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GPCP Global Combined Precipitation Data
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Readme Contents

Data Set Overview
Sponsor
Original Archive
Future Updates

The Data
Characteristics
Source

The Files
Format
Name and Directory Information
Companion Software

The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data

Contacts
Points of Contact

References

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Data Set Overview

This global precipitation dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit satellite infrared data, and rain gauge observations. The dataset is comprised of monthly gridded area-mean rainfall totals and error estimates, for the period covering July 1987 to December 1997. For consistency with the other datasets in the Goddard DAAC's Climate Interdisciplinary Data Collection (CIDC), the original 2.5x2.5 degree gridded precipitation data received from NOAA National Climate Data Center is regridded to 1x1 degree grid. The original dataset is formally referred to as the "GPCP Version 1a Combined Precipitation Data Set", which is often abbreviated to "GPCP Combined Data Set" or "Version 1a Data Set". It has been produced for the Global Precipitation Climatology Project(GPCP), an international effort organized by GEWEX/WCRP/WMO to provide an improved long-term precipitation record over the globe(for details see WMO ,1985; and WMO/ICSU,1990) with the purpose of evaluating and providing global gridded data sets of monthly precipitation based on all suitable observation techniques as a basis for: Sponsor
The production and distribution of this data set are being funded by NASA's Earth Science enterprise. The data are not copyrighted; however, we request that when you publish data or results using these data please acknowledge as follows:

The authors wish to thank the Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, MD, 20771, for producing the data in its present format and distributing them. The original data products were produced by the science investigators Dr. George Huffman and Dr. Robert Adler of Laboratory of Atmospheres, Code 912, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771 USA, as the Global Precipitation Climatology Project (GPCP) Merge Development Centre, and is archived at World Data Center A (WDC-A) for Meteorology at the National Climate Data Center (NCDC) in Asheville, North Carolina. Goddard DAAC's share in these activities was sponsored by NASA's Earth Science enterprise.

Original Archive
The original combined version 1a precipitation data along with intermediate products (on 2.5 x 2.5 degree grid) and detailed document is currently available from the archive WDC-A, NOAA National Climatic Data Center (NCDC) . The original dataset including the precipitation estimates from individual input fields (microwave and infrared satellite estimates, their combinations, and rain gauge analysis) is also available through our Hydrology Data collection site. The anonymous FTP site for GPCP v1a Combined Precipitation is ftp://daac.gsfc.nasa.gov/data/hydrology/precip/gpcp/gpcp_v1a_combined.

Future Updates
The Goddard DAAC will update this data set as new data are processed and made available at NCDC.

The Data

Characteristics

Source
This GPCP global combined precipitation data on 1x1 degree grid is derived from the original GPCP Version 1a Combined Precipitation Data Set (Huffman,1997b) which contains the final product satellite and rain gauge merged precipitation estimates as well as the intermediate products (the individual input fields such as Infrared Geosynchronous Precipitation Index (GPI), Special Sensor Microwave/Imager (SSM/I), and rain gauge estimates, their combinations, and error estimates) as supporting information on a 2.5 degree by 2.5 degree grid for the period July 1987 to June 1997.

The input fields for producing the GPCP version 1a combined product has been provided to the GPCP Merge Developement Center (Huffman et al., 1995) by following GPCP participating institutions:

GPCP Polar Satellite Precipitation Data Center (SSM/I emission estimates)
NOAA Office of Research and Application (SSM/I scattering estimates)
GPCP Geostationary Satellite Precipitation Data Center (GPI estimates)
GPCP Global Precipitation Climatology Centre (rain gauge analyses)

These individual data sets, as well as the combinations based on them are contained in the original version 1a data set.

The Files

The global combined precipitation data set contains global gridded rainfall estimates. There are two files for each month of the data. One file is the the satellite and gauge merged precipitation estimates and the other file contains the error estimates in the precipitation for that month. Data in each file progresses from North to South and from West to East beginning at 180 degrees West and 90 degrees North. Thus first point represents the grid cell centered at 89.5 degree North and 179.5 West. Grids with missing values are filled with missing value code ( -99.99).

