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	<title>copernicus-rag — architecture</title>
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	<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\nDOI (product): \nhttps://doi.org/10.48670/moi-00168\n\nReferences:\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---\nDEFINITION\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\nUpstreams: 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\nDOI (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\nDetailed 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\nDOI (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\nTable 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\nFigure 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\nFigureÂ 22 shows time series of monthly mean biases from the three reanalyses\nagainst AERONET for several areas, and TableÂ 8 shows the corresponding mean\nbiases and their standard deviations. FigureÂ 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.Â 21, CAMSRA has\nthe smallest bias with respect to the AERONET data in most areas (see also\nTableÂ 8) and has the largest correlation coefficient (Fig.Â 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 AprilÂ 2012. Over Southeast Asia all reanalyses have\na negative bias, with CAMSRA having the smallest and CIRA the largest\nbias (see TableÂ 8). In the NH, the bias changes with season and is largest\n(positive) during the summer months.\n\nFigureÂ 23Time 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.Â 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. FigureÂ 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. FigureÂ 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\n3Leaf 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\nFigure 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\nTable 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\nFigure 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",
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	"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": [
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	{
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	"title": "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level",
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	}
	],
	"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",
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	"title": "Testing the capability of CMIP6 GCMs to represent precipitation intra-seasonal variability",
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	},
	{
	"notebook_id": "climate_projections-cmip6_validation_q12",
	"title": "Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands",
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	"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": [
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	"download",
	"other",
	"plot"
	],
	"n_code_lines": 612,
	"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment"
	},
	{
	"notebook_id": "projections-cmip6",
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	}
	],
	"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",
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	"title": "Evaluation of the hit-rate of seasonal forecasts for tercile categories of monthly anomalies",
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	"title": "Assessing possible outcomes of seasonal temperature forecast",
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	"notebook_id": "sf-anomalies",
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	"source_repo": "ecmwf-projects/copernicus-training-c3s"
	},
	{
	"notebook_id": "sf-verification",
	"title": "Seasonal Forecast Verification",
	"recipe_kinds": [
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	"download",
	"other",
	"plot"
	],
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	"score": 0.8333333333333333
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	"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.",
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	"title": "Seasonal forecasts bias assessment for impact models",
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	"notebook_id": "seasonal_seasonal-monthly-single-levels_forecast-skill_q04",
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	},
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	"notebook_id": "sf-verification",
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	{
	"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",
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	"other",
	"plot"
	],
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	"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"
	],
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	}
	],
	"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",
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	"download",
	"other",
	"plot"
	],
	"n_code_lines": 698,
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	},
	{
	"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": [
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	"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,
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	},
	{
	"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",
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	"plot"
	],
	"n_code_lines": 678,
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	},
	{
	"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",
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	"analyze",
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	"aspect": null,
	"source_repo": "ecmwf-projects/c3s2-eqc-quality-assessment",
	"license": "Apache-2.0"
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	"aspect": null,
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	"license": "NONE"
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	"license": "NONE"
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	"license": "NONE"
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	"title": "Daily accumulation for non-UTC timezone for the ERA5-land accumulated variables",
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	"n_code_cells": 8,
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	"license": "NONE"
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	{
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	"title": "Getting information about data store collections (datasets) using `ecmwf-datastores-client`",
	"scope": "generic",
	"recipe_kinds": [
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	"download",
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	"n_code_cells": 11,
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	"notebook_id": "documentation_ecmwf_datastore_client_3_retrieve_data",
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	"title": "Advanced Data Retrieval with `ecmwf-datastores-client`",
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	"n_code_cells": 14,
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	"aspect": null,
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	"license": "NONE"
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	"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"
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	"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"
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	"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\nCopernicus 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\nCiting 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": [
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	"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
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	{
	"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>
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