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@@ -57,3 +57,17 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+eval/raw/ecmwf_eval_3.grib filter=lfs diff=lfs merge=lfs -text
+eval/raw/glosea_eval_3.grib filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_1.grib filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_1.grib.923a8.idx filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_2.grib filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_2.grib.923a8.idx filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_2023.grib filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_3.grib filter=lfs diff=lfs merge=lfs -text
+raw/ecmwf_3.grib.923a8.idx filter=lfs diff=lfs merge=lfs -text
+raw/glosea_1.grib filter=lfs diff=lfs merge=lfs -text
+raw/glosea_2.grib filter=lfs diff=lfs merge=lfs -text
+raw/glosea_2023.grib filter=lfs diff=lfs merge=lfs -text
+raw/glosea_2023.grib.5b7b6.idx filter=lfs diff=lfs merge=lfs -text
+raw/glosea_3.grib filter=lfs diff=lfs merge=lfs -text

access.ipynb

@@ -0,0 +1,1548 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = ['access', 'ecmwf', 'glosea5', 'silo']\n",
+    "frames = []\n",
+    "for file in files:\n",
+    "    df = pd.read_parquet(f'data/processed/{file}.parquet')\n",
+    "    df['model'] = file\n",
+    "    frames.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(583632, 5)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "access = frames[0]\n",
+    "access.reset_index(inplace=True, drop=True)\n",
+    "columns = access.columns\n",
+    "# Convert time to string\n",
+    "access['time'] = access['time'].astype(str)\n",
+    "access.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-29. -28. -27. -26. -25. -24. -23. -22. -20. -19. -18. -17.]\n",
+      "[150. 151. 152. 153. 142. 143. 144. 145. 146.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print access unique lat and lon\n",
+    "print(access['lat'].unique())\n",
+    "print(access['lon'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>pr</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.220000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>3.170000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>13.210000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>25.549999</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-28.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.160000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583627</th>\n",
+       "      <td>2018-02-10 12:00:00</td>\n",
+       "      <td>-18.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583628</th>\n",
+       "      <td>2018-02-10 12:00:00</td>\n",
+       "      <td>-17.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583629</th>\n",
+       "      <td>2018-02-10 12:00:00</td>\n",
+       "      <td>-17.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>0.290000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583630</th>\n",
+       "      <td>2018-02-10 12:00:00</td>\n",
+       "      <td>-17.0</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>1.150000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583631</th>\n",
+       "      <td>2018-02-10 12:00:00</td>\n",
+       "      <td>-17.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>583632 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       time   lat    lon         pr   model\n",
+       "0       1983-01-01 12:00:00 -29.0  150.0   0.220000  access\n",
+       "1       1983-01-01 12:00:00 -29.0  151.0   3.170000  access\n",
+       "2       1983-01-01 12:00:00 -29.0  152.0  13.210000  access\n",
+       "3       1983-01-01 12:00:00 -29.0  153.0  25.549999  access\n",
+       "4       1983-01-01 12:00:00 -28.0  150.0   0.160000  access\n",
+       "...                     ...   ...    ...        ...     ...\n",
+       "583627  2018-02-10 12:00:00 -18.0  146.0   0.100000  access\n",
+       "583628  2018-02-10 12:00:00 -17.0  143.0   0.100000  access\n",
+       "583629  2018-02-10 12:00:00 -17.0  144.0   0.290000  access\n",
+       "583630  2018-02-10 12:00:00 -17.0  145.0   1.150000  access\n",
+       "583631  2018-02-10 12:00:00 -17.0  146.0   0.000000  access\n",
+       "\n",
+       "[583632 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "access"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(627300, 5)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ecmwf = frames[1]\n",
+    "ecmwf.rename(columns={'date': 'time', 'precip': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, inplace=True)\n",
+    "ecmwf = ecmwf[columns]\n",
+    "ecmwf.reset_index(inplace=True, drop=True)\n",
+    "# Convert time to string\n",
+    "ecmwf['time'] = ecmwf['time'].astype(str)\n",
+    "ecmwf.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>pr</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1981-01-29</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1981-01-29</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1981-01-29</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1981-01-29</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1981-01-29</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627295</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627296</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627297</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627298</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627299</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>627300 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              time   lat    lon   pr  model\n",
