "5. Detailed data description and availability > 5.1. CERRA > 5.1.4. Parameters on model levels",
"text": "**Table 4:** Overview of parameters on model levels\n\n| | |\n| --- | --- |\n| **Metadata** | |\n| **Horizontal coverage** | The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Herewith, it covers entire Europe. See Figure 2 for an overview of the model domain. |\n| **Horizontal resolution** | 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members |\n| **Vertical coverage** | From approximately 10m (model level 106) above the surface to a height of 1 hPa (model level 1) |\n| **Vertical levels** | 106 hybrid atmospheric model levels (106, 105, 104 ... 3, 2, 1) |\n| **Temporal coverage** | 1984-09-01 00 UTC – 2021-06-30 21 UTC |\n| **Temporal resolution** | CERRA high-resolution reanalysis: 3-hourly analyses at 00, 03, 06, 09, 12, 15, 18 and 21 UTC CERRA ensemble members: 6-hourly analyses at 00, 06, 12 and 18 UTC Note: forecast data are not saved for the parameters on model levels |\n| **Data type and format** | Gridded data in GRIB2 |\n| **Grid** | Lambert conformal conic grid; 1069x1069 grid points for CERRA high-resolution reanalysis; 565x565 grid points for CERRA-EDA |\n\n**Table 4:** Overview of parameters on model levels\n\n| | | | | | |\n| --- | --- | --- | --- | --- | --- |\n| | Parameter | Unit | GRIB code | Analysis 3 hourly | forecast 1,2,3,… |\n| 1. | Specific humidity | kg/kg | 133 | yes | - |\n| 2. | Temperature | K | 130 | yes | - |\n| 3. | U-velocity | m/s | 131 | yes | - |\n| 4. | V-velocity | m",
"score": 0.41025641025641024
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">C22</span><span class="qa-tool">search_deep_docs</span><span class="qa-q">SEAS5 bias correction calibration</span><span class="qa-dt num">0.9s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "SEAS5 bias correction calibration",
"store": "CDS/ADS/EWDS",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"store": "CDS",
"dataset_ids": [
"sis-hydrology-variables-derived-seasonal-reforecast"
],
"doc_title": "Hydrological model specification",
"doc_url": "https://dast.copernicus-climate.eu/documents/sis-hydrology-derived/C3S_D424.SMHI.2.5_HydrologicalModelsSpecification_v2.pdf",
"section": "Hydrological model specification",
"text": "the CDS-Catalogue. The dataset covers most of Europe, with a\nresolution of 5 km at daily time steps, and the temporal coverage is from 1990 until present.\n\nCopernicus Climate Change Service\n\nC3S_424_SMHI_2021SC3-Hydrological Model Specification\nPage 9 of 30\n3.2 ERA5\nThe VIC-WUR model requires additional variables from what EFAS-Meteo provides, and the\nfollowing variables are retrieved from ERA5:\nSurface solar radiation, surface thermal radiation, surface wind components, surface pressure and\ndewpoint temperature.\n3.3 SEAS5\nThe seasonal forecasting currently used is the latest long-range forecasting system from ECMWF,\nknown as SEAS5. SEAS5 is the fifth generation for seasonal forecasting from ECMWF including a\nnumber of upgrades in comparison to the previous System4, in particular in the ocean model,\natmospheric resolution, and land surface initialisation. Details of SEAS5 are presented in Table 1.\nThe hydrological seasonal reforecasts make use of SEAS5 in its original resolution and were\naccessed directly from the MARS archive at ECMWF. This was performed in order to benefit from\nthe higher resolution compared to the common resolution of all seasonal forecast systems that are\navailable through the CDS-catalogue.\nTable 1. Characteristics of ECMWF SEAS5 seasonal forecasting system.\nCharacteristics\nECMWF SEAS5\nIFS Cycle\n43r1\nIFS horizontal resolution\nTCO319\nIFS Gaussian grid\nO320 (35 km)\nIFS vertical resolution (TOA)\nL91 (0.01 hPa)\nIFS model stochastic physics\n3-lev SPPT and SPBS\nOcean model\nNEMO v3.4\nOcean horizontal resolution\nORCA 0.25\nOcean vertical resolution\nL75\nSea ice ",
"score": 0.5344827586206896
},
{
"store": "CDS",
"dataset_ids": [
"sis-energy-derived-projections",
"sis-energy-derived-reanalysis"
],
"doc_title": "Technical description of methodologies followed in the development of each product",
"doc_url": "https://confluence.ecmwf.int/x/_FmaE",
"section": "Introduction > 3. Seasonal Forecast stream > 3.3. Bias adjustment of the climate indicators",
"text": "Both hindcasts and forecasts of SF systems are markedly affected by bias. In mid-latitudes, mean error commonly ranges from ±4° for temperature, ±3 m/s for wind and up to 5 mm/day for total precipitation (see figure below). Raw bias maps and time series plots are available for all initialization months, models and variables on the CDS's public partition:\n\n/data/public/C3S\\_ENERGY/SEAS/<clim,ener>/<ec,mo,mf>//FIG/\\*mean\\_error\\_ H\\_raw\\_1993-2016\\*\n\n**Figure** **13**: Raw mean bias for TA (K, METO SY14), TP (mm/day, ECMW SY05) and WS (10m, m/s, MTFR SY07). Initialization on November, validity Dec-Feb.\n\nThe inherent systematic bias in seasonal forecast is reduced through a quantile mapping (QM) approach. QM was chosen as it is a robust and widely used approach. The bias-adjustment is trained over the hindcast period. For each variable, hindcasts cumulative distribution function (CDF) is constructed. Then same process is applied to ERA5 since it is taken as the reference (graphical explanation on Figure 14). Finally, with the same statistical transform enabling to pass form hindcast CDS to ERA5 CDS, the same treatment is applied to the raw forecast's CDF in order to obtain the bias adjusted forecast.\n\nThe quantile mapping method may be summarized in two steps:\n\n1. Determine the percentile rank of a biased forecast value using its own empirical CDF;\n2. Retrieve and replace the reference value associated with the same percentile within the reference CDF.\n\nFew technical steps are also important to consider:\n\n* **Percentile computation:** Given a RUN-month and a forecast length, e",
"score": 0.5
},
{
"store": "CDS",
"dataset_ids": [
"sis-hydrology-variables-derived-seasonal-forecast"
],
"doc_title": "Hydrological model specification",
"doc_url": "https://dast.copernicus-climate.eu/documents/sis-hydrology-derived/C3S_D424.SMHI.2.3c_HydrologicalModelsSpecification_v1-2.pdf",
"section": "Hydrological model specification",
"text": "a conceptual illustration of the model. The runoff\nproduced at every grid cell is routed through the river network using a kinematic wave approach.\nThe model also includes options to simulate lakes, reservoirs and water abstraction.\n\nLISFLOOD is coded using the Python programming language and a PCRaster Python extension.\nLISFLOOD and its associated tools are all open-source. The LISFLOOD source code, model\ndocumentation, test catchments and tools can be found at the LISFLOOD Open Source page\nhttps://ec-jrc.github.io/lisflood/. The LISFLOOD used in EFAS is updated through official releases\nwhich have been versioned. For more info on the versioning, we refer to:\nhttps://confluence.ecmwf.int/display/COPSRV/EFAS+versioning+system\n\nFigure 1: Conceptual diagram of the major hydrological processes in the LISFLOOD model.\n\nCopernicus Climate Change Service\n\nC3S_424_SMHI_2020SC2-Hydrological Model Specification\nPage 10 of 37\n3. Meteorological forcing data\n3.1 EFAS-Meteo\nObservational meteorological forcing for model calibration and forecast initialization is using EFAS-\nMeteo (Ntegeka et al. 2013). The version used here is from May 2019, and was a pre-release for the\nversion that is being ingested into the CDS-Catalogue. The dataset covers most of Europe, with a\nresolution of 5 km at daily time steps, and the temporal coverage is from 1990 until present.\n3.2 ERA5\nThe VIC model requires additional variables from what EFAS-Meteo provides, and the following\nvariables are retrieved from the regularly updated reanalysis data set ERA5T:\nSurface solar radiation, surface thermal radiation, s",
"score": 0.3550724637681159
}
]
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>L2 · EQC quality</b><span>EQC quality assessment reports</span></div>
<details class="qa"><summary><span class="qa-tag num">D23</span><span class="qa-tool">get_eqc_quality_report</span><span class="qa-q">completeness of the satellite methane XCH4 record</span><span class="qa-dt num">0.6s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "completeness of the satellite methane XCH4 record",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"dataset_id": "satellite-methane",
"report_id": "satellite_satellite-methane_uncertainty_q01",
"aspect": "uncertainty_q01",
"title": "Methane satellite observations uncertainty and completeness assessment for carbon cycle",
"section": "Methane satellite observations uncertainty and completeness assessment for carbon cycle > Quality assessment statement",
"text": "to present derived from satellite observations\" ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).\n\n**[](template:section-3)**\n * [[9]](https://doi.org/10.1016/j.rse.2018.02.011) used a 'proxy (PR)' data product (CH4_GOS_OCPR) to take advantage of the higher data availability compared to the 'full physics (FP)' product (CH4_GOS_OCFP) in regions such as the tropics, which are characterised by the prevalence of clouds and high aerosol loading. PR products typically contain more data points and better coverage compared to FP: PR products suffer less from potential biases and therefore require less strict quality filtering resulting in more data points with quality flag “good” in the final product files ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).\n\n## 📈 Analysis and results\n\n(template:section-1)=\n### 1. Uncertainty assessment\n\nThe data product used by [[9]](https://doi.org/10.1016/j.rse.2018.02.011) was characterised by a random error of 13 ppb and a relative spatial bias of 2 ppb with respect to TCCON co-located observations ([[10]](https://doi.org/10.5194/acp-21-4339-2021)). According with [[8]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PQAR_GHG_main_latest.pdf), the **Level 2 XCH4 products** of the dataset \"Methane data from 2002 to present derived from satellite observations\" have **uncertainties comparable to those of the dataset used by [[9]](https://doi.org/10.1016/j.rse.2018.02.011)**: individual measurement accuracies are typically less than 20",
"code_notebooks": [
{
"notebook_id": "satellite_satellite-methane_completeness_q03",
"title": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 225,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_completeness_q04",
"title": "Methane satellite observations completeness assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 155,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_extremes-detection_q02",
"title": "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 151,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.527027027027027
},
{
"dataset_id": "satellite-methane",
"report_id": "satellite_satellite-methane_completeness_q03",
"aspect": "completeness_q03",
"title": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"section": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"text": "Production date: 21-11-2025\n\nProduced by: Davide Putero (CNR), Paolo Cristofanelli (CNR)",
"code_notebooks": [
{
"notebook_id": "satellite_satellite-methane_completeness_q03",
"title": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 225,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_completeness_q04",
"title": "Methane satellite observations completeness assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 155,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_extremes-detection_q02",
"title": "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 151,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.5
},
{
"dataset_id": "satellite-methane",
"report_id": "satellite_satellite-methane_completeness_q03",
"aspect": "completeness_q03",
"title": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"section": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring > ℹ️ If you want to know more > Key resources",
"text": "The CDS catalogue entries for the data used were:\n* Methane data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview\n\nCode libraries used:\n* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [B-Open](https://www.bopen.eu/)\n\nUsers interested in obtaining updated figures for methane growth rates and global trends are directed to official C3S sources for precise reporting: [[7]](https://doi.org/10.24381/14j9-s541), [[8]](https://climate.copernicus.eu/global-climate-highlights-2024).\n\nUsers interested in near-real time detection of hot-spot locations for methane emissions can consider to use the CAMS Methane Hotspot Explorer: https://atmosphere.copernicus.eu/ghg-services/cams-methane-hotspot-explorer?utm_source=press&utm_medium=referral&utm_campaign=CH4-app-2025",
"code_notebooks": [
{
"notebook_id": "satellite_satellite-methane_completeness_q03",
"title": "Methane satellite observations uncertainty assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 225,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_completeness_q04",
"title": "Methane satellite observations completeness assessment for greenhouse gas monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 155,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-methane_extremes-detection_q02",
"title": "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 151,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.43333333333333335
}
],
"source": "c3s2-eqc-quality-assessment (CDS EQC QA reports)",
"next_step": "where a result has code_notebooks[], call get_dataset_code(dataset_id, notebook_id=...) for the runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">D24</span><span class="qa-tool">get_eqc_quality_report</span><span class="qa-q">are CMIP6 models consistent for temperature projections</span><span class="qa-x">dataset_id=projections-cmip6</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "are CMIP6 models consistent for temperature projections",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"dataset_id": "projections-cmip6",
"report_id": "climate_projections-cmip6_validation_q12",
"aspect": "validation_q12",
"title": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
"section": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
"text": "Production date: 27-06-2025\n\nProduced by: CMCC foundation - Euro-Mediterranean Center on Climate Change. Albert Martinez Boti and Lorenzo Sangelantoni.",
"code_notebooks": [
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q01",
"title": "Bias in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 702,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q02",
"title": "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 684,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q03",
"title": "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 587,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q01",
"title": "Biases in energy-consumption-related indices in Europe",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 622,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q02",
"title": "Uncertainty in projected changes of energy consumption in Europe",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 539,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q03",
"title": "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 526,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q04",
"title": "Projections of future ice-free periods for the Arctic and Antarctic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 884,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_model-performance_q02",
"title": "Historical accuracy of sea ice extent in the CMIP6 experiments",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 563,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_model-performance_q03",
"title": "Evaluation of the Arctic sea ice thickness in the CMIP6 historical experiments",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 325,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q10",
"title": "Testing the capability of CMIP6 GCMs to represent precipitation inter-annual variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 476,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q11",
"title": "Testing the capability of CMIP6 GCMs to represent precipitation intra-seasonal variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 632,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q12",
"title": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 342,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q13",
"title": "CMIP6 biases in the SPEI6 drought index over the Mediterranean region",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 612,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "projections-cmip6",
"title": "Plot an Ensemble of CMIP6 Climate Projections",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 118,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.5
},
{
"dataset_id": "projections-cmip6",
"report_id": "climate_projections-cmip6_climate-and-weather-extremes_q02",
"aspect": "climate-and-weather-extremes_q02",
"title": "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector",
"section": "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Quality assessment statement",
"text": "These are the key outcomes of this assessment\n\n* Looking ahead to future projections (2015-2099), all models within the subset agree on projecting general positive trends for both indices across Europe during the temporal aggregation of JJA, with a particularly notable positive projected trend in the Mediterranean Basin for 'TX90p'. This finding is consistent with the results of Josep Cos et al. (2022) [[6]](https://doi.org/10.5194/esd-13-321-2022), who evaluated the Mediterranean climate change hotspot using CMIP6 projections.\n\n* While certain regions exhibit near-zero trends for the 'SU' index, possibly due to threshold temperature constraints, others show higher values, highlighting the importance of considering both statistically and physically based extreme indices for comprehensive assessments.\n\n* Utilising CMIP6 projections presents a valuable opportunity to anticipate future trends in air temperature extremes across Europe, enabling the insurance industry to refine risk management strategies. While all considered models show a positive trend for these indices, the magnitude of these trends and their uncertainty (quantified by the inter-model spread) vary spatially and need to be considered.\n\n* A separate assessment evaluates the biases in climatology and trends of these indices for the historical period from 1971 to 2000 (\"CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector\"). The results of that assessment show an overall underestimation of the trends for both indices and the climatology of 'SU', as well as difficult",
