This page systematically presents the climate datasets used in this study, covering their key characteristics, accessibility procedures, and their specific value for advancing agroecological research. The datasets were evaluated and prioritized based on criteria critical for agricultural applications, data integration, and scientific rigor (Prioritization method)

Dataset Name	Short Description	Provider/Source	Spatial Coverage	Temporal Coverage	Spatial Resolution	Temporal Resolution	Key Variables	Data Quality Indicators	Metadata Completeness	Update Frequency	Data Provenance	Preprocessing Steps	Access Methods	API/Package Links	Example Scripts	License/Terms	Integration Challenges	Data Ownership	Versioning/Change Log	Data Citation	Benchmarking/Validation Studies	Potential Applications (Agroecological)	Priority Level
CHIRPS	High-resolution, quasi-global rainfall dataset blending satellite imagery and station data for trend analysis and seasonal drought monitoring	Climate Hazards Center (CHC), UCSB	50°S–50°N, 180°W–180°E	1981–present (updated frequently)	0.05° x 0.05° (~5.5 km at equator)	Daily, pentadal (5-day), dekadal (10-day), monthly	Precipitation (mm)	Validated against station data; uncertainty estimates; provisional data for ~20 days after month end	Spatial/temporal coverage, source, version, units, coordinate system (WGS84)	Updated within 45 days; provisional data replaced with final	Satellite + station blend; processing steps documented on CHC website	Resampling, masking, unit conversion, missing data handling	FTP/HTTP (CHC data portal), FEWS NET Data Portal, Google Earth Engine (GEE), R package (chirps on CRAN)	GEE API Docs, R package chirps	Example: Script to Download CHIRPS in R (GitHub)	Open for research and non-commercial use	Large file sizes, need for specific libraries (e.g., netCDF4, xarray), handling provisional data, managing missing data flags	Climate Hazards Center, UCSB	Version history on CHC website	Cite as: "CHIRPS dataset provided by Climate Hazards Group, UCSB"	CHIRPS Validation on CHC website, CHIRPS v2.0: Increased resolution from climate station evaluation (Funk et al., 2015)	Rainfall pattern analysis, drought monitoring, crop yield modeling, water resource management, agroclimatic zoning, validation of local climate data	High (core dataset for agroecology)
AgERA5	Reanalysis dataset tailored for agriculture, based on ERA5 but with additional variables and adjustments for agricultural applications	ECMWF/C3S	Global	1979–present (updated daily with a short delay)	0.1° x 0.1° (~10 km)	Hourly	Standard ERA5 variables plus: Evapotranspiration (actual/potential), Soil water content (various depths), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), crop-specific indicators	Model-based, validated against ground observations where possible; uncertainty and bias information in documentation; see ECMWF documentation	Full metadata in GRIB files (variable definitions, units, provenance, coordinate system, version)	Updated daily with a short delay	Derived from ERA5 reanalysis; further processed for agricultural relevance; see AgERA5 documentation	Convert GRIB to NetCDF for easier handling (cfgrib, eccodes, xarray); spatial/temporal subsetting; handle large file sizes; check units/definitions for ag variables	Copernicus Data Space Ecosystem (CDSE), ECMWF AgERA5 page	CDS API Docs, cfgrib Python package, eccodes	Example: Script to download AgERA5 in R(GitHub)	Copernicus license (free for research/educational use, registration required)	Registration required, GRIB format requires specific tools, large data volume, agricultural variable definitions may differ from other datasets	ECMWF, Copernicus Climate Change Service	Versioning and change log available on CDSE and ECMWF AgERA5	Cite as: "AgERA5 dataset provided by ECMWF and Copernicus Climate Change Service (C3S)"	AgERA5: Dataset evaluation and use for crop modeling (ECMWF), AgERA5 technical note (PDF)	Crop yield forecasting, irrigation management, pest/disease modeling, land suitability assessment, drought/heat stress monitoring, vegetation dynamics (LAI, FAPAR), water resource management, climate change impact assessment, soil moisture studies	High (core dataset for agroecology and climate-adaptive agriculture)
TerraClimate	High-resolution (~4 km, 1/24°) monthly global dataset of climate and climatic water balance variables for terrestrial surfaces, combining climatological normals with coarser time-varying data for detailed, temporally consistent records	Climatology Lab, University of California Merced (UCM)	Global (all terrestrial surfaces)	1958–present (updated annually)	1/24° (~4 km)	Monthly	Precipitation, max/min temperature, wind speed, vapor pressure, vapor pressure deficit, downward shortwave radiation, reference/actual evapotranspiration, climatic water deficit, soil moisture, runoff, snow water equivalent, Palmer Drought Severity Index (PDSI)	Validated against station and streamflow data; improved mean absolute error and spatial realism; inherits biases from parent datasets (e.g., precipitation bias in mountains)	Metadata available for variables, units, provenance, and grid; NetCDF metadata includes time, lat, lon, variable	Updated annually when parent datasets become available	WorldClim v2 (climatology), CRU Ts4.0 and JRA55 (monthly anomalies), climatically aided interpolation; water balance via modified Thornthwaite-Mather model	Check missing data flags; handle large files; use geospatial tools (netCDF4, xarray, raster, R)	Climatology Lab website, Microsoft Planetary Computer, Google Earth Engine, THREDDS servers	Google Earth Engine API Docs, Planetary Computer API Docs, THREDDS server access, R package (datazoom.amazonia vignette)	Download TerraClimate in Python/R via THREDDS server, or use GEE script example	Public domain (CC0 license); free for research and operational use	Large file sizes; requires geospatial software; annual updates; monthly resolution limits capture of short-term extremes; model-derived variables have uncertainties in data-sparse regions	Climatology Lab, University of California Merced	Versioning/change log not specified; annual updates tracked via parent datasets	Abatzoglou et al. (2018): TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5, https://doi.org/10.1038/sdata.2017.191	Scientific Data article (Abatzoglou et al., 2018), Climate Data Guide summary	Climate trend/variability analysis, drought monitoring, water balance and agricultural risk assessment, crop yield modeling, agroclimatic zoning, ecological/hydrological modeling, validation/downscaling of coarser datasets	High (core dataset for agroecological and climate-adaptive research)
IMERG (Integrated Multi-satellitE Retrievals for GPM)	NASA/JAXA quasi-global (90°S–90°N), high-resolution (0.1° x 0.1°) precipitation estimates by merging GPM satellite constellation data with microwave, infrared, and gauge observations	NASA GPM / JAXA	Quasi-global (90°S–90°N, 180°W–180°E)	June 2000–present (updated with latency: Early, Late, Final Runs)	0.1° x 0.1° (~10 km)	Half-hourly, daily, monthly	Precipitation rate (mm/hr), accumulated precipitation (mm), error estimates, counts of contributing retrievals	Validation against global gauge networks; error estimates included; Final Run is highest quality; see IMERG Algorithm Theoretical Basis Document	NetCDF files include variable/unit metadata, time, lat, lon, run type, version, and error variables	Half-hourly to monthly, depending on product and run (Early: ~4h latency; Late: ~12h; Final: ~3 months)	Merges GPM satellite data (microwave/IR sensors) with gauge data; multiple runs differ by latency and data sources; see IMERG documentation	Handle large files; use netCDF4/xarray/rioxarray (Python) or raster (R); check fill values; aggregate temporally as needed; select appropriate run (Early/Late/Final)	NASA GES DISC, Google Earth Engine, ERDDAP servers (example)	GEE API Docs, GES DISC Data Access, IMERG Data Cookbook	GES DISC IMERG Python Example, GEE JavaScript Example	NASA Data Policy (open for research/non-commercial use, acknowledge NASA/JAXA/GPM)	Large file sizes; software requirements; different runs have different latency/accuracy; missing data flags; only precipitation (no temperature, etc.)	NASA / JAXA	IMERG Versioning (version and change log per product)	"Huffman, G.J., et al., 2020. NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), Version 07. NASA's GES DISC. https://doi.org/10.5067/GPM/IMERG/3B-HH/07"	IMERG Validation Reports, IMERG Algorithm Theoretical Basis	Rainfall pattern analysis, drought monitoring, extreme precipitation event analysis, crop yield modeling, water resource management, agroclimatic zoning, validation of local rainfall data, sub-daily/daily hydrological modeling	High (core precipitation dataset for agroecological and climate studies)
TAMSAT	Long-term, high-resolution rainfall dataset for Africa, blending Meteosat thermal infrared satellite imagery with ground-based rain gauge observations for operational rainfall monitoring and climate risk assessment.	Department of Meteorology, University of Reading	Africa (continental)	1983–present (updated regularly)	0.0375° x 0.0375° (~4 km)	Daily, dekadal (10-day), monthly	Rainfall/precipitation (mm), soil moisture estimates (for some products)	Calibrated with ground-based rain gauges; validation and uncertainty assessments published in peer-reviewed literature; quality control flags in data files	NetCDF files include variable names, units, time, lat, lon, version, and quality flags; documentation available on TAMSAT website	Updated regularly as new satellite and gauge data become available	Derived from Meteosat TIR imagery, calibrated with gauge observations; see TAMSAT documentation and publications	Handle large files; use NetCDF-compatible tools (e.g., xarray, netCDF4, raster); check for missing data flags and quality control layers; aggregate or subset as needed	TAMSAT Data Portal, FTP/HTTP download (see website for latest), may require registration	No formal public API; use TAMSAT Data Portal for downloads; process with xarray, netCDF4 (Python), or raster (R); check documentation for updates	-	Terms of Use: Free for non-commercial research and educational use; citation required	Large file sizes; NetCDF format requires specific tools; access methods may change; rainfall only (no temperature, radiation, etc.); accuracy limited in regions with sparse gauge data	University of Reading	Versioning and updates noted in file names and documentation	"TAMSAT rainfall dataset, University of Reading. See TAMSAT website for citation guidance."	TAMSAT Publications, validation studies listed on the website	Rainfall pattern analysis, drought monitoring and early warning, agricultural planning, crop yield modeling, water availability assessment, climate risk analysis, validation of other rainfall datasets over Africa	High (core rainfall dataset for agroecological research in Africa)
