Precipitation Data Sets: Overview & Comparison table

Main content

Precipitation (rain, snow, hail, ...) is one of the key components of the hydrological cycle. Its societal importance cannot be over stated. For climate research, precipitation is one of the key terms for balancing the energy budget, and one of the most challenging aspects of climate modeling. Hence, high quality estimates of precipitation's distribution, amounts and intensity are essential. However, the very nature of precipitation and the limitations of the observing system make the quantification of precipitation challenging.  Unlike (say) temperature which has a high degree of spatial and temporal correlation, precipitation can be fractal in space and discontinuous in time. Further, regional variations in topography can affect precipitation amounts significantly. Most precipitation data sets may be categorized into one of three broad categories: gauge data sets (e.g. CRU TS, GPCC, APHRODITE, PREC/L), satellite-only data sets (e.g., CHOMPS) and merged satellite-gauge products (e.g. GPCP, CMAP, TRMM 3B42). 

Even with the near-global coverage of satellites, most satellites fly over a region only twice per day, potentially missing precipitation events. For this reason, many data sets combine observations from multiple satellite platforms that carry passive microwave and/or infrared instruments.  Infrared sensors are used to estimate cloud top temperatures, which must be calibrated to some other precipitation estimate. The microwave-based algorithms derive the precipitation signal from both scattering and emission but only the scattering signal is useful over land because of strong variations in surface emissivity that distort the emission. Microwave-only products include CHOMPS and the SSM/I datasets; PERSIANN and CMORPH combine information from passive microwave and infrared.

Climate-quality, gauge-based data sets can be difficult to construct due to the widely distributed and heterogeneous nature of the source data. Moreover, wind and evaporation effects on the gauge measurments, typically resulting in under-catch, need to be considered.

Despite many advances in observing systems and algorithms over the years, validating global precipitation observations, for example through the energy budget approach, has proven challenging. Adler et al (2012) estimate the global (ocean+land)-mean precipitation as 2.6 ± 7% mm/day. Over oceans only,Behrangi et al (2014),  'estimate for 3-yr (2007–09) near-global (80S-80N) oceanic mean precipitation rate is ~2.94 mm/day which is about 9% than the CPC/CMAP rate (2.68 mm/day) and 4% higher than the GPCP rate (2.82 mm/day)'.

Key Strengths

Key Strengths

Provide critical information on the hydrologic cycle and water resources

Key Limitations

Key Limitations

Considerable gaps occur in both satellite- and gauge-based observations

Low intensity and short duration precipitation events tend to be under-sampled

Large uncertainties (~50%) of mean precipitation polewards of 50 degrees in both hemispheres

Other Information

Earth system components and main variables
Type of data product
Dataset collections

Years of record
Data time period extended
Input Data
Vertical Levels:
Missing Data Flag
Ocean or Land
Spatial Resolution
Model Resolution (reanalysis)
Data Assimilation Method
Model Vintage (reanalysis)

Key Publications
Not available...

Summary of precipitation datasets

Name Snapshot Source Domain Years of record Timestep Spatial Resolution Formats Input Data
APHRODITE: Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources Figure 1 (Contributed by A. Yatagai) Hirosaki University Asia to Climatology, Daily 0.25x0.25 , 0.5x0.5 (whole Asia), , 0.05x0.05 (Japan only) netCDF, ascii, binary
CHELSA high-resolution land surface temperature and precipitation CHELSA high resolution comparison  (contributed by D Karger) Swiss Federal Research Institute WSL , University of Hamburg , University of Zurich , University of Goettingen Global to Climatology, Monthly 30 arc sec GeoTIFF

ERA-Interim downscaled with geographic predictors and bias-corrected precip

CHOMPS: CICS High-Resolution Optimally Interpolated Microwave Precipitation from Satellites CICS/ Renu Joseph Global to Daily 0.25x0.25 binary

multiple satellite microwave sensors

CMAP: CPC Merged Analysis of Precipitation CMAP precipitation in January NOAA CPC/Dr. Pingping Xie Global to Monthly, Pentad 2.5 x 2.5 ascii, netCDF

gague analysis; microwave and infrared observations from polar orbiting and geostationary satellites

CMORPH (CPC MORPHing technique): High resolution precipitation (60S-60N) Climate Data Guide Image: CMORPH Climate Prediction Center/R. Joyce, J.Janowiak Global to Sub-daily, Daily 0.25x0.25 netCDF, binary

satellite microwave

COREv2 Air-Sea Surface Fluxes NCAR / Bill Large & Steve Yeager Global to Monthly netCDF
CPC Unified Gauge-Based Analysis of Global Daily Precipitation Climate Data Guide: CPC Unified Guage Based precipitation Climate Prediction Center Global to Daily 0.5x0.5 binary

station gague data

CRU TS Gridded precipitation and other meteorological variables since 1901 Climatic Research Unit (CRU) / Ian Harris, Phil Jones Global to Climatology, Monthly 0.5x0.5 netCDF, ascii

~4000 station records primarily from CLIMAT, Monthly Climatic Data from the World, and World Weather Records