"code_notebooks": [
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q01",
"title": "Bias in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 702,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q02",
"title": "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 684,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q03",
"title": "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 587,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q01",
"title": "Biases in energy-consumption-related indices in Europe",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 622,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q02",
"title": "Uncertainty in projected changes of energy consumption in Europe",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 539,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q03",
"title": "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 526,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q04",
"title": "Projections of future ice-free periods for the Arctic and Antarctic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 884,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_model-performance_q02",
"title": "Historical accuracy of sea ice extent in the CMIP6 experiments",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 563,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_model-performance_q03",
"title": "Evaluation of the Arctic sea ice thickness in the CMIP6 historical experiments",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 325,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q10",
"title": "Testing the capability of CMIP6 GCMs to represent precipitation inter-annual variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 476,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q11",
"title": "Testing the capability of CMIP6 GCMs to represent precipitation intra-seasonal variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 632,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q12",
"title": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 342,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q13",
"title": "CMIP6 biases in the SPEI6 drought index over the Mediterranean region",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 612,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "projections-cmip6",
"title": "Plot an Ensemble of CMIP6 Climate Projections",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 118,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.5
},
{
"dataset_id": "projections-cmip6",
"report_id": "climate_projections-cmip6_validation_q12",
"aspect": "validation_q12",
"title": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
"section": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands > Quality assessment question",
"text": "* **How is warming propagating across pressure levels and different latitudinal bands?**\n* **Is there signal consistence between CMIP6 ensemble results and ERA5, and among CMIP6 ensemble GCMs?**\n\nThe vertical profile of temperature trends is widely recognised as an important fingerprint of climate change (e.g., [[1]](https://doi.org/10.1002/joc.1756),[[2]](https://doi.org/10.1126/science.274.5290.1170)),[[3]](https://doi.org/10.1073/pnas.1305332110). One of the most studied features of this vertical structure is the amplified warming in the tropical upper troposphere compared to the surface, commonly referred to as tropical tropospheric amplification [[4]](https://doi.org/10.1126/science.1114867). This behaviour is consistent with the basic theory of moist adiabatic processes, which predicts stronger warming in the tropical free troposphere than near the surface due to latent heat release during deep convection [[5]](https://doi.org/10.1175/1520-0469%281979%29036<0415:ALRRAT>2.0.CO;2).\n\nWhile tropical amplification has been widely studied, other regions and their representation in CMIP6 models remain less explored. To develop a more comprehensive evaluation framework, it is essential to assess how warming propagates vertically across pressure levels and latitudinal bands. Understanding the vertical structure of temperature trends across these bands provides critical insights into model performance and the physical processes driving climate change. This vertical perspective is key for model developers to identify and diagnose persistent biases, such as errors in atmospheric",
"code_notebooks": [
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q01",
"title": "Bias in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 702,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q02",
"title": "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 684,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-and-weather-extremes_q03",
"title": "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 587,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q01",
"title": "Biases in energy-consumption-related indices in Europe",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 622,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q02",
"title": "Uncertainty in projected changes of energy consumption in Europe",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 539,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q03",
"title": "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 526,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_climate-impact-indicators_q04",
"title": "Projections of future ice-free periods for the Arctic and Antarctic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 884,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_model-performance_q02",
"title": "Historical accuracy of sea ice extent in the CMIP6 experiments",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 563,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_model-performance_q03",
"title": "Evaluation of the Arctic sea ice thickness in the CMIP6 historical experiments",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 325,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q10",
"title": "Testing the capability of CMIP6 GCMs to represent precipitation inter-annual variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 476,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q11",
"title": "Testing the capability of CMIP6 GCMs to represent precipitation intra-seasonal variability",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 632,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q12",
"title": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 342,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cmip6_validation_q13",
"title": "CMIP6 biases in the SPEI6 drought index over the Mediterranean region",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 612,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "projections-cmip6",
"title": "Plot an Ensemble of CMIP6 Climate Projections",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 118,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.45
}
],
"source": "c3s2-eqc-quality-assessment (CDS EQC QA reports)",
"next_step": "where a result has code_notebooks[], call get_dataset_code(dataset_id, notebook_id=...) for the runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">D25</span><span class="qa-tool">get_eqc_quality_report</span><span class="qa-q">seasonal forecast skill for precipitation</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "seasonal forecast skill for precipitation",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"dataset_id": "seasonal-monthly-single-levels",
"report_id": "seasonal_seasonal-monthly-single-levels_resolution_q06",
"aspect": "resolution_q06",
"title": "Assessing the impact of spatial scale and temporal trends on seasonal forecast quality",
"section": "Assessing the impact of spatial scale and temporal trends on seasonal forecast quality > ℹ️ If you want to know more > References",
"text": "[1] Greuell, W., Franssen, W. H. P., and Hutjes, R. W. A.: Seasonal streamflow forecasts for Europe – Part 2: Sources of skill, Hydrol. Earth Syst. Sci., 23, 371–391, https://doi.org/10.5194/hess-23-371-2019, 2019.\n\n[2] Prodhomme, C., Materia, S., Ardilouze, C. et al. Seasonal prediction of European summer heatwaves. Clim Dyn 58, 2149–2166 (2022). https://doi.org/10.1007/s00382-021-05828-3\n\n[3] Gubler, S., and Coauthors, 2020: Assessment of ECMWF SEAS5 Seasonal Forecast Performance over South America. Wea. Forecasting, 35, 561–584, https://doi.org/10.1175/WAF-D-19-0106.1\n\n[4] Calì Quaglia, F., Terzago, S. & von Hardenberg, J. Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods. Clim Dyn 58, 2167–2191 (2022). https://doi.org/10.1007/s00382-021-05895-6",
"code_notebooks": [
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q02",
"title": "Seasonal forecasts bias assessment for impact models",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 304,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q04",
"title": "Evaluation of the hit-rate of seasonal forecasts for tercile categories of monthly anomalies",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 303,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_resolution_q06",
"title": "Assessing the impact of spatial scale and temporal trends on seasonal forecast quality",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 276,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_uncertainty-quality-flags_q01",
"title": "Assessing possible outcomes of seasonal temperature forecast",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_lines": 241,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "sf-anomalies",
"title": "Seasonal Forecast Anomalies",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 194,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
},
{
"notebook_id": "sf-verification",
"title": "Seasonal Forecast Verification",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 305,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.8333333333333333
},
{
"dataset_id": "seasonal-monthly-single-levels",
"report_id": "seasonal_seasonal-monthly-single-levels_validation_q05",
"aspect": "validation_q05",
"title": "Benefit and challenges of a multi-model approach to seasonal forecasts",
"section": "Benefit and challenges of a multi-model approach to seasonal forecasts > ℹ️ If you want to know more > Key resources",
"text": "Explore the [C3S Seasonal Forecast Products](https://climate.copernicus.eu/seasonal-forecasts)\n\nExplore the [North American Multi-Model Ensemble products](https://nmme.earth.miami.edu/forecasts/nmme_prob_forecast_all_var.html)\n\n[PyCPT](https://iri-pycpt.github.io/PyCPT2-Seasonal-Forecast-User-Guide/intro.html) - a tool calibrate and verify multi-model seasonal forecasts of precipitation based on the NOAA North American Multi-Model Ensemble (NMME) and European Copernicus Climate Change Service (C3S) databases.",
"code_notebooks": [
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q02",
"title": "Seasonal forecasts bias assessment for impact models",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 304,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q04",
"title": "Evaluation of the hit-rate of seasonal forecasts for tercile categories of monthly anomalies",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 303,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_resolution_q06",
"title": "Assessing the impact of spatial scale and temporal trends on seasonal forecast quality",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 276,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_uncertainty-quality-flags_q01",
"title": "Assessing possible outcomes of seasonal temperature forecast",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_lines": 241,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "sf-anomalies",
"title": "Seasonal Forecast Anomalies",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 194,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
},
{
"notebook_id": "sf-verification",
"title": "Seasonal Forecast Verification",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 305,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.5454545454545454
},
{
"dataset_id": "seasonal-monthly-single-levels",
"report_id": "seasonal_seasonal-monthly-single-levels_validation_q05",
"aspect": "validation_q05",
"title": "Benefit and challenges of a multi-model approach to seasonal forecasts",
"section": "Benefit and challenges of a multi-model approach to seasonal forecasts > ℹ️ If you want to know more > References",
"text": "sembles in seasonal forecasting — I. Basic concept. Tellus A: Dynamic Meteorology and Oceanography, 57(3), 219–233. https://doi.org/10.3402/tellusa.v57i3.14657\n\n[[23]](https://doi.org/10.1002/qj.210) Weigel, A. P., Liniger, M. A., & Appenzeller, C. (2008). Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts?. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 134(630), 241-260.\n\n[[24]](https://doi.org/10.1007/s00382-020-05314-2) Hemri, S., Bhend, J., Liniger, M.A. et al. How to create an operational multi-model of seasonal forecasts?. Clim Dyn 55, 1141–1157 (2020). https://doi.org/10.1007/s00382-020-05314-2\n\n[[25]](https://doi.org/10.3402/tellusa.v57i3.14658) Doblas-Reyes, F. J., Hagedorn, R., & Palmer, T. N. (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination. Tellus A: Dynamic Meteorology and Oceanography, 57(3), 234–252.\n\n[[26]](https://doi.org/10.1007/s00382-018-4404-z) Mishra N, Prodhomme C, Guemas V (2018) Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe. Clim Dyn.\n\n[[27]](https://doi.org/10.1175/MWR2818.1) Robertson, A. W., Lall, U., Zebiak, S. E., & Goddard, L. (2004). Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Monthly Weather Review, 132(12), 2732-2744.\n\n[[28]](https://doi.org/10.1175/BAMS-84-12-1783) Barnston, A. G., Mason, S. J., Goddard, L., DeWitt, D. G., & Zebiak, S. E.",
"code_notebooks": [
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q02",
"title": "Seasonal forecasts bias assessment for impact models",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 304,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q04",
"title": "Evaluation of the hit-rate of seasonal forecasts for tercile categories of monthly anomalies",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 303,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_resolution_q06",
"title": "Assessing the impact of spatial scale and temporal trends on seasonal forecast quality",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 276,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "seasonal_seasonal-monthly-single-levels_uncertainty-quality-flags_q01",
"title": "Assessing possible outcomes of seasonal temperature forecast",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_lines": 241,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "sf-anomalies",
"title": "Seasonal Forecast Anomalies",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 194,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
},
{
"notebook_id": "sf-verification",
"title": "Seasonal Forecast Verification",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 305,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.3619047619047619
}
],
"source": "c3s2-eqc-quality-assessment (CDS EQC QA reports)",
"next_step": "where a result has code_notebooks[], call get_dataset_code(dataset_id, notebook_id=...) for the runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">D26</span><span class="qa-tool">get_eqc_quality_report</span><span class="qa-q">glacier mass change trend assessment maturity</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "glacier mass change trend assessment maturity",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"dataset_id": "derived-gridded-glacier-mass-change",
"report_id": "satellite_derived-gridded-glacier-mass-change_data-completeness_q01",
"aspect": "data-completeness_q01",
"title": "Glacier mass change data from satellite and in-situ observations: uncertainty analysis for glaciological and climate change monitoring",
"section": "Glacier mass change data from satellite and in-situ observations: uncertainty analysis for glaciological and climate change monitoring > Quality assessment question",
"text": "* **\"Is the glacier mass change dataset sufficiently adequate in terms of its uncertainty to effectively monitor and evaluate global (cumulative) glacier mass changes over time, including their associated impacts on global sea level rise?\"**\n\nGlaciers significantly impact global sea-level rise, freshwater resources, natural hazards, hydro-power generation, recreation and tourism. Assessing glacier mass changes due to climate warming is therefore crucial for addressing these issues. The \"[Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database](https://cds.climate.copernicus.eu/datasets/derived-gridded-glacier-mass-change?tab=overview)\" (version WGMS-FOG-2023-09) on the Climate Data Store (CDS), offers a global coverage of glacier mass changes by integrating in-situ, aerial, and satellite data [[1](https://wgms.ch/), [2](https://doi.org/10.5194/essd-17-1977-2025)]. The gridded glaciers mass change dataset that is on the CDS is currently one of the most complete dataset of glacier mass change data in terms of its spatial coverage. It is generally considered the main reference dataset to determine long-term glacier mass changes across the globe.\n\nWhen measured over a long period and at extended geographical scales, trends in glacier mass balance can be considered a clear indicator of global climate change [[3](https://doi.org/10.1038/s41586-019-1071-0)]. Despite some known issues, this dataset provides valuable insights into glacier mass changes across spatial and temporal scales. In that regard, this notebook investigates how w",
"code_notebooks": [
{
"notebook_id": "satellite_derived-gridded-glacier-mass-change_data-completeness_q01",