Format

Data Files

Name and Directory Information

Naming Convention:

The file naming convention for the GPCP Global Combined Precipitation Dataset is

gpcp_v1a.psg.1nmegg.[yymm].ddd (precipitation values)
gpcp_v1a.esg.1nmegg.[yymm].ddd (error estimates)
where:
gpcp_v1a = data product designator
psg(or esg) = parameter name: precipitation(or error)satellite-gauge
1 = number of levels
n = vertical coordinate, n= not applicable
m = temporal period, m = monthly
e = horizontal grid resolution, e = 1 x 1 degree
go = spatial coverage, gg = global (land & ocean)
yy = year
mm = month
ddd = file type designation, (bin=binary, ctl=GrADS control file)

Directory Path to Data Files

/data/inter_disc/hydrology/precip/gpcp/gpcp_v1a_cmb/yyyy/
where yyyy refers to year.

Companion Software
Several software packages have been made available on the CIDC CD-ROM set. The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is currently in use worldwide for the analysis and display of earth science data. GrADS meta-data files (.ctl) have been supplied for each of the data sets. A GrADS gui interface has been created for use with the CIDC data. See the GrADS document for information on how to use the gui interface.

Decompression software for PC and Macintosh platforms have been supplied for datasets which are compressed on the CIDC CD-ROM set. For additional information on the decompression software see the aareadme file in the directory:

software/decompression/

Sample programs in FORTRAN, C and IDL languages have also been made available to read these data. You may also acquire this software by accessing the software/read_cidc_sftwr directory on each of the CIDC CD-ROMs

The Science
Theoretical Basis of Data
Knowledge of the spatial and temporal distribution of large scale precipitation is required in the study of climate change. Spatial distribution of the precipitation identifies the regions of maximum latent heat release which is a major driving force of the atmospheric circulation. The precipitation estimates are available from different satellite and surface observations. However, each source has strengths and weaknesses. The geostationary infrared observations provide good temporal resolution and diurnal coverage of precipitation systems. However, the relation between infrared radiance and instantaneous surface precipitation is relatively weak and useful primarily for deep convective systems in the 40 deg N-S latitude zone. The SSM/I microwave radiances have strong connection with surface rainfall, especially over the ocean, and are useful to much higher latitudes. However, the SSM/I observations have poor temporal sampling. Surface rain-gauge measurements are accurate but mostly limited to land areas. Recognizing such shortcomings, World Climate Research Programme (WCRP 1986) initiated the Global Precipitation Climatology Project (GPCP) with the goal of the production of an improved long-record estimates of precipitation over the globe from the blend of the various satellite and surface precipitation estimates(Huffman et al. 1997).

The GPCP sponsored several Algorithm Intercomparison Projects (referred to as AIP-1, AIP-2, and AIP-3) for the purpose of evaluating and intercomparing a variety of satellite precipitation estimation techniques. As well, the NASA Wetnet Project has sponsored several such projects (referred to as Precipitation Intercomparison Projects, and labeled PIP-1, PIP-2, and PIP-3). One use of these projects has been to identify competitive techniques for use in the GPCP combined data set. Various groups in the international science community are given the tasks of preparing precipitation estimates from individual data sources, then the GPCP Merge Development Centre (GMDC), located at NASA Goddard Space Flight Center in the Laboratory for Atmospheres is charged with combining these into a "best" global product. The satellite-gauge precipitation product of the GPCP Version 1a Combined Precipitation Data Set is the "final" blended precipitation estimate produced by an algorithm developed by Huffman et al.(1995) at GMDC, NASA/GSFC. Only a few similar data sets are available. The earlier combined precipitation data set produced by the GPCC is superseded by the Version 1a Data Set, produced at NASA/GSFC. The combination data set by Xie and Arkin (1996) uses similar input data and has similar temporal and spatial coverage, but is carried out with a much different technique.

Processing Sequence and Algorithms
The algorithm used by Huffman et al.(1995) at GMDC, NASA/GSFC, for estimating the area-average precipitation first produces a multi-satellite precipitation product based on a merged analysis using all available satellite estimates and then finally combining the multi-satellite analysis with rain-gauge analysis.