CHIRTS (Climate Hazards Center InfraRed Temperature with Stations)	High-resolution, quasi-global maximum and minimum temperature dataset blending satellite thermal infrared data with ground station observations for climate monitoring and agricultural risk assessment	Climate Hazards Center (CHC), UCSB	60°S–70°N, 180°W–180°E	1983–2016 (v1.0), 1983–present (v2.0, ongoing updates)	0.05° x 0.05° (~5.5 km at equator)	Daily	Maximum and minimum temperature (°C)	Validated against global station data; quality flags and uncertainty estimates included; see CHIRTS documentation	Metadata includes variable, unit, time, lat, lon, version, and processing details	Updated as new versions or extensions are released (check CHC website for latest)	Blends satellite thermal infrared observations with ground station data; processing described in CHIRTS documentation	Handle NetCDF files; use xarray, netCDF4 (Python), or raster (R); check for missing data flags	CHC Data Portal (CHIRTS), FTP/HTTP download (see website)	No formal API; use CHC data portal for downloads and documentation	Example: Download and process CHIRTS in R (GitHub)	Open for research and non-commercial use (see CHC data use policy)	Large file sizes; requires geospatial software; limited to temperature variables	Climate Hazards Center, UCSB	Version history and change log on CHC website	"CHIRTS dataset provided by Climate Hazards Group, UCSB" (see CHC citation guidance)	Validation and methodology described in CHIRTS documentation and associated peer-reviewed publications	Heat stress monitoring, crop modeling, climate trend analysis, agricultural risk assessment, drought impact studies, validation of other temperature datasets	High (core temperature dataset for agroecological research)
ERA5 (ECMWF Reanalysis v5)	Fifth generation ECMWF atmospheric reanalysis, providing hourly estimates for a large number of atmospheric, land, and ocean climate variables globally	European Centre for Medium-Range Weather Forecasts (ECMWF)	Global	1950 to near-present (updated daily)	0.25° x 0.25° (~31 km at equator) for atmospheric variables; other variables may differ	Hourly (monthly means also available)	Air temperature (various levels), precipitation, surface radiation, wind speed/direction, soil moisture, evaporation, sea surface temperature, sea ice, and more	Model-based with uncertainties estimated via ensemble; validated against observations; see ERA5 documentation	Full metadata in GRIB files (variable definitions, units, provenance, coordinate system, version)	Updated daily with a delay of 2–3 months for final data	Combines model data with global observations using 4D-Var data assimilation; see ERA5 documentation[1][3][5]	Use cfgrib/eccodes (Python) for GRIB; convert to NetCDF; subset spatially/temporally; check units/definitions	Copernicus Data Space Ecosystem (CDSE), ECMWF Data Catalogue (MARS), Cloud Platforms (AWS, GCP, Azure)	CDS API Docs, cfgrib Python package, eccodes	ERA5 Python example (CDS API)	Copernicus license (free for research/educational use, registration required)	Registration required; large dataset volume; GRIB format needs specific tools; high computational demands; complexity of access and format	ECMWF, Copernicus Climate Change Service (C3S)	Versioning and change log available on ECMWF documentation	"ERA5 data provided by ECMWF and Copernicus Climate Change Service (C3S)"	ERA5 evaluation and publications (ECMWF), Hersbach et al. (2020)	Temperature analysis, precipitation/drought monitoring, evapotranspiration, solar radiation, wind patterns, soil moisture, extreme weather, climate change impact studies	High (core dataset for agroecological and climate research)
NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)	Global, high-resolution (0.25°) daily climate projections downscaled from CMIP5 and CMIP6 GCMs, including key variables for climate impact studies	NASA Earth Exchange (NEX)	CMIP5: 50°S–50°N; CMIP6: 60°S–90°N	CMIP5: 1950–2099; CMIP6: 1950–2100	0.25° x 0.25° (~25 km)	Daily	tasmin (daily min temperature, K), tasmax (daily max temperature, K), precipitation (kg m⁻² s⁻¹)	Bias-corrected and spatially disaggregated; multiple GCMs/scenarios; see NEX-GDDP Technical Notes (CMIP5), NEX-GDDP-CMIP6 Technical Note	NetCDF metadata includes variable, unit, scenario, model, time, lat, lon, and processing info	Dataset is not updated in real time; new versions may be released for CMIP6/other projects	Downscaled from CMIP5/CMIP6 GCMs using Bias-Correction Spatial Disaggregation (BCSD); see technical documentation (CMIP5), NEX-GDDP-CMIP6 Technical Note	Handle large NetCDF files; use xarray, netCDF4, CDO; check for missing data flags; convert units as needed	NASA NCCS THREDDS, AWS S3, Google Earth Engine	Google Earth Engine API Docs, THREDDS NetCDFSubset, AWS S3 API	GEE example scripts, AWS S3 download example, GitHub script to download data	Open for scientific research; not recommended for commercial/engineering use without expert consultation	Large file sizes (up to 38 TB for CMIP6), requires technical expertise, limited to temperature/precipitation, projections subject to GCM/downscaling uncertainties	NASA Earth Exchange (NEX)	Versioning and change log available via NEX-GDDP Technical Notes (CMIP5), NEX-GDDP-CMIP6 Technical Note	"NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)" (see homepage for citation details)	NEX-GDDP Technical Notes (CMIP5), NEX-GDDP-CMIP6 Technical Note	Climate risk assessment, crop yield modeling, drought/heat stress analysis, water resource planning, agroclimatic zoning, validation of local climate data	High (core dataset for climate impact and agroecological research)
NASA POWER (Prediction Of Worldwide Energy Resources)	Freely available, gridded solar and meteorological datasets derived from NASA satellite observations and climate models for renewable energy, agriculture, and building energy efficiency applications	NASA Langley Research Center (LaRC) POWER Project	Global	Varies by parameter; many from 1981 to near-present	0.5° x 0.5° (~50 km); some parameters may be higher resolution	Daily, monthly, hourly (parameter-dependent)	Solar radiation (insolation, direct normal irradiance, diffuse horizontal irradiance), surface meteorology (temperature, wind speed/direction, humidity, pressure), precipitation, evapotranspiration, soil moisture (some datasets), cloud cover	Derived from NASA satellite products and reanalysis; validation and uncertainty information in documentation; see POWER Documentation	Metadata included in NetCDF, JSON, and CSV outputs; variable names, units, coordinates, time, and processing info	Updated regularly as new satellite and reanalysis data are processed	Combines satellite observations and climate models; see POWER Documentation for details	Handle missing data flags; use appropriate tools for CSV, JSON, NetCDF (e.g., xarray, pandas, netCDF4); check units and spatial/temporal resolution	POWER Data Access Viewer, POWER API, direct download (CSV, JSON, NetCDF, GeoJSON)	API Getting Started, POWER Python client (pynasapower), POWER R client (nasapower)	API Python Example, R Example, API Tutorials	Public domain; free for use with attribution to NASA/POWER (citation guidance)	0.5° resolution may be too coarse for local studies; temporal coverage varies by parameter; API requires programming knowledge; handle units and missing data carefully	NASA Langley Research Center (LaRC)	Versioning and updates documented in POWER Documentation	NASA POWER Citation Guidance	POWER Publications, Validation/Uncertainty	Solar resource assessment, crop modeling, irrigation planning, climate risk analysis, energy efficiency studies, agroclimatic zoning, weather-driven agricultural decision support	High (widely used for agroclimatology and renewable energy)
CMIP6 (Coupled Model Intercomparison Project Phase 6)	International coordinated climate modeling effort providing a comprehensive ensemble of global climate projections for understanding past, present, and future climate change	World Climate Research Programme (WCRP) WGCM, data via Earth System Grid Federation (ESGF)	Global (resolution varies by model)	Historical: ~1850–present; Projections: up to 2100+ (varies by experiment)	Varies by model (from ~100 km to ~25 km or finer)	Daily, monthly, sub-daily (model/variable-dependent)	Temperature (surface, ocean, etc.), precipitation, radiation, wind, humidity, sea level pressure, sea ice, ocean currents, salinity, soil moisture, runoff, carbon cycle, and more	Each modeling group provides validation and uncertainty info; ensemble approach allows assessment of robustness; see Eyring et al., 2016	NetCDF files with CF-compliant metadata (variable/unit, model, experiment, grid, version, etc.)	Not updated in real time; new experiments released as available	Data produced by international modeling centers; standardized protocols; see CMIP6 Overview	Handle large NetCDF files; use xarray, netCDF4, dask; understand DRS structure; regrid as needed for analysis	ESGF Search, Copernicus CDS, CEDA STAC API	ESGF API Docs, pyesgf Python library, Copernicus CDS API	Example: Script to download rowCMIP6 in R(GitHub)	Data access and use governed by contributing modeling group terms (see ESGF Terms), generally free for research/education with attribution	Huge data volume; variable model resolutions; complex DRS structure; requires technical expertise; model biases and uncertainties; license terms vary by group	WCRP WGCM, Modeling Centers	Versioning and change log per model/experiment; see CMIP6 DRS	See CMIP6 Citation Guidance	Eyring et al., 2016, CMIP6 Publications	Climate change impact assessment, crop suitability, extreme event analysis, adaptation strategy development, regional downscaling, climate risk analysis	High (core dataset for climate and agroecological research)