+       "0       1981-01-29 -29.0  138.0  0.0  ecmwf\n",
+       "1       1981-01-29 -29.0  139.0  0.0  ecmwf\n",
+       "2       1981-01-29 -29.0  140.0  0.0  ecmwf\n",
+       "3       1981-01-29 -29.0  141.0  0.0  ecmwf\n",
+       "4       1981-01-29 -29.0  142.0  0.0  ecmwf\n",
+       "...            ...   ...    ...  ...    ...\n",
+       "627295  2018-12-31 -15.0  150.0  0.0  ecmwf\n",
+       "627296  2018-12-31 -15.0  151.0  0.0  ecmwf\n",
+       "627297  2018-12-31 -15.0  152.0  0.0  ecmwf\n",
+       "627298  2018-12-31 -15.0  153.0  0.0  ecmwf\n",
+       "627299  2018-12-31 -15.0  154.0  0.0  ecmwf\n",
+       "\n",
+       "[627300 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ecmwf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(331008, 5)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glosea = frames[2]\n",
+    "glosea.rename(columns={'date': 'time', 'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, inplace=True)\n",
+    "glosea = glosea[columns]\n",
+    "glosea.reset_index(inplace=True, drop=True)\n",
+    "# Convert time to string\n",
+    "glosea['time'] = glosea['time'].astype(str)\n",
+    "glosea.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(350624, 5)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "silo = frames[3]\n",
+    "silo.rename(columns={'daily_rain': 'pr'}, inplace=True)\n",
+    "silo = silo[columns]\n",
+    "# Convert lat and lon to float32\n",
+    "silo['lat'] = silo['lat'].astype('float32')\n",
+    "silo['lon'] = silo['lon'].astype('float32')\n",
+    "silo.reset_index(inplace=True, drop=True)\n",
+    "# Convert time to string\n",
+    "silo['time'] = silo['time'].astype(str)\n",
+    "silo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([142., 143., 144., 145., 150., 151., 152., 153.], dtype=float32)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "silo.lon.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-25., -24., -23., -22., -29., -28., -27., -26.], dtype=float32)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "silo.lat.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>pr</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.220000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>3.170000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>13.210000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>25.549999</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-28.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.160000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892559</th>\n",
+       "      <td>2018-12-27</td>\n",
+       "      <td>-26.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>silo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892560</th>\n",
+       "      <td>2018-12-28</td>\n",
+       "      <td>-26.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>2.699707</td>\n",
+       "      <td>silo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892561</th>\n",
+       "      <td>2018-12-29</td>\n",
+       "      <td>-26.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>silo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892562</th>\n",
+       "      <td>2018-12-30</td>\n",
+       "      <td>-26.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>silo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892563</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>-26.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>silo</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1892564 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        time   lat    lon         pr   model\n",
+       "0        1983-01-01 12:00:00 -29.0  150.0   0.220000  access\n",
+       "1        1983-01-01 12:00:00 -29.0  151.0   3.170000  access\n",
+       "2        1983-01-01 12:00:00 -29.0  152.0  13.210000  access\n",
+       "3        1983-01-01 12:00:00 -29.0  153.0  25.549999  access\n",
+       "4        1983-01-01 12:00:00 -28.0  150.0   0.160000  access\n",
+       "...                      ...   ...    ...        ...     ...\n",
+       "1892559           2018-12-27 -26.0  153.0   0.000000    silo\n",
+       "1892560           2018-12-28 -26.0  153.0   2.699707    silo\n",
+       "1892561           2018-12-29 -26.0  153.0   0.000000    silo\n",
+       "1892562           2018-12-30 -26.0  153.0   0.000000    silo\n",
+       "1892563           2018-12-31 -26.0  153.0   0.000000    silo\n",
+       "\n",
+       "[1892564 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfs = [access, ecmwf, glosea, silo]\n",
+    "master_df = pd.concat(dfs)\n",
+    "master_df.reset_index(inplace=True, drop=True)\n",
+    "# print(master_df.shape)\n",
+    "# master_df = master_df.groupby(['time', 'lat', 'lon', 'model']).agg({'pr': 'sum'}).reset_index()\n",
+    "# print(master_df.shape)\n",
+    "master_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df.to_parquet('data/processed/master.parquet')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating an evaluation parquet for 2023"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cfgrib\n",