"title": "Glacier mass change data from satellite and in-situ observations: uncertainty analysis for glaciological and climate change monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 244,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_derived-gridded-glacier-mass-change_trend-assessment_q02",
"title": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 222,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.625
},
{
"dataset_id": "derived-gridded-glacier-mass-change",
"report_id": "satellite_derived-gridded-glacier-mass-change_trend-assessment_q02",
"aspect": "trend-assessment_q02",
"title": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis",
"section": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis",
"text": "Production date: 31-05-2025\n\nDataset version: WGMS-FOG-2023-09\n\nProduced by: Yoni Verhaegen and Philippe Huybrechts (Vrije Universiteit Brussel)",
"code_notebooks": [
{
"notebook_id": "satellite_derived-gridded-glacier-mass-change_data-completeness_q01",
"title": "Glacier mass change data from satellite and in-situ observations: uncertainty analysis for glaciological and climate change monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 244,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_derived-gridded-glacier-mass-change_trend-assessment_q02",
"title": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 222,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.5
},
{
"dataset_id": "derived-gridded-glacier-mass-change",
"report_id": "satellite_derived-gridded-glacier-mass-change_trend-assessment_q02",
"aspect": "trend-assessment_q02",
"title": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis",
"section": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis > Analysis and results > 4. Short summary and take-home messages",
"text": "When measured over long periods and extensive geographical scales, trends in glacier mass changes are clear indicators of global climate change [[3](https://doi.org/10.1038/s41586-019-1071-0), [9](https://doi.org/10.1038/s41586-024-08545-z)]. To be able to derive such trends of glacier mass changes and for climate change analysis/monitoring to become reliable and possible, the glacier mass change dataset should at least exhibit a comprehensive spatial coverage (i.e. global), a long and continuous temporal coverage (> 30 years), quantified and transparent pixel-by-pixel uncertainty estimates that meet international proposed thresholds [[4](https://library.wmo.int/records/item/58111-the-2022-gcos-ecvs-requirements-gcos-245)], a validation effort or a comparison to theoretical models, and an adequate spatio-temporal resolution (cfr. the \"Maturity Matrix\" [[10](https://doi.org/10.1175/BAMS-D-21-0109.1)]).\n\nThe [glacier mass change dataset on the CDS](https://cds.climate.copernicus.eu/datasets/derived-gridded-glacier-mass-change?tab=overview) is found to exhibit a consistent suitable spatial (0.5° x 0.5°) and temporal (annual) resolution, as well as an extensive coverage (> 30 years globally), to conduct a meaningful analysis of linear and quadratic trends in glacier mass changes at local (pixel-by-pixel), regional, and global scales. The spatial and temporal resolution/extent of the data furthermore align with international standards such as those proposed by GCOS [[4](https://library.wmo.int/records/item/58111-the-2022-gcos-ecvs-requirements-gcos-245)] and data gaps are practi",
"code_notebooks": [
{
"notebook_id": "satellite_derived-gridded-glacier-mass-change_data-completeness_q01",
"title": "Glacier mass change data from satellite and in-situ observations: uncertainty analysis for glaciological and climate change monitoring",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 244,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_derived-gridded-glacier-mass-change_trend-assessment_q02",
"title": "Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 222,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.43333333333333335
}
],
"source": "c3s2-eqc-quality-assessment (CDS EQC QA reports)",
"next_step": "where a result has code_notebooks[], call get_dataset_code(dataset_id, notebook_id=...) for the runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">D27</span><span class="qa-tool">get_eqc_quality_report</span><span class="qa-q">uncertainty of Greenland ice sheet velocity</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "uncertainty of Greenland ice sheet velocity",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"dataset_id": "satellite-greenland-ice-sheet-velocity",
"report_id": "satellite_satellite-greenland-ice-sheet-velocity_uncertainty_q02",
"aspect": "uncertainty_q02",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"section": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications > Quality assessment question",
"text": "* **\"How does the uncertainty of the horizontal ice sheet surface flow velocity vary in space and time and is the dataset sufficiently accurate and precise to be used in an ice sheet modeling framework?\"**\n\nIce sheets are not static, but instead flow. The total velocity magnitude is the sum of the velocity related to internal ice deformation, and is complemented by basal sliding and bed deformation components. Mapping ice sheet surface flow velocities and their temporal changes provides key information for investigating the dynamic response of the ice sheets to climate change. Remote sensing techniques, such as the use of satellites, are an important feature to derive and study these flow velocities. Satellites are able to inspect directly and repeatedly large areas of ice, and, as such are able to detect the movement of its surface. Remote sensing techniques that use satellite data are therefore considered the only feasible manner to derive accurate surface velocities of the ice sheets on a regular basis. In this dataset, they are derived by applying offset tracking techniques using Sentinel-1 synthetic aperture radar (SAR) satellite data. The main principle of the \"[Ice sheet velocity for Antarctica and Greenland derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-greenland-ice-sheet-velocity?tab=overview)\" dataset therefore relies on the preservation of surface features or other detectable patterns (e.g. speckle) in between multiple image acquisition periods. The extraction of these features/patterns from images acquired over differe",
"code_notebooks": [
{
"notebook_id": "satellite_satellite-greenland-ice-sheet-velocity_trend-assessment_q01",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial coverage and data completeness for trend analysis in glaciological applications",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 238,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-greenland-ice-sheet-velocity_uncertainty_q02",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 368,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.75
},
{
"dataset_id": "satellite-greenland-ice-sheet-velocity",
"report_id": "satellite_satellite-greenland-ice-sheet-velocity_uncertainty_q02",
"aspect": "uncertainty_q02",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"section": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"text": "Production date: 31-05-2025\n\nDataset version: 1.3 to 1.5\n\nProduced by: Yoni Verhaegen and Philippe Huybrechts (Vrije Universiteit Brussel)",
"code_notebooks": [
{
"notebook_id": "satellite_satellite-greenland-ice-sheet-velocity_trend-assessment_q01",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial coverage and data completeness for trend analysis in glaciological applications",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 238,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-greenland-ice-sheet-velocity_uncertainty_q02",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 368,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.5
},
{
"dataset_id": "satellite-greenland-ice-sheet-velocity",
"report_id": "satellite_satellite-greenland-ice-sheet-velocity_uncertainty_q02",
"aspect": "uncertainty_q02",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"section": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications > ℹ️ If you want to know more > Key resources",
"text": "- \"[Ice sheet velocity for Antarctica and Greenland derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-greenland-ice-sheet-velocity?tab=overview)\" on the CDS\n- [Documentation on the CDS](https://cds.climate.copernicus.eu/datasets/satellite-greenland-ice-sheet-velocity?tab=documentation) and the [ECMWF Confluence Wiki](https://confluence.ecmwf.int/pages/viewpage.action?pageId=355348656) (Copernicus Knowledge Base).\n- [Copernicus climate change indicators: ice sheets](https://climate.copernicus.eu/climate-indicators/ice-sheets)\n- [A web article about remote sensing-derived ice velocity acquisition](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1/Sentinel-1_s_decade_of_essential_data_over_shifting_ice_sheets)\n- [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control` prepared by [B-Open](https://www.bopen.eu).",
"code_notebooks": [
{
"notebook_id": "satellite_satellite-greenland-ice-sheet-velocity_trend-assessment_q01",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial coverage and data completeness for trend analysis in glaciological applications",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 238,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "satellite_satellite-greenland-ice-sheet-velocity_uncertainty_q02",
"title": "Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 368,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
}
],
"score": 0.4583333333333333
}
],
"source": "c3s2-eqc-quality-assessment (CDS EQC QA reports)",
"next_step": "where a result has code_notebooks[], call get_dataset_code(dataset_id, notebook_id=...) for the runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">D28</span><span class="qa-tool">get_eqc_quality_report</span><span class="qa-q">ERA5 representation of extreme events heat waves</span><span class="qa-dt num">0.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "ERA5 representation of extreme events heat waves",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"dataset_id": "thermaltrace",
"report_id": "application_thermaltrace_extreme-events_q01",
"aspect": "extreme-events_q01",
"title": "Visualisation of heat and cold stress with Thermal Trace",
"section": "Visualisation of heat and cold stress with Thermal Trace > ℹ️ If you want to know more > Key resources",
"text": "More about the ERA5 reanalysis and ERA5-HEAT dataset:\n* [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803)\n* [The ERA5 global reanalysis from 1940 to 2022](https://doi.org/10.1002/qj.4803)\n* [ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis](https://doi.org/10.1002/gdj3.102)\n* [Thermal comfort indices derived from ERA5 reanalysis (ERA5-HEAT)](https://doi.org/10.24381/cds.553b7518) on the CDS\n * [Time series optimised version of the dataset](https://cds.climate.copernicus.eu/datasets/derived-utci-historical-timeseries)\n\nRelated datasets on the CDS:\n* [Climate extreme indices and heat stress indicators derived from CMIP6 global climate projections](https://doi.org/10.24381/cds.776e08bd)\n* [Heat waves and cold spells in Europe derived from climate projections](https://doi.org/10.24381/cds.9e7ca677)\n\nMore about feels-like temperature and UTCI:\n* [Literature Review on UTCI Applications](https://doi.org/10.1007/978-3-030-76716-7_3)\n* [Principles of the New Universal Thermal Climate Index (UTCI) and its Application to Bioclimatic Research in European Scale](https://doi.org/10.2478/mgrsd-2010-0009)\n* [Thermofeel: A python thermal comfort indices library](https://doi.org/10.1016/j.softx.2022.101005)\n* [UTCI—Why another thermal index?](https://doi.org/10.1007/s00484-011-0513-7)\n\nMore about cold snaps:\n* [Could an extremely cold central European winter such as 1963 happen again despite climate change?](https://doi.org/10.5194/wcd-5-943-2024)\n* [The UK winter of 2009/2010 compared with severe winters of the last 100 y",
"code_notebooks": [],
"score": 0.6111111111111112
},
{
"dataset_id": "projections-cordex-domains-single-levels",
"report_id": "climate_projections-cordex-domains-single-levels_climate-and-weather-extremes_q01",
"aspect": "climate-and-weather-extremes_q01",
"title": "Bias in extreme temperature indices for the reinsurance sector",
"section": "Bias in extreme temperature indices for the reinsurance sector",
"text": "Production date: 29-05-2024\n\nProduced by: CMCC foundation - Euro-Mediterranean Center on Climate Change. Albert Martinez Boti.",
"code_notebooks": [
{
"notebook_id": "climate_projections-cordex-domains-single-levels_climate-and-weather-extremes_q01",
"title": "Bias in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 698,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cordex-domains-single-levels_climate-and-weather-extremes_q02",
"title": "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 678,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cordex-domains-single-levels_climate-and-weather-extremes_q03",
"title": "Bias in precipitation-based indices for impact models",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 698,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cordex-domains-single-levels_climate-and-weather-extremes_q04",
"title": "Projected changes in precipitation-based indices for impact models",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 678,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cordex-domains-single-levels_validation_q06",
"title": "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 606,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cordex-domains-single-levels_validation_q07",
"title": "CORDEX biases in the SPEI6 drought index over the Mediterranean region",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 731,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "climate_projections-cordex-domains-single-levels_validation_q08",
"title": "Near-future CORDEX projections of the SPEI6 drought index over the Mediterranean region",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 671,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
},
{
"notebook_id": "datasets_projections_cordex_domains_single_levels_arco_access",
"title": "Access the analysis ready CORDEX regional climate projections data",
"recipe_kinds": [
"analyze",
"download"
],
"n_code_lines": 50,
"source_repo": "ecmwf-training/dss-notebooks"
},
{
"notebook_id": "projections-cordex",
"title": "Analysis of Projected versus Historical Climatology with CORDEX Data",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_lines": 96,
"source_repo": "ecmwf-projects/copernicus-training-c3s"
}
],
"score": 0.5
},
{
"dataset_id": "climate-pulse",
"report_id": "application_climate-pulse_extreme-events_q01",
"aspect": "extreme-events_q01",
"title": "Visualisation of temperature during extreme weather events with Climate Pulse",
"section": "Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 2. Heatwaves > Introduction",
"text": "There is no universal definition of a [heatwave](https://climate.copernicus.eu/heatwaves-brief-introduction),\nwith different organisations and countries having established their own criteria.\nHowever, we can generally define them as a period of time, from a few days up to a few weeks,\nof temperatures significantly higher than the normal range for that location at that time of year\n[[Lhotka+22](https://doi.org/10.1029/2022EA002567), [Stefanon+12](https://doi.org/10.1088/1748-9326/7/1/014023)].\nFor example, the [European State of the Climate 2023](https://climate.copernicus.eu/heatwaves-brief-introduction) used the definition\n\"a period of at least three consecutive days when both the daily surface air temperature minima and maxima are higher than the highest 5% of values for the day in question during the 1991–2020 reference period\".\n\nOne of the main impacts of heatwaves is the danger they pose to human health.\nRoughly 489&nbsp;000 heat-related deaths occur each year [[Zhao+21](https://doi.org/10.1016/S2542-5196%2821%2900081-4)],\nand high-intensity heatwave events are known to increase mortality rates.\nFor example,\nin Europe alone, in the summer of 2022,\nan estimated 61&nbsp;672 heat-related excess deaths occurred [[Ballester+23](https://doi.org/10.1038/s41591-023-02419-z)].\nDue to these impacts, heatwaves often generate significant news coverage.\nThe June 2024 Eastern Mediterranean heatwave led to real-time reporting of its increasing death toll,\nas well as journalistic investigations into the overall impact and the drivers of this extreme event\n[[Bali+24](https://www.dw.com",
"code_notebooks": [],
"score": 0.36666666666666664
}
],
"source": "c3s2-eqc-quality-assessment (CDS EQC QA reports)",
"next_step": "where a result has code_notebooks[], call get_dataset_code(dataset_id, notebook_id=...) for the runnable code"
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>L2 · code notebooks</b><span>ready-to-run code per dataset</span></div>
<details class="qa"><summary><span class="qa-tag num">E29</span><span class="qa-tool">get_dataset_code</span><span class="qa-q">reanalysis-era5-single-levels</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"dataset_id": "reanalysis-era5-single-levels",
"store": "CDS",
"n_notebooks": 3,
"notebooks": [
{
"notebook_id": "datasets_reanalysis_era5_single_levels_arco_access",
"title": "Access the analysis ready ERA5 single levels data",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download"
],
"n_code_cells": 4,
"n_code_lines": 57,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "datasets_reanalysis_era5_single_levels_demo",
"title": "ERA5 hourly data on single levels from 1940 to present",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 5,
"n_code_lines": 26,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "reanalysis-heatwave",
"title": "Analysis of September 2020 European Heatwave using ERA5 Climate Reanalysis Data from C3S",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 25,
"n_code_lines": 124,
"aspect": null,
"source_repo": "ecmwf-projects/copernicus-training-c3s",
"license": "NONE"
}
],
"generic_how_to": [
{
"notebook_id": "datasets_dss_jupyter_form",
"title": "The DSS Jupyter Form",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 4,
"n_code_lines": 11,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_daily_statistics",
"title": "Daily statistics in the CDS",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 6,