CHIRPS is a high-resolution (0.05° x 0.05° latitude/longitude) quasi-global (50°S–50°N) rainfall dataset blending satellite imagery and station data, designed for applications like trend analysis and seasonal drought monitoring.

Attribute	Description
Short Description	CHIRPS is a 35+ year quasi-global rainfall dataset blending satellite imagery and station data for high-resolution precipitation information.
Provider/Source	Climate Hazards Center (CHC), University of California, Santa Barbara (UCSB)
Homepage/Link	https://www.chc.ucsb.edu/data/chirps
License/Terms of Use	Generally open for research and non-commercial use.
Spatial Coverage	Quasi-global, 50°S to 50°N latitude, 180°W to 180°E longitude
Temporal Coverage	January 1, 1981, to near-present (updated frequently)
Spatial Resolution	0.05° x 0.05° latitude/longitude (approximately 5 km x 5 km at the equator)
Temporal Resolution	Daily, pentadal (5-day totals), dekadal (10-day totals), monthly aggregations
Key Variables	Precipitation (mm)

Attribute	Description
Access Methods	Download via FTP/HTTP (CHC website), FEWS NET Data Portal, Google Earth Engine (GEE), potential intermediary APIs.
Data Formats	Primarily NetCDF (.nc), potentially GeoTIFF (.tif) for some derived products.
Data Organization	By temporal resolution (daily, monthly, etc.), then by year within each resolution. NetCDF files contain multi-dimensional arrays (time, latitude, longitude, variable).
Potential Challenges	Large file sizes, need for specific software libraries (e.g., netCDF4, xarray), potential missing data (handle fill values), awareness of data update frequency.

Attribute	Description
File Naming Conventions	Typically includes product name (chirps), temporal resolution (v2.0.daily), year, and sometimes day or month (e.g., chirps-v2.0.daily.1981.01.01.nc).
Variable Names & Units	Primary variable is usually precip (precipitation) with units of millimeters (mm). Verify metadata within the NetCDF files.
Coordinate Systems	Latitude and longitude based on World Geodetic System 1984 (WGS 84) datum. Coordinate variables are typically named lat and lon.
Data Processing	Use libraries like xarray, rioxarray, netCDF4 (Python) or raster (R) for NetCDF handling. Correctly handle latitude/longitude coordinates and the time dimension. Be aware of and manage missing data flags. Consider the need for data aggregation or resampling.

API Availability	Link to API Information	How to Utilize
No Direct API	Google Earth Engine API Docs (if using GEE) R Package chirps CRAN	Access via Google Earth Engine API (if applicable). Download files and use libraries like xarray (Python) or raster (R). Explore R package chirps.

Application/Strength/Limitation	Description
Potential Applications	Rainfall pattern analysis, drought monitoring, extreme precipitation event analysis, climate risk assessment, crop yield modeling, water resource management, agroclimatic zoning, validation of local climate data.
Strengths for AE Research	High spatial resolution for localized analysis, long temporal coverage for trend analysis, quasi-global coverage for broad studies, integration of satellite and station data for spatial completeness and accuracy.
Limitations for AE Research	Indirect satellite measurements with potential biases, does not include other crucial variables (temperature, solar radiation, etc.), spatial resolution might still be coarse for very localized microclimatic studies.

Resource Type	Description/Link
User Guides/Documentation	Primary source is the CHC website: https://www.chc.ucsb.edu/data/chirps. Look for sections like "Documentation" or "About CHIRPS."
Community/Support	No dedicated CHIRPS forum is likely. General remote sensing or climate data forums might have discussions. Contact the Climate Hazards Center directly for specific support inquiries via their website.

AgERA5 is a tailored version of the ECMWF ERA5 reanalysis dataset, specifically adapted for agricultural applications by providing enhanced near-surface parameters relevant to agriculture at an hourly resolution and the same spatial resolution as ERA5.