In the first step preliminary combinations and adjustments are made. Microwave measurements are used to adjust the IR based GPI and form the Adjusted Geosynchronous Precipitation Index (AGPI) in the latitude belt 40 deg N-S. The geosynchronous meteorological satellites give three hourly temporal coverage but their sensors detect high cold clouds which are normally associated with rain storms. The association with rain amounts is statistical and is only reasonably accurate at mid and low latitudes ((Arkin and Meisner,1987; Arkin et al. 1994). In addition geosynchronous measurements are not available for all longitudes at all times. Microwave sensors on lower sun synchronous satellites detect the rain directly although determining the rain amounts is still a difficult art. In the GPCP Version 1a Combined product two microwave rain algorithms are used, one for ocean regions based on emission (Chang et al., 1995; Wilheit et al., 1991)) and one for land based on scattering ( Ferraro, et al., 1994; Weng and Grody, 1994, Grody 1991). The microwave measurements cover the entire globe but at a lower temporal and spatial resolution. The AGPI has the (usually low) bias of the microwave measurements together with the smoothness and temporal coverage of the geosynchronous IR measurements. In addition a microwave composite precipitation product is formed by combining the SSM/I emission estimate over water and the SSM/I scattering estimate over land. Since the emission technique eliminates land-contaminated pixels individually, a weighted transition between the two results is computed in the coastal zone.

In the second step, the various satellite data sets are merged to produce a best global satellite estimate. AGPI estimates are taken where available in the latitudes 40 deg N-S belt. Where these are missing in this belt, a weighted combination of the SSM/I composite estimate and the microwave-adjusted low-orbit IR are inserted. The combination weights are the inverse (estimated) error variances of the respective estimates. Such weighted combination of microwave and microwave-adjusted low-orbit IR is done because the low-orbit IR lacks the sampling to warrant the AGPI adjustment scheme. At higher latitudes the SSM/I composite values are used since IR estimates become less accurate at high latitudes.

The rain gauge precipitation product is produced by the Global Precipitation Climatology Centre (GPCC) under the direction of B. Rudolf, located in the Deutscher Wetterdienst, Offenbach A.M., Germany (Rudolf 1993,1996). Rain gauge reports are archived from about 6700 stations around the globe, both from Global Telecommunications Network reports, and from other regional or national data collections. An extensive quality-control system is run, featuring an automated step and then a manual step designed to retain legitimate extreme events that typify precipitation. A variant of the SPHEREMAP spatial interpolation routine (Willmott et al. 1985) is used to analyze station values to area averages. The analyzed values have been corrected for systematic error following Legates (1987).

The final product is blend of multi-satellite and rain-gauge estimates.

Final Combined Multi-Satellite and Gauge Precipitation Product:

The satellite-gauge precipitation product is produced as part of the GPCP Version 1a Combined Precipitation Data Set by the GPCP Merge Development Center 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 but only 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.

Missing Value Estimation:

There is generally no effort to "estimate missing values" in the single-source data sets, although a few missing days of gauge data are tolerated in computing monthly values.

However, two cases of missing data are considered while computing the "AGPI coefficients". First, when SSM/I data are missing in a region, but GPI data exist, the coefficients are smoothly filled across the blank. Second, when low-orbit IR data are used to fill holes in the geosynchronous-orbit IR data, the low-orbit IR data are used to estimate a smoothed AGPI. Specifically, the ratio of the AGPI and the GPI computed from low-orbit IR data is computed around the edge of the hole, the ratio is smoothly filled across the hole, and the ratio is multiplied by the low-orbit GPI at each point in the hole.

Error Estimation:

The "absolute error variable" is produced as part of the GPCP Version 1a Combined Precipitation Data Set by the GPCP Merge Development Center. 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 

      H * ( rbar + S) * [ 720 + 268 * SQRT ( rbar ) ]
VAR = -----------------------------------------------                (1)   
                         Ni

for absolute 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/mo, 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 (1) 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 Surface Reference Data Center (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. Table 1 shows the numerical values of H and S which are used to estimate random error for various precipitation estimates.

All absolute error fields have been converted from their original units of mm/mo to mm/d.

                         Table 1.

                    H and S constants 
                                
                       |    S    |
  Technique            | (mm/mo) |        H
  ---------------------+---------+-----------------------
                       |         |     
  SSMI Emission [se]   |   30    |  3.25 (55 km images)
                       |         |     
  SSMI Scattering [ss] |   30    |  4.5 (55 km images)
                       |         |     
  AGPI [ag]            |   20    |  0.6 (2.5 deg images)
                       |         |     
  Rain Gauge [ga]      |    6    |  0.005 (gauges)

Quality and Confidence Estimates:

The "accuracy" of the precipitation products can be broken into systematic departures from the true answer (bias) and random fluctuations about the true answer (sampling), as dicussed in Huffman (1997a). The former are the biggest problem for climatological averages, since they will not average out. However, on the monthly time scale the low number of samples tends to present a more serious problem. That is, for most of the data sets the sampling is spotty enough that the collection of values over one month is not yet representative of the true distribution of precipitation.

