- GPCC Global Rain Gauge Analysis Data
- 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
- Data Access and Contacts
- FTP Site
- Points of Contact
Precipitation data are main input to global hydrological cycles and climate models. The conventional rain-gauge measurements are the only direct measure of rain-fall. This dataset is comprised of monthly gridded area-mean rainfall totals for the period January 1986 to June 1997 on a 1 by 1 degree global grid.
The original precipitation data set has been produced at Global Precipitation Climatology Centre (GPCC), in an effort to provide global data sets of area-averaged and time-integrated precipitation fields based on surface rain gauge measurements. The GPCC collects monthly precipitation totals received from CLIMAT and SYNOP reports via the World Weather Watch GTS (Global Telecommunication System) of the World Meteorological Organization (WMO ). The GPCC also acquires monthly precipitation data from international/national meteorological and hydrological services/institutions. An interim database of about 6700 meteorological stations is defined. Surface rain-gauge based monthly precipitation data from these stations are analyzed over land areas and a gridded dataset is created (Rudolf, 1996; Rudolf et. al. 1994 ; and Rudolf, 1993), using a spatial objective analysis method.
GPCC is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany) as a contribution to international climate observation and research activities. The Centre participates in international programs and projects such as GEWEX and ACSYS of the World Climate Research Programme, WCP-Water and in the development of GCOS. It is member of the GEWEX Hydrometeorology Panel and a component of the Global Precipitation Climatology Project (GPCP).
The main purpose of the GPCP (for details see WCRP, 1990; WMO ,1985; WMO/ICSU,1990) is to evaluate and provide global gridded data sets of monthly precipitation based on all suitable observation techniques as a basis for:
- verification of climate model simulations,
- investigations of the global hydrological cycle and
- climate change detection studies.
Sponsor
The distribution of this data set is 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 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 investigator Dr. Bruno Rudolf at GPCC , Deutscher Wetterdienst, Germany. Goddard's share in these activities was sponsored by NASA's Earth Science enterprise.
Original Archive
The global land precipitation data set on the 1x1 degree grid in its original format is produced by GPCC . The Goddard DAAC transforms the original dataset to a uniform format for inclusion in its Climate Interdisciplinary Data Collection (CIDC). To maintain the uniform format of the Interdisciplinary data collection the original data (in ascii) is put into binary format. The global land precipitation dataset is also available on 2.5 x 2.5 degree grid from the archive WDC-A, NOAA/NCDC NESDIS/National Climatic Data Center Climate Analysis Center and the Goddard DAAC Hydrology Data Site. By request both the 1x1 and 2.5x2.5 degree grid can also be obtained from Dr. Bruno Rudolf at the GPCC.Future Updates
The Goddard DAAC will update this data set as new data are processed and made available at GPCC.
- Parameters:
pcp: Accumulated surface precipitation in mm/month
obs: Number of stations per grid
err: Systematic error estimates (%) based on Legates climatology (=100 * Legates climatology corrected for systematic error/Legates climatology as measured)
- Temporal Coverage: January 1986 - June 1997
- Temporal Resolution: Monthly Means
- Spatial Coverage: Global Land
- Spatial Resolution: 1 degree x 1 degree
Source
The GPCC land surface precipitation data set is derived from the monthly precipitation totals based on conventional surface raingauge measurements.A large variety of surface rain-gauges are in use world-wide . The geometry and size of these instruments vary considerably (Sevruk, 1982).
Generally, national daily standard-gauges measure precipitation at or near the ground, and are observed at least once a day . The size of these gauges are made big enough to collect more than the average one-day or maximum 1-2 hour precipitation which differs according to various climatic conditions. The standard gauges are also commonly used to measure both rain and snow, and the latter affects fundamentally the form and dimensions of a particular national gauge (snow gauges are bigger). Thus, in countries with negligible snowfall but much rain or where different gauges are used for rain and snow (e.g., Canada), the height of the gauge orifice varies between zero and more than 1 m above the ground. This is defined by country's national standards. It is advantageous if the gauge orifice is small or the collector is shallow with a steep funnel. In both cases, the wetting losses tend to be relatively small. In areas with little snowfall, gauges can be installed so that the rim is near to the ground. This reduces losses from wind and consequently the systematic error. In contrast, in countries with heavy snowfall the gauges are in general large and the collectors deep. Thus the wetting losses for rain tend to be relatively large. In addition, the precipitation gauges in these countries are set high above ground-level and the systematic error for measurement of rain is somewhat greater. In some countries or regions which experience heavy snowfall, the daily standard precipitation gauges are even equipped with windshields or special snow gauges may be used.