"n_code_lines": 27,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_daily_accumulation_for_era5_land",
"title": "Daily accumulation for non-UTC timezone for the ERA5-land accumulated variables",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 8,
"n_code_lines": 44,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_1_getting_started",
"title": "Getting Started with `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"other"
],
"n_code_cells": 1,
"n_code_lines": 10,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_2_getting_information_about_collections",
"title": "Getting information about data store collections (datasets) using `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 11,
"n_code_lines": 57,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_3_retrieve_data",
"title": "Retrieve Sample ERA5 Data Using `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"download",
"other"
],
"n_code_cells": 7,
"n_code_lines": 26,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_4_submit_download_requests_advanced_data_retrieval",
"title": "Advanced Data Retrieval with `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"download",
"other"
],
"n_code_cells": 14,
"n_code_lines": 53,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_5_managing_your_jobs",
"title": "Managing Your Data Requests with `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 10,
"n_code_lines": 51,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_getting_started_with_the_dss_jupyterhub",
"title": "Getting started with the DSS JupyterHub",
"scope": "generic",
"recipe_kinds": [
"download"
],
"n_code_cells": 2,
"n_code_lines": 28,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_grib_to_netcdf",
"title": "GRIB to NetCDF4 conversion in the CDS and ADS",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 6,
"n_code_lines": 146,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
}
],
"next_step": "call get_dataset_code(dataset_id, notebook_id=...) for one notebook's full runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">E30</span><span class="qa-tool">get_dataset_code</span><span class="qa-q">GLOBAL_ANALYSISFORECAST_PHY_001_024</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"dataset_id": "GLOBAL_ANALYSISFORECAST_PHY_001_024",
"store": "CMEMS",
"n_notebooks": 0,
"notebooks": [],
"generic_how_to": [
{
"notebook_id": "doc-usage-quickoverview",
"title": "Quick overview",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 16,
"n_code_lines": 124,
"aspect": null,
"source_repo": "mercator-ocean/copernicus-marine-toolbox",
"license": "EUPL-1.2"
},
{
"notebook_id": "PythonNotebooks__IndexFilePlots__IndexFile_Folium_Visalization",
"title": "Index file visualization",
"scope": "generic",
"recipe_kinds": [
"analyze",
"plot"
],
"n_code_cells": 8,
"n_code_lines": 28,
"aspect": null,
"source_repo": "CopernicusMarineInsitu/INSTACTraining",
"license": "MIT"
},
{
"notebook_id": "PythonNotebooks__PlatformPlots__Read_TimeSeries_2",
"title": "Reading a remote file using OPeNDAP",
"scope": "generic",
"recipe_kinds": [
"analyze",
"plot"
],
"n_code_cells": 7,
"n_code_lines": 19,
"aspect": null,
"source_repo": "CopernicusMarineInsitu/INSTACTraining",
"license": "MIT"
},
{
"notebook_id": "PythonNotebooks__PlatformPlots__Read_TimeSeries_3",
"title": "Reading a file using CF module",
"scope": "generic",
"recipe_kinds": [
"analyze",
"plot"
],
"n_code_cells": 10,
"n_code_lines": 26,
"aspect": null,
"source_repo": "CopernicusMarineInsitu/INSTACTraining",
"license": "MIT"
},
{
"notebook_id": "01_11_CMEMS_handson_CLASS4",
"title": "CMEMS_1 A view on the ocean: the physical or the statistical view (CLASS4 validation, Biscay)",
"scope": "generic",
"recipe_kinds": [
"analyze",
"plot"
],
"n_code_cells": 16,
"n_code_lines": 480,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_12_CMEMS_handson_UPWELLING",
"title": "CMEMS_1 VIEW UPWELLING: Physical/Biogeochemical processes off Portugal and west of Galicia",
"scope": "generic",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 12,
"n_code_lines": 160,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_21_CMEMS_handson_CYCLONES",
"title": "CMEMS_2 SAIL CYCLONES: impact of cyclone FELLENG on ocean temperature (Indian Ocean)",
"scope": "generic",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 12,
"n_code_lines": 253,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_22_CMEMS_handson_OPENDRIFT",
"title": "CMEMS_2 SAIL OpenDrift: Lagrangian particle trajectories through the Indonesian throughflow",
"scope": "generic",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 7,
"n_code_lines": 61,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_31_CMEMS_handson_DIVEp1",
"title": "CMEMS_3 DIVE p1: Interannual variability of the Leeuwin current (3D virtual ocean)",
"scope": "generic",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 24,
"n_code_lines": 253,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_32_CMEMS_handson_DIVEp2",
"title": "CMEMS_3 DIVE p2: The Leeuwin current annual cycle",
"scope": "generic",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 15,
"n_code_lines": 168,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_33_CMEMS_handson_DIVEp3",
"title": "CMEMS_3 DIVE p3: Validation of analyses with in-situ observations in the Leeuwin current",
"scope": "generic",
"recipe_kinds": [
"analyze",
"plot"
],
"n_code_cells": 5,
"n_code_lines": 93,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
},
{
"notebook_id": "01_34_CMEMS_handson_DIVEp4",
"title": "CMEMS_3 DIVE p4: Assimilating altimetry in a 3D model (OSE)",
"scope": "generic",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 25,
"n_code_lines": 395,
"aspect": null,
"source_repo": "copernicusmarine/copernicus-marine-notebook-gallery",
"license": "MIT"
}
],
"next_step": "call get_dataset_code(dataset_id, notebook_id=...) for one notebook's full runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">E31</span><span class="qa-tool">get_dataset_code</span><span class="qa-q">cems-glofas-historical</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"dataset_id": "cems-glofas-historical",
"store": "EWDS",
"n_notebooks": 3,
"notebooks": [
{
"notebook_id": "glofas-bangladesh-floods",
"title": "Observing major flood events with GLOFAS",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 15,
"n_code_lines": 179,
"aspect": null,
"source_repo": "ecmwf-projects/copernicus-training-c3s",
"license": "NONE"
},
{
"notebook_id": "cems-global-flood-meeting-session-2-part-2-best-practices",
"title": "Part 2 : Best practices to download GloFAS data from CEMS Early Warning Data Store",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 7,
"n_code_lines": 130,
"aspect": null,
"source_repo": "ecmwf-training/2025-glofas-annual-meeting",
"license": "Apache-2.0"
},
{
"notebook_id": "cems-global-flood-meeting-session-2-part-3-observations-vs-simulations",
"title": "Part 3 : Mapping discharge gauge station to glofas cell",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 14,
"n_code_lines": 239,
"aspect": null,
"source_repo": "ecmwf-training/2025-glofas-annual-meeting",
"license": "Apache-2.0"
}
],
"generic_how_to": [],
"next_step": "call get_dataset_code(dataset_id, notebook_id=...) for one notebook's full runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">E32</span><span class="qa-tool">get_dataset_code</span><span class="qa-q">cams-europe-air-quality-forecasts</span><span class="qa-x">kind=plot</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"dataset_id": "cams-europe-air-quality-forecasts",
"store": "ADS",
"n_notebooks": 4,
"notebooks": [
{
"notebook_id": "cams-regional-forecast",
"title": "CAMS Regional Air Quality Forecast Practical",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 21,
"n_code_lines": 87,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
},
{
"notebook_id": "proc-aq-index",
"title": "European Air Quality Index Calculation",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 38,
"n_code_lines": 171,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
},
{
"notebook_id": "ak3-no2-tropomi-cams-regional",
"title": "Comparing TROPOMI NO2 columns with the CAMS regional air quality ensemble product",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 20,
"n_code_lines": 108,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
},
{
"notebook_id": "xesmf-3b-no2-tropomi-cams-regional-xesmf",
"title": "Comparing TROPOMI NO2 columns with CAMS regional (xESMF conservative regridding variant)",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"other",
"plot"
],
"n_code_cells": 13,
"n_code_lines": 95,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
}
],
"generic_how_to": [
{
"notebook_id": "vis-maps",
"title": "Create Maps from CAMS Data",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 16,
"n_code_lines": 87,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
},
{
"notebook_id": "vis-animations",
"title": "Create Animations from CAMS Data",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 16,
"n_code_lines": 48,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
},
{
"notebook_id": "vis-time-series",
"title": "Plot Time Series of CAMS Data",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 11,
"n_code_lines": 43,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
},
{
"notebook_id": "vis-profiles",
"title": "Profile Plots and Zonal Means",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other",
"plot"
],
"n_code_cells": 25,
"n_code_lines": 80,
"aspect": null,
"source_repo": "ecmwf-training/cams-training",
"license": "Apache-2.0"
}
],
"next_step": "call get_dataset_code(dataset_id, notebook_id=...) for one notebook's full runnable code"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">E33</span><span class="qa-tool">get_dataset_code</span><span class="qa-q">satellite-sea-surface-temperature</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"dataset_id": "satellite-sea-surface-temperature",
"store": "CDS",
"n_notebooks": 3,
"notebooks": [
{
"notebook_id": "satellite_satellite-sea-surface-temperature_consistency_q01",
"title": "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"plot"
],
"n_code_cells": 9,
"n_code_lines": 292,
"aspect": null,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment",
"license": "Apache-2.0"
},
{
"notebook_id": "satellite_satellite-sea-surface-temperature_consistency_q02",
"title": "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download",
"plot"
],
"n_code_cells": 6,
"n_code_lines": 337,
"aspect": null,
"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment",
"license": "Apache-2.0"
},
{
"notebook_id": "datasets_satellite_sea_surface_temperature_arco_access",
"title": "Access the analysis ready satellite sea surface temperature data",
"scope": "dataset",
"recipe_kinds": [
"analyze",
"download"
],
"n_code_cells": 4,
"n_code_lines": 48,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
}
],
"generic_how_to": [
{
"notebook_id": "datasets_dss_jupyter_form",
"title": "The DSS Jupyter Form",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 4,
"n_code_lines": 11,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_daily_statistics",
"title": "Daily statistics in the CDS",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 6,
"n_code_lines": 27,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_daily_accumulation_for_era5_land",
"title": "Daily accumulation for non-UTC timezone for the ERA5-land accumulated variables",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 8,
"n_code_lines": 44,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_1_getting_started",
"title": "Getting Started with `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"other"
],
"n_code_cells": 1,
"n_code_lines": 10,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_2_getting_information_about_collections",
"title": "Getting information about data store collections (datasets) using `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 11,
"n_code_lines": 57,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_3_retrieve_data",
"title": "Retrieve Sample ERA5 Data Using `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"download",
"other"
],
"n_code_cells": 7,
"n_code_lines": 26,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_4_submit_download_requests_advanced_data_retrieval",
"title": "Advanced Data Retrieval with `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"download",
"other"
],
"n_code_cells": 14,
"n_code_lines": 53,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_ecmwf_datastore_client_5_managing_your_jobs",
"title": "Managing Your Data Requests with `ecmwf-datastores-client`",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 10,
"n_code_lines": 51,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_getting_started_with_the_dss_jupyterhub",
"title": "Getting started with the DSS JupyterHub",
"scope": "generic",
"recipe_kinds": [
"download"
],
"n_code_cells": 2,
"n_code_lines": 28,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
},
{
"notebook_id": "documentation_grib_to_netcdf",
"title": "GRIB to NetCDF4 conversion in the CDS and ADS",
"scope": "generic",
"recipe_kinds": [
"analyze",
"download",
"other"
],
"n_code_cells": 6,
"n_code_lines": 146,
"aspect": null,
"source_repo": "ecmwf-training/dss-notebooks",
"license": "NONE"
}
],
"next_step": "call get_dataset_code(dataset_id, notebook_id=...) for one notebook's full runnable code"
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>L3 · publications</b><span>search over 430k paper chunks</span></div>
<details class="qa"><summary><span class="qa-tag num">F34</span><span class="qa-tool">search_publications</span><span class="qa-q">marine heatwave detection from SST reanalysis</span><span class="qa-dt num">410.1s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "marine heatwave detection from SST reanalysis",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.21203/rs.3.rs-6503093/v1",
"title": "Synergistic Impacts of Climate Variabilities on Marine Heatwaves inShelf Seas",
"journal": "Research Square",
"year": 2025,
"domains": [
"ocean/marine",
"atmosphere"
],
"section": "MHW detection",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "MHW charactetics were identified using the MATLAB Marine Heatwaves (M\\_MHW) toolbox $^{49}$ . For each grid cell, we calculated daily SST anomalies (1982-2021) after removing the long-term warming trend.",
"score": 0.5
},
{
"doi": "10.1038/s43247-024-01215-y",
"title": "Arctic marine heatwaves forced by greenhouse gases and triggered by abrupt sea-ice melt",
"journal": "Communications Earth & Environment",
"year": 2024,
"domains": [
"ocean/marine",
"atmosphere",
"cryosphere"
],
"section": "Conclusions",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "The summer of 2007 marked the beginning of a shift towards a new era of marine heatwaves over the shallow marginal seas of the Arctic Ocean. Severe marine heatwave events are predominantly concentrated in the first-year-ice area along the edges, where stratification in summer inhibits the downward dispersion of energy from solar radiation $^{8}$ resulting in unusual fluctuations in SST. Here we show that 82% of the sea surface temperature variability over the shallow Arctic marginal seas can be attributed to changes in the net atmospheric heat fluxes. Marine heatwaves in the Arctic are primarily triggered by an early and abrupt sea-ice retreat, which coincides with the midsummer (July) maximum downward radiative fluxes. The 2020 marine heatwave was stronger than the 2012 event, even though 2012 had experienced more total sea-ice loss, owing to an abrupt sea-ice retreat in 2020 across a broad area near the peak of downward radiative fluxes in July.\n\nEvent attribution analysis reveals that any marine heatwave event with an intensity larger than $1.5^{\\circ}$ C has $<1\\%$ occurrence probability under no-greenhouse gas effect. Thus, the occurrence of the recently observed extreme marine heatwave, such as that occurred in 2007 (with $3.4^{\\circ}$ C intensity) and in 2020 (with $4^{\\circ}$ C intensity), would have been exceptionally unlikely in the absence of GHG forcing. Thus, GHG forcing is necessary for the occurrence of these events, though not sufficient. This means that GHG forcing must be present for such events to happen, but the inclusion of GHG forcing alone is not enou",
"score": 0.5
},
{
"doi": "10.5194/bg-22-6583-2025",
"title": "Marine heatwaves deeply alter marine food web structure and function",
"journal": "Biogeosciences",
"year": 2025,
"domains": [
"ocean/marine",
"atmosphere"
],
"section": "A1 MHW characterisation and detection",
"orphan": true,
"linked_products": [],
"text": "To charactee MHW in each ocean spatial cell, we analysed daily SST observations from the NOAA's AVHRR data (DOC/NOAA/NESDIS/NCDC, 2008; https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00680, last access: 3 May 2022). We defined MHW as a discrete, prolonged and anomalously warm water event when the daily SSTs exceed an extreme temperature threshold value for at least 5 consecutive days (Hobday et al., 2016). The extreme temperature threshold value was calculated for each $1^{\\circ}$ latitude $\\times 1^{\\circ}$ longitude spatial cell as the 90th percentile of daily SST from the 30-year historical time series from January 1982 to December 2011. We did not calculate threshold values by season; thus, MHW events were identified by a single threshold across the year. As a result, we detected MHWs mostly occurring during the year's warmest months (see Fig. A1a for a schematic explanation). This approach to identifying the MHW threshold represents biological extreme temperature in the local (spatial cell) context. It is appropriate to assess the direct mortality associated with MHWs (Oliver et al., 2021). We determined a reference average sea surface temperature (SST average) for each spatial cell by analysing data from 1 January 1982 to 31 December 2011. Utilising this reference average SST, we classified each spatial cell into thermal classes, with each class representing a $1^{\\circ}\\mathrm{C}$ increment of the reference SST average, ranging from $-1$ to $29^{\\circ}\\mathrm{C}$ (refer to Fig. A1b). We used the R package heatwaveR, described at http",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F35</span><span class="qa-tool">search_publications</span><span class="qa-q">ERA5 wind energy resource assessment</span><span class="qa-x">domain=atmosphere</span><span class="qa-dt num">50.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "ERA5 wind energy resource assessment",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.1038/s44168-025-00332-4",
