This dataset has 1-day (daily) averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. 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.

CHIRPS is an abbreviation for Climate Hazards Group InfraRed Precipitation with Station Data (Version 2.0 final). The CHIRPS is a 30+ year quasi-global rainfall dataset and incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. The data of the CHIRPS pentad is derived from Google Earth Engine with earth engine snippet as https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2FUCSB-CHG_CHIRPS_PENTAD . With the dataset in a global format, it is clipped with the Cambodia boundary and generated the data visualized chart through the obtained data.

This dataset contains Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Quasi-global pentadal satellite and observation based precipitation estimates over land from 1981 to near-real time. 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.

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. It is a gridded rainfall time series for trend analysis and seasonal drought monitoring, spans 50°S-50°N (and all longitudes) and ranges from 1981 to near-present. The anomaly refers to the difference between current rainfall and the average rainfall that occurred between 1981 and 2010 in millimeters. For more information visit the CHIRPS site.

This dataset contains the latest available CHIRPS anomaly data. The full list of data available is available from USGS for Mar-May data, Oct-Dec data, and others.

Additionally, subnational statistics (mean, min, max) have been calculated for Ethiopia, Kenya, and Somalia and are available in the csv resource.

Scientists at Famine and Early Warning System (FEWS NET) who are members of the SERVIR Applied Sciences Team used 30+ years' (1981-present) worth of multiple satellite data sources and ground observations to produce an unprecedented, global, spatially and temporally consistent and continuous 30-year record of satellite-derived rainfall data. Spanning 50°S-50°N (and all longitudes), 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. This CHIRPS global dataset makes it possible to accurately assess and monitor large-scale rainfall patterns and analyze how they may be affected by climate change.

Two CHIRPS products, both reported in millimeters (mm), 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, the World Meteorological Organization's (WMO) Global Telecommunication System (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.

The data are available in various formats for download via the Climate Hazard Center FTP site. (see below)

Through ClimateSERV (https://climateserv.servirglobal.net), the SERVIR Program provides the ability to extract zonal statistics (average, min, max) over a user-specified area of interest (AOI) for a specific time period. Data are downloadable as charts and underlying tabular data (in comma separated values - .csv files). Subsets of the data in raster format (.tif files) for an AOI can also be extracted. ClimateSERV also exposes an API to allow data retrieval requests into third party applications. ClimateSERV combines CHIRPS data with the most recently available CHIRP (no stations) data, which is overwitten as new CHIRPS data become available.

Please see Online Resources further below for links.

For more information on FEWS NET, visit https://fews.net For more information on SERVIR, visit https://servirglobal.net

This dataset has annual averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

Dados sobre a região obtidos da Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS).

This dataset has 5-day (pentad) averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

This map is part of SDGs Today. Please see sdgstoday.orgUnderstanding how rainfall varies across geography and time is important for environmental monitoring and drought prediction. Calculations of rainfall from rain gauges often result in incomplete coverage, and an exclusive reliance on satellite imagery can underestimate extreme precipitation events. The Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) is a joint project between the US. Geological Survey and UC Santa Barbara. The project dates back to 1981 and brings together both categories of data to provide nearly global gridded rainfall estimates, which are helpful for trend analysis and seasonal drought monitoring. For this dataset, researchers combine historical monthly averages from rain gauges with five different satellite products, and local rainfall is calculated using regression techniques. They then adjust biases in the estimates by blending in available daily rain gauge data. Estimates are available at a high spatial resolution (0.05°) and are updated daily with a two-day lag. Read more about the methodology here.

CHIRPS-compatible GEFS rainfall forecasts for anticipating flood and drought hazards. CHIRPS-GEFS is a bias-corrected and downscaled version of NCEP Global Ensemble Forecast System precipitation forecasts made to be spatially compatible with various CHIRPS products. The ESRL/PSD version 12 Reforecast Project version-12 runs an instance of the Global Ensemble Forecast System (GEFS) model 16 days into the future (https://www.noaa.gov/media-release/noaa-upgrades-global-ensemble-forecas...). These data consist of 5 ensemble members, and the mean of these members is used as the target forecast for this product. Daily rainfall forecasts are accumulated to create 5-/10-/15-day totals. The rank-based quantile of these totals is then quantile-matched to the empirical distribution of CHIRPS rainfall for the corresponding period. The result of the quantile-matching scheme is that the average and variance of the CHIRPS data is approximately retained in the resulting CHIRPS-GEFS values. The CHIRPS-GEFS forecast data product is a valuable resource for CHIRPS users in particular, as it provides 5-day to 15-day GEFS forecast precipitation totals and anomalies that are compatible with the historical CHIRPS. This feature allows for the timely assessment of how the latest forecast could alter the current agro-climatological situation. Updated daily at a spatial resolution of 5 km across the globe.