Accordingly, the "random error" is assumed to be dominant, and estimates are computed as discussed for the "absolute error variable". Note that the rain gauge analysis' random error is just as real as that of the satellite data, even if somewhat smaller. Random error cannot be corrected.

The "bias error" is not corrected in the SSM/I emission, SSM/I scattering, SSM/I composite, and GPI precipitation estimates. In the AGPI the GPI is adjusted to the large-scale bias of the SSM/I, which is assumed lower than the GPI's. As noted in the "satellite-gauge precipitation product" discussion, the Multi-Satellite product is adjusted to the large-scale bias of the Gauge analysis before the combination is computed. It continues to be the case that biases over ocean are not corrected by gauges in the Multi-Satellite and Satellite-Gauge products. 

  Four types of "known errors" are contained in part or all of the current 
  data set, and will be corrected in a future general re-run.  They have 
  been uncovered by visual inspection of the combined data fields over 
  several years of production, but are considered too minor or insufficiently 
  understood to provoke an immediate reprocessing.
  
  1. Limit checks on sea ice contamination in the SSM/I emission estimates
     have been refined as additional cases are uncovered.  The 1997 fields
     should be noticably cleaner.
  2. The climatological bias correction to the gauge data was capped at
     a maximum multiplier of 3, starting in 1997.  A few isolated areas in
     snowy regions had higher values, particularly in Antarctica and 
     Siberia.
  3. Exact-zero values in marginally snowy land regions (from the SSM/I 
     scattering field) are probably not reliable, and should simply be
     "small."
  4. Isolated exact-zero values surrounded by significantly non-zero values
     (i.e., >30 mm/mo) in oceanic regions are not reliable.  Starting in
     1997 they are replaced with the average of the surrounding points (but
     none actually occured in the first 6 months of 1997).

Additional Processing

GPCP Version 1a Combined Precipitation Data on 2.5x2.5 degree grid (array dimension 144x72) has been remapped to 1x1 grid (array dimension 360x180). The following steps were performed in the regridding process:

  1. Starting with the first latitude band in the original data set (87.5N to 90N), the first pair of grid cells (total of 5 degrees in longitude) was partitioned into five cells each of width 1 degree; cells 1 and 2 were assigned the value of the first 2.5 degree cell, cells 4 and 5 the value of the second 2.5 degree cell, and cell 3 the arithmetic average of the values of the first and second 2.5 degree cells.

  2. In Step 1, if either (but not both) of the original 2.5 degree cells is a fill value, then no average is performed and cell 3 is assigned the value of the unfilled 2.5 degree cell. If both of the original cells are fill values, then cell 3 is likewise assigned this fill value.

  3. Steps 1 and 2 were repeated for the remaining 71 pairs of 2.5 grid cells in the original data set

  4. Steps 1 through 3 were performed for the remaining 71 latitude bands in the original data set to arrive at a temporary array of size 360 x 72 (1 degree longitude by 2.5 degrees latitude)

  5. The entire procedure above was repeated in the latitudinal direction using the same grid cell partitioning scheme to arrive at the final 360 x 180 (1 degree longitude by 1 degree latitude) array.

  6. The regridded data were visually examined to ensure consistency with the original data.

Scientific Potential of Data
The spatial distribution of precipitation identifies the regions of maximum latent heat release which is a major driving force of the atmospheric circulation. The Observed precipitation data need to be temporarily and spatially integrated (e.g. monthly mean on a grid area) if it is to be used for the assessment of the earth's energy, water balance, and monitoring of short-term climate variability and long-term trends (Hauschild et al., 1994).

Some of the main applications of these precipitation data sets are:

Validation of Data
An early validation against the Surface Reference Data Center analysis yields the statistics in Table 2. Overall, the combination appears to be working as expected. 