"title": "Assessing renewable wind and solar energy yield with gridded climate datasets",
"journal": "npj Climate Action",
"year": 2026,
"domains": [
"atmosphere"
],
"section": "Gridded datasets for wind and solar resources assessment",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "Gridded meteorological datasets, particularly global and regional reanalyses, have become central to the long-term assessment of wind and solar energy resources. Their increasing use is driven by the variable nature of renewable energy sources and the corresponding need for long and homogeneous time series to support project planning, investment k analysis, and the calibration or debiasing of climate simulations. Among the most widely utilized are ERA5 $^{18}$ , MERRA2 $^{19}$ , CFSR $^{20}$ , and JRA55 $^{21}$ , which assimilate large volumes of historical observations into numerical weather prediction models to reconstruct past atmospheric states with temporal consistency and global coverage. Wind energy applications using reanalyses. ERA5, developed by ECMWF within the Copernicus Climate Change Service, is particularly valued for its hourly temporal resolution, 31 km horizontal resolution, and direct provision of wind speeds at 100 m above ground level—an altitude directly relevant for modern wind turbines. This represents a substantial improvement over its predecessor ERA-Interim (e.g., 6-hourly data, \\~79 km resolution) and has positioned ERA5 as the leading reanalysis product in many contemporary wind energy studies $^{22,23}$ . Compared to MERRA2, which offers wind speed data up to 50 m and coarser resolution (\\~50 km), ERA5 delivers better spatial fidelity and wind speed representation $^{24}$ . The availability of wind data at 100 m is essential as turbine hub heights now commonly exceed 80 m both onshore and offshore $^{25-27}$ . ERA5 has been successfully used to",
"score": 0.5666666666666667
},
{
"doi": "10.1038/s43247-024-01260-7",
"title": "Identification of reliable locations for wind power generation through a global analysis of wind droughts",
"journal": "Communications Earth & Environment",
"year": 2024,
"domains": [
"atmosphere",
"land"
],
"section": "Methods",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "To conduct our analysis, we used wind speeds, pressures, and temperatures retrieved from the ERA5 dataset $^{35}$ . ERA5 is produced by the Copernicus Climate Change Service (C3S) at the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 is the fifth-generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to the present. It provides hourly estimates of a large number of atmospheric, land, and oceanic climate variables. The data cover the Earth on a regular latitude-longitude grid of 0.25 degrees (about 30 km in the mid-latitudes) and resolve the atmosphere using 137 levels from the surface up to a height of 80 km. We retrieved the 100-m components of wind speed, surface pressure, and 2-m temperature from January 1979 to December 2022 at hourly resolution, thus focusing our analysis on data that assimilate widespread satellite-based observations. This translates to about 400 billion data points for each of the four variables and about 3 TB of total data volume. From the wind speed time series, we estimate the power density time series. We then introduce an energy deficit metric to quantify the wind resource variability and wind droughts. The energy deficit metric quantifies the energy deficit of a wind-based power system over an annual time scale when we set a target generation profile. Note that the target generation profiles that we consider (constant or climatological) are aimed at characterizing the wind resource and variability and do not intend to represent the potential\n\ncomplementarity of the wind generation p",
"score": 0.5
},
{
"doi": "10.5194/wes-10-733-2025",
"title": "Tall wind profile validation of ERA5, NORA3, and NEWA datasets using lidar observations",
"journal": "Wind energy science",
"year": 2025,
"domains": [
"atmosphere"
],
"section": "6 Conclusions",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "This study examines the capability of three wind atlases, NORA3, NEWA, and ERA5, in modelling wind speed profiles up to 500 m above the surface for wind energy applications. Reference wind speed profiles were obtained from Doppler wind lidar (DWL) measurements conducted at five distinct sites in northern Europe. These sites encompass diverse topographies such as flat coastal terrain, mountainous regions, and offshore environments. The study aims to broaden the validation scope to altitudes critical for large wind turbines and airborne wind energy (AWE) systems. This study addresses a significant challenge in wind energy, as there has been relatively limited investigation into tall wind speed profiles using scanning DWL in profiler modes for wind resource assessment at heights up to 500 m above the surface.\n\nThe study found that the three wind atlases perform well in offshore locations, with ERA5 and NORA3 showing the closest correlation to lidar data. More specifically, NORA3 and ERA5 perform almost equally well in terms of correlation coefficient, root-mean-square error (RMSE), and Earth mover's distance (EMD). However, ERA5 has a lower bias above 200 m. Onshore, NORA3 outperforms ERA5 and NEWA at all heights for most error metrics. While the agreement between the models and lidar measurements generally improves with height, this trend is less consistent at coastal and complex-terrain sites, where significant deviations occur, especially for ERA5 and NEWA.\n\nIn terms of wind turbine capacity factor (CF), all datasets show good agreement with CF derived from lidar data offsh",
"score": 0.369047619047619
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F36</span><span class="qa-tool">search_publications</span><span class="qa-q">validation of ERA5-Land snow depth against stations</span><span class="qa-dt num">34.6s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "validation of ERA5-Land snow depth against stations",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.5194/tc-13-2221-2019",
"title": "Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations",
"journal": "The cryosphere",
"year": 2019,
"domains": [
"cryosphere"
],
"section": "Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations",
"orphan": false,
"linked_products": [
"seasonal-monthly-single-levels",
"seasonal-original-pressure-levels",
"seasonal-original-single-levels"
],
"text": "Yvan Orsolini $^{1}$ , Martin Wegmann $^{2,a}$ , Emanuel Dutra $^{3}$ , Boqi Liu $^{4}$ , Gianpaolo Balsamo $^{5}$ , Kun Yang $^{6,7}$ , Patricia de Rosnay $^{5}$ , Congwen Zhu $^{4}$ , Wenli Wang $^{6,7}$ , Retish Senan $^{5}$ , and Gabriele Arduini $^{5}$\n\n$^{1}$ NILU – Norwegian Institute for Air Research, Kjeller, Norway $^{2}$ Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany $^{3}$ Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal $^{4}$ Institute of Climate System, Chinese Academy of Meteorological Sciences, Beijing, China $^{5}$ European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK $^{6}$ Department of Earth System Science, Tsinghua University, Beijing, China $^{7}$ Institute of Tibetan Plateau Research of Chinese Academy of Sciences, Beijing, China $^{a}$ formerly at: Institut des Géosciences de l'Environnement, Université Grenoble Alpes, Grenoble, France\n\nCorrespondence: Yvan Orsolini (orsolini@nilu.no)\n\nRevised: 17 June 2019 – Accepted: 8 July 2019 – Published: 27 August 2019\n\nAbstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at",
"score": 0.5714285714285714
},
{
"doi": "10.1038/s41597-023-01959-w",
"title": "Bioclimatic atlas of the terrestrial Arctic",
"journal": "Scientific Data",
"year": 2023,
"domains": [
"cryosphere",
"land"
],
"section": "Technical Validation",
"orphan": false,
"linked_products": [
"reanalysis-era5-land",
"reanalysis-era5-land-monthly-means",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "The ARCLIM dataset is derived from ERA5-Land, which is a state-of-the-art global dataset for various land applications. As ERA5-Land is still a relatively new dataset, only a few studies so far have validated its performance against in-situ observations. <details> <summary>flowchart</summary>\n\n```mermaid graph TD A[\"Hourly ERA5-Land data for 1950-2021: 2-metre temperature, 2-metre dew point temperature, skin temperature, total precipitation, snowfall, snow cover, 10-metre U- and V wind components\"] --> B[\" the ERA5-Land data from Climate Data Store\"] B --> C[\"Pre-process the data from hourly to daily resolution\"] C --> D[\"Daily ERA5-Land data for 1950-2021: daily maximum, minimum, mean and sum of the variables\"] D --> E[\"Read the pre-processed data and calculate the ARCLIM variables\"] E --> F[\"Annual ARCLIM values for 1950-2021\"] F --> G[\"Average conditions for 1991-2020\"] F --> H[\"Trends for 1951-2021\"] G --> I[\"Technical validation\"] H --> J[\"ARCLIM variables as NETCDF4 and GeoTIFF format\"] ``` </details>\n\nRegarding snow depth, Muñoz-Sabater et al. $^{34}$ documented that ERA5-Land performs varyingly compared to ERA5. The ERA5-Land dataset outperforms ERA5 in the US and in complex terrain where ERA5-Land benefits from its higher spatial resolution. Instead, in Scandinavia, ERA5 was found to perform better than ERA5-Land. Räisänen $^{76}$ compared mean snow depth in March for 39 winter seasons (1982–2020) from ERA5-Land predictions with two Finnish station observations: Sodankylä and Helsinki. He found high interannual correlation (0.92–0.97), but a slight negative bias fo",
"score": 0.5263157894736842
},
{
"doi": "10.1007/s00382-022-06376-0",
"title": "Snow depth and snow cover over the Tibetan Plateau observed from space in against ERA5: matters of scale",
"journal": "Climate Dynamics",
"year": 2022,
"domains": [
"cryosphere"
],
"section": "Abstract",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "There has been a statement that the satellite and reanalyses significantly overestimate snow depths (SDs) as compared with in-situ observations over the Tibetan Plateau (TP). The inconsistency may be partly due to representations related to different spatial resolutions. To further clarify matters of scale, this work estimates and compares the fractional snow cover (FSC) from MODIS (500 m), SDs from Sentinel-1 C-band SAR (1 km), ERA5-Land (9 km) and ERA5 (31 km), together with ground observations from GHCN-D and at 6 newly-established stations in the Namco watershed. SD and FSC from MERRA-2 (50 km) are also discussed in compaon with ERA5s. Results indicate that SD from fine resolution reanalysis has better consistency to in-situ observations over the TP. SD of ERA5-Land matches in-situ measurements better than ERA5 and MERRA-2. Overestimates of SDs in reanalyses are likely for shallow snowpack. However, underestimates are found for deep snow particularly late in the snow season. Improvements are displayed in ERA5-Land, while increased SD is attributed by altitude in fine resolution. Overall SDs of ERA5 and ERA5-Land have similar spatial distributions and annual cycle patterns over the TP, consistent with satellite-based datasets. A notable defect of ERA5s is related to delayed ablation of deep snowpack during spring and early summer. It causes cold temperature biases at surface that may impact on the land-atmosphere interaction. Snow analysis that combines the information of FSC into SD has been demonstrated in MERRA-2 data. Snow analysis has the potential to improve SDs of",
"score": 0.41666666666666663
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F37</span><span class="qa-tool">search_publications</span><span class="qa-q">machine learning downscaling of reanalysis fields</span><span class="qa-dt num">19.9s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "machine learning downscaling of reanalysis fields",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.1017/eds.2023.1",
"title": "AtmoDist: Self-supervised representation learning for atmospheric dynamics",
"journal": "Environmental Data Science",
"year": 2023,
"domains": [
"atmosphere"
],
"section": "Abstract",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "Representation learning has proven to be a powerful methodology in a wide variety of machine-learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show how to sidestep the difficulty and introduce a self-supervised learning task that is applicable to a wide variety of unlabeled atmospheric datasets. Specifically, we train a neural network on the simple yet intricate task of predicting the temporal distance between atmospheric fields from distinct but nearby times. We demonstrate that training with this task on the ERA5 reanalysis dataset leads to internal representations that capture intrinsic aspects of atmospheric dynamics. For example, when employed as a loss function in other machine-learning applications, the derived AtmoDist distance leads to improved results compared to the $\\ell_{2}$ -loss. For downscaling one obtains higher resolution fields that match the true statistics more closely than previous approaches and for the interpolation of missing or occluded data the AtmoDist distance leads to results that contain more realistic fine-scale features. Since it is obtained from observational data, AtmoDist also provides a novel perspective on atmospheric predictability.",
"score": 0.5909090909090909
},
{
"doi": "10.1038/s41612-025-01103-y",
"title": "Global spatio-temporal ERA5 precipitation downscaling to km and sub-hourly scale using generative AI",
"journal": "npj Climate and Atmospheric Science",
"year": 2025,
"domains": [
"atmosphere",
"climate-modeling"
],
"section": "Global spatio-temporal ERA5 precipitation downscaling to km and sub- hourly scale using generative AI",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "Here, we present spateGAN-ERA5, a conditional generative adversarial network for robust deep learning-based spatio-temporal downscaling of ERA5 precipitation data. Our model transforms hourly, 24 km ( $\\sim0.25^{\\circ}$ ) resolved ERA5 precipitation estimates into rainfields that resemble weather radar observations at a resolution of 10 min and 2 km. SpateGAN-ERA5 is trained on high-resolution quantitative precipitation estimates (QPE) from a gauge-adjusted and climatology-corrected weather radar product in Germany and is evaluated across three climatically diverse regions on the globe. The model generalizes well outside the training domain and enables computationally efficient global rainfall downscaling to a resolution that is fine enough to capture the spatio-temporal complexity of rainfall, especially for rainfall events with convective cells. It generates realistic extreme value distributions, spatial structures, and advection patterns, all in a well-calibrated ensemble that addresses the underdetermined nature of the downscaling problem. Thus, spateGAN-ERA5 significantly advances downscaling methodologies and opens up a wide field of possible scientific investigations in a variety of domains like hydrology, k analysis, or agriculture.",
"score": 0.5
},
{
"doi": "10.1007/s00704-020-03489-6",
"title": "Deep learning–based downscaling of summer monsoon rainfall data over Indian region",
"journal": "Theoretical and Applied Climatology",
"year": 2021,
"domains": [
"atmosphere",
"climate-modeling"
],
"section": "Abstract",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to nonlinear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL) based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season. Among the three algorithms, namely SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data postprocessing, in particular, ERA5 data. Downscaled ERA5 ",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F38</span><span class="qa-tool">search_publications</span><span class="qa-q">storm surge modelling satellite altimetry sea level</span><span class="qa-x">domain=ocean/marine</span><span class="qa-dt num">28.9s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "storm surge modelling satellite altimetry sea level",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.1007/s10712-016-9392-0",
"title": "Monitoring Sea Level in the Coastal Zone with Satellite Altimetry and Tide Gauges",
"journal": "Surveys in Geophysics",
"year": 2016,
"domains": [
"ocean/marine"
],
"section": "Monitoring Sea Level in the Coastal Zone with Satellite Altimetry and Tide Gauges",
"orphan": false,
"linked_products": [],
"text": "Paolo Cipollini $^{1}$ · Francisco M. Calafat $^{2}$ · Svetlana Jevrejeva $^{2}$ · Angelique Melet $^{3,4}$ · Pierre Prandi $^{5}$\n\nAbstract We examine the issue of sustained measurements of sea level in the coastal zone, first by summarizing the long-term observations from tide gauges, then showing how those are now complemented by improved satellite altimetry products in the coastal ocean. We present some of the progresses in coastal altimetry, both from dedicated reprocessing of the radar waveforms and from the development of improved corrections for the atmospheric effects. This trend towards better altimetric data at the coast comes also from technological innovations such as Ka-band altimetry and SAR altimetry, and we discuss the advantages deriving from the AltiKa Ka-band altimeter and the SIRAL altimeter on CryoSat-2 that can be operated in SAR mode. A case study along the UK coast demonstrates the good agreement between coastal altimetry and tide gauge observations, with root mean square differences as low as 4 cm at many stations, allowing the characterization of the annual cycle of sea level along the UK coasts. Finally, we examine the evolution of the sea level trend from the open to the coastal ocean along the western coast of Africa, comparing standard and coastally improved products. Different products give different sea level trend profiles, so the recommendation is that additional efforts are needed to study sea level trends in the coastal zone from past and present satellite altimeters. Further improvements are expected from more refined processing and scr",
"score": 0.5
},
{
"doi": "10.21203/rs.3.rs-3791026/v1",