+    "import xarray as xr\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>step</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>number</th>\n",
+       "      <th>surface</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>tprate</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1915</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>300.915039</td>\n",
+       "      <td>2.158828e-08</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1916</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>299.213379</td>\n",
+       "      <td>2.551912e-08</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1917</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>296.932617</td>\n",
+       "      <td>2.889698e-08</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1918</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>295.119629</td>\n",
+       "      <td>3.888960e-08</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1919</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>295.313965</td>\n",
+       "      <td>4.453392e-08</td>\n",
+       "      <td>ecmwf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1920 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           time    step  latitude  longitude  number  surface valid_time  \\\n",
+       "0    2023-03-01 30 days     -22.0      142.0       0      0.0 2023-03-31   \n",
+       "1    2023-03-01 30 days     -22.0      143.0       0      0.0 2023-03-31   \n",
+       "2    2023-03-01 30 days     -22.0      144.0       0      0.0 2023-03-31   \n",
+       "3    2023-03-01 30 days     -22.0      145.0       0      0.0 2023-03-31   \n",
+       "4    2023-03-01 30 days     -22.0      146.0       0      0.0 2023-03-31   \n",
+       "...         ...     ...       ...        ...     ...      ...        ...   \n",
+       "1915 2023-12-01 31 days     -29.0      149.0       0      0.0 2024-01-01   \n",
+       "1916 2023-12-01 31 days     -29.0      150.0       0      0.0 2024-01-01   \n",
+       "1917 2023-12-01 31 days     -29.0      151.0       0      0.0 2024-01-01   \n",
+       "1918 2023-12-01 31 days     -29.0      152.0       0      0.0 2024-01-01   \n",
+       "1919 2023-12-01 31 days     -29.0      153.0       0      0.0 2024-01-01   \n",
+       "\n",
+       "             t2m        tprate  model  \n",
+       "0            NaN           NaN  ecmwf  \n",
+       "1            NaN           NaN  ecmwf  \n",
+       "2            NaN           NaN  ecmwf  \n",
+       "3            NaN           NaN  ecmwf  \n",
+       "4            NaN           NaN  ecmwf  \n",
+       "...          ...           ...    ...  \n",
+       "1915  300.915039  2.158828e-08  ecmwf  \n",
+       "1916  299.213379  2.551912e-08  ecmwf  \n",
+       "1917  296.932617  2.889698e-08  ecmwf  \n",
+       "1918  295.119629  3.888960e-08  ecmwf  \n",
+       "1919  295.313965  4.453392e-08  ecmwf  \n",
+       "\n",
+       "[1920 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read in ecwmf_2023.grib and access_2023.grib as GRIB files\n",
+    "ecmwf = cfgrib.open_datasets('data/raw/ecmwf_2023.grib')\n",
+    "\n",
+    "# Convert the xarray dataset to a pandas dataframe\n",
+    "ecmwf_df = ecmwf[0].to_dataframe().reset_index()\n",
+    "ecmwf_df['model'] = 'ecmwf'\n",
+    "ecmwf_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>step</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>number</th>\n",
+       "      <th>surface</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>tprate</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>30 days</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1915</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>302.622101</td>\n",
+       "      <td>2.736340e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1916</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>300.857300</td>\n",
+       "      <td>2.981721e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1917</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>298.743988</td>\n",
+       "      <td>3.446928e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1918</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>297.252991</td>\n",
+       "      <td>4.342598e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1919</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>31 days</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>296.756409</td>\n",
+       "      <td>4.949413e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1920 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           time    step  latitude  longitude  number  surface valid_time  \\\n",
+       "0    2023-03-01 30 days     -22.0      142.0       0      0.0 2023-03-31   \n",
+       "1    2023-03-01 30 days     -22.0      143.0       0      0.0 2023-03-31   \n",
+       "2    2023-03-01 30 days     -22.0      144.0       0      0.0 2023-03-31   \n",
+       "3    2023-03-01 30 days     -22.0      145.0       0      0.0 2023-03-31   \n",
+       "4    2023-03-01 30 days     -22.0      146.0       0      0.0 2023-03-31   \n",
+       "...         ...     ...       ...        ...     ...      ...        ...   \n",
