What is CHIRPS?
Latest Preliminary CHIRPS v2.0 Africa Pentad

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. As of February 12th, 2015, version 2.0 of CHIRPS is complete and available to the public. For detailed information on CHIRPS, please refer to our paper in Scientific Data.

History and Intent

Since 1999, U.S. Geological Survey (USGS) and CHG scientists, supported by funding from the U.S. Agency for International Development (USAID), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA), have been developing techniques for producing rainfall maps, especially where surface data is sparse.

Estimating rainfall variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving dryer-than-normal season must be placed in historical context so that the severity of rainfall deficits may be quickly evaluated. However, estimates derived from satellite data provide areal averages that suffer from biases due to complex terrain which often underestimate the intensity of extreme precipitations events. Conversely, precipitation grids produced from station data suffer in more rural regions where there are less rain gauge stations. CHIRPS was created in collaboration with scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in order to deliver reliable, up to date, and more complete datasets for a number of early warning objectives (such as trend analysis and seasonal drought monitoring).

Early research focused on combining models of terrain-induced precipitation enhancement with interpolated station data. More recently, new resources of satellite observations such as gridded satellite-based precipitation estimates from NASA and NOAA have been leveraged to build high resolution (0.05°) gridded precipitation climatologies. When applied to satellite-based precipitation fields, these improved climatologies can remove systematic bias, a key technique in the production of the 1981 to near-present CHIRPS dataset. The creation of CHIRPS has supported drought monitoring efforts by the USAID Famine Early Warning Systems Network (FEWS NET).


The current best publication of record for CHIRPS is:

Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell & Joel Michaelsen.
"The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015.

The data reviewed for this paper extend from January 1981 to August 2015. The CHIRPSv2.0 dataset will continue to grow over time as new TIR and station data become available.

[+] Related Publications [-] Related Publications
CHG Publications
External Publications and Reports

See "A Quasi-Global Precipitation Time Series for Drought Monitoring" for citations used in developing and reporting on CHIRPS.

Screening and Diagnostics

The Climate Hazards Group has developed a number of screening processes and diagnostic tools to evaluate the various aspects of CHIRP and CHIRPS.

[+] Screen for False Zeroes [-] Screen for False Zeroes

The daily GTS and GSOD values underwent screening to flag potential missing values coded with zeros. This is a substantial problem with both of these information sources, and can produce completely erroneous 'droughts' in the midst of rainy season, as missing data are coded as zeros and passed through the automated GSOD and GTS networks. If the daily GSOD and GTS values were zero for a given day, but the daily CHIRP precipitation was above the long-term (1981-2014) average daily rainfall intensity, that daily station value was treated as missing.

[+] Screen for Duplicates [-] Screen for Duplicates

Some GSOD data were found to exhibit repeating values on adjacent days. For example, a string of daily observations might be: 0.0000, 0.0000, 87.653, 87.653, 0.0000, .... If a monthly GSOD record had three duplicate identical values or, the amount of precipitation being duplicated was greater than 30 mm in a month, the monthly record was omitted. The probability of five digits being the same two times in a month is vanishingly small.

[+] Maps of Quality Control Excluded Stations [-] Maps of Quality Control Excluded Stations

For each new run of CHIRPS a number of quality control steps are performed. Along the way, maps are made showing the location and number of points excluded due to:

  • Bad Zscore: Zscore for pixel over entire time series is greater than 4.
  • False Zeroes: Station is zero, but CHIRP > 7 for pentad 20 for monthly.
  • Extreme Values: Station value greater than 2000, or greater than 5 times CHIRP.

The monthly png's can be found here.

The pentad png's can be found here.

