Global Precipitation Climatology Project (GPCP) Version 2 Combined Precipitation Data Set Comments

Comments on the data:

The *satellite-gauge precipitation product* is produced as part of the GPCP Version 2 Combined Precipitation Data Set by the GPCP Merge Development Centre in two steps (Huffman et al. 1995). First, the multi-satellite estimate is adjusted toward the large-scale gauge average for each grid box over land. That is, the multi-satellite value is multiplied by the ratio of the large-scale (5x5 grid-box) average gauge analysis to the large-scale average of the multi-satellite estimate. Alternatively, in low-precipitation areas the difference in the large-scale averages is added to the multi-satellite value when the averaged gauge exceeds the averaged multi-satellite. In the second step, the gauge-adjusted multi-satellite estimate and the gauge analysis are combined in a weighted average, where the weights are the inverse (estimated) error variance of the respective estimates.

The *absolute random error variable* is produced as part of the GPCP Version 2 Combined Precipitation Data Set by the GPCP Merge Development Centre . Following Huffman (1997a), bias error is neglected compared to random error (both physical and algorithmic), then simple theoretical and practical considerations lead to the functional form

absolute random error forumla

for absolute random error, where VAR is the estimated error variance of an average over a finite set of observations, H is taken as constant (actually slightly dependent on the shape of the precipitation rate histogram), rbar is the average precipitation rate in mm/d, S is taken as constant (approximately SQRT(VAR) for rbar=0), Ni is the number of INDEPENDENT samples in the set of observations, and the expression in square brackets is a parameterization of the conditional precipitation rate based on work with the Goddard Scattering Algorithm, Version 2 (Adler et al. 1994) and fitting of the equation to the Surface Reference Data Center analyses (McNab 1995). The "constants" H and S are set for each of the data sets for which error estimates are required by comparison of the data set against the SRDC and GPCC analyses and tropical Pacific atoll gauge data (Morrissey and Green 1991). The computed value of H actually accounts for multiplicative errors in Ni and the conditional rainrate parameterization (the [] term), in addition to H itself. The table shows the numerical values of H and S. All absolute random error fields have been converted from their original units of mm/mo to mm/d.

Table. Numerical values of H and S constants used to estimate absolute error for various precipitation estimates.

Technique S (mm/d) H
SSMI Emission [se] 1 3.25 (55 km images)
SSMI Scattering [ss] 1 4.5 (55 km images)
TOVS [tv] 1 0.0045
OPI [op] 1 0.0045
AGPI [ag] 20 0.6 (2.5 deg images)
Rain Gauge [ga] 6 0.005 (gauges)

For the independent data sets rbar is taken to be the independent estimate of rain itself. However, when these errors are used in the combination, theory and tests show that the result is a low bias. Rbar needs to have the same value in all the error estimates; so we estimate it as the simple average of all rainfall values contributing to the combination. Note that this scheme is only used in computing errors used in the combination. The formalism mixes algorithm and sampling error, and should be replaced by a more complete method when additional information is available from the single-source estimates. However, W. Krajewski et al. (2000) developed and applied a methodology for assessing the expected random error in a gridded precipitation field. Their estimates of expected error agree rather closely with the errors estimated for the multi-satellite and satellite-gauge combinations.

See also the Official GPCP v.2 documentation.


  • Adler, R.F., G.J. Huffman, and P.R. Keehn 1994: Global rain estimates from microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11, 125-152.
  • Huffman, G.J., R.F. Adler, B. Rudolf, U. Schneider, and P.R. Keehn, 1995: Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information. J. Climate, 8, 1284-1295.
  • Huffman, G.J., 1997a: Estimates of root-mean-square random error contained in finite sets of estimated precipitation. J. Appl. Meteor., 36, 1191-1201.
  • Krajewski, W.F., G.J. Ciach, J.R. McCollum, and C. Bacotiu, 2000: Initial validation of the Global Precipitation Climatology Project over the United States. J. Appl. Meteor., 39, 1071-1087.
  • McNab, A., 1995: Surface Reference Data Center Product Guide. National Climatic Data Center, Asheville,NC, 10 pp.
  • Morrissey, M.L., and J. S. Green, 1991: The Pacific Atoll Raingauge Data Set. Planetary Geosci. Div. Contrib. 648, Univ. of Hawaii, Honolulu, HI, 45 pp.