The land surface dataset contains global gridded rainfall estimates. 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 over ocean are filled by missing value code ( -999.99).
Compressed:
Uncompressed:
- The precipitation error estimate data files have been compressed using Lempel-Ziv coding. The .bin filename ending has been replaced with the .gz ending, indicating that the files are compressed. When decompressing the files use the -N option so that the original .bin file name ending is restored. aareadme file in the directory:
- software/decompression/
Data Files
- File Size: 259200 bytes, 64800 data values
- Data Format: IEEE floating point notation
- Headers, trailers, and delimiters: none
- Data Missing Code: -999.99
- Image orientation: North to South
- Start position: (179.5W, 89.5N)
- End position: (179.5E, 89.5S)
Name and Directory Information
Naming Convention:
- The file naming convention for the Global land Precipitation Dataset is
- gpcc_gag.pcp.1nmegl.[yymm].ddd
- gpcc_gag.obs.1nmegl.[yymm].ddd
- gpcc_gag.err.1nmegl.[mm].ddd
- where:
- gpcc_gag = data product designator: GPCC gauge analysis
- pcp, obs, err = parameter name: precipitation, number of observations, error estimates
- 1 = number of levels
- n = vertical coordinate, n= not applicable
- m = temporal period, m = monthly
- e = horizontal grid resolution, e = 1 x 1 degree
- gl = spatial coverage, gl = global (land)
- yy = year (xx signifies year independence)
- mm = month
- ddd = file type designation (gz=compressed, bin=binary, ctl=GrADS control file)
NOTE: When decompressing the data files be sure to use the -N option. This will restore the original .bin filename. For additional information on decompression see the format section of this readme and the aareadme file in the directory:
- software/decompression/
Directory Path to Data Files
- For Precipitation estimates and Number of observations:
- /data/hydrology/precip/gpcp/gpcc/yyyy/
where yyyy refers to year.- For error estimates :
- /data/hydrology/precip/gpcp/gpcc/systematic_errors/
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
Theoretical Basis of Data
The algorithm used for estimating the area-average precipitation is calculated from the precipitation-gauge point measurements by using the spatial objective analysis method. Legates(1987) evaluated several interpolation algorithms and Shepard's (1968) empirical weighting scheme was found to be reliable. Since the region of consideration is entire globe, a spherical adaptation of the code (following Willmont et al., 1985 ) was used. This method overcomes some deficiencies typical of a pure distance weighting , first by using only the four to 10 nearest stations, second by clustering near-neighbor measurements (directional weighting), and third by the extrapolation of estimated gradients of the precipitation field to yield extremes not covered by measurements.The Shepard's(1968) weight used is:
W(i) = [S(i) ^ k )] [1 + D(i)]where S(i) is the distance component for station i and D(i) is the directional component. Following Shepard (1968) a value of 2.0 is used for the exponent k. In the analysis, the gauge- measured precipitation is weighted by 1/w(i).
Interpolation for Antarctica is made separately from the interpolation run for the other continents to avoid any influence of far distant stations.
Using this code known as SPHEREMAP, the conventionally measured monthly precipitation depths from about 6700 stations are interpolated on a 0.5 degree grid, and the areal means on a 1x1 degree grid are calculated by averaging the interpolated values( Rudolf et. al. 1994; Rudolf 1993; and Rudolf et. al. 1992).