"title": "Accounting for surge dynamics is key to modeling tropical cyclone-induced coastal flooding",
"journal": "Research Square",
"year": 2024,
"domains": [
"ocean/marine",
"atmosphere",
"emergency"
],
"section": "Data from GTSM (simulated hydrographs)",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "We use outputs of the Global Tide and Surge Model (GTSM) with forcing from the European Reanalysis (ERA5) $^{80}$ from the extreme sea level dataset CoDEC-ERA5 $^{30}$ . For this study, we obtained a version of CoDEC-ERA5 that comes as hourly-resolved sea level time series at 18,719 virtual gauge locations. The virtual gauges are equidistantly located every 50 km along the smoothed global coastlines. We used the CoDEC-ERA5 water levels to derive inundation maps with the Aqueduct bathtub-type inundation model (see above). For that, we translated water levels from anomalies relative to the local annual average sea level to the EGM96 vertical datum using satellite altimetry (see above). For the compaon of GTSM with GeoClaw, we restricted to the virtual gauge locations that lie within the IBTrACS storm geometries, and for which satellite altimetry data is available (as with the GESLA3 stations, see above). This added up to a total of 2588 time series at 1484 distinct gauge locations for 63 of the 71 storm events.",
"score": 0.5
},
{
"doi": "10.1007/s10661-026-15166-8",
"title": "Improving coastal water level estimation by merging nadir-only satellite altimetry data into a hydrodynamic model",
"journal": "Environmental Monitoring and Assessment",
"year": 2026,
"domains": [
"ocean/marine"
],
"section": "Research highlights",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "- Nadir-only satellite altimetry improves storm surge model performance. - Model skill increases when overpasses capture surge events. - Multi-mission altimetry assimilation yields the highest overall accuracy.\n\nKeywords ADCIRC · SWOT · Storm surge · Data assimilation · Altimetry",
"score": 0.41666666666666663
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F39</span><span class="qa-tool">search_publications</span><span class="qa-q">PM2.5 estimation from CAMS aerosol</span><span class="qa-dt num">27.2s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "PM2.5 estimation from CAMS aerosol",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.1007/s11869-023-01464-4",
"title": "Modeling of carbonaceous aerosols for air pollution health impact studies in Europe",
"journal": "Air Quality Atmosphere & Health",
"year": 2023,
"domains": [
"atmosphere"
],
"section": "Abstract",
"orphan": false,
"linked_products": [
"insitu-gridded-observations-europe",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "Air pollution from fine particulate matter (PM2.5) has been associated with various health implications that can lead to increased morbidity and excess mortality. Epidemiological and toxicological studies have shown that carbonaceous particles (black carbon and organic aerosols) may be more hazardous to human health than inorganic ones. Health impact studies and emission reduction policies are based on total PM2.5 concentration without differentiating the more harmful components. In such assessments, PM2.5 and their sub-component concentrations are usually modeled with air quality models. Organic aerosols have been shown to be consistently underestimated, which may affect excess mortality estimates. Here, we use the WRF-Chem model to simulate PM2.5 (including carbonaceous particles) over the wider European domain and assess some of the main factors that contribute to uncertainty. In particular, we explore the impact of anthropogenic emissions and meteorological modeling on carbonaceous aerosol concentrations. We further assess their effects on excess mortality estimates by using the Global Exposure Mortality Model (GEMM). We find that meteorological grid nudging is essential for accurately representing both PM2.5 and carbonaceous aerosols and that, for this application, results improve more significantly compared to spectral nudging. Our results indicate that the explicit account of organic precursors (semi-volatile and intermediate-volatile organic carbons—SVOCs/IVOCs) in emission inventories would improve the accuracy of organic aerosols modeling. We conclude that uncerta",
"score": 0.525
},
{
"doi": "10.21203/rs.3.rs-7564554/v1",
"title": "CAMS products for analyzing atmospheric dynamics to develop QA indicators at regional scale",
"journal": "Research Square",
"year": 2025,
"domains": [
"atmosphere"
],
"section": "Abstract",
"orphan": false,
"linked_products": [
"cams-global-reanalysis-eac4",
"cams-global-reanalysis-eac4-monthly"
],
"text": "Air pollution episodes involving fine particulate matter ( $PM_{2.5}$ ) are tightly linked to synoptic meteorology, which regulates accumulation and dispersion. This study evaluates the ability of Copernicus Atmosphere Monitoring Service (CAMS) reanalyses (2015–2023) to support a daily-scale classification of circulation regimes relevant for air quality in eastern France. CAMS near-surface parameters (temperature, relative humidity, wind) were compared with the high-resolution SAFRAN reanalyses, and CAMS sea-level pressure fields were used to derive a reproducible classification benchmarked against Großwetterlagen.\n\nThe present study highlights three main regimes. Anticyclonic situations promote strong $PM_{2.5}$ accumulation under stable, poorly ventilated conditions. Low-pressure regimes enhance dispersion through stronger winds and mixing, limiting concentrations. An intermediate regime, less documented in previous classifications, combines moderate pressure gradients and variable transport pathways, producing heterogeneous pollution levels and occasional long-range particle transport.\n\nResults show good climatological agreement between CAMS and SAFRAN, with CAMS reproduces the main meteorological and synoptic patterns, while smoothing finer-scale contrasts. The classification explains both seasonal patterns and interannual variability, while underlining the persistent contribution of local emissions (traffic, heating, industry).\n\nOverall, CAMS provides a robust synoptic-scale framework for meteorological typologies relevant to air quality. Although its coarse resolution",
"score": 0.5
},
{
"doi": "10.26833/ijeg.1841923",
"title": "Spatiotemporal Analysis and Forecasting of PM2.5 Air Pollution in Azerbaijan Using SARIMA and Prophet Models",
"journal": "International Journal of Engineering and Geosciences",
"year": 2026,
"domains": [
"climate-general"
],
"section": "2.1. Data Sources and Initial Preparation",
"orphan": false,
"linked_products": [
"cams-global-reanalysis-eac4",
"cams-global-reanalysis-eac4-monthly"
],
"text": "We constructed the PM2.5 time series for Azerbaijan using the ECMWF CAMS Near-Real-Time (NRT) reanalysis as the sole numerical source $[34]$ . This product provides daily global fields of surface PM2.5 concentration by assimilating satellite aerosol optical depth with a chemical transport model $[35]$ . The Sentinel-5P TROPOMI Level-3 products (processed on Google Earth Engine) $[36, 37]$ were used only for qualitative visual compaon and contextual interpretation, particularly over bright surfaces such as deserts and snow-covered mountains where CAMS retrievals may have higher uncertainty $[38]$ . No numerical fusion, averaging, or calibration between the two datasets was performed.\n\nThe study period runs from 1 January 2019 to 31 December 2024—six full years that capture a complete cycle of seasonal heating demand, dust events, and recent changes in economic activity. All images were first clipped to the official national boundary of Azerbaijan (including the Nakhchivan exclave) and only the PM2.5 band was retained for further processing.\n\nTo smooth day-to-day noise while preserving monthly-scale signals, we aggregated daily images into monthly mean composites directly in Google Earth Engine. The resulting rasters (10 km × 10 km spatial resolution) were converted to monthly country-wide average values for time-series modelling.",
"score": 0.35964912280701755
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F40</span><span class="qa-tool">search_publications</span><span class="qa-q">drought monitoring soil moisture anomalies</span><span class="qa-x">domain=land</span><span class="qa-dt num">30.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "drought monitoring soil moisture anomalies",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.18174/637197",
"title": "Soil moisture droughts evaluated using Earth observation",
"journal": "",
"year": 2023,
"domains": [
"land"
],
"section": "Abstract",
"orphan": false,
"linked_products": [],
"text": "Recent advances in satellite soil moisture retrievals now allow for satellite soil moisture retrievals at sub-field scale. Their relevance for drought monitoring remains to be studied. We take the opportunity of the unique availability of three high resolution (60 m, 100 m, 1 km) soil moisture datasets over Luxembourg for an intercompaon between drought data. Monthly anomalies of satellite retrieved soil moisture were evaluated with those of reference in situ soil moisture and precipitation (SPI). An analysis on the national scale showed that the 1 km data most often correctly identifies a drought observed in the in situ data. Compared to SPI, the 60 m dataset also performs well, while the 100 m data shows a poorer performance. On smaller scales, both the 1 km and 100 m datasets show little spatial variability in their soil moisture anomalies. Only the 60 m dataset is able to distinguish between local variations in soil moisture. Hence, while 1 km data allowed for soil moisture drought monitoring on the national scale, higher resolution data is needed to adequately monitor droughts on (sub-)field scales.",
"score": 1.0
},
{
"doi": "10.1007/s41748-025-00609-y",
"title": "Trends and Variability of Drought in Northern South America Between 1982 and 2022",
"journal": "Earth Systems and Environment",
"year": 2025,
"domains": [
"land"
],
"section": "2.3.2 Soil Moisture Anomaly Index (SMAI)",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "The SMAI is critical in determining the onset and duration of agricultural drought conditions. This is achieved by closely monitoring soil moisture levels and analyzing data using advanced analytical techniques. The SMAI then disseminates its findings to relevant stakeholders promptly and effectively. By providing accurate and reliable information, the SMAI assists in the sustainable management of agricultural resources and developing effective mitigation strategies. SMAI is calculated from Eq. 3.\n\nS M A I = \\frac {S M - \\overline {{S M}}}{\\sigma_ {S M}} \\tag {3}\n\nWhere $SM$ , $\\overline{SM}$ , and $\\sigma_{SM}$ represent the monthly soil moisture value, and mean and the standard deviation of the month, respectively. The SMAI measures agricultural drought by comparing current conditions to the normal status of water availability. Typically, negative anomalies between $-1$ and $-1.5$ indicate moderate drought conditions, while values between $-1.5$ and $-2$ and values less than $-2$ are classified as severe and extreme seasonal drought, respectively.",
"score": 0.3958333333333333
},
{
"doi": "10.18174/637197",
"title": "Soil moisture droughts evaluated using Earth observation",
"journal": "",
"year": 2023,
"domains": [
"land"
],
"section": "Summary",
"orphan": false,
"linked_products": [],
"text": "To that end, the 60 m dataset that was used in Chapter 4, was extended to cover Luxembourg. In Chapter 5, two additional soil moisture datasets over Luxembourg were used: the vanderSat dataset with a 100 m spatial resolution and the Copernicus dataset with a 1 km resolution. Monthly anomalies of the three satellite retrieved soil moisture datasets were evaluated with those of reference in situ soil moisture and precipitation. An analysis on the national scale showed that the Copernicus data most often correctly identified a drought observed in the in situ data. Compared to precipitation anomalies, the 60 m dataset also performed well, while the 100 m data showed lower correlations. On smaller scales, both the Copernicus and vanderSat datasets showed spatially constant anomalies. Only the 60 m dataset could\n\ndistinguish local soil moisture variations. Hence, while Copernicus data allowed for soil moisture drought monitoring on the national scale, higher resolution data is needed to adequately monitor droughts on local scales. Although this research showed that high resolution soil moisture retrieval for drought monitoring currently still has its limitations, its potential was also shown. The data can already be useful on bare soil fields for sub-field scale drought monitoring and mitigation. That means that the data can be used for precision agriculture purposes. This is especially useful when soil moisture data is combined with other types of (remote sensing) data that can provide information on vegetation functioning. For an efficient drought mitigation strategy, all these",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">F41</span><span class="qa-tool">search_publications</span><span class="qa-q">RMSE comparison against buoy observations table</span><span class="qa-dt num">37.5s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "RMSE comparison against buoy observations table",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.5194/essd-17-1191-2025",
"title": "Global ocean surface heat fluxes derived from the maximum entropy production framework accounting for ocean heat storage and Bowen ratio adjustments",
"journal": "Earth system science data",
"year": 2025,
"domains": [
"ocean/marine"
],
"section": "4.3.1 Evaluation of net radiation",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "[FIGURE CAPTION]\nTable 3. Evaluation of global monthly net radiation products against buoy observations.",
"score": 0.5
},
{
"doi": "10.1109/jstars.2024.3395798",
"title": "Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data",
"journal": "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
"year": 2024,
"domains": [
"climate-general"
],
"section": "B. Comparison with independent observations",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "[TABLE in section: B. Comparison with independent observations]\nCaption: Fig. 8. Comparison of Gaofen-3 SWH retrievals using DB-CNN with buoy and altimeter SWH observations.\n\n| | Corr | RMSE | Bias | SI |\n| ------ | ------ | ------ | ------ | ------ |\n| Buoy | 0.90 | 0.427m | -0.017m| 15.68% |\n| | | 0.337m | 0.141m | 13.16% |",
"score": 0.5
},
{
"doi": "10.5194/essd-12-1929-2020",
"title": "The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations",
"journal": "Earth System Science Data",
"year": 2020,
"domains": [
"waves"
],
"section": "4.1 Comparisons against in situ data and model results",
"orphan": false,
"linked_products": [
"GLOBAL_MULTIYEAR_WAV_001_032",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "[FIGURE CAPTION]\nTable 5. Statistical metrics based on altimeter–buoy and altimeter–model comparisons for different subsets of data (see Fig. 1).",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>L3 · linkage</b><span>dataset ↔ its literature</span></div>
<details class="qa"><summary><span class="qa-tag num">G42</span><span class="qa-tool">get_dataset_publications</span><span class="qa-q">reanalysis-era5-single-levels</span><span class="qa-dt num">1.2s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"id": "reanalysis-era5-single-levels",
"n_linked_publications": 5990,
"results": [
{
"doi": "10.1038/s43247-022-00498-3",
"title": "The Arctic has warmed nearly four times faster than the globe since 1979",
"journal": "Communications Earth & Environment",
"year": "2022",
"citations_count": 2910,
"link_via": [
"flagship"
],
"flagship_labels": [
"ERA5 Hersbach 2020"
],
"full_text_available": true
},
{
"doi": "10.1038/s41586-023-06185-3",
"title": "Accurate medium-range global weather forecasting with 3D neural networks",
"journal": "Nature",
"year": "2023",
"citations_count": 1428,
"link_via": [
"flagship"
],
"flagship_labels": [
"ERA5 Hersbach 2020"
],
"full_text_available": true
},
{
"doi": "10.1029/2019jd032361",
"title": "An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set",
"journal": "Journal of Geophysical Research Atmospheres",
"year": "2020",
"citations_count": 968,
"link_via": [
"flagship"
],
"flagship_labels": [
"ERA5 Hersbach 2020",
"OSTIA Donlon 2012"
],
"full_text_available": true
},
{
"doi": "10.1126/science.abn6301",
"title": "A global transition to flash droughts under climate change",
"journal": "Science",
"year": "2023",
"citations_count": 766,
"link_via": [
"flagship"
],
"flagship_labels": [
"ERA5 Hersbach 2020"
],
"full_text_available": true
},
{
"doi": "10.1016/s0140-6736(24)01822-1",
"title": "The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action",
"journal": "The Lancet",
"year": "2024",
"citations_count": 698,
"link_via": [
"flagship"
],
"flagship_labels": [
"ERA5 Hersbach 2020",
"ERA5-Land Munoz-Sabater 2021"
],
"full_text_available": true
}
],
"next_step": "read_publication(doi) for full text where full_text_available; otherwise metadata only for now"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">G43</span><span class="qa-tool">search_publications</span><span class="qa-q">evaluation</span><span class="qa-x">dataset_or_product_id=reanalysis-era5-land</span><span class="qa-dt num">49.9s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "evaluation",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.5194/essd-15-2445-2023",
"title": "CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies",
"journal": "Earth system science data",
"year": 2023,
"domains": [
"climate-general"
],
"section": "3 Evaluation",
"orphan": false,
"linked_products": [
"reanalysis-era5-land",