Attribute	Description
Short Description	AgERA5 is a reanalysis dataset specifically tailored for agricultural applications, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with the Copernicus Climate Change Service (C3S). It is based on the ERA5 dataset but includes additional variables and adjustments relevant for agriculture.
Provider/Source	European Centre for Medium-Range Weather Forecasts (ECMWF) / Copernicus Climate Change Service (C3S).
Homepage/Link to Dataset	https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/agera5 (This is the main ECMWF page; specific access might be through the Copernicus Data Space Ecosystem).
License and Terms of Use	Likely the same as ERA5: Copernicus license. Registration with the Copernicus Data Space Ecosystem (CDSE) will be required. See: https://cds.climate.copernicus.eu/terms-and-conditions.
Spatial Coverage	Global, consistent with ERA5.
Temporal Coverage	1979 to near-present (updated daily with a delay).
Resolution	Spatial: 0.1° x 0.1° latitude/longitude (approximately 10 km x 10 km at the equator). This is a higher spatial resolution than standard ERA5. Temporal: Hourly.
Key Variables	Includes all standard ERA5 variables, plus additional and enhanced variables relevant for agriculture, such as: - Evapotranspiration (potential, actual) - Soil water content (at various levels) - Leaf area index (LAI) - Fraction of absorbed photosynthetically active radiation (FAPAR) - Crop-specific indicators

Attribute	Description
Access Methods	Primarily through the Copernicus Data Space Ecosystem (CDSE) via the Web Interface and CDS API (Python). It might also be accessible through other platforms that host Copernicus data. Check the AgERA5 ECMWF page and the CDSE catalogue for the most up-to-date access methods.
Data Formats	Likely GRIB (GRIdded Binary) format, consistent with ERA5. Conversion to other formats like NetCDF will likely be supported through CDSE tools or other software.
Data Organization	Similar to ERA5: organized by variable, by time (hourly), and often separated by year. GRIB files contain the gridded data and associated metadata. The specific organization might differ slightly due to the additional agricultural variables. Consult the CDSE documentation.
Potential Challenges	Registration with CDSE required, potentially large dataset volume (though higher resolution might mean larger files than standard ERA5 for the same area), GRIB format (requires specific libraries), understanding the specific agricultural variables and their units (refer to documentation), computational resources for analysis.

Attribute	Description
File Naming Conventions	Likely similar to ERA5 GRIB files but will include indicators for the AgERA5 product and the specific agricultural variables. Consult the CDSE catalogue and the metadata within the downloaded files for precise naming conventions.
Variable Names & Units	Will include standard ERA5 variable names as well as specific names for the agricultural variables (e.g., e, swvl1, lai, fapar). Units will be specified in the GRIB metadata. Refer to the ECMWF and CDSE documentation for the definitions and units of the AgERA5 specific variables.
Coordinate Systems	Likely the same regular latitude-longitude grid as ERA5 (WGS 84), but at the higher 0.1° resolution. Coordinate information will be in the GRIB metadata.
Data Processing	Use GRIB decoding libraries (cfgrib, eccodes in Python). Utilize the CDS API for efficient data access. Consider converting to NetCDF for easier manipulation with libraries like xarray. Pay close attention to the specific units and definitions of the agricultural variables. Implement spatial and temporal subsetting to manage data volume.

API Availability	Link to API Information	How to Utilize
Yes (CDSE API)	CDSE API Documentation	Register on CDSE, install cdsapi Python library, specify "agera5" dataset in requests.
	ECMWF AgERA5 Info

Application/Strength/Limitation	Description
Potential Applications	Crop yield forecasting, irrigation management, pest and disease modeling, assessment of land suitability for different crops, drought and heat stress monitoring specific to agriculture, analysis of vegetation dynamics (LAI, FAPAR), water resource management in agricultural contexts, climate change impact assessments on agriculture, soil moisture studies for plant growth.
Strengths for AE Research	Higher spatial resolution (0.1°) compared to standard ERA5, providing more detailed information for agricultural landscapes. Includes agriculture-specific variables like evapotranspiration, soil water at multiple levels, LAI, and FAPAR, which are directly relevant to agroecological studies. Long temporal coverage (since 1979) for historical analysis. Hourly resolution for capturing diurnal variations important for plant processes. Globally consistent dataset for comparative studies.
Limitations for AE Research	Reanalysis data (model-based with inherent uncertainties, potentially biases that might be different from standard ERA5). Still might not capture very localized microclimatic variations relevant for small farms. Requires registration and familiarity with the CDSE and potentially GRIB format. Can be computationally demanding to process large volumes of high-resolution data. Understanding the specific definitions and limitations of the agricultural variables is crucial.

Resource Type	Description/Link
Publications	Search Google Scholar and the ECMWF website specifically for "AgERA5" related publications and documentation. Look for scientific reports and validation studies.
User Guides/Documentation	The main ECMWF AgERA5 page: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/agera5. The Copernicus Data Space Ecosystem documentation (https://cds.climate.copernicus.eu/cdsapp#!/documentation) will also be crucial for access and understanding the data. Look for sections specifically mentioning AgERA5.
Community/Support	The Copernicus Discourse forum (https://community.copernicus.eu/) is the recommended platform for questions and discussions related to Copernicus datasets, including AgERA5. ECMWF support channels might also be relevant.

CMIP6 (Coupled Model Intercomparison Project Phase 6) is a major international climate modeling initiative that coordinates climate model experiments from numerous research groups worldwide, providing a comprehensive ensemble of global climate projections for understanding past, present, and future climate change.

Attribute	Description
Short Description	CMIP6 is the sixth phase of the Coupled Model Intercomparison Project, a major international effort to coordinate climate model experiments and make the output publicly available. It provides a framework for understanding past, present, and future climate changes.
Provider/Source	An international effort coordinated by the World Climate Research Programme (WCRP) Working Group on Coupled Modelling (WGCM). Data is produced by numerous modeling centers worldwide.
Homepage/Link	https://wcrp-cmip.org/cmip-phases/cmip6/
License/Terms of Use	Data access and use are governed by terms specified by the contributing modeling groups. These terms vary, so it's crucial to check the license associated with each specific dataset on the Earth System Grid Federation (ESGF) or other access points. Generally, data is available for research and educational purposes with proper attribution.
Spatial Coverage	Global. The spatial resolution varies significantly depending on the climate model used.
Temporal Coverage	Historical simulations (typically 1850-near present) and future projections covering various Shared Socioeconomic Pathways (SSPs) extending to 2100 and beyond, depending on the experiment.
Spatial Resolution	Varies greatly depending on the climate model. Resolutions can range from coarse (e.g., ~100 km) to relatively high (e.g., ~25 km or finer) depending on the model and the specific output.
Temporal Resolution	Output is available at various temporal resolutions, including daily, monthly, and sometimes sub-daily, depending on the model and the requested variables. Monthly data is common for many analyses.
Key Variables	A vast array of atmospheric, oceanic, land surface, and sea ice variables are available, including: - Temperature (surface air, ocean, etc.) - Precipitation (total, convective, large-scale) - Radiation (solar, thermal, top-of-atmosphere, surface) - Wind (surface and at various levels) - Humidity - Sea level pressure - Sea ice concentration and thickness - Ocean currents and salinity - Land surface variables (soil moisture, runoff, etc.) - Biogeochemical variables (carbon cycle, etc. - depending on the Earth System Model).

Attribute	Description
Access Methods	Primarily through the Earth System Grid Federation (ESGF) (https://esgf.llnl.gov/). Users can search and download data from a distributed network of data nodes. Access may require registration depending on the data provider. Some data is also available through platforms like the Copernicus Climate Data Store (CDS) (https://cds.climate.copernicus.eu/).
Data Formats	All CMIP6 output is stored in NetCDF (.nc) files. These files adhere to the Climate and Forecast (CF) Metadata Conventions, which provide a standardized description of the data, including variable definitions and spatial/temporal properties.
Data Organization	Data is organized following a specific directory structure based on the CMIP6 Data Reference Syntax (DRS). This structure includes: mip_era/activity_id/institution_id/source_id/experiment_id/variant_label/table_id/variable_id/grid_label/version. Filenames also follow a standardized pattern: <variable_id>_<table_id>_<source_id>_<experiment_id>_<variant_label>_<grid_label>_<time_range>.nc.
Potential Challenges	The sheer volume of data can be overwhelming. Understanding the CMIP6 experimental design, model variations (ensemble members), and the DRS is crucial for effective data discovery. Different models have different spatial resolutions and variable availability. License terms vary. Downloading large datasets can be time-consuming and require significant storage.

Attribute	Description
File Naming Conventions	Adherence to the CMIP6 DRS is crucial for programmatic data access. Developers need to understand how to parse the directory structure and filenames to identify specific models, experiments, variables, and time periods. Refer to the official CMIP6 documentation for the complete DRS specification.
Variable Names & Units	Variable names are standardized according to the CMIP6 data request and CF conventions. Units are included as metadata within the NetCDF files. Developers should rely on the metadata within the files for accurate unit information. Standard libraries (e.g., netCDF4, xarray in Python) can be used to access this metadata.
Coordinate Systems	Typically uses latitude and longitude coordinates. The specific grid information (grid and grid_label global attributes in NetCDF files) should be examined to understand the spatial referencing of the data. Regridding to a common grid might be necessary for comparisons across different models.
Data Processing	Utilize Python libraries like xarray, rioxarray, netCDF4, or dask for efficient handling of multi-dimensional NetCDF data. Be prepared to handle large datasets and potentially implement parallel processing techniques. Implement robust methods for spatial and temporal subsetting and for regridding data to common grids if needed. Ensure the toolkit can handle the complexities of the CMIP6 DRS for automated data discovery and access.