[+] Station Comparison Graphs [-] Station Comparison Graphs

Rcheck_stations_by_source_country compares monthly time series station precipitation data to those of it's neighboring stations, generating plots to be used for visual comparisons to determine the station's data quality. The algorithm selects a station from our precipitation database for a given source and searches for neighbor stations within an iteratively increasing radius from the station until a minimum number of "quality" stations is found (usually 3). A quality station is one with enough observations (5-15 depending on the data set's temporal length) within the time series of interest (for CHIRPS 1981-present). At each search iteration, if the minimum number of quality stations are not found, the search radius is increased up to a maximum of 150km. If the minimum is not met at the maximum search radius, the station is skipped and noted in a log file. When enough quality neighbors are found, the a distance weighted mean (DWM) of the neighbors is calculated and the correlation coefficient (R) and mean standard error (MSE) between the station and the mean are calculated. These values are used to compute a composite index of the overall "badness" of the station compared to the neighbor's DWM. The badness index (BI) is calculated with:

BI = (1.0 - max(R, 0.0) + min(MSE, 10.0)) * 50.0

The BI is calculated for the 3 wettest months at the station location and added together to produce a "total badness index" (TBI) for each station. The time series of the station (blue), and it's neighbors (distance weighted shades of grey), is plotted along with the DWM (green) for each of the wettest three months. Each of these graphics contains; the latitude and longitude and elevation of the station and it's neighbors, as well as the names and station identification numbers. A plot of the locations of each station is generated in the upper right corner of the graph as color coded symbols to depict the differences in elevation as compared to the station. The climatological precipitation value for the given month, and at each nearby station location, is plotted along the right side of the plot as a reference to use in determining station validity. The names of the three graphics files are then prepended with the TBI to allow for numerical sorting by the computer operating systemÕs file system. Typically, only stations with TBI greater than 100 are saved for viewing since TBI values below that are stations in very good agreement with their neighbors. Read more...

[+] EWX Reality Checks [-] EWX Reality Checks

You can use our internal EWX server to evaluate how station data differs from the CHIRP.

EWX R-Checks Stations is a set of imagery we have prepared to examine the station data going into the CHIRPS data product. It can be used to o identify erroneous station data as well as agreement between stations and the CHIRPS fields (click here for examples).

[+] Regional Time Series Statistics [-] Regional Time Series Statistics

On a monthly basis, we examine several measures of the new CHIRPS values over the entire time series for consistency. Regional and global means are plotted for the CHIRPS values, maximum value, standard deviation, number of pixels with rain, Z score and the difference between CHIRPS and CHIRP for the current month. (Examples coming soon!)

[+] Stations Used by Country by Month [-] Stations Used by Country by Month

Select a continent to view available pngs showing the number of stations for a given country. Plots shown are smoothed over a moving seven month window.

[+] Station Density [-] Station Density

For every month, we provide GeoTiff files of the CHIRPS station density for 0.05 degree resolution and 0.25 degree resolution.

GET ADDED TO OUR USERS LIST: If you would like to recieve updates on CHIRPS processing/validations/publications etc., send an email to chirps@geog.ucsb.edu.
Data (ftp)

To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). This work is published from: United States.

Two CHIRPS products are produced operationally: a rapid preliminary version, and a later final version. The preliminary CHIRPS product is available, for the entire domain, two days after the end of a pentad (2nd, 7th, 12th, 17th, 22nd and 27th). The preliminary CHIRPS uses two station sources, GTS and Mexico. The final CHIRPS product takes advantage of several other stations sources and is complete sometime in the third week of the following month. Final products for all times/domains/formats are calculated at that time.

Updated regularly at: CHIRPS version 2.0

For the time being, the Climate Hazards Group CHIRPS team will continue to support version 1.8, found here.


The FAQ for CHIRPS can be found on our wiki.

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Affiliated Organizations

The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 "Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World" with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for "Famine Early Warning Systems Network Support", the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for "A Land Data Assimilation System for Famine Early Warning", SERVIR award #NNH12AU22I for "A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa", The National Oceanic and Atmospheric Administration award NA11OAR4310151 for "A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network", and the USGS Land Change Science Program.