"reanalysis-era5-land-monthly-means",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "The evaluation of the downscaling from low (0.5°) to high (30 arcsec) resolution follows the evaluation approach outlined in Karger et al. (2021) and compares measurements at meteorological stations with data from both the low and the high spatial resolution. Since many observations at stations are already included in the W5E5 data due to the bias correction applied, we do not only evaluate the actual measurements at the stations but rather focus on the difference between evaluation metrics achieved by the 0.5° data and the downscaled data. This will directly evaluate the downscaling but not the forcing of the downscaling (see Karger et al., 2021). We use two observational datasets, GHCN-D (Global Historical Climatology Network Daily) and GEBA (Global Energy Balance Archive), as references for the evaluation. The evaluation is performed at daily, seasonal, and long-term climatological normals. The compaon to the station data is global, whereas the compaon to the dynamically down-scaled data is constrained to the United States, where model output as well as a dense network of observational station data is available.",
"score": 0.5416666666666666
},
{
"doi": "10.5194/essd-15-2445-2023",
"title": "CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies",
"journal": "Earth system science data",
"year": 2023,
"domains": [
"climate-general"
],
"section": "3.1 Evaluation datasets",
"orphan": false,
"linked_products": [
"reanalysis-era5-land",
"reanalysis-era5-land-monthly-means",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "To evaluate the performance of the downscaling algorithm we compute several test statistics at the original $0.5^{\\circ}$ resolution of the W5E5 data and the downscaled data at 30 arcsec from CHELSA-W5E5. We use observations at meteorological stations (Table 1) and compare those to W5E5 and CHELSA-W5E5 data from the corresponding $0.5^{\\circ}$ and 30 arcsec grid cells, respectively, to assess the value added by the downscaling.",
"score": 0.5
},
{
"doi": "10.5194/tc-19-201-2025",
"title": "Benchmarking of snow water equivalent (SWE) products based on outcomes of the SnowPEx+ Intercomparison Project",
"journal": "The cryosphere",
"year": 2025,
"domains": [
"cryosphere"
],
"section": "2.2 Overall evaluation strategy",
"orphan": false,
"linked_products": [
"reanalysis-era5-land",
"reanalysis-era5-land-monthly-means",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "We evaluate the 23 gridded SWE products (Table 1) on their ability to represent aspects of SWE climatology, variability, and trends across 14 combinations of regions and seasons as summarized in Table 2. The choices of regions and seasons that we test are controlled in part by the reference data, as we detail in Sect. 2.3. While ideally we would use a single reference dataset applied in the same manner for all tests, the charactetics of our primary reference data (referred to in Table 2 as “combined snow course + gamma SWE”) limit the types of evaluations for which they are most appropriate. Therefore, in order to facilitate the compaon of product performance among all tests, we implement a relative point system as our overall evaluation strategy. For each combination of region and season listed in Table 2, the products that perform best on a given test are rewarded and the ones that perform the worst are penalized. Results from this reward/penalty system are tallied over all 14 evaluations, allowing us to provide total relative rankings (Sect. 4) that indicate a product’s overall performance compared to the entire suite of products.\n\nFor as many tests as possible, the particular reward/penalty applied to the products is determined using a two-component skill score (the skill score itself is described in Sect. 2.4). For each product, its similarity to the specified reference data is measured in terms of this skill score, and the distribution of scores among all products on the given test is used to determine the rewards and penalties. Any products performing above the 90th ",
"score": 0.38596491228070173
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">G44</span><span class="qa-tool">search_publications</span><span class="qa-q">flood</span><span class="qa-x">dataset_or_product_id=cems-glofas-historical</span><span class="qa-dt num">25.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "flood",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.1002/2013wr014710",
"title": "Real‐time global flood estimation using satellite‐based precipitation and a coupled land surface and routing model",
"journal": "Water Resources Research",
"year": 2014,
"domains": [
"atmosphere",
"land"
],
"section": "4.2. Recent Floods in Mississippi Upstream Subbasin Rivers",
"orphan": false,
"linked_products": [
"cems-glofas-forecast",
"cems-glofas-historical",
"cems-glofas-reforecast",
"cems-glofas-seasonal"
],
"text": "[FIGURE CAPTION]\nFigure 3. Snapshots from the real-time GFMS (online: http://flood.umd.edu) for major two flood waves, covering April to early June, 2013, in subbasin rivers upstream of the Mississippi River, including (a and b) the flood detection and intensity (water depth above flood threshold), (c and d) previous 7 day accumulated precipitation according TMPA RT, (e and f) streamflow. All data are at 1/8th ( $\\sim$ 12km) resolution.",
"score": 0.5
},
{
"doi": "10.47672/ajcs.1931",
"title": "Assessment of Flood Vulnerability and Coping Strategies of Communities Living along River Tana in Madogo Ward, Kenya",
"journal": "American Journal of Climatic Studies",
"year": 2024,
"domains": [
"land",
"emergency"
],
"section": "Conceptual Framework",
"orphan": false,
"linked_products": [
"cems-glofas-forecast",
"cems-glofas-historical",
"cems-glofas-reforecast",
"cems-glofas-seasonal"
],
"text": "A conceptual framework in Figure 1 indicates the link between the study variables.\n\n<details> <summary>flowchart</summary>\n\n```mermaid graph TD A[\"FLOODS\"] --> B[\"FLOODS CHARACTETICS\"] A --> C[\"FLOOD VULNERABILITY\"] A --> D[\"FLOOD ADAPTATION STRATEGIES\"] B --> B1[\"Flood duration\"] B --> B2[\"Flood frequencies\"] C --> C1[\"Factors contributing to flood vulnerability\"] D --> D1[\"Flood adaptation strategies\"] D --> D2[\"Flood coping strategies\"] ``` </details>\n\nSource: Author (2022) Based on Literature Review\n\nFlood is the overflow of rivers into adjacent low-lying plains usually produced by prolonged heavy rainfall, snow melt, dam-breaks, tidal and cyclonic surges, and development projects (Lyon & Yetman, 2005). Flood has different charactetics like magnitudes, durations, and magnitudes which make community living in the flood prone areas to developed flood adaptations strategies and flood coping strategies. Social and economic charactetic of people coupled with flood charactetics of flood contribute to flood vulnerability of the residents of flood prone areas.\n\nThe study was also inspired by the research gaps in the earlier studies which have not narrowed down to flood vulnerability factors and coping strategies along River Tana in the lower sub basin. Therefore, to fill these research gaps, this study was conducted. Munyai (2017) looked at an assessment of community flood vulnerability and adaptation: A case study of Greater Tzaneen Local Municipality, South Africa. The studies in South Africa present a contextual research gap and given the contextual difference between the co",
"score": 0.5
},
{
"doi": "10.21203/rs.3.rs-6718520/v1",
"title": "Resolving the cascade of uncertainty in global flood projections",
"journal": "Research Square",
"year": 2025,
"domains": [
"climate-general"
],
"section": "Abstract",
"orphan": false,
"linked_products": [
"cems-glofas-historical"
],
"text": "Projections of river flooding are crucial for adaptation, but conventional impact modelling is plagued by a ‘cascade of uncertainty’ aing from global climate model (GCM) outputs, statistical corrections, and hydrologic model structures. To overcome this cascade, we developed a data-driven approach that uses machine learning (ML) models to directly predict 10-year flood magnitudes from spatially-varying summary statistics derived from 19 uncorrected CMIP6 GCMs. We find that ML models trained on uncorrected GCMs are 40% more accurate than hydrologic models driven with bias-corrected GCMs. ML rectifies spatially-variable GCM biases by adjusting the contribution of basin attributes, correcting heavy rainfall underestimation in wet river basins. Our results indicate that precipitation intensification is more likely to increase river flood magnitude in dry and high-altitude regions. By 2100, only 34% of rivers are projected to experience larger floods under SSP5-8.5, with the largest increases found in dry climates and high-altitude, steep river segments.",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">G45</span><span class="qa-tool">search_publications</span><span class="qa-q">sea level trend</span><span class="qa-x">dataset_or_product_id=SEALEVEL_GLO_PHY_L4_MY_008_047</span><span class="qa-dt num">42.7s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "sea level trend",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.3389/feart.2021.698876",
"title": "The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis",
"journal": "Frontiers in Earth Science",
"year": 2021,
"domains": [
"physics",
"waves"
],
"section": "Sea Level Time Evolution",
"orphan": false,
"linked_products": [
"GLOBAL_ANALYSISFORECAST_PHY_001_024",
"GLOBAL_MULTIYEAR_PHY_001_030",
"GLOBAL_MULTIYEAR_WAV_001_032",
"SEALEVEL_GLO_PHY_L4_MY_008_047",
"SEALEVEL_GLO_PHY_L4_NRT_008_046",
"reanalysis-oras5"
],
"text": "Of particular importance for sea level trends, along-track altimetric observations from various missions are assimilated in GLORYS12 and G4, together with in situ temperature and salinity profiles and other observations. Altimetric observations capture sea level trends due to land ice mass loss and land water storage changes, in addition to trends due to sterodynamic sea level changes (e.g., Gregory et al., 2019). As mentioned in the description of the ocean model in Description of GLORYS12, a global mean sea level (GMSL) trend is added at each time step to the modeled dynamic sea level. This added GMSL signal is composed of the diagnosed global mean steric sea level change and of a barystatic (land ice related, Gregory et al., 2019) sea level trend of 1.31 mm/yr over 1993–2001 and of 2.20 mm/yr over 2002–2016. The GMSL change is added to all simulations, prior to data assimilation for GLORYS12 and G4.",
"score": 0.5666666666666667
},
{
"doi": "10.5194/essd-10-281-2018",
"title": "An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative",
"journal": "Earth System Science Data",
"year": 2018,
"domains": [
"sea_level"
],
"section": "3 Description of the SL\\_cci v2.0 ECV",
"orphan": false,
"linked_products": [
"OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies",
"OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies",
"OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies",
"OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies",
"OMI_CLIMATE_SL_GLOBAL_regional_trends",
"OMI_CLIMATE_SL_IBI_area_averaged_anomalies",
"OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies",
"OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"
],
"text": "[FIGURE CAPTION]\nFigure 2. Regional MSL trend (from SL\\_cci v2.0 ECV) during the period 1993–2015.",
"score": 0.5
},
{
"doi": "10.5194/essd-11-1189-2019",
"title": "Uncertainty in satellite estimates of global mean sea-level changes, trend and acceleration",
"journal": "Earth System Science Data",
"year": 2019,
"domains": [
"sea_level"
],
"section": "6 Uncertainty in GMSL trend and acceleration",
"orphan": false,
"linked_products": [
"OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies",
"OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies",
"OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies",
"OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies",
"OMI_CLIMATE_SL_GLOBAL_regional_trends",
"OMI_CLIMATE_SL_IBI_area_averaged_anomalies",
"OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies",
"OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"
],
"text": "[FIGURE CAPTION]\nFigure 9 indicates that for periods of 5 years and longer, the trend in GMSL is always significant at the 90% CL over the whole record. At the end of the record the trend tends to increase. This is consistent with the acceleration plot in Fig. 6. Over the 25 years of satellite altimetry, we find a sea-level rise of $3.35 \\pm 0.4 \\, mm yr^{-1}$ (90% CL) after correcting for the TOPEX-A GMSL drift. The differences due to the different TOPEX-A corrections are negligible (< 0.05 mm yr $^{-1}$ ).",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">G46</span><span class="qa-tool">search_publications</span><span class="qa-q">coastal erosion</span><span class="qa-x">orphan_only=True</span><span class="qa-dt num">37.4s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "coastal erosion",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.21203/rs.3.rs-3120217/v1",
"title": "Assessing Coastal Erosion and Sediment Removal in the sand bar of the Isla Salamanca: Implications for the Barranquilla-Ciénaga Highway and coastal marine biodiversity – Colombia",
"journal": "",
"year": null,
"domains": [
"ocean/marine"
],
"section": "3 Results and Discussion",
"orphan": true,
"linked_products": [],
"text": "The medium-term analysis showed that along the 43,230 m of the Isla Salamanca coastal barrier, most of it (37,850 m) is in a process of frank transgression, with average annual rates of -4.8 m·yr $^{-1}$ , with a maximum erosion of -277.6 m from the shoreline, between March 2004 and January 2021. On the other hand, adjacent to the mouth of the Magdalena River, at Boca de Ceniza, the western 5,535 m of the coastal barrier presents a maximum deposition of 499.8 m in extent (Fig. 2A and Table 1). The two specific areas of the coastal barrier studied are the areas with the highest erosion rates near the road connecting the cities of Barranquilla and Pueblo Viejo. At km 19 the average erosion rate was -10.0 m·yr $^{-1}$ , attaining a maximum erosion rate of -284 m and with the lowest retreat of -25.8 m from the shoreline (Fig. 2B and Table 1). These lower erosion rates are in the central portion of km 19, in which is due to the containment works with the construction of the seawall from the year of 2014. While at km 29 erosion rates reach a maximum of -6.2 m·yr $^{-1}$ with a shoreline retraction of -96 m (Fig. 2C y Table 1). Sheet piling is a technique used to control coastal erosion and protect coastal infrastructure, such as Highway, bridges, and homes, from the damaging effects of storms and ing sea levels (Griggs and Reguero 2021). This technique involves placing large rocks or concrete blocks along the shoreline to reduce the force of waves and stabilize the beach (Isla et al. 2018). Fencing can be a temporary solution to protect coastal infrastructure as it does not addre",
"score": 0.6428571428571428
},
{
"doi": "10.21203/rs.3.rs-4668096/v1",
"title": "Coastal Vulnerability of Archeological Sites of Southeastern Crete, Greece",
"journal": "",
"year": null,
"domains": [
"ocean/marine",
"emergency"
],
"section": "The Study Area",
"orphan": true,
"linked_products": [],
"text": "Coastal erosion. The majority of coasts within the study region primarily consist of solid rock formations, contributing to their relative stability in terms of coastal erosion when compared to global erosion trends (Donchyts et al. 2017; Prémaillon et al. 2018). However, certain parts of the studied coasts are rather vulnerable. The evidence of coastal erosion is especially visible in the western part of the lerapetra area. The ruins of the Roman-era structure near Stomio, which had been on land in the middle of the previous century, are submerged now (Figs. 1, 2a). The Roman-period town Lefki, located at the north of Koufonisi island (Fig. 1), is subject to severe coastal erosion, which appears to have been destroying the buildings of the town row by row over a very long time, since many ruins have been discovered in the shallow water of the area (Fig. 3).",
"score": 0.5833333333333333
},
{
"doi": "10.1007/s00367-024-00765-6",
"title": "Assessing erosion and sediment removal in the Isla Salamanca coastal barrier: implications for the Barranquilla-Ciénaga highway and coastal marine biodiversity – Colombia",
"journal": "",
"year": null,
"domains": [
"ocean/marine"
],
"section": "Actions to protect the coast and its ecosystems",
"orphan": true,
"linked_products": [],
"text": "The question is, when will we adapt our human development to natural processes? We must be aware of the forcings acting in the coastal zone and have the habit of living in changing coastal environments. We must consider that the artificial structures present at the land-sea interface are temporary and only as a last resort consider the construction of engineered structures for coastal protection or preservation, only in heavily populated areas.",
"score": 0.5
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">G47</span><span class="qa-tool">search_publications</span><span class="qa-q">ERA5 global reanalysis applications</span><span class="qa-x">dataset_or_product_id=reanalysis-era5-single-levels</span><span class="qa-dt num">35.5s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"query": "ERA5 global reanalysis applications",
"retrieval": "hybrid(dense+bm25)",
"reranked": false,
"n_results": 3,
"results": [
{
"doi": "10.1007/s00521-024-10139-9",
"title": "Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models",
"journal": "Neural Computing and Applications",
"year": 2024,
"domains": [
"atmosphere",
"climate-modeling"
],
"section": "3.1 ERA5",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "ERA5 [1], short for the Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, is a well known and vastly utilized dataset in the realm of climate and atmospheric research. ERA5, encompassing a period from 1940 to the present, provide a detailed portrait of the Earth's atmospheric system. It provides hourly estimates of a large number of atmospheric, land and oceanic climate variables with a horizontal resolution of 0.25 degrees (around 30 km) and 137 levels in the vertical from the surface to 0.01 hPa (about 80 km). An innovative aspect of The ERA5 reanalyses is its timely availability. Collecting the necessary data and their computation results in a production latency of approximately 5 days. This timely availability makes ERA5 a valuable tool for studying events in the recent past and forecasting future weather.\n\nThe assimilation system used in ERA5, based on the ECMWF Integrated Forecasting System (IFS), incorporates advancements in numerical weather prediction models, radiative transfer schemes, and satellite data utilization, contributing to the dataset's reliability and precision [67].\n\nERA5 is a state-of-the-art global atmospheric reanalysis, and it serves as a robust tool for understanding and analyzing the Earth's climate system, offering a global representation of atmospheric, land, and oceanic conditions spanning several decades. ERA5 over the years it has been a crucial asset for scientists from a variety of fields, with the dataset appearing in a vast amount of studies. Its applications span several key areas:\n\n\\- Analys",