Daymet: Daily Surface Weather and Climatological Summaries Climate Data Guide Image: Daymet U. Montana/Peter Thornton North America to Climatology, Daily 1km x 1km in 2x2 tiles netCDF, ascii
E-OBS: High-resolution gridded mean/max/min temperature, precipitation and sea level pressure for Europe & Northern Africa E-OBS_sample_picture_Daily_Maximum_Temperature (contributed by G van Der Shrier) Copernicus Climate Change Service , Royal Netherlands Meteorological Institute Europe to Daily 0.1 x 0.1 degrees netCDF

station data

GHCN-D: Global Historical climatology Network daily temperatures Distribution of GHCN-D stations (contributed by K. McKinnon) NOAA/ NCEI Global to Daily ascii
Global (land) precipitation and temperature: Willmott & Matsuura, University of Delaware U. Delaware / Cort J. Willmott, Kenji Matsuura Global to Climatology, Monthly 0.5x0.5 degree netCDF, ascii

land stations from GHCNv2 and a few other sources

GPCC: Global Precipitation Climatology Centre Climate Data Guide Image: GPCC precipitation for May,2012 GPCC operated by DWD under the auspices of the World Meteorological Organization (WMO) Global to Climatology, Daily, Monthly 0.5x0.5, 1x1, 2.5x2.5 netCDF, ascii

station gague data

GPCP (Daily): Global Precipitation Climatology Project Zonal-mean rain frequency in GPCC 1dd (contributed by A. Pendergrass) GSFC (NASA): G. Huffmann, D. Bolvin, R. Adler Global to Daily 1x1 netCDF, binary

station rain guage, satellite data

GPCP (Monthly): Global Precipitation Climatology Project Map of significant trends in precipitation, according to GPCP v2.2. (contributed by A. Pendergrass) GSFC (NASA) / G. Huffman, D. Bolvin, R. Adler, JJ Wang Global to Climatology, Monthly 2.5 x 2.5 (monthly) binary, netCDF

rain gauge stations, satellites, and sounding observations

HOAPS: Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data Climate Data Guide Image: HOAPS-3: Annual mean freshwater flux (E-P) for 2005. A. Andersson (DWD), S. Bakan (MPI), K. Fennig (DWD), C. Klepp (Uni. Hamburg) Global to Climatology, Sub-daily, Monthly, Pentad 0.5 x 0.5 netCDF

SSM/I passive microwave radiometers, except for the SST, which is taken from AVHRR

Livneh gridded precipitation and other meteorological variables for continental US, Mexico and southern Canada Figure 1 (contibuted by B. Livneh) Dr. Ben Livneh, University of Colorado North America to Daily, Monthly 1/16 degree (~6 km) netCDF

GHCN-daily; Environment Canada; Servicio Meteorológico Nacional (Mexico); NCEP-NCAR Reanalysis

NASA Energy and Water cycle Study (NEWS) Climatology of the 1st Decade of the 21st Century Dataset NASA/GSFC/HSL; M. Rodell, T. L'Ecuyer, H. Kato Beaudoing Africa, Antarctic, Asia, Atlantic Ocean, Australia, Europe, Indian Ocean, Maritime Continent, North America, South America to Climatology, Monthly, Annual netCDF, ascii, Spreadsheet
NLDAS: North American Land Data Assimilation System Climate Data Guide Image: NLDAS NASA, NOAA, Princeton, U. Washington North America to Climatology, Sub-daily, Monthly 0.125 (224x464) netCDF, GRIB

observations & model

PERSIANN-CDR: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record infographic (contributed by R Cullather) UCI Center for Hydrometeorology & Remote Sensing (CHRS): S. Sorooshian Global to Sub-daily, Daily 0.25x0.25 netCDF, binary

directly: IR satellite, indirectly: NCEP stage IV precipitation data, rain gauge, PMW satellite data

Precipitation Reconstruction Land (PREC/L): 1948-present Climate Data Guide Image: PREC/L NOAA/Chan, Xie, Arkin Global to Monthly 2.5 x 2.5, 1x1, 0.5 x 0.5 netCDF, ascii, binary

GHCN v2 gauge measurements

PRISM High-Resolution Spatial Climate Data for the United States: Max/min temp, dewpoint, precipitation PRISM winter preciptiation in western Colorado. Oregon State Univ (USDA, NOAA)/ C. Daly North America to Climatology, Daily, Monthly 4 km, 800m netCDF, ARC/INFO ASCII GRID
SSM/I, SSMIS: Special Sensor Microwave/Imager and Sounder NASA's Pathfinder Program Global to Daily, Monthly, Weekly 0.25 x 0.25 binary
SSMI Version 7: Ocean Product Suite NASA/RSS Global Sub-daily, Daily, Monthly netCDF

satellite microwave

TerraClimate: Global, high-resolution gridded temperature, precipitation, and other water balance variables 1981-2010 average Dec-February climatic water deficit for Tasmania as represented in TerraClim (contributed by J. Abatzoglou) John Abatzoglou, University of California - Merced Global to Climatology, Monthly ~4 km (1/24th degree) netCDF

WorldClim, CRUTS4.0

TRMM: Tropical Rainfall Measuring Mission TRMM and GPCP (contributed by A. Pendergrass) NASA & Japan's National Space Development Agency Tropics to Sub-daily, Daily, Monthly 0.25x0.x25 , ~40S - 40N and ~50S - 50N HDF, netCDF

satellite microwave and IR; gauge (for calibration)

Tropical Moored Buoy System: TAO, TRITON, PIRATA, RAMA (TOGA) USA, Japan, France, Brazil, Indonesia, China/ M.J. McPhaden, others Tropics to Sub-daily, Daily, Monthly, Weekly netCDF, ascii

Moored Bouy