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[CIDC FTP Data]
[GPCC PCP IDC Data on FTP]
Data Access
GPCC Global Rain Gauge Analysis Data
[rule]
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
Data Access and Contacts
FTP Site
Points of Contact
References
[rule]
Data Set Overview
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.
The Data
Characteristics
* 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 Files
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).
Format
Compressed:
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/
Uncompressed:
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/inter_disc/hydrology/precip/gpcp/gpcc/yyyy/
where yyyy refers to year.
For error estimates :
/data/inter_disc/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
The Science
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:
1. reformatting of the acquired station-related data,
2. quality control of the meta-data (station coordinates),
3. 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,
4. first objective analysis using the uncontrolled precipitation
records,
5. automatic quality control of the precipitation record
(comparison with area means from step 4 and station-related
climatic means),
6. visual expert quality control and interactive correction with
graphical workstation assistance (comparison with climate
maps, orographical data, extreme-event-catalogs, etc.),
7. second objective analysis using the controlled precipitation
data,
8. filling the results of the second analysis run (step 7) into
precipitation grid data bank (GDB),