Processing Sequence and Algorithms
Since the large errors and biases may exist from station to station, the rain-gauge precipitation data are carefully quality controlled. In this process, every effort is made to retain as much data as possible, in part by correcting many obvious errors as described in Schneider, 1993. This procedure avoids inadvertently eliminating useful information in data sparse areas of the globe. First, the monthly precipitation amounts are checked for extreme values and also compared with climatology. Next, the point-measured precipitation data from different sources are intercompared to check for discrepancies. As a last step in the automatic quality-control procedure, the spatial homogeneity of the point-measured monthly precipitation data is checked. Subsequent to these automatic quality-control checks, data flagged as incorrect or questionable during this process are checked manually at a graphics workstation which can display all station-related information (e.g. geographical coordinates, elevation) and overlay topographic fields (such as orography) as background information.The processing steps for the data set are:
- reformatting of the acquired station-related data,
- quality control of the meta-data (station coordinates),
- filling the precipitation point data bank (PDB), i.e. merging the station-related data from different sources (GTS, national data sets, other collections) to one worldwide data set,
- first objective analysis using the uncontrolled precipitation records,
- automatic quality control of the precipitation record (comparison with area means from step 4 and station-related climatic means),
- visual expert quality control and interactive correction with graphical workstation assistance (comparison with climate maps, orographical data, extreme-event-catalogs, etc.),
- second objective analysis using the controlled precipitation data,
- filling the results of the second analysis run (step 7) into precipitation grid data bank (GDB),
- correction of the results from step 8 with regard to systematic measuring errors,
- calculation of the grid-related stochastic error of the precipitation results (step 8) from station density, precipitation variance fields, individual data uncertainties, and uncertainty of the systematic measuring error corrections.
Sources of errors:
Although analyses of conventional rain-gauge measurements are considered to provide the most reliable precipitation information over land areas, they can be affected by different sources of uncertainty, which can be classified into two major error types:
- a methodological component in obtaining area-average precipitation from point measurements depending on the analysis method used ( Bussieres and Hogg, 1989), on the spatial density and on the distribution of the point measurements ( WMO, 1985; Schneider et al., 1993) and
- inaccuracies of the point precipitation measurements themselves.
The second error type consists of two parts, the systematic gauge- measuring error and a random error component. The systematic error generally results in an under measurement of the true precipitation mainly due to wind effects, especially on snowfall, and wetting as well as evaporative losses (Sevruk, 1982; Legates and Willmott, 1990). For rainfall the systematic error is about 5%, whereas for snowfall it can reach 50% or even more. Random errors can be caused by the gauge (e.g., leakage from or damage to the gauge), by the observer (e.g., inaccuracies in reading the instrument) or can be introduced in the course of data processing and transmission (see Groisman and Legates, 1994; Schneider et al., 1994).
The systematic error in the measurement of precipitation is affected by gauge characteristics, such as dimensions, form and material. Differences in the characteristics of various types of gauges complicate the comparison of both precipitation measurements and correction formulae. There is, as yet, no generally accepted theory for the physical nature of the problems associated with precipitation gauges. Consequently, if a correction formula developed for one type of gauge is to be used for another, special field and/or laboratory investigations are required. In each case, a review is made of the results of comparisons made elsewhere together with an examination of the gauges involved.
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:
- Initialization and validation of mesoscale and large-scale general circulation models ( Hulme, 1992)
- Verification of monthly satellite based precipitation estimates (Janowiak, 1992)
- Input fields in global hydrological studies ( Lapin, 1994)
- Simulations of the present-day climate and forecasting of global climate (Krishnamurti et al., 1994)
- Correlation studies, especially during transient events or periodic events such as El Nino ( Nicholls, 1988)
- changes and design of culverts and stream channels (Rosenzweig and Parry, 1994 )
Validation of Data
The rain-gauge analyses (on the 2.5 degree grid) have been intercompared to different precipitation climatologies, to satellite-based precipitation estimates derived from IR and microwave images and to results accumulated from daily forecasts of the operational weather prediction model of ECMWF as global, continental and zonal averages, as difference fields and in regression analyses.These case studies indicated that at least 2 to 8 stations per 2.5 degree grid (depending on orographic and climatological conditions in the grid) are required to estimate area- average precipitation with a relative error of less than 10% ( Schneider et al., 1993). An assessment of the other error components is in preparation (Schneider et al., 1994).