The data is accessible via the Climate Hazards Center (resource provider) website, as well as the SERVIR ClimateSERV application. Through ClimateSERV (https://climateserv.servirglobal.net), the SERVIR Program provides the ability to extract zonal statistics (average, min, max) over a user-specified area of interest (AOI) for a specific time period. Data are downloadable as charts and underlying tabular data (in comma separated values - .csv files). Subsets of the data in raster format (.tif files) for an AOI can also be extracted. ClimateSERV also exposes an API to allow data retrieval requests into third party applications.

Please see Online Resources further below for links.

For more information on SERVIR, visit https://www.servirglobal.net

Contains data from Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) precipitation dataset. CHIRPS integrates 0.05° resolution satellite imagery with ground station data to generate gridded rainfall time series.

The datacude includes daily precipitation measurements from 01-Oct-2019 to 30-Sep-2021 for the Boeotikos Kifissos river basin.

• Dimentions:
  1. time: 1096
  2. latitude: 9
  3. longitude: 19
• Data variables: (time, latitude, longitude)
• Size: 759kB

Dimensions: (time: 1096, latitude: 9, longitude: 19)

This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. CHIRPS (Climate Hazards Group InfraRed Precipitation with Station, https://www.chc.ucsb.edu/data/chirps) is a global precipitation dataset with a spatial resolution of 0.05° (approximately 5 km) that provides information from 1981 to the present, with daily temporal resolution. This dataset contains total, mean, minimum and maximum rainfall in mm averaged per epidemiological week. Each instance represents one epidemiological week with its respective sum, minimum, average and maximum rainfall, and number of rainy days. 1. The original data are in NETCDF (.nc) format, which is a multidimensional data format. The data were processed and converted into a tabular format, separated by semicolons (;). The total precipitation, as well as the minimum, average, and maximum precipitation values per epidemiological week were calculated. The number of rainy days was also added, indicating the number of days with rainfall in each epidemiological week. 2. The variables LAT and LON indicate the location of the center of each pixel.

High-resolution reanalysis products could improve the representativeness of rainfall on high Andean basins, but their performance must be locally validated. We addressed the performance, accuracy, and ability of TerraClimate and CHIRPSv.2 to represent 36 years of rainfall (1985–2020) from 23 stations in the Upper Chicamocha River, a Colombian basin of complex terrain and tropical hydrometeorology. Using several statistical metrics at monthly, seasonal and annual scales, we found how both datasets overestimate rainfall as a function of elevation, with better performance and accuracy from CHIRPS (r ∼0.76, R2 ∼0.58, NSE ∼0.56, and low RMSE ∼33.7 mm/month, MAE ∼25.2 mm/month, ME ∼6.4 mm/month, and PBIAS ∼9.3), while TerraClimate overestimates inter-annual variability, especially between June and August. Seasonally, the datasets exhibit different spatial patterns and magnitudes, even after bias correction. The findings highlight the potential use and challenges of high-resolution datasets in basins with similar topography and hydrometeorology in the Andean region.

Annual precipitation in Mexico over 30 years from 1995 to 2024. The dataset used was CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The units are in mm/year.

Precipitacion anual en México durante 30 años, de 1995 a 20024. Los datos climaticos empleados fueron de CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). Las unidades de cada pixel estan en mm/año.

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. 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.

Approximately 5km (0.05°)

unit: "mm"

dataType: "Float32"

noDataValue: -9999

Data revision: 2018-10-24

Contact points:

Metadata Contact: FAO-Data

Online resources:

Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) est un ensemble de données quasi mondiales sur les précipitations qui couvre plus de 30 ans. CHIRPS intègre des images satellite d'une résolution de 0,05° avec des données de stations in situ pour créer des séries temporelles de précipitations maillées pour l'analyse des tendances et la surveillance saisonnière de la sécheresse.