+       "1915 2023-12-01 31 days     -29.0      149.0       0      0.0 2024-01-01   \n",
+       "1916 2023-12-01 31 days     -29.0      150.0       0      0.0 2024-01-01   \n",
+       "1917 2023-12-01 31 days     -29.0      151.0       0      0.0 2024-01-01   \n",
+       "1918 2023-12-01 31 days     -29.0      152.0       0      0.0 2024-01-01   \n",
+       "1919 2023-12-01 31 days     -29.0      153.0       0      0.0 2024-01-01   \n",
+       "\n",
+       "             t2m        tprate   model  \n",
+       "0            NaN           NaN  glosea  \n",
+       "1            NaN           NaN  glosea  \n",
+       "2            NaN           NaN  glosea  \n",
+       "3            NaN           NaN  glosea  \n",
+       "4            NaN           NaN  glosea  \n",
+       "...          ...           ...     ...  \n",
+       "1915  302.622101  2.736340e-08  glosea  \n",
+       "1916  300.857300  2.981721e-08  glosea  \n",
+       "1917  298.743988  3.446928e-08  glosea  \n",
+       "1918  297.252991  4.342598e-08  glosea  \n",
+       "1919  296.756409  4.949413e-08  glosea  \n",
+       "\n",
+       "[1920 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# same for glosea_2023.grib\n",
+    "glosea = cfgrib.open_datasets('data/raw/glosea_2023.grib')\n",
+    "\n",
+    "glosea_df = glosea[0].to_dataframe().reset_index()\n",
+    "glosea_df['model'] = 'glosea'\n",
+    "glosea_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine dfs\n",
+    "dfs = [ecmwf_df, glosea_df]\n",
+    "master_df = pd.concat(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>pr</th>\n",
+       "      <th>model</th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_year</th>\n",
+       "      <th>days_since_start</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>ecmwf</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>ecmwf</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>ecmwf</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>ecmwf</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-01</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>ecmwf</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1915</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>2.736340e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>12</td>\n",
+       "      <td>335</td>\n",
+       "      <td>11255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1916</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2.981721e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>12</td>\n",
+       "      <td>335</td>\n",
+       "      <td>11255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1917</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>3.446928e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>12</td>\n",
+       "      <td>335</td>\n",
+       "      <td>11255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1918</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>4.342598e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>12</td>\n",
+       "      <td>335</td>\n",
+       "      <td>11255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1919</th>\n",
+       "      <td>2023-12-01</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>4.949413e-08</td>\n",
+       "      <td>glosea</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>12</td>\n",
+       "      <td>335</td>\n",
+       "      <td>11255</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3840 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           time   lat    lon            pr   model  year  month  day_of_year  \\\n",
+       "0    2023-03-01 -22.0  142.0  0.000000e+00   ecmwf  2023      3           60   \n",
+       "1    2023-03-01 -22.0  143.0  0.000000e+00   ecmwf  2023      3           60   \n",
+       "2    2023-03-01 -22.0  144.0  0.000000e+00   ecmwf  2023      3           60   \n",
+       "3    2023-03-01 -22.0  145.0  0.000000e+00   ecmwf  2023      3           60   \n",
+       "4    2023-03-01 -22.0  146.0  0.000000e+00   ecmwf  2023      3           60   \n",
+       "...         ...   ...    ...           ...     ...   ...    ...          ...   \n",
+       "1915 2023-12-01 -29.0  149.0  2.736340e-08  glosea  2023     12          335   \n",
+       "1916 2023-12-01 -29.0  150.0  2.981721e-08  glosea  2023     12          335   \n",
+       "1917 2023-12-01 -29.0  151.0  3.446928e-08  glosea  2023     12          335   \n",
+       "1918 2023-12-01 -29.0  152.0  4.342598e-08  glosea  2023     12          335   \n",
+       "1919 2023-12-01 -29.0  153.0  4.949413e-08  glosea  2023     12          335   \n",
+       "\n",
+       "      days_since_start  \n",
+       "0                10980  \n",
+       "1                10980  \n",
+       "2                10980  \n",
+       "3                10980  \n",
+       "4                10980  \n",
+       "...                ...  \n",
+       "1915             11255  \n",
+       "1916             11255  \n",
+       "1917             11255  \n",
+       "1918             11255  \n",
+       "1919             11255  \n",
+       "\n",
+       "[3840 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns = ['time', 'lat', 'lon', 'pr', 'model', 'year', 'month', 'day_of_year', 'days_since_start']\n",