The rain-gauge measurements have not been corrected for the systematic gauge-measuring error (in general an under estimation of the true precipitation by about 10% on global average).
Points of Contact
For information about or assistance in using any DAAC data, contact
- EOS Distributed Active Archive Center (DAAC)
- Code 902.2
- NASA Goddard Space Flight Center
- Greenbelt, Maryland 20771
- Internet: daacuso@daac.gsfc.nasa.gov
- 301-614-5224 (voice)
- 301-614-5268 (fax)
- To inquire about or order the original GPCC precipitation data set, contact
- Dr. Bruno Rudolf
- Global Precipitation Climatology Centre (GPCC)
- at Deutscher Wetterdienst
- Frankfurter Str. 135
- D-63067 Offenbach/Main
- Germany.
- email: brudolf@dwd.d400.de
- Tel: 49-69-8062-2765
- Fax: 49-69-8062-2880
Bussieres, N., and W.D. Hogg, 1989. The objective analysis of daily rainfall by distance weighting schemes on a meso-scale grid. Canadian Meteorol. and Oceanographic Society, Atmosphere-Ocean, 27(3):521-541.
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.
Groisman, P.Y., and D.R. Legates 1994. The accuracy of United States precipitation data. Bull. Amer. Met. Soc., 75(2): 215-227.
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.
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.
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., C.J. Willmott, 1990. Mean seasonal and spatial variability in gauge-corrected global precipitation. Internat. J. Climatol., 9:111-127.
Legates, D.R., 1987. A climatology of global precipitation. Publ. in Climatology, 40 (1), Newark, Delaware, 85 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., H. Hauschild, W. Rueth, and U. Schneider, 1994. Terrestrial Precipitation Analysis: Operational Method and Required Density of Point Measurements. Global Precipitations and Climate Change, M. Desbois and F. Desalmand, Eds., NATO ASI Series, Vol. 1, No. 26, Springer-Verlag, 173-186.
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.
Rudolf, B., H. Hauschild, M. Reiss, U. Schneider, 1992. Beitraege zum Weltzentrum fuer Niederschlagsklimatologie - Contributions to the Global Precipitation Climatology Centre. Meteorol. Zeitschrift N.F. , 1(1):7-84 (In German, with Abstracts and Summary in English).
Schneider, U., W. Rueth, B. Rudolf, 1994. Estimating the error-range associated with area-average monthly precipitation analyzed from rain-gauge measurements on a global scale. In preparation.
Schneider, U., 1993. The GPCC quality-control system for gauge-measured precipitation data. In: Report of a GEWEX workshop "Analysis methods of precipitation on a global scale", Koblenz, Germany, September 1992, WCRP-81, WMO/TD-No. 558, June 1993, A5-A7.
Schneider, U., B. Rudolf, W. Rueth, 1993. The spatial sampling error of areal mean monthly precipitation totals analyzed from gauge-measurements. Proc. 4th Internat. Conf. on Precipitation "Hydrological and meteorological aspects of rainfall measurement and predictability", Iowa City, Iowa, April 1993, pg. 80-82.
Sevruk, B., 1982. Methods of correction for systematic error in point precipitation measurement for operational use. Operational Hydrology ,Rep.-No. 21, World Meteorological Organization, Geneva, WMO Rep.-No.589, 91 pp.
Shepard, D., 1968. A two-dimensional interpolation function for irregularly spaced data. Proc. 23rd ACM Nat. Conf., Brandon/Systems Press, Princeton, NJ, 517-524.
Willmott, C.J., Rowe, C.M., Philpot, W.D. (1985): Small-Scale Climate Maps: A Sensitivity Analysis of Some Common Assumptions Associated with Grid-Point Interpolation and Contouring. The American Cartographer, 12(1), 5-16.
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.
WCRP, 1990. The Global Precipitation Climatology Project - Implementation and Data Management Plan. WMO/TD-No. 367, Geneva, June 1990, 47 pp. and appendices.
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Last update:Wed Oct 1 12:54:16 EDT 1997