                             
                                Table 2
                                
       Summary statistics for all cells and months comparing the 
  SSM/I composite, Multi-satellite, Gauge, and Satellite-gauge products 
                         to the SRDC analysis for July 1987 -- December 1991.

                  |  Bias   |Avg of|Diff|| RMS Error
  Product         | (mm/mo) |  (mm/mo)   |  (mm/mo)
  ----------------+---------+------------+----------
                  |         |            |
  SSM/I composite |  4.03   |   60.10    |   88.05
                  |         |            |
  Multi-satellite | -5.80   |   44.20    |   62.47
                  |         |            |
  Gauge (GPCC)    |  6.77   |   18.85    |   35.11
                  |         |            |
  Satellite-gauge |  3.70   |   20.29    |   32.98

The "quality index" variable has recently been proposed by Huffman et al. (1997) and developed in Huffman (1997a) as a way of comparing the errors computed for different techniques. Absolute error tends to zero as the average precipitation tends to zero, while relative error tends to infinity. According to (1), the dependence is approximately SQRT(rbar) and 1/SQRT(rbar), respectively. Thus, it is hard to illustrate overall dependence on sample size with either representation. However, if one inverts (1) it is possible to get an expression for a number of samples as a function of precipitation rate and the estimated error variance:

      Hg * ( rbarx + Sg) * [ 720 + 268 * SQRT ( rbarx ) ]
Neg = ---------------------------------------------------             (2)     
                          VARx

where rbarx and VARx are the precipitation rate and estimated error variance for technique X, Hg and Sg are the values of H and S for the gauge analysis, and Neg is the number of "equivalent gauges," an estimate of the number of gauges that corresponds to this case. Tests show that Neg is well-behaved over the range of rbar, largely reflecting the sampling that provided rbarx and VARx, but also showing differences in the functional form of absolute error over the range of rbar for different techniques.

Qualitatively, higher Neg denotes more confident answers. Values above 10 are relatively good. The SSM/I composite estimates tend to have Neg around 1 or 2, while the AGPI has Neg around 3 or 4. The rain gauge analysis runs the whole range from 0 to a few grid boxes in excess of 40.

Contacts
Points of Contact
For information about or assistance in using any DAAC data, contact

EOS Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)

The original GPCP Combined Precipitation Data Set(on 2.5 by 2.5 degree grid) can be accessed from the Goddard DAAC via this document GPCP v1a Combined Precipitation Data (Binary data files)

or via FTP

ftp daac.gsfc.nasa.gov
login: anonymous
password: < your internet address >
cd /data/hydrology/precip/gpcp/gpcp_v1a_combined

or contact NCDC Archive

Dr. Alan McNab
World Data Center A (WDC-A)
National Climate Data Center (NCDC)
Rm 514
151 Patton Ave.
Asheville, NC 28801-5001 USA
Internet:amcnab@ncdc.noaa.gov
704-271-4592 (voice)
704-271-4328 (fax)

For algorithm questions related to original data, please contact the data producers:

Dr. George J. Huffman
Code 912
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Internet: huffman@agnes.gsfc.nasa.gov
301-286-9785 (voice)
301-286-1762 (fax)

and

Dr. Robert Adler
Code 912
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Internet: Adler@agnes.gsfc.nasa.gov
301-286-9086 (voice)
301-286-1762 (fax)

References
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.

Arkin, P. A., R. Joyce, and J. E. Janowiak, 1994: IR techniques: GOES precipitation index, Remote Sens. Rev., 11, 107-124.

Arkin, P.A., and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982-1984. Mon. Wea. Rev., 115, 51-74.

Chang, A. T., L. S. Chiu, and G. Yang, 1995: Diurnal cycle of oceanic precipitation from SSM/I data. Mon. Wea. Rev., 123, 3371-3380.

Ferraro, R. R., N. C. Grody, and G. F. Marks, 1994: Effects of surface conditions on rain identification using the SSM/I. Remote Sens. Rev., 11, 195-209.

GPCC, 1993. Global area-mean monthly precipitation totals for the year 1988 (preliminary estimates, derived from rain-gauge measurements, satellite observations and numerical weather prediction results). Ed. by WCRP and Deutscher Wetterdienst, Rep.-No. DWD/K7/WZN-1993/07-1, Offenbach, July 1993.

GPCC, 1992. Monthly precipitation estimates based on gauge measurements on the continents for the year 1987 (preliminary results) and future requirements. Ed. by WCRP and Deutscher Wetterdienst, Rep.-No. DWD/K7 WZN-1992/08-1, Offenbach, August 1992.