+    "\n",
+    "# Extract year, month, day_of_year, and days_since_start from time\n",
+    "master_df['time'] = pd.to_datetime(master_df['time'])\n",
+    "master_df['year'] = master_df['time'].dt.year\n",
+    "master_df['month'] = master_df['time'].dt.month\n",
+    "master_df['day_of_year'] = master_df['time'].dt.dayofyear\n",
+    "\n",
+    "# days_since_start is days since 1993-02-06\n",
+    "start_date = pd.to_datetime('1993-02-06')\n",
+    "master_df['days_since_start'] = (master_df['time'] - start_date).dt.days\n",
+    "\n",
+    "# Rename latitude to lat and longitude to lon\n",
+    "master_df.rename(columns={'latitude': 'lat', 'longitude': 'lon'}, inplace=True)\n",
+    "# Rename tprate to pr\n",
+    "master_df.rename(columns={'tprate': 'pr'}, inplace=True)\n",
+    "\n",
+    "# Fill NaN pr values with 0\n",
+    "master_df['pr'] = master_df['pr'].fillna(0)\n",
+    "\n",
+    "master_df = master_df[columns]\n",
+    "master_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save as parquet\n",
+    "master_df.to_parquet('data/processed/master_2023.parquet')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Actual master df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "path = 'data/processed/master.parquet'\n",
+    "df = pd.read_parquet(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>pr</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.220000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>3.170000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>13.210000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-29.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>25.549999</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1983-01-01 12:00:00</td>\n",
+       "      <td>-28.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>0.160000</td>\n",
+       "      <td>access</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  time   lat    lon         pr   model\n",
+       "0  1983-01-01 12:00:00 -29.0  150.0   0.220000  access\n",
+       "1  1983-01-01 12:00:00 -29.0  151.0   3.170000  access\n",
+       "2  1983-01-01 12:00:00 -29.0  152.0  13.210000  access\n",
+       "3  1983-01-01 12:00:00 -29.0  153.0  25.549999  access\n",
+       "4  1983-01-01 12:00:00 -28.0  150.0   0.160000  access"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ensemble_data.py

@@ -0,0 +1,234 @@
+from tqdm import tqdm
+import requests
+import os
+import xarray as xr
+import pandas as pd
+from glob import glob
+import cfgrib
+
+# ### GLOSEA5 ###
+# path = "raw/glosea.grib"
+# print("Processing Glosea5...")
+# # Open the GRIB file as an xarray dataset
+# ds_1 = cfgrib.open_datasets("raw/glosea_1.grib")
+# ds_2 = cfgrib.open_datasets("raw/glosea_2.grib")
+# ds_3 = cfgrib.open_datasets("raw/glosea_3.grib")
+# glosea_xarray_1, glosea_xarray_2, glosea_xarray_3 = ds_1[0], ds_2[0], ds_3[0]
+# # Convert to pandas DataFrame
+# df_1 = glosea_xarray_1.to_dataframe().reset_index()
+# df_2 = glosea_xarray_2.to_dataframe().reset_index()
+# df_3 = glosea_xarray_3.to_dataframe().reset_index()
+# # Concatenate the two DataFrames
+# glosea_df = pd.concat([df_1, df_2, df_3], ignore_index=True)
+# # Convert tprate NaN to 0.0
+# glosea_df['tprate'] = glosea_df['tprate'].fillna(0.0)
+# # Aggregate across steps
+# glosea_df = glosea_df.groupby(['valid_time', 'latitude', 'longitude'])['tprate'].mean().reset_index()
+# # Keep between 1981 and 2019
+# glosea_df = glosea_df[glosea_df['valid_time'].between('1981-01-01', '2018-12-31')]
+# # Rename columns
+# glosea_df.rename(columns={'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon', 'valid_time': 'time'}, inplace=True)
+# glosea_df.reset_index(inplace=True, drop=True)
+# print(f'Saving Glosea5 data to parquet (length of dataframe = {len(glosea_df)})...')
+# glosea_df.to_parquet('processed/glosea5.parquet')
+# # Delete any files ending in .idx from the raw folder
+# for file in os.listdir("raw"):
+#     if file.endswith(".idx"):
+#         os.remove(os.path.join("raw", file))
+
+# ### ECMWF ###
+# path = "raw/ecmwf.grib"
+# print("Processing ECMWF...")
+# # Open the GRIB file as an xarray dataset
+# ds_1 = cfgrib.open_datasets("raw/ecmwf_1.grib")
+# ds_2 = cfgrib.open_datasets("raw/ecmwf_2.grib")
+# ds_3 = cfgrib.open_datasets("raw/ecmwf_3.grib")
+# ecmwf_xarray_1, ecmwf_xarray_2, ecmwf_xarray_3 = ds_1[0], ds_2[0], ds_3[0]
+# # Convert to pandas DataFrame
+# df_1 = ecmwf_xarray_1.to_dataframe().reset_index()
+# df_2 = ecmwf_xarray_2.to_dataframe().reset_index()
+# df_3 = ecmwf_xarray_3.to_dataframe().reset_index()
+# # Concatenate the two DataFrames
+# ecmwf_df = pd.concat([df_1, df_2, df_3], ignore_index=True)
+# # Convert tprate NaN to 0.0
+# ecmwf_df['tprate'] = ecmwf_df['tprate'].fillna(0.0)
+# # Aggregate across steps
+# ecmwf_df = ecmwf_df.groupby(['valid_time', 'latitude', 'longitude'])['tprate'].mean().reset_index()
+# # Keep between 1981 and 2019
+# ecmwf_df = ecmwf_df[ecmwf_df['valid_time'].between('1981-01-01', '2018-12-31')]
+# # Rename columns
+# ecmwf_df.rename(columns={'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon', 'valid_time': 'time'}, inplace=True)
+# ecmwf_df.reset_index(inplace=True, drop=True)
+# print(f'Saving ECMWF data to parquet (length of dataframe = {len(ecmwf_df)})...')
