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 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 .

This dataset contains Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Quasi-global 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.

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 .

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.

This file contains the dataset (both raw observed precipitation data and figures obtained as output of the analysis) accompanying the manuscript 'hess-2016-453' submitted to the HESS journal (http://www.hydrology-and-earth-system-sciences.net/).

Title: "Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile"

Abstract

Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access (e.g., high elevation zones). Recently, satellite-based rainfall estimates (SRE) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.

This study attempts to exhaustively evaluate -for the first time- the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-adj, MSWEPv1.1 and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE. The modified Kling-Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, several categorical indices were used to assess the ability of each SRE to correctly identify different precipitation intensities.

Results revealed that most SRE products performed better for the humid South (36.4-43.7ºS) and Central Chile (32.18-36.4ºS), in particular at low- and mid-elevation zones (0-1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (MAM-JJA) compared to summer (DJF) and autumn (SON). In addition, all SREs were able to correctly identify the occurrence of no rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all time scales, which can be clearly attributed to its bias-correction procedure using 213 stations from Chile. Good results were also obtained by CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR and PERSIANN-CCS-adj were not able to represent observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resources assessments. We finally conclude that despite improvements of most SRE products, a site-specific calibration is still needed before any use in catchment-scale hydrological studies.

The 3-month Standardized Precipitation Index (SPI-3) is an indicator used to monitor meteorological drought based on precipitation anomalies over 3-month accumulation periods. SPI-3 serves as a proxy for medium-term impacts, such as reduced stream flow and reservoir storage. The input data for calculating the SPI-3 is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates from rain gauge and satellite observations.

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 provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

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.

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 Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data products provide a measure of precipitation in mm over land that incorporates both station and satellite information. This image contains the monthly sum of precipitation observations continent of Africa for December 2017. It is updated monthly.

This dataset contains satellite rainfall data for eight major cities in the Sirba River Basin, Burkina Faso, derived from two products: the CHIRPS model and the TAMSAT model. The two zip archives, named according to the reference model, contain within them 8 folders, one for each city investigated. Each folder contains within it a file (nameofthecityMODEL_19832023).xlsx with the 40-year rainfall series and four folders (indices, log, plots, trend) containing the computations obtained using RClimdex software to calculate ETCCDI rainfall climate indices.

The data presented can be used for future insights and researches on the watershed.

The Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data archive is a quasi-global (50S-50N), gridded 0.05 degree resolution, 1981 to near-real time precipitation time series. The terrestrial preciptation estimates, are available in daily to annual time intervals. Africa is available. Data is available in several formats (NetCDF, TIFF, BIL, PNG) and are located in separate subdirectories. 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 only Global Telecommunications System (GTS) and Conagua (Mexico) data. 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.

This dataset contains dekadal rainfall indicators computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2, aggregated by subnational administrative units. Included indicators are (for each dekad): 10 day rainfall mm rainfall 1-month rolling aggregation mm rainfall 3-month rolling aggregation mm rainfall long term average mm rainfall 1-month rolling aggregation long term average mm rainfall 3-month rolling aggregation long term average mm rainfall anomaly % rainfall 1-month anomaly % *rainfall 3-month anomaly % The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the n_pixelscolumn.

A daily precipitation estimation derived from WHU-SGCC method (Wuhan University Satellite and Gauge precipitation Collaborated Correction), blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin during summer seasons from 1990 to 2014.

A daily precipitation estimation derived from WHU-SGCC method (Wuhan University Satellite and Gauge precipitation Collaborated Correction), blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin during the different seasons from 1990 to 2014.

The Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data archive is a quasi-global (50S-50N), gridded 0.05 degree resolution, 1981 to near-real time precipitation time series. The terrestrial preciptation estimates, are available in daily to annual time intervals. In addition to the quasi-global extent, subsets of Western Hemisphere, Africa and the Central Americia/Caribbean regions are available. These are available in several formats (NetCDF, TIFF, BIL, PNG) and are located in separate subdirectories. 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 only Global Telecommunications System (GTS) and Conagua (Mexico) data. 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.

Information on the spatio-temporal distribution of rainfall is very critical for addressing water related disasters, especially in the arid to semi-arid regions of the Middle East and North Africa region. However, availability of reliable rainfall datasets for the region is limited. In this study we combined observation from satellite-based rainfall data, in situ rain gauge observation and rainfall climatology to create a reliable regional rainfall dataset for Jordan, West Bank and Lebanon. First, we validated three satellite-based rainfall products using rain gauge observations obtained from Jordan (205 stations), Palestine (44 stations) and Lebanon (8 stations). We used the daily 25-km Tropical Rainfall Measuring Mission over 2000 – 2016; daily 10-km Rainfall Estimate for Africa (RFE) rainfall over 2001 – 2016; daily 5-km Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall over 1981-2015; daily 25-km Multi-Source Weighted-Ensemble Precipitation (MSWEP) over 1984-2015. The validation was conducted between in situ rain gauge observation and satellite rainfall data and resulted in utilizing the MSWEP dataset in correlation with a bias correction grid. The created rainfall dataset was used to estimate stream flow in the region and determine suitable areas of aquifer recharge.

This dataset contains satellite rainfall data for eight major cities in the Sirba River Basin, Burkina Faso, derived from two products: the CHIRPS model and the TAMSAT model. The two archives, named according to the reference model, contain within them 8 folders, one for each city investigated. Each folder contains within it a file (nameofthecityMODEL_19832023).xlsx with the 40-year rainfall series and four folders (indices, log, plots, trend) containing the computations obtained using RClimdex software to calculate ETCCDI rainfall climate indices.

The data presented can be used for future insights and researches on the watershed.