Grody, N.C., 1991: Classification of snow cover and precipitation using the Special Sensor Microwave/Imager (SSM/I). J. Geophys. Res., 96, 7423-7435.

Hauschild, H., M. Reis, and B. Rudolf, 1994 . Global and terrestrial precipitation climatologies: An overview and some intercomparisons. Global Precipitations and Climate Change, M. Desbois and F. Desalmand, Eds., NATO ASI Series, Vol. 1, No. 26, Springer-Verlag, 419-434.

Huffman, G.J., 1997a: Estimates of root-mean-square random error contained in finite sets of estimated precipitation. J. Appl. Meteor., 36, 1191-1201.

Huffman, G.J., ed., 1997b: The Global Precipitation Climatology Project monthly mean precipitation data set. WMO/TD No. 808, WMO, Geneva, Switzerland. 37pp.

Huffman, G.J., R.F. Adler, P.A. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, R.J. Joyce, A. McNab, B. Rudolf, U. Schneider, and P. Xie, 1997: The Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set. Bull. Amer. Meteor. Soc., 78, 5-20.

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.

Hulme, M., 1992. A 1951-80 global land precipitation climatology for the evaluation of General Circulation Models, Climate Dynamics, 7, 57-72.

Janowiak, J. E.,1992: Tropical rainfall: A comparison of satellite-derived rainfall estimates with model precipitation forecasts, climatologies and observations. Mon. wea. Rev.,120, 448-462.

Janowiak, J.E., and P.A. Arkin, 1991: Rainfall variations in the tropics during 1986-1989. J. Geophys. Res., 96, 3359-3373.

Krishnamurti, T.N., G.D. Rohaly, and H. S. Bedi, 1994: Improved precipitation forecast skill from the use of physical initialization.Global Precipitations and Climate Change, M. Desbois and F. Desalmand, Eds., NATO ASI Series, Vol. 1, No. 26, Springer-Verlag, 309-324.

Lapin, M., 1994 . Possible impacts of climate change upon the water balance in central Europe Global Precipitations and Climate Change, M. Desbois and F. Desalmand, Eds., NATO ASI Series, Vol. 1, No. 26, Springer-Verlag, 161-170.

Legates, D. R, 1987: A climatology of global precipitation. Pub. in Climatol., 40, U. of Delware.

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.

Nicholls, N., 1988. El Nino-Southern Oscillation and rainfall variability. J. Climate, 1:418-421.

Rosenzweig, C., and M.L. Parry, 1994. Potential impact of climate change on world food supply, Nature, 367, 133-138.

Rudolf, B., 1996. Global Precipitation Climatology Center activities. GEWEX News, vol. 6, No. 1.

Rudolf, B., 1993. Management and analysis of precipitation data on a routine basis. Proc. Internat. WMO/IAHS/ETH Symp. on Precipitation and Evaporation. Slovak Hydrometeorol. Inst., Bratislava, Sept. 1993, (Eds. M. Lapin, B. Sevruk), 1:69-76.

Weng, F., and N.C. Grody, 1994: Retrieval of cloud liquid water using the Special Sensor Microwave Imager (SSM/I). J. Geophys. Res., 99, 25535-25551.

Wilheit, T., A. Chang and L. Chiu, 1991: Retrieval of monthly rainfall indices from microwave radiometric measurements using probability distribution function. J. Atmos. Ocean. Tech., 8, 118-136.

Willmott, C.J., C.M. Rowe, and W.D. Philpot, 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Amer. Cartographer, 12, 5-16.

WCRP, 1986: Report of the workshop on global large scale precipitation data sets for the World Climate Research Programme. WCP-111, WMO/TD - No. 94, WMO, Geneva, 45 pp.

WMO/ICSU ,1990: The Global Precipitation Climatology Project - Implementation and Data Management Plan. WMO/TD-No. 367, Geneva, June, 1990.

WMO , 1985. Review of requirements for area-averaged precipitation data, surface-based and space-based estimation techniques, space and time sampling, accuracy and error; data exchange. WCP-100, WMO/TD-No. 115, 57 pp. and appendices.

Xie, P., and P.A. Arkin, 1996: Analysis of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. J. Climate, 9, 840-858.

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