+# ecmwf_df.to_parquet('processed/ecmwf.parquet')
+# # Delete any files ending in .idx from the raw folder
+# for file in os.listdir("raw"):
+#     if file.endswith(".idx"):
+#         os.remove(os.path.join("raw", file))
+
+
+# ### ACCESS-S2 ###
+
+# # Define the output file path
+# output_file = 'processed/access.parquet'
+
+# # Define the path and file pattern
+# path = "/g/data/ux62/access-s2/hindcast/calibrated/atmos/pr/daily/e09/"
+
+# # Check if the output file already exists and read it if it does
+# if os.path.exists(output_file):
+#     master_df = pd.read_parquet(output_file)
+#     # Extract already processed years
+#     processed_years = master_df['time'].dt.year.unique()
+# else:
+#     # Initialise an empty DataFrame if the file does not exist
+#     master_df = pd.DataFrame()
+#     processed_years = []
+
+# # Generate file patterns for each year from 1983 to 2018 and get matching files
+# files = []
+# for year in range(1983, 2018):
+#     if year not in processed_years:
+#         pattern = f"*pr_{year}*.nc"
+#         files.extend(glob(os.path.join(path, pattern)))
+
+# print(f"Processing data for years: {set(range(1983, 2018)) - set(processed_years)}")
+
+# # Loop through the list of files and load each one
+# for file in tqdm(files):
+#     # Load the xarray dataset
+#     ds = xr.open_dataset(file)
+
+#     # Slice the dataset for three specific lat/lon grids
+#     ds_sliced1 = ds.sel(lon=slice(142, 145), lat=slice(-25, -22))
+#     ds_sliced2 = ds.sel(lon=slice(150, 153), lat=slice(-29, -26))
+#     ds_sliced3 = ds.sel(lon=slice(143, 146), lat=slice(-20, -17))
+
+    
+#     # Flatten the sliced data for 'pr' variable for both slices
+#     df1 = ds_sliced1['pr'].to_dataframe().reset_index()
+#     df2 = ds_sliced2['pr'].to_dataframe().reset_index()
+#     df3 = ds_sliced3['pr'].to_dataframe().reset_index()
+
+#     # Concatenate the two DataFrames
+#     combined_df = pd.concat([df1, df2, df3], ignore_index=True)
+    
+#     # Filter rows where latitude and longitude are integers
+#     combined_df = combined_df[combined_df['lat'].apply(lambda x: x.is_integer())]
+#     combined_df = combined_df[combined_df['lon'].apply(lambda x: x.is_integer())]
+
+
+#     # Append the DataFrame to the master DataFrame
+#     master_df = pd.concat([master_df, combined_df], ignore_index=True)
+    
+#     # Close the xarray dataset
+#     ds.close()
+
+#     # Save the updated master_df to the Parquet file
+#     os.makedirs(os.path.dirname(output_file), exist_ok=True)
+#     master_df.to_parquet(output_file)
+
+#     # Print the processed year for tracking
+#     processed_year = pd.to_datetime(master_df['time'].max()).year
+#     print(f"Year {processed_year} processed and saved.")
+
+# # After the loop, perform any final processing needed on master_df
+# if os.path.exists(output_file):
+#     master_df = pd.read_parquet(output_file)
+#     master_df['time'] = pd.to_datetime(master_df['time'])
+
+#     # Group by time, lat, and lon, then sum the pr values
+#     deduped_df = master_df.groupby(['time', 'lat', 'lon']).agg({'pr': 'sum'}).reset_index()
+
+#     # Save the final processed DataFrame to a Parquet file
+#     deduped_df.to_parquet(output_file)
+#     print(f"Final file saved to {output_file}")
+# else:
+#     print(f"No data processed. {output_file} does not exist.")
