Climate Data

Precipitation Data Sets: Overview & Comparison table

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:

  • Provide critical information on the hydrologic cycle and water resources

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
  1. Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E. Trenberth, 2016: Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales. Journal of Climate, 29, 7773–7795,
  2. Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K. Hsu, 2018: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. Rev. Geophys., 56, 79–107,
  3. Adler, Robert F., Guojun Gu, George J. Huffman, 2012: Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP). J. Appl. Meteor. Climatol., 51, 84–99.
  4. Behrangi, A. et al (2014): An Update on Oceanic Precipitation Rate and Its Zonal Distribution in Light of Advanced Observations from Space. J. Climate, 27(11)
  5. Bosilovich, Michael G., Junye Chen, Franklin R. Robertson, Robert F. Adler, 2008: Evaluation of Global Precipitation in Reanalyses. J. Appl. Meteor. Climatol., 47, 2279–2299
  6. Huffman, G. J. (2006): Satellite-Based Estimation of Precipitation Using Microwave Sensors. Encyclopedia of Hydrological Sciences
  7. Kidd, C., P. Bauer, J. Turk, G. J. Huffman, R. Joyce, K.-L. Hsu, D. Braithwaite, 2012: Intercomparison of High-Resolution Precipitation Products over Northwest Europe. J. Hydrometeor, 13, 67–83.
  8. Maggioni, V. et al (2014): An Error Model for Uncertainty Quantification in High-Time Resolution Precipitation Products. J. Hydrometeor., 15(3)
  9. Sapiano, M. R. P., P. A. Arkin, 2009: An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data. J. Hydrometeor, 10, 149–166.
  10. Trenberth, K. E., A. Dai, G. van der Schrier, P. D. Jones, J. Barichivich, K. R. Briffa, and J. Sheffield^, 2014: Global warming and changes in drought. Nature Climate Change, 4, 17-22
  11. Trenberth, K. (2011): Changes in Precipitation with Climate Change. Clim. Res. 47: 123-138
  12. Wang, J-J et al (2014): An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation. J. of Climate, 27(1), 273-284

Cite this page

National Center for Atmospheric Research Staff (Eds). Last modified 03 Aug 2020. "The Climate Data Guide: Precipitation Data Sets: Overview & Comparison table." Retrieved from

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