Accurate and reliable high-resolution spatial precipitation data are crucial for hydrometeorology research. But most of the precipitation products have significant differences in terms of estimation accuracy owning to the influence of sensors, climate and terrain. Moreover, due to the neglect of the precipitation feature and the sparse distribution of gauge stations, the existing bias correction methods often have great uncertainties under different precipitation intensities. Thus, we developed a Daily Precipitation Bias Correction Approach Based on Feature Space Construction and Gauge-Satellite Fusion (BCFS). First, the precipitation feature space under different precipitation intensities was reconstructed, considering the attribute similarities of the spatial values, non-spatial values and trends. Then, the numerical relationships of correlated neighboring pixels were established taking account of these three similarities. Finally, the effective correction of the daily precipitation bias based on a small number of stations and a great number of pixels was achieved by the integration methods of variational mode decomposition, multivariate random forest regression model, and the spatial interpolation method. Using gauge station observations and the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) (1998-2019) and taking the Han River basin (China) as a case study, we quantitatively analyzed the accuracy of the bias correction results comparing the BCFS with the original CHIRPS precipitation estimations and the Wuhan University Satellite and Gauge precipitation Collaborated Correction method (WHU-SGCC). The results demonstrated the BCFS can effectively improve the estimation accuracy under different daily precipitation intensities. Therefore, the method is meaningful to make up for the deficiency of satellite-based estimations and provide high-precision daily precipitation for hydrometeorological and environmental monitoring and forecasting.

The 1-month Standardized Precipitation Index (SPI-1) is an indicator used to monitor meteorological drought based on precipitation anomalies over 1-month accumulation periods. SPI-1 serves as a proxy indicator for immediate impacts of droughts such as reduced soil moisture, snowpack, and flow in smaller creeks. The input data for calculating the SPI-1 is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates from rain gauge and satellite observations.

This database contains spatial information with a 0.05° grid resolution of specific agroclimatic indices for maize, dry beans, soybeans, and coffee regions in Angola. In total, the database comprises 13 agroclimatic indices for each crop, grouped as follows: 1. Dry Conditions Indices: • Number of Dry Days • Number of Dry Spells • Average Length of Dry Spells 2. Wet Conditions Indices: • Number of Wet Days • Number of Wet Spells • Average Length of Wet Spells • Total Precipitation 3. Heatwave Indices: • Number of Hot Days • Number of Heatwaves • Maximum Length of Heatwaves 4. Crop Water Requirement Index: • Potential Evapotranspiration (ETo) 5. Water Balance Index: • Standardized Precipitation and Evapotranspiration Index (SPEI) These indices were calculated using historical climatic data for the period 1981 to 2020, considering the typical growth and development periods of each crop of interest, detailed as follows: • Maize: September - April • Beans: November – March • Soybeans: October – April • Coffee: September – August Additionally, six "El Niño" events (1982-1983, 1987-1988, 1991-1992, 1997-1998, 2009-2010, 2015-2016) and six "La Niña" events (1984-1985, 1988-1989, 1998-1999) were considered to characterize the behavior of each indicator under the influence of different phases of the ENSO phenomenon. Metodology:Regarding the climatic data used to calculate each of the indices, the following information is provided: 1. Dry and Wet Conditions Indices: Historical daily rainfall data from the Climate Hazards Group InfraRed Precipitation Measurement (CHIRPS) dataset (https://www.chc.ucsb.edu/data) were used. 2. Heatwave Indices: Historical daily maximum temperature data were obtained from the AgERA5 database (https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview), and a resampling process was applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. 3. Crop Water Requirement Indices: The Priestley-Taylor equation was used to calculate Potential Evapotranspiration (ETo) due to its simplicity and suitability for tropical conditions. Daily maximum and minimum temperature data, as well as solar radiation, were obtained from the AgERA5 database. A resampling process was also applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. 4. Water Balance Indices: The SPEI indicator calculation was based on daily precipitation data from CHIRPS and ETo calculated using daily maximum and minimum temperature data, as well as solar radiation, from the AgERA5 database. This database provides a valuable tool for understanding and managing agroclimatic aspects in key crop-producing regions in Angola, which can have a significant impact on the country's agriculture and food security.