+
+
+# def create_master_parquet():
+#     files = ['access', 'ecmwf', 'glosea5', 'silo']
+#     frames = []
+#     for file in files:
+#         df = pd.read_parquet(f'processed/{file}.parquet')
+#         df['model'] = file
+#         frames.append(df)
+
+#     access = frames[0]
+#     access.reset_index(inplace=True, drop=True)
+#     columns = access.columns
+#     # Convert time to string
+#     access['time'] = access['time'].astype(str)
+
+#     ecmwf = frames[1]
+#     ecmwf.rename(columns={'date': 'time', 'precip': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, inplace=True)
+#     ecmwf = ecmwf[columns]
+#     ecmwf.reset_index(inplace=True, drop=True)
+#     # Convert time to string
+#     ecmwf['time'] = ecmwf['time'].astype(str)
+
+#     glosea = frames[2]
+#     glosea.rename(columns={'date': 'time', 'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, inplace=True)
+#     glosea = glosea[columns]
+#     glosea.reset_index(inplace=True, drop=True)
+#     # Convert time to string
+#     glosea['time'] = glosea['time'].astype(str)
+
+#     silo = frames[3]
+#     silo.rename(columns={'daily_rain': 'pr'}, inplace=True)
+#     silo = silo[columns]
+#     # Convert lat and lon to float32
+#     silo['lat'] = silo['lat'].astype('float32')
+#     silo['lon'] = silo['lon'].astype('float32')
+#     silo.reset_index(inplace=True, drop=True)
+#     # Convert time to string
+#     silo['time'] = silo['time'].astype(str)
+
+#     dfs = [access, ecmwf, glosea, silo]
+#     master_df = pd.concat(dfs)
+#     master_df.reset_index(inplace=True, drop=True)
+
+#     master_df.to_parquet('processed/master.parquet')
+#     print(f"Final file saved to processed/master.parquet")
+
+import pandas as pd
+
+def standardize_df(df, rename_dict, default_columns):
+    """Standardize the DataFrame structure."""
+    df = df.rename(columns=rename_dict)
+    df = df[default_columns]
+    df.reset_index(inplace=True, drop=True)
+    df['time'] = df['time'].astype(str)
+    return df
+
+def create_master_parquet():
+    files = ['access', 'ecmwf', 'glosea5', 'silo']
+    rename_dicts = [
+        {}, 
+        {'date': 'time', 'precip': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, 
+        {'date': 'time', 'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, 
+        {'daily_rain': 'pr'}
+    ]
+
+    # Read and append the 'model' column to each DataFrame
+    frames = []
+    for file, rename_dict in zip(files, rename_dicts):
+        df = pd.read_parquet(f'processed/{file}.parquet')
+        df['model'] = file
+        df = standardize_df(df, rename_dict, default_columns=['time', 'lat', 'lon', 'pr', 'model'])
+        frames.append(df)
+
+    # Use the first DataFrame (access) as a template for column names
+    columns = frames[0].columns
+
+    # Standardize each DataFrame
+    for i in range(1, len(frames)):
+        frames[i] = standardize_df(frames[i], rename_dicts[i], columns)
+
+    master_df = pd.concat(frames)
+    master_df.reset_index(inplace=True, drop=True)
+    master_df.to_parquet('processed/master.parquet')
+    print("Final file saved to processed/master.parquet")
+
+create_master_parquet()

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+from tqdm import tqdm
+import requests
+import os
+import xarray as xr
+import pandas as pd
+
+# Define constants
+BASE_URL = "https://s3-ap-southeast-2.amazonaws.com/silo-open-data/Official/annual/"
+VARIABLE = "daily_rain"  # or whatever variable you are interested in
+years = range(1989, 2021)  # Replace with your actual range
+
+# def clean_silo(path, lat_slice1, lon_slice1, lat_slice2, lon_slice2):
+#     ds = xr.open_dataset(path)
+#     filtered_data1 = ds.sel(lat=lat_slice1, lon=lon_slice1)
+#     print(filtered_data1)
+#     filtered_data1 = filtered_data1.where(
+#         (filtered_data1['lat'] % 1 == 0) & (filtered_data1['lon'] % 1 == 0), drop=True
+#     )
+#     filtered_data2 = ds.sel(lat=lat_slice2, lon=lon_slice2)
+#     filtered_data2 = filtered_data2.where(
+#         (filtered_data2['lat'] % 1 == 0) & (filtered_data2['lon'] % 1 == 0), drop=True
+#     )
+#     combined_data = xr.concat([filtered_data1, filtered_data2], dim='lat_lon')
+#     df = combined_data.to_dataframe().reset_index()
+#     df.drop(columns=['crs'], inplace=True)
+#     df.reset_index(inplace=True, drop=True)
+#     return df
+
+# # List to store cleaned DataFrames
+# df_list = []
+
+# # Loop over each year to download the corresponding NetCDF file
+# print(f'Generating SILO data for {years[0]} to {years[-1]-1}...')