"score": 0.6
},
{
"doi": "10.21203/rs.3.rs-3390987/v1",
"title": "Rising geopotential height under global warming",
"journal": "Research Square",
"year": 2023,
"domains": [
"atmosphere"
],
"section": "Data Availability Statements",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "All the datasets adopted in this study can be accessed online via the following URLs.\n\n1. CMIP6 model data\n\n2. Met Office Hadley Centre global historical surface temperature version 5 (HadCRUT5)\n\n3. ERA5 global gridded monthly reanalysis data (ERA5)\n\n4. NCEP/NCAR reanalysis (NCEP1)\n\n5. NCEP/DOE reanalysis version 2 (NCEP2)\n\n6. Japanese 55-year reanalysis (JRA55)\n\n7. Modern-Era Retrospective Analysis for Research and Applications (MERRA2)",
"score": 0.5
},
{
"doi": "10.3389/fmars.2022.1034644",
"title": "Effect of the Grijalva-Usumacinta system on the circulation adjacent to the eastern shelf of Yucatan",
"journal": "Frontiers in Marine Science",
"year": 2023,
"domains": [
"ocean/marine",
"atmosphere"
],
"section": "Winds ERA-5",
"orphan": false,
"linked_products": [
"reanalysis-era5-pressure-levels",
"reanalysis-era5-pressure-levels-monthly-means",
"reanalysis-era5-single-levels",
"reanalysis-era5-single-levels-monthly-means"
],
"text": "The ERA5 reanalysis (spanning 1979 onwards) of the European Center for Medium-Range Weather Forecast (ECMWF) embodies a detailed record of the global atmosphere, land surface, and ocean waves. The new reanalysis ERA5 replaces the ERA-Interim reanalysis. ERA5 has a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA-Interim. In addition, ERA5 has hourly outputs (Hersbach et al., 2020). The dataset is publicly available (Copernicus Climate Change Service [C3S], 2017, https://cds.climate.copernicus.eu/).",
"score": 0.3333333333333333
}
],
"next_step": "read_publication(doi) pulls a paper's full parsed text"
}</pre></details>
</div>
<div class="qa-grp"><div class="qa-gt"><b>metadata / full text</b><span>full metadata and paper full text</span></div>
<details class="qa"><summary><span class="qa-tag num">H48</span><span class="qa-tool">dataset_metadata</span><span class="qa-q">reanalysis-era5-single-levels</span><span class="qa-dt num">0.2s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"matched_by": "dataset_id",
"store": "cds",
"product_id": null,
"title": "ERA5 hourly data on single levels from 1940 to present",
"doi": "10.24381/cds.adbb2d47",
"variables": [
{
"short_name": "10m_u_component_of_wind"
},
{
"short_name": "10m_v_component_of_wind"
},
{
"short_name": "2m_dewpoint_temperature"
},
{
"short_name": "2m_temperature"
},
{
"short_name": "mean_sea_level_pressure"
},
{
"short_name": "mean_wave_direction"
},
{
"short_name": "mean_wave_period"
},
{
"short_name": "sea_surface_temperature"
},
{
"short_name": "significant_height_of_combined_wind_waves_and_swell"
},
{
"short_name": "surface_pressure"
},
{
"short_name": "total_precipitation"
},
{
"short_name": "ice_temperature_layer_1"
},
{
"short_name": "ice_temperature_layer_2"
},
{
"short_name": "ice_temperature_layer_3"
},
{
"short_name": "ice_temperature_layer_4"
},
{
"short_name": "maximum_2m_temperature_since_previous_post_processing"
},
{
"short_name": "minimum_2m_temperature_since_previous_post_processing"
},
{
"short_name": "skin_temperature"
},
{
"short_name": "100m_u_component_of_wind"
},
{
"short_name": "100m_v_component_of_wind"
},
{
"short_name": "10m_u_component_of_neutral_wind"
},
{
"short_name": "10m_v_component_of_neutral_wind"
},
{
"short_name": "10m_wind_gust_since_previous_post_processing"
},
{
"short_name": "instantaneous_10m_wind_gust"
},
{
"short_name": "mean_boundary_layer_dissipation"
},
{
"short_name": "mean_convective_precipitation_rate"
},
{
"short_name": "mean_convective_snowfall_rate"
},
{
"short_name": "mean_eastward_gravity_wave_surface_stress"
},
{
"short_name": "mean_eastward_turbulent_surface_stress"
},
{
"short_name": "mean_evaporation_rate"
},
{
"short_name": "mean_gravity_wave_dissipation"
},
{
"short_name": "mean_large_scale_precipitation_fraction"
},
{
"short_name": "mean_large_scale_precipitation_rate"
},
{
"short_name": "mean_large_scale_snowfall_rate"
},
{
"short_name": "mean_northward_gravity_wave_surface_stress"
},
{
"short_name": "mean_northward_turbulent_surface_stress"
},
{
"short_name": "mean_potential_evaporation_rate"
},
{
"short_name": "mean_runoff_rate"
},
{
"short_name": "mean_snow_evaporation_rate"
},
{
"short_name": "mean_snowfall_rate"
},
{
"short_name": "mean_snowmelt_rate"
},
{
"short_name": "mean_sub_surface_runoff_rate"
},
{
"short_name": "mean_surface_direct_short_wave_radiation_flux"
},
{
"short_name": "mean_surface_direct_short_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_surface_downward_long_wave_radiation_flux"
},
{
"short_name": "mean_surface_downward_long_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_surface_downward_short_wave_radiation_flux"
},
{
"short_name": "mean_surface_downward_short_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_surface_downward_uv_radiation_flux"
},
{
"short_name": "mean_surface_latent_heat_flux"
},
{
"short_name": "mean_surface_net_long_wave_radiation_flux"
},
{
"short_name": "mean_surface_net_long_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_surface_net_short_wave_radiation_flux"
},
{
"short_name": "mean_surface_net_short_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_surface_runoff_rate"
},
{
"short_name": "mean_surface_sensible_heat_flux"
},
{
"short_name": "mean_top_downward_short_wave_radiation_flux"
},
{
"short_name": "mean_top_net_long_wave_radiation_flux"
},
{
"short_name": "mean_top_net_long_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_top_net_short_wave_radiation_flux"
},
{
"short_name": "mean_top_net_short_wave_radiation_flux_clear_sky"
},
{
"short_name": "mean_total_precipitation_rate"
},
{
"short_name": "mean_vertically_integrated_moisture_divergence"
},
{
"short_name": "clear_sky_direct_solar_radiation_at_surface"
},
{
"short_name": "downward_uv_radiation_at_the_surface"
},
{
"short_name": "forecast_logarithm_of_surface_roughness_for_heat"
},
{
"short_name": "instantaneous_surface_sensible_heat_flux"
},
{
"short_name": "near_ir_albedo_for_diffuse_radiation"
},
{
"short_name": "near_ir_albedo_for_direct_radiation"
},
{
"short_name": "surface_latent_heat_flux"
},
{
"short_name": "surface_net_solar_radiation"
},
{
"short_name": "surface_net_solar_radiation_clear_sky"
},
{
"short_name": "surface_net_thermal_radiation"
},
{
"short_name": "surface_net_thermal_radiation_clear_sky"
},
{
"short_name": "surface_sensible_heat_flux"
},
{
"short_name": "surface_solar_radiation_downward_clear_sky"
},
{
"short_name": "surface_solar_radiation_downwards"
},
{
"short_name": "surface_thermal_radiation_downward_clear_sky"
},
{
"short_name": "surface_thermal_radiation_downwards"
},
{
"short_name": "toa_incident_solar_radiation"
},
{
"short_name": "top_net_solar_radiation"
},
{
"short_name": "top_net_solar_radiation_clear_sky"
},
{
"short_name": "top_net_thermal_radiation"
},
{
"short_name": "top_net_thermal_radiation_clear_sky"
},
{
"short_name": "total_sky_direct_solar_radiation_at_surface"
},
{
"short_name": "uv_visible_albedo_for_diffuse_radiation"
},
{
"short_name": "uv_visible_albedo_for_direct_radiation"
},
{
"short_name": "cloud_base_height"
},
{
"short_name": "high_cloud_cover"
},
{
"short_name": "low_cloud_cover"
},
{
"short_name": "medium_cloud_cover"
},
{
"short_name": "total_cloud_cover"
},
{
"short_name": "total_column_cloud_ice_water"
},
{
"short_name": "total_column_cloud_liquid_water"
},
{
"short_name": "vertical_integral_of_divergence_of_cloud_frozen_water_flux"
},
{
"short_name": "vertical_integral_of_divergence_of_cloud_liquid_water_flux"
},
{
"short_name": "vertical_integral_of_eastward_cloud_frozen_water_flux"
},
{
"short_name": "vertical_integral_of_eastward_cloud_liquid_water_flux"
},
{
"short_name": "vertical_integral_of_northward_cloud_frozen_water_flux"
},
{
"short_name": "vertical_integral_of_northward_cloud_liquid_water_flux"
},
{
"short_name": "lake_bottom_temperature"
},
{
"short_name": "lake_cover"
},
{
"short_name": "lake_depth"
},
{
"short_name": "lake_ice_depth"
},
{
"short_name": "lake_ice_temperature"
},
{
"short_name": "lake_mix_layer_depth"
},
{
"short_name": "lake_mix_layer_temperature"
},
{
"short_name": "lake_shape_factor"
},
{
"short_name": "lake_total_layer_temperature"
},
{
"short_name": "evaporation"
},
{
"short_name": "potential_evaporation"
},
{
"short_name": "runoff"
},
{
"short_name": "sub_surface_runoff"
},
{
"short_name": "surface_runoff"
},
{
"short_name": "convective_precipitation"
},
{
"short_name": "convective_rain_rate"
},
{
"short_name": "instantaneous_large_scale_surface_precipitation_fraction"
},
{
"short_name": "large_scale_rain_rate"
},
{
"short_name": "large_scale_precipitation"
},
{
"short_name": "large_scale_precipitation_fraction"
}
],
"spatial_bbox": [
0.0,
-89.0,
360.0,
89.0
],
"temporal_range": [
"1940-01-01T00:00:00+00:00",
"2026-06-25T00:00:00+00:00"
],
"depth_range": null,
"update_frequency": "Daily",
"processing_level": null,
"production_center": "ECMWF",
"sources": null,
"keywords": [
"Product type: Reanalysis",
"Temporal coverage: Past",
"Spatial coverage: Global",
"Variable domain: Atmosphere (surface)",
"Variable domain: Atmosphere (upper air)",
"Provider: Copernicus C3S"
],
"published": "2018-06-14T00:00:00Z",
"updated": "2026-07-01T00:00:00Z",
"message": null,
"documentation_links": [
{
"title": "ERA5 data documentation",
"url": "https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation",
"rel": "documentation",
"description": "Detailed information relating to the ERA5 data archive can be found in the web link above."
},
{
"title": "The ERA5 global reanalysis: Preliminary extension to 1950",
"url": "https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4174",
"rel": "documentation",
"description": "Journal article describing the ERA5 preliminary extension."
},
{
"title": "The ERA5 global reanalysis",
"url": "https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803",
"rel": "documentation",
"description": "Journal article describing ERA5."
},
{
"title": "Renamed variable: form ocean waves 10m wind to ocean surface stress equivalent 10m neutral wind",
"url": "https://confluence.ecmwf.int/x/ggleC",
"rel": "documentation",
"description": "The reason for the change was a parameter name clash between variables in ERA5 wind and ERA5 ocean waves."
},
{
"title": "Analysis of September 2020 European heatwave using ERA5 climate reanalysis",
"url": "https://jupyterhub.ecmwf.int/hub/user-redirect/lab/tree/notebooks-library/c3s-training/reanalysis/reanalysis-heatwave.ipynb?profile=dss-singleuser",
"rel": "documentation",
"description": "Analyse a heatwave with ERA5 data - compare temperatures with climatology, and plot figures."
},
{
"title": "CC-BY licence",
"url": "https://spdx.org/licenses/CC-BY-4.0",
"rel": "license",
"description": null
}
],
"references": [
{
"text": "#### Guidelines\n\nIn addition to the requirements of the applicable license(s), users **must**:\n\n- cite the CDS catalogue entry;\n\n- provide clear and visible attribution to the Copernicus programme and attribute each data product used; \n\n\n\n\n#### Citing the CDS catalogue entry \n\nCopernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: [10.24381/cds.adbb2d47](https://doi.org/10.24381/cds.adbb2d47) (Accessed on DD-MMM-YYYY)\n\n\n\n\n#### Attribution\n\n**Copernicus programme:** \n\nGenerated using or contains modified Copernicus Climate Change Service information <YYYY>. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.\n\n**Citing the data:**:\n\nHersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: [10.24381/cds.adbb2d47](https://doi.org/10.24381/cds.adbb2d47) (Accessed on DD-MMM-YYYY)\n",
"doi": "10.24381/cds.adbb2d47",
"title": "Citation and attribution"
},
{
"text": "DOI: [10.24381/cds.adbb2d47](https://doi.org/10.24381/cds.adbb2d47)",
"doi": "10.24381/cds.adbb2d47",
"title": null
}
],
"licence": "CC-BY-4.0",
"related_collections": [
"reanalysis-era5-single-levels-timeseries",
"derived-era5-single-levels-daily-statistics",
"reanalysis-era5-single-levels-monthly-means",
"derived-utci-historical-timeseries",
"derived-utci-historical",
"reanalysis-era5-pressure-levels",
"reanalysis-era5-complete",
"reanalysis-era5-pressure-levels-monthly-means",
"derived-era5-pressure-levels-daily-statistics"
],
"dataset_id": "reanalysis-era5-single-levels",
"variables_truncated": "142 more omitted"
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">H49</span><span class="qa-tool">list_dataset_documents</span><span class="qa-q">GLOBAL_MULTIYEAR_PHY_001_030</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"product_id": "GLOBAL_MULTIYEAR_PHY_001_030",
"product_title": "Global Ocean Physics Reanalysis",
"dataset_ids": [
"cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m",
"cmems_mod_glo_phy_my_0.083deg_P1D-m",
"cmems_mod_glo_phy_my_0.083deg_P1M-m",
"cmems_mod_glo_phy_my_0.083deg_static"
],
"doi": "10.48670/moi-00021",
"regions": [
"global"
],
"domains": [
"physics"
],
"n_documents": 3,
"documents": [
{
"doc_id": "CMEMS-GLO-PUM-001-030",
"doc_type": "PUM",
"size_bytes": 35635,
"available": true
},
{
"doc_id": "CMEMS-GLO-QUID-001-030",
"doc_type": "QUID",
"size_bytes": 200810,
"available": true
},
{
"doc_id": "CMEMS-GLO-SQO-001-030",
"doc_type": "SQO",
"size_bytes": 27849,
"available": true
}
]
}</pre></details>
<details class="qa"><summary><span class="qa-tag num">H50</span><span class="qa-tool">read_publication</span><span class="qa-q">10.1002_2016jc012391</span><span class="qa-x">max_chars=1200</span><span class="qa-dt num">0.0s</span><span class="qa-ok">ok</span></summary><pre>{
"ok": true,
"doi": "10.1002/2016jc012391",
"title": "Winter to summer oceanographic observations in the A rctic O cean north of S valbard",
"journal": "Journal of Geophysical Research: Oceans",
"year": "2017",
"total_chars": 114556,
"offset": 0,
"returned_chars": 1200,
"next_offset": 1200,
"text": "# Winter to summer oceanographic observations in the Arctic Ocean north of Svalbard\n\nAmelie Meyer, Arild Sundfjord, Ilker Fer, Christine Provost, Nicolas Villacieros Robineau, Zoé Koenig, Ingrid H. Onarheim, Lars H. Smedsrud, Pedro Duarte, Paul A. Dodd, et al.\n\n# ▶ To cite this version:\n\nAmelie Meyer, Arild Sundfjord, Ilker Fer, Christine Provost, Nicolas Villacieros Robineau, et al.. Winter to summer oceanographic observations in the Arctic Ocean north of Svalbard. Journal of Geophysical Research. Oceans, 2017, 122 (8), pp.6218-6237. <10.1002/2016JC012391>. <hal-01492014>\n\nHAL Id: hal-01492014\n\nhttps://hal.science/hal-01492014v1\n\nSubmitted on 4 Jan 2022\n\nHAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.\n\nL'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou p",
"outline": [
{
"heading": "# Winter to summer oceanographic observations in the Arctic Ocean north of Svalbard",
"offset": 0
},
{
"heading": "# ▶ To cite this version:",
"offset": 262
},
{
"heading": "# RESEARCH ARTICLE",
"offset": 1290
},
{
"heading": "# Special Section:",
"offset": 1332
},
{
"heading": "# Key Points:",
"offset": 1482
},
{
"heading": "# Correspondence to:",
"offset": 1843
},
{
"heading": "# Citation:",
"offset": 1899
},
{
"heading": "# Winter to summer oceanographic observations in the Arctic Ocean north of Svalbard",
"offset": 2612
},
{
"heading": "# 1. Introduction",
"offset": 4568
},
{
"heading": "# 2. Data and Methods",
"offset": 9709
},
{
"heading": "# 2.1. N-ICE2015 Expedition",
"offset": 9732
},
{
"heading": "# 2.2. Collected Data",
"offset": 16737
},
{
"heading": "# 2.2.1. Ship-Board CTD Data",
"offset": 19476
},
{
"heading": "# 2.2.2. IAOOS Profilers Data",
"offset": 20745
},
{
"heading": "# 2.2.3. Microstructure Profiler Data",
"offset": 22046
},
{
"heading": "# 2.2.4. Dissolved Oxygen Data (Water Samples)",
"offset": 23437
},
{
"heading": "# 2.2.5. Current Data: Vessel-Mounted ADCP Data",
"offset": 24682
},
{
"heading": "# 2.2.6. Current Data: Long Ranger ADCP Data",
"offset": 25210
},
{
"heading": "# 2.3. Tides",
"offset": 26326
},
{
"heading": "# 2.3.1. Tidal Model: AOTIM-5 Tidal Current Predictions",
"offset": 26688
},
{
"heading": "# 2.3.2. Tidal Current Observations: Complex Demodulation",
"offset": 27201
},
{
"heading": "# 2.4. Climatologies and Reanalysis Products",
"offset": 28277
},
{
"heading": "# 2.4.1. Ocean Climatology: MIMOC",
"offset": 28323
},
{
"heading": "# 2.4.2. Reanalysis Product: ERA-Interim",
"offset": 28927
},
{
"heading": "# 3. Results",
"offset": 29352
},
{
"heading": "# 3.1. Environmental Conditions",
"offset": 29366
},
{
"heading": "# 3.2. Hydrography and Water Masses",
"offset": 33497
},
{
"heading": "# 3.2.1. Hydrographic Overview",
"offset": 33534
},
{
"heading": "# 3.2.2. Water Masses",
"offset": 34427
},
{
"heading": "# 3.3. Mixed-Layer Characteristics",
"offset": 36895
},
{
"heading": "# 3.4. Seasonal and Regional Variability",
"offset": 45477
},
{
"heading": "# 3.4.1. In the Observations",
"offset": 45519
},
{
"heading": "# 3.4.2. Comparison With Ocean Climatology",
"offset": 49680
},
{
"heading": "# 3.4.3. Cold Halocline and Sea Ice",
"offset": 73229
},
{
"heading": "# 3.5. Surface Currents and Drift",
"offset": 75924
},
{
"heading": "# 3.6. Deeper Circulation",
"offset": 77686
},
{
"heading": "# 3.7. Tides and Oscillations",
"offset": 79258
},
{
"heading": "# 4. Discussion",
"offset": 81545
},
{
"heading": "# 4.1. Upper Layer Characteristics and Formation of the Cold Halocline",
"offset": 90177
},
{
"heading": "# 4.2. Atlantic Water Characteristics and Circulation",
"offset": 91709
},
{
"heading": "# 5. Conclusions",
"offset": 95906
},
{
"heading": "# Acknowledgments",
"offset": 97680
},
{
"heading": "# References",
"offset": 99005
},
{
"heading": "# Erratum",
"offset": 114338
}
]
}</pre></details>
</div>
</div>
<footer>
originals intact · snapshot: HF <b>dmpantiu/copernicus-rag-core</b> 8.4 GB · open: table-id collisions
</footer>
</div>
|