API Availability	Link to API Information	How to Utilize
Yes (ESGF API)	ESGF API Nodes	Use Python libraries like pyesgf.search or CLI tools like esgpull to query datasets. Construct requests using facets (e.g., project=CMIP6, variable=tas). Access OpenDAP URLs via xarray for analysis.
	CMIP6 Data Request	Search variables/experiments by name, MIP, or frequency. Use spreadsheets or web interfaces to verify requested variables and metadata standards (CF conventions).
	CMIP6 Citation API	Submit JSON-formatted author lists, ORCIDs, and references via API or GUI. Use error-checking tools to validate citations before publishing.
	CEDA STAC API	Query CMIP6 metadata using SpatioTemporal Asset Catalog (STAC) standards. Filter by model, experiment, or variable.
	Copernicus CDS API	Install cdsapi Python library. Requires registration and dataset-specific syntax (e.g., experiment_id=ssp585). Limited to subset of CMIP6 data hosted by Copernicus.

Application/Strength/Limitation	Description
Potential Applications	Long-term climate change impact assessments on agriculture, analysis of future temperature and precipitation changes relevant for crop suitability, studying the frequency and intensity of extreme events (heatwaves, droughts, floods) and their agricultural impacts, informing adaptation strategies, downscaling CMIP6 data for regional agricultural modeling, assessing climate risks under different socioeconomic scenarios.
Strengths for AE Research	Provides a wide range of future climate projections based on different scenarios, allowing for the exploration of uncertainties. The multi-model ensemble approach helps in assessing the robustness of climate change signals. The long historical simulations provide context for understanding past climate variability. The global coverage allows for studies in diverse agroecological zones. The standardized NetCDF format and metadata conventions facilitate data processing and comparison across models.
Limitations for AE Research	The spatial resolution of many CMIP6 models might be too coarse to directly capture the climate variability relevant for local-scale agricultural systems. Downscaling techniques are often required. Biases in climate models can affect the accuracy of regional projections. The complexity of the CMIP6 experiment design and model variations requires careful consideration when selecting and interpreting data. Some agricultural-specific variables (like detailed soil moisture or vegetation indices) might not be directly available and may need to be derived or obtained from other datasets.

Resource Type	Description/Link
Publications	The overview paper on the CMIP6 experimental design and organization (Eyring et al., 2016) is a key reference. Search for "CMIP6" in scientific databases like Google Scholar and the IPCC reports (AR6) for analyses based on CMIP6 data.
User Guides/Documentation	The official CMIP6 website (https://wcrp-cmip.org/cmip-phases/cmip6/) provides extensive information. The PCMDI (Program for Climate Model Diagnosis and Intercomparison) at LLNL also hosts valuable resources (https://pcmdi.llnl.gov/CMIP6/). The ESGF website (https://esgf.llnl.gov/) has user guides and documentation for data access. The CMIP6 Data Request documentation provides details on variables and experiments.
Community/Support	The ESGF user support forums and mailing lists are good resources for technical questions related to data access. The CMIP community is large and active; relevant scientific conferences and workshops can also provide opportunities for interaction.

NEX-GDDP is a NASA dataset providing downscaled daily climate projections for the globe, derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model runs using the Bias-Corrected Spatial Disaggregation (BCSD) method.

Attribute	Description
Short Description	NEX-GDDP provides global, high-resolution (0.25°) daily climate projections downscaled from CMIP5 and CMIP6 GCMs, including key variables for climate impact studies.
Provider/Source	NASA Earth Exchange (NEX)
Homepage/Link	NEX-GDDP CMIP5, NEX-GDDP-CMIP6
License/Terms of Use	Open for scientific research; not recommended for commercial/engineering use without expert consultation.
Spatial Coverage	Global (CMIP5: 50°S–50°N; CMIP6: 60°S–90°N).
Temporal Coverage	1950–2100 (CMIP5: 1950–2099; CMIP6: 1950–2100).
Spatial Resolution	0.25° x 0.25° (~25 km).
Temporal Resolution	Daily
Key Variables	tasmin (daily min temperature, K), tasmax (daily max temperature, K), precipitation (kg m⁻² s⁻¹).

Attribute	Description
Access Methods	Download via NASA NCCS THREDDS, AWS S3, Google Earth Engine.
Data Formats	netCDF4 (classic and enhanced).
Data Organization	Organized by scenario (RCP/SSP), model, variable, and year. Files are typically per variable per year per model.
Potential Challenges	Large dataset size (up to 38 TB for CMIP6), requires netCDF-compatible tools (e.g., xarray, netCDF4, CDO).

Attribute	Description
File Naming Conventions	Typically includes model, scenario, variable, and year (e.g., pr_day_ACCESS-CM2_historical_2014.nc).
Variable Names & Units	tasmin (K), tasmax (K), pr (kg m⁻² s⁻¹).
Coordinate Systems	Latitude/longitude, WGS84 datum.
Data Processing	Downscaling via Bias-Correction Spatial Disaggregation (BCSD); time in days since model-dependent reference.
Handling Missing Data	Standard netCDF conventions; check metadata for fill values.

API Availability	Description
Direct API	No dedicated REST API.
Google Earth Engine	Datasets available for analysis via GEE Python/JavaScript APIs.
Programmatic Access	Download and subsetting via THREDDS (NetCDFSubset), AWS S3, wget/cURL scripting.

Application/Strength/Limitation	Description
Potential Applications	Climate risk assessment, crop yield modeling, drought and heat stress analysis, water resource planning, agroclimatic zoning, validation of local climate data.
Strengths	High spatial and temporal resolution for local/regional studies, global coverage, daily data for temperature and precipitation, bias-corrected projections from multiple GCMs and scenarios.
Limitations	Does not include other agroclimatic variables (e.g., solar radiation, humidity), file sizes can be challenging, requires technical expertise for data handling, and projections are subject to GCM and downscaling uncertainties.

Resource Type	Description/Link
User Guides	NEX-GDDP Technical Note (CMIP5)
	NEX-GDDP-CMIP6 Technical Note
Community/Support	Contact NASA NEX team or NCCS Support for technical questions.
Documentation	Dataset homepages (see above), Google Earth Engine dataset pages.

ERA5 is a high-resolution (0.25° x 0.25° for atmospheric parameters, 0.25° x 0.25° for land surface, 0.5° x 0.5° for ocean waves) global reanalysis dataset from ECMWF, providing hourly estimates for a vast array of atmospheric, land, and oceanic variables since 1950.

Attribute	Description
Short Description	ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate, providing hourly estimates for a large number of atmospheric, land, and ocean climate variables.
Provider/Source	European Centre for Medium-Range Weather Forecasts (ECMWF)
Homepage/Link	https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5
License/Terms of Use	Copernicus license. Registration with the Copernicus Data Space Ecosystem (CDSE) is required. See: https://cds.climate.copernicus.eu/terms-and-conditions.
Spatial Coverage	Global
Temporal Coverage	1950 to near-present (updated daily)
Spatial Resolution	0.25° x 0.25° latitude/longitude (approximately 31 km x 31 km at the equator) for atmospheric variables. Other variables may have different resolutions.
Temporal Resolution	Hourly. Monthly means are also available.
Key Variables	Air temperature (various levels), precipitation (total, convective, large-scale), surface radiation (solar, thermal), wind speed and direction (various levels), soil moisture, evaporation, sea surface temperature, sea ice, and many more.

Attribute	Description
Access Methods	Copernicus Data Space Ecosystem (CDSE) via Web Interface and CDS API (Python), ECMWF Data Catalogue (MARS), Cloud Platforms (AWS, GCP, Azure). CDSE is the recommended primary access point.
Data Formats	Primarily GRIB (GRIdded Binary). Can be converted to other formats like NetCDF.
Data Organization	By variable, by time (hourly/monthly), often separated by year. GRIB files contain gridded data with metadata.
Potential Challenges	Registration required, large dataset volume, GRIB format (requires specific libraries), vast number of variables (careful selection needed), potentially high computational demands for analysis.

Attribute	Description
File Naming Conventions	GRIB file names vary depending on the download method from CDSE. Typically include variable name, date, time, and other metadata. Refer to file metadata.
Variable Names & Units	Variable names in GRIB are often abbreviated (ECMWF conventions). Units are in the metadata. Consult ECMWF/CDSE documentation.
Coordinate Systems	Typically regular latitude-longitude grid. Projection and datum info in GRIB metadata.
Data Processing	Use libraries like cfgrib or eccodes (Python) for GRIB. Explore CDS API for efficient download. Consider conversion to NetCDF. Implement spatial and temporal subsetting. Be mindful of unit conversions.

API Availability	Link to API Information	How to Utilize
Yes (CDSE API)	CDSE API Documentation	Register on CDSE, install cdsapi Python library, follow documentation for making requests.
	CDSE Platform

Application/Strength/Limitation	Description
Potential Applications	Temperature analysis (growing degree days, frost risk, heat stress), precipitation analysis (drought monitoring, variability), evapotranspiration estimation, solar radiation assessment, wind patterns, soil moisture monitoring, extreme weather event analysis, climate change impact studies.
Strengths for AE Research	Comprehensive variable set, long temporal coverage (since 1950), global coverage, hourly resolution allows for detailed analysis, continuously updated.
Limitations for AE Research	Coarser spatial resolution than CHIRPS (may miss localized variations), reanalysis data (model-based with inherent uncertainties), complexity of access and format, potentially high computational demands.

Resource Type	Description/Link
Publications	Search Google Scholar and the ECMWF website for ERA5 related publications.
User Guides/Documentation	ECMWF website: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. Copernicus Data Space Ecosystem documentation: https://cds.climate.copernicus.eu/cdsapp#!/documentation.
Community/Support	Copernicus Discourse forum: https://community.copernicus.eu/. ECMWF also provides support via their website.

NASA POWER (Prediction Of Worldwide Energy Resources) provides satellite-derived global meteorological and solar energy data tailored for renewable energy, sustainable buildings, and agroclimatology applications, offering hourly, daily, monthly, and climatological time series of essential variables like temperature, precipitation, and solar radiation.

Attribute	Description
Short Description	NASA POWER provides freely available gridded solar and meteorological datasets derived from NASA satellite observations and climate models. It is designed to support renewable energy, agricultural, and building energy efficiency applications.
Provider/Source	NASA Langley Research Center (LaRC) POWER Project, supported by the NASA Earth Science Directorate Applied Sciences Program.
Homepage/Link	https://power.larc.nasa.gov/
License and Terms of Use	NASA POWER data is generally considered public domain and freely available for use with proper attribution to NASA/POWER. Consult the "About" or "Documentation" section on the POWER website for specific citation guidelines.
Spatial Coverage	Global.
Temporal Coverage	Varies depending on the specific data product. Some parameters are available from 1981 to near-present, while others might have different temporal ranges. Consult the POWER website for the specific temporal coverage of each variable and dataset.
Spatial Resolution	The standard spatial resolution is 0.5° x 0.5° latitude/longitude (approximately 50 km x 50 km at the equator). Some parameters might be available at higher resolutions. Check the specific dataset details on the POWER website.
Temporal Resolution	Data is available at various temporal resolutions, including daily, monthly, and sometimes hourly (depending on the parameter and dataset). Users can often select the desired temporal aggregation through the web interface or API.
Key Variables	A wide range of solar and meteorological parameters are available, including: - Solar radiation (insolation, direct normal irradiance, diffuse horizontal irradiance) - Surface meteorology (temperature, wind speed and direction, humidity, pressure) - Precipitation - Evapotranspiration - Soil moisture (for some datasets) - Cloud cover.

Attribute	Description
Access Methods	POWER Web Data Viewer: An interactive web interface allows users to select parameters, specify geographic locations and time ranges, and download data in various formats. POWER API (Application Programming Interface): Provides programmatic access to the data, allowing developers to integrate POWER data directly into their applications and workflows. Documentation for the API is available on the POWER website.
Data Formats	Data can be downloaded in various formats, including: - CSV (Comma Separated Values): Suitable for simple data extraction and analysis. - JSON (JavaScript Object Notation): A lightweight format often used with web APIs. - NetCDF (.nc): A standard format for gridded climate data, often preferred for more complex spatial and temporal analyses. - GeoJSON: For geospatial applications. The availability of formats may vary depending on the access method and the selected parameters.
Data Organization	When using the web interface, data is typically downloaded as a time series for a specified location or as gridded data for a spatial extent. The organization within NetCDF files follows standard conventions with dimensions for time, latitude, and longitude, and variables representing the meteorological parameters. API responses are usually structured in JSON. CSV files contain columns for time and the selected parameters.
Potential Challenges	Understanding the different datasets and their temporal coverage is important. The spatial resolution (0.5°) might be too coarse for very localized agricultural studies. While the API is powerful, it requires some programming knowledge. Ensure proper handling of units and potential missing data flags as described in the data documentation. Be aware of any rate limits or usage policies when using the API.

Attribute	Description
File Naming Conventions	When downloading via the web interface, filenames typically include the location (latitude/longitude) and date range. When using the API, the structure of the returned data (JSON or NetCDF) is more important than a specific filename convention. For NetCDF downloads, standard CF conventions are generally followed.
Variable Names & Units	Variable names are generally descriptive (e.g., T2M for 2-meter temperature, ALLSKY_SFC_SWDN for surface downwelling shortwave radiation). Units are clearly specified in the metadata when downloading NetCDF files or within the JSON API responses. The POWER website provides documentation on the available parameters and their units.
Coordinate Systems	Uses latitude and longitude coordinates. The standard projection is typically a regular latitude-longitude grid (Plate Carrée). Coordinate information is included in the metadata of NetCDF files and within the structure of JSON responses.
Data Processing	For API integration, developers will need to handle HTTP requests and parse JSON responses. For NetCDF data, use libraries like xarray or netCDF4 in Python. Be prepared to handle spatial extraction (selecting data for specific areas) and temporal filtering. Pay close attention to the units of each variable and perform any necessary conversions. Handle missing data as indicated in the data values or metadata. The POWER API allows for specifying the desired output format (CSV, JSON, NetCDF), which can simplify integration with different parts of your toolkit.

API Availability	Link to API Information	How to Utilize
Yes (REST API)	API Getting Started	Construct API URLs with parameters (lat, lon, dates, variables). Use output formats (JSON/CSV/NetCDF) directly in scripts. Python/R clients available (pynasapower, nasapower).
	API Documentation	Follow parameter guidelines for temporal ranges and spatial resolution (0.5° for meteo). Handle HTTP codes (200=success, 429=throttling).
	Data Access Viewer	Generate starter API URLs via GUI. Copy/paste requests into scripts. Supports multi-point downloads through CSV coordinate uploads.
	Python Client	Install via pip. Use get_data() with coordinates/time ranges. Handles API pagination and rate limits automatically.
	R Client	Install from CRAN. Use get_power() with community codes ("ag", "sb") and temporal resolutions ("daily", "monthly"). Returns tidy data frames.

Application/Strength/Limitation	Description
Potential Applications	Assessing solar radiation for crop growth and solar energy potential in agricultural areas, analyzing temperature and humidity impacts on crop and livestock, estimating potential evapotranspiration for irrigation planning, monitoring precipitation patterns, studying wind patterns for wind erosion or pollination, assessing soil moisture availability (for some datasets), providing input data for agricultural models, climate risk assessments related to solar and meteorological variables.
Strengths for AE Research	Freely available and easy to access through both a user-friendly web interface and a programmatic API. Global coverage allows for studies in any agricultural region worldwide. Provides a wide range of relevant solar and meteorological variables in a single source. Multiple temporal resolutions offer flexibility for different types of analyses. Data is derived from reliable NASA satellite observations and climate models.
Limitations for AE Research	The standard 0.5° spatial resolution can be too coarse for detailed local-scale agricultural analysis. While precipitation data is available, datasets like CHIRPS or TAMSAT might be preferred for higher resolution rainfall information in specific regions. The temporal coverage varies by parameter and dataset, so careful selection is needed. Direct access to very high temporal resolution (e.g., sub-hourly) might be limited for some parameters.

Resource Type	Description/Link
Publications	The NASA POWER website (https://power.larc.nasa.gov/) usually lists relevant publications and citations related to the dataset and its methodology.
User Guides/Documentation	The "Documentation" or "About" sections on the NASA POWER website are comprehensive resources, providing details on the available datasets, parameters, temporal coverage, spatial resolution, data access methods (web interface and API), and data formats. The API documentation is particularly important for developers.
Community/Support	The NASA POWER website may provide contact information for questions or support. You might also find discussions or examples of using POWER data in online forums related to remote sensing, renewable energy, or agricultural modeling.

TerraClimate is a dataset of high-resolution (1/24° (~4 km)) monthly climate and water balance variables for global land surfaces from 1958 to near-present, created by combining climate model output with observational data to provide a spatially and temporally consistent record of variables like temperature, precipitation, soil water balance, and evapotranspiration.

Attribute	Description
Short Description	TerraClimate is a global, high-resolution (~4 km, 1/24°) monthly dataset of climate and climatic water balance variables for terrestrial surfaces, spanning from 1958 to the present. It blends high-resolution climatological normals with coarser time-varying data to provide detailed, temporally consistent climate information.
Provider/Source	Climatology Lab, University of California Merced (UCM)
Homepage/Link	Climatology Lab TerraClimate
License/Terms of Use	Public domain (Creative Commons CC0 license); free for research and operational use.
Spatial Coverage	Global (all terrestrial surfaces).
Temporal Coverage	January 1958 to present (updated annually).
Spatial Resolution	1/24° (~4 km) latitude/longitude grid.
Temporal Resolution	Monthly.
Key Variables	Precipitation, maximum temperature, minimum temperature, wind speed, vapor pressure, vapor pressure deficit, downward shortwave radiation, reference evapotranspiration, actual evapotranspiration, climatic water deficit, soil moisture, runoff, snow water equivalent, Palmer Drought Severity Index (PDSI).

Attribute	Description
Access Methods	Download via Climatology Lab website, Microsoft Planetary Computer, Google Earth Engine, and THREDDS servers.
Data Formats	NetCDF (.nc), GeoTIFF (.tif) for some derived products.
Data Organization	Files organized by variable and year/month; NetCDF files contain multi-dimensional arrays (time, latitude, longitude, variable).
Potential Challenges	Large file sizes; requires software (e.g., netCDF4, xarray, raster, R) for handling; be aware of missing data flags and annual updates.

Attribute	Description
File Naming Conventions	Typically includes variable name, year, and month (e.g., TerraClimate_ppt_1981.nc).
Variable Names & Units	See documentation; e.g., ppt (precipitation, mm), tmax/tmin (max/min temperature, °C), ws (wind speed, m/s), srad (solar radiation, W/m²), aet (actual evapotranspiration, mm), pet (reference evapotranspiration, mm), etc.
Coordinate Systems	Latitude and longitude (WGS 84 datum), grid cell centers.
Data Processing	Combines WorldClim v2 climatological normals with CRU Ts4.0 and JRA55 reanalysis for monthly variability, using climatically aided interpolation. Water balance variables are generated using a modified Thornthwaite-Mather model.
Handling Missing Data	Missing values are flagged; users should check metadata and handle fill values appropriately.

API Availability	Description
Direct API	No dedicated API.
Programmatic Access	Accessible via Google Earth Engine, Microsoft Planetary Computer, and THREDDS servers; can be scripted in Python, R, or other languages using standard geospatial libraries.

Application/Strength/Limitation	Description
Potential Applications	- Climate trend and variability analysis at local to global scales - Drought monitoring, water balance studies, and agricultural risk assessment - Crop yield modeling, agroclimatic zoning, and ecological/hydrological modeling - Validation and downscaling of coarser climate datasets
Strengths	- High spatial resolution (~4 km) and long temporal coverage (1958–present) - Comprehensive set of climate and water balance variables - Freely available and widely validated against station and streamflow data
Limitations	- Monthly temporal resolution may not capture short-term extremes - Large data volumes require robust data handling tools - Some variables are model-derived and may have uncertainties in data-sparse regions

Resource Type	Description/Link
User Guides/Documentation	Climatology Lab TerraClimate, Scientific Data article
Community/Support	No dedicated forum; general support via Climatology Lab or climate data user communities.
R Package	datazoom.amazonia vignette for TerraClimate

IMERG is a NASA/JAXA product providing quasi-global (90°S-90°N), high-resolution (0.1° x 0.1°) estimates of surface precipitation by merging data from the GPM satellite constellation and incorporating microwave, infrared, and gauge information.

Attribute	Description
Short Description	IMERG is a NASA product estimating global surface precipitation rates by combining data from the GPM satellite constellation, including microwave, infrared, and gauge data.
Provider/Source	NASA (National Aeronautics and Space Administration) / JAXA (Japan Aerospace Exploration Agency) as part of the Global Precipitation Measurement (GPM) mission.
Homepage/Link	GPM Website and GES DISC IMERG Page
License/Terms of Use	Generally open for research and non-commercial use. Users should acknowledge NASA/JAXA/GPM in publications. See specific data access portals for detailed terms.
Spatial Coverage	Quasi-global, 90°S to 90°N latitude, 180°W to 180°E longitude.
Temporal Coverage	June 2000 to near-present (updated with a latency depending on the product: Early, Late, and Final Runs).
Spatial Resolution	0.1° x 0.1° latitude/longitude (approximately 10 km x 10 km at the equator).
Temporal Resolution	Half-hourly (30-minute), daily, and monthly aggregations available depending on the product (Early, Late, Final).
Key Variables	Precipitation rate (mm/hr), accumulated precipitation (mm), and associated error estimates. Different runs (Early, Late, Final) offer varying latency and accuracy.

Attribute	Description
Access Methods	Download via NASA GES DISC (HTTP/FTP), potential access through облачные платформы like Google Earth Engine (GEE), and possibly through intermediary APIs or ERDDAP servers.
Data Formats	Primarily NetCDF (.nc), sometimes available in GeoTIFF (.tif) for certain products or through specific portals.
Data Organization	By temporal resolution (half-hourly, daily, monthly), then by year and sometimes month. NetCDF files contain multi-dimensional arrays (time, latitude, longitude, variable). Different "Runs" (Early, Late, Final) are often organized as separate datasets.
Potential Challenges	Large file sizes, especially for high temporal resolution data. Requires specific software libraries (e.g., netCDF4, xarray, rioxarray in Python) for handling. Be aware of the latency and accuracy differences between the Early, Late, and Final Runs. Potential missing data (handle fill values).

Attribute	Description
File Naming Conventions	Typically includes product name (e.g., 3B-HHR, 3B-DAY), version number (e.g., V06, V07), date (YYYYMMDD), start and end times for half-hourly data (HHMMSS), and sometimes a time-of-day indicator (in minutes). For example: 3B-HHR.MS.MRG.3IMERG.20241201-S233000-E235959.1410.V07B.nc4.
Variable Names & Units	Primary variable is usually precipitationCal or similar, representing the merged satellite-gauge precipitation estimate, often with units of millimeters per hour (mm/hr) or millimeters per day (mm/day) for daily products. Verify metadata within the NetCDF files for precise variable names and units. Other variables include error estimates (err), and counts of contributing satellite retrievals (nobs, HQprecipitation).
Coordinate Systems	Latitude and longitude based on the World Geodetic System 1984 (WGS 84) datum. Coordinate variables are typically named lat and lon. The time coordinate is usually in UTC (Coordinated Universal Time) as seconds or days since a reference epoch.
Data Processing	Use libraries like xarray, rioxarray, netCDF4 (Python) or raster (R) for NetCDF file handling. Pay close attention to latitude and longitude coordinates and the time dimension. Handle missing data flags appropriately. Consider the need for temporal aggregation (e.g., daily or monthly totals) or spatial averaging Runs) and their implications for data quality and latency.

API Availability	Link to API Information	How to Utilize
No Direct API	Google Earth Engine API Docs (if using GEE)	Access via Google Earth Engine API if the IMERG dataset is available in the GEE catalog. Use GEE's Python or JavaScript API to filter by time and spatial extent, and to download or process the data.
ERDDAP Servers (Potential)	Search for ERDDAP servers hosting IMERG data (e.g., NOAA ERDDAP).	ERDDAP provides a web interface and APIs (e.g., OPeNDAP, griddap) to access and download subsets of the data in various formats (NetCDF, CSV, etc.).
Python Libraries	Libraries like requests or urllib can be used to directly download files from the GES DISC FTP/HTTP servers if you know the file URLs.	Construct the appropriate URLs based on the GES DISC file structure and use Python to download the files. Then, use netCDF4 or xarray to read and process the downloaded NetCDF files.

Application/Strength/Limitation	Description
Potential Applications	Rainfall pattern analysis, drought monitoring, extreme precipitation event analysis relevant to agricultural impacts (floods, dry spells), crop yield modeling (as precipitation is a key input), water resource management for irrigation, agroclimatic zoning based on rainfall regimes, validation of local rainfall data or downscaled climate model outputs. The half-hourly and daily resolutions can capture sub-daily and daily rainfall variability important for hydrological processes affecting agriculture.
Strengths for AE Research	High spatial resolution allows for localized analysis relevant to farm-level studies. Long temporal coverage (since 2000) enables trend analysis and understanding of historical precipitation variability. Quasi-global coverage allows for studies across different agroecological zones. Integration of multiple satellite sensors and ground gauge data generally leads to higher accuracy compared to satellite-only products, especially in the Final Run.
Limitations for AE Research	Indirect satellite measurements can still have biases, especially in regions with complex terrain, coastlines, or during frozen precipitation. Does not include other crucial agroecological variables like temperature, solar radiation, humidity, etc. Spatial resolution might still be too coarse for very localized microclimatic studies within farms. Different IMERG runs have varying latency and accuracy; the near real-time products (Early, Late) might be less accurate than the research-oriented Final Run.

Resource Type	Description/Link
User Guides/Documentation	Primary source is the NASA GES DISC website: GES DISC IMERG Documentation. Look for documents related to the algorithm, data quality, and user guides. The GPM mission website (https://gpm.nasa.gov/) also has relevant information.
Community/Support	NASA GES DISC provides user support. Contact information can usually be found on their website. Online forums related to remote sensing, climate data, or specific software (e.g., Earth Engine Developer Forum) might also have discussions and solutions related to IMERG data.

TAMSAT is a long-term (1983 to present) quasi-global (African continent) high-resolution (~4km) rainfall dataset derived from Meteosat thermal infrared (TIR) satellite imagery, calibrated using rain gauge measurements for operational rainfall monitoring and climate risk assessment in Africa.

Attribute	Description
Short Description	TAMSAT is a long-term, high-resolution rainfall dataset for Africa, developed by the University of Reading. It blends infrared satellite imagery with ground-based rain gauge observations to provide reliable rainfall estimates, particularly valuable in regions with sparse gauge networks.
Provider/Source	Department of Meteorology, University of Reading.
Homepage/Link	https://www.tamsat.org.uk/
License and Terms of Use	The TAMSAT dataset is generally made available for non-commercial research, educational, and applications use. Specific terms of use, including citation requirements, are usually outlined on the TAMSAT website. It's important to consult the "Data Access" or "Terms of Use" section on their site.
Spatial Coverage	Africa (continental).
Temporal Coverage	Typically spans from 1983 to near-present, with updates occurring regularly.
Spatial Resolution	0.0375° x 0.0375° latitude/longitude (approximately 4 km x 4 km at the equator). This is a relatively high spatial resolution.
Temporal Resolution	Daily, dekadal (10-day), and monthly rainfall estimates are commonly available. Other aggregations might also be provided.
Key Variables	Rainfall/Precipitation(Units are typically millimeters (mm)) and soil moisture estimates

Attribute	Description
Access Methods	Data can usually be accessed through the TAMSAT website, often via a dedicated "Data Access" or "Download" section. This might involve direct download via FTP or HTTP. They may also have a data portal or require registration for access. Inquire about any potential API access from their website or contact them directly.
Data Formats	Data is often provided in NetCDF (.nc) format, as well as potentially other formats like GeoTIFF (.tif) for easier integration with GIS software. The format can vary depending on the specific product and how it's accessed.
Data Organization	Files are typically organized by temporal resolution (daily, dekadal, monthly) and then by year. Filenames often include the date or date range and the product version. NetCDF files will contain multi-dimensional arrays (time, latitude, longitude, rainfall).
Potential Challenges	Access methods might vary over time, so always refer to the TAMSAT website for the latest instructions. Large file sizes can be a consideration, especially for long time series at high spatial resolution. NetCDF files require specific software libraries for processing. Ensure you understand any specific data flags or quality control information provided with the dataset.

Attribute	Description
File Naming Conventions	Typically include the product name (TAMSAT), temporal resolution (e.g., daily, dekadal, monthly), year, and potentially the day or month. Refer to the TAMSAT website and the filenames of downloaded data for the exact conventions.
Variable Names & Units	The primary rainfall variable is usually named precip. Units are typically millimeters (mm). Check the metadata within the NetCDF files for more information on variables and units.
Coordinate Systems	Uses latitude and longitude coordinates. The specific datum is usually WGS 84 but should be verified in the NetCDF metadata.
Data Processing	Utilize standard geospatial and NetCDF processing libraries in your chosen programming language (e.g., xarray, rioxarray, netCDF4 in Python; raster in R). Be prepared to handle spatial subsets for specific agricultural regions within Africa. Consider implementing functions for temporal aggregation or disaggregation if needed. Pay attention to any missing data flags or quality control layers that might be provided.

Available API	Link to API Information	How to Utilize
No formal, public REST API	TAMSAT Website (for data access information)	Programmatic access likely involves downloading files from the website or provided links and then using libraries like xarray (Python) or raster (R) to process the data. Check the TAMSAT website for any potential future API updates.

Application/Strength/Limitation	Description
Potential Applications	Rainfall pattern analysis across Africa, drought monitoring and early warning systems, agricultural planning and decision-making, crop yield modeling (especially in rainfed agriculture), assessment of water availability for irrigation, climate risk assessment in agricultural regions, validation of other rainfall datasets or climate models over Africa, studies on the impact of rainfall variability on vegetation and agricultural productivity.
Strengths for AE Research	High spatial resolution is particularly valuable for capturing localized rainfall patterns relevant to agricultural fields. Long temporal coverage allows for historical analysis of rainfall trends and variability. Focus on Africa makes it a directly relevant dataset for agroecological research on the continent, especially in areas with limited ground station data. Blending of satellite and gauge data aims to provide more accurate estimates than satellite-only products.
Limitations for AE Research	Primarily a rainfall dataset; does not include other essential agroclimatic variables like temperature, solar radiation, or evapotranspiration. Accuracy can still be limited in very remote regions with minimal gauge input. While high for regional data, the 4 km resolution might still be too coarse for very micro-scale agricultural studies. Understanding the specific algorithms and potential biases in the TAMSAT product is important for interpretation.

Resource Type	Description/Link
Publications	The TAMSAT website (https://www.tamsat.org.uk/) often lists key publications describing the dataset methodology and validation. Search for "TAMSAT rainfall" in scientific databases like Google Scholar.
User Guides/Documentation	The TAMSAT website is the primary source for user guides, data documentation, and any FAQs. Look for sections like "Data," "Documentation," or "About TAMSAT." They provide specific guidelines for data access and usage.
Community/Support	The TAMSAT website might provide contact information for inquiries or support. You could also look for relevant discussions in online forums or communities focused on African climate data or remote sensing in agriculture.