+# for year in tqdm(years):
+#     url = f"{BASE_URL}{VARIABLE}/{year}.{VARIABLE}.nc"
+#     response = requests.get(url)
+    
+#     # Temporary path to save the downloaded NetCDF file
+#     temp_path = f"{year}.{VARIABLE}.nc"
+    
+#     # Check if the request was successful
+#     if response.status_code == 200:
+#         # Save the NetCDF file
+#         with open(temp_path, "wb") as f:
+#             f.write(response.content)
+        
+#         # Clean the data
+#         cleaned_df = clean_silo(temp_path, slice(-25, -22), slice(142, 145), slice(-29, -26), slice(150, 153))
+#         print(cleaned_df)
+#         df_list.append(cleaned_df)
+        
+#         # Remove the temporary NetCDF file to save space
+#         os.remove(temp_path)
+#     else:
+#         print(f"Failed to download data for {year}")
+
+# # Concatenate all the cleaned DataFrames
+# final_df = pd.concat(df_list, ignore_index=True)
+# final_df.rename(columns={'daily_rain': 'pr'}, inplace=True)
+
+# # Save to parquet
+# print('Saving data to parquet...')
+# final_df.to_parquet('processed/silo.parquet')
+
+def convert_to_dataframe(ds, lat_slice, lon_slice):
+    # Filter and convert to DataFrame
+    filtered_data = ds.sel(lat=lat_slice, lon=lon_slice).to_dataframe().reset_index()
+    return filtered_data
+
+def aggregate_precipitation(df, grid_size=0.05):
+    # Define the range for grid aggregation
+    lat_range = df['lat'].apply(lambda x: round(x)).unique()
+    lon_range = df['lon'].apply(lambda x: round(x)).unique()
+
+    aggregated_records = []
+    for lat_int in lat_range:
+        for lon_int in lon_range:
+            # Define grid boundaries
+            lat_min, lat_max = lat_int - grid_size, lat_int + grid_size
+            lon_min, lon_max = lon_int - grid_size, lon_int + grid_size
+
+            # Filter and aggregate data within the grid
+            grid_df = df[(df['lat'] >= lat_min) & (df['lat'] <= lat_max) &
+                         (df['lon'] >= lon_min) & (df['lon'] <= lon_max)]
+            aggregated = grid_df.groupby('time')['daily_rain'].sum().reset_index()
+            aggregated['lat'] = lat_int
+            aggregated['lon'] = lon_int
+            aggregated_records.append(aggregated)
+
+    # Combine all records into a single DataFrame
+    return pd.concat(aggregated_records, ignore_index=True)
+
+# Main loop to process data
+df_list = []
+for year in tqdm(years):
+    url = f"{BASE_URL}{VARIABLE}/{year}.{VARIABLE}.nc"
+    response = requests.get(url)
+    
+    # Temporary path to save the downloaded NetCDF file
+    temp_path = f"{year}.{VARIABLE}.nc"
+    
+    # Check if the request was successful
+    if response.status_code == 200:
+        # Save the NetCDF file
+        with open(temp_path, "wb") as f:
+            f.write(response.content)
+
+        # Convert Dataset to DataFrame
+        ds = xr.open_dataset(temp_path)
+        df1 = convert_to_dataframe(ds, slice(-25, -22), slice(142, 145))
+        df2 = convert_to_dataframe(ds, slice(-29, -26), slice(150, 153))
+        df3 = convert_to_dataframe(ds, slice(-20, -17), slice(143, 146))
+        
+        # Combine the two DataFrames
+        combined_df = pd.concat([df1, df2, df3], ignore_index=True)
+
+        # Aggregate precipitation data
+        aggregated_df = aggregate_precipitation(combined_df)
+        df_list.append(aggregated_df)
+
+        os.remove(temp_path)
+        
+    else:
+        print(f"Failed to download data for {year}")
+
+
+# Concatenate all DataFrames and save to parquet
+final_df = pd.concat(df_list, ignore_index=True)
+final_df.to_parquet('processed/silo.parquet')

tensors/climatology_targets_240.pt

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+size 516268

tensors/targets_120.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4489fd1dc60eeb774acd3c71960b63fb7d9a7b90efe0efb96df4f6803f9e5711
+size 523804

tensors/targets_240.pt

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+oid sha256:0195280fd617d8147beca6b1d0d356467d10bffcf57fcde439d67435eaff8925
+size 516144