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| CHIRPS | |
|---|---|
| Name | CHIRPS |
| Caption | Climate-sensitive precipitation dataset |
| Country | Global |
| Developer | Climate Hazards Center; Famine Early Warning Systems Network; University of California, Santa Barbara |
| Released | 2011 |
| Frequency | Daily, pentadal, monthly |
| Format | GeoTIFF, NetCDF, ASCII |
CHIRPS
CHIRPS is a climate-focused precipitation dataset combining satellite imagery, station observations, and climatology to produce high-resolution gridded rainfall estimates. It was created to support drought monitoring, agriculture planning, and humanitarian early warning by integrating long-term records with near-real-time inputs. CHIRPS has been used by international agencies and research institutions to assess precipitation variability across Africa, Central America, South Asia, and other regions vulnerable to hydrometeorological extremes.
CHIRPS delivers quasi-global precipitation fields at ~0.05° (~5 km) spatial resolution and multiple temporal aggregations (daily, pentadal, monthly), designed to bridge gaps between coarse satellite products and sparse in-situ networks. The dataset blends station-based observations from networks such as Global Historical Climatology Network, FAO, National Oceanic and Atmospheric Administration, and regional services with satellite estimates derived from infrared and microwave sensors aboard platforms like NOAA-19, MetOp, and Himawari-8. CHIRPS supports operational programs including Famine Early Warning Systems Network, United Nations Office for the Coordination of Humanitarian Affairs, and research efforts at University of California, Santa Barbara and international research centers.
Development of the dataset was led by the Climate Hazards Center in collaboration with partners including FEWS NET, the US Agency for International Development, and academic groups at University of California, Santa Barbara. The methodology fuses three components: a high-resolution climatology, satellite-only precipitation estimates, and in-situ station data using interpolation and bias-correction techniques. Satellite precipitation optical sensor retrievals and microwave products are calibrated against station records from sources such as Global Precipitation Climatology Centre, TRMM, and regional meteorological agencies. Spatial interpolation uses inverse-distance weighting and local scaling to preserve orographic and coastal patterns observed in datasets like WorldClim while maintaining continuity with long-term records from archives such as Climatic Research Unit.
CHIRPS provides daily, pentadal (5-day), and monthly precipitation totals in standard geospatial formats including GeoTIFF, NetCDF, and ASCII grids suitable for integration with geographic information systems used by NASA, European Space Agency, and national meteorological services. Derivative products include anomaly fields, standardized precipitation index inputs, and climatological normals computed for baseline periods aligned with datasets like World Meteorological Organization standards. Time series are packaged for direct use in hydrological models developed by groups affiliated with International Water Management Institute and Consultative Group on International Agricultural Research.
CHIRPS is applied in diverse operational and research contexts: drought monitoring and declaration by FEWS NET and Famine Early Warning Systems Network partners; crop yield forecasting for programs run by USAID and FAO; flood risk assessment in collaboration with UNICEF and national disaster management agencies; and climate variability studies published by teams at Columbia University, University of Oxford, and Imperial College London. It also supports ecological modeling for organizations such as WWF and hydrological forecasting frameworks used by World Bank projects and regional development banks.
Validation exercises compare CHIRPS against station networks, reanalysis datasets like ERA5, and satellite products such as IMERG and TRMM Multi-satellite Precipitation Analysis. Performance metrics typically include correlation coefficients, bias, mean absolute error, and categorical statistics for dry/wet day detection. Independent evaluations by groups at International Research Institute for Climate and Society and national meteorological services have shown that CHIRPS improves spatial realism relative to coarse satellite-only products, particularly in data-rich regions and in capturing orographic precipitation patterns documented in case studies from Ethiopia, Peru, and India.
Despite strengths, CHIRPS inherits limitations linked to station density, satellite retrieval errors, and climatology assumptions. In data-sparse regions such as parts of Sahara Desert and remote oceanic islands, estimates rely heavily on satellite signals that can misclassify convection or miss light stratiform precipitation, issues noted in comparisons with ship- and buoy-based observations from International Comprehensive Ocean-Atmosphere Data Set. Bias correction methods may underperform during regime shifts associated with events like the El Niño–Southern Oscillation or sudden land-cover changes. Users should be cautious when applying CHIRPS for extreme-event attribution without supplementary ground-based validation from national services.
CHIRPS datasets are disseminated through repositories and portals used by humanitarian, academic, and operational actors, compatible with data infrastructures hosted by NASA Earthdata, Google Earth Engine, and institutional FTP services maintained by the Climate Hazards Center. Licensing and access policies facilitate integration into workflows used by FEWS NET, United Nations Development Programme, and research groups at institutions like Massachusetts Institute of Technology and Stanford University. Data consumers typically access CHIRPS for offline analysis, web-based visualization, and input into decision-support tools deployed by international organizations.
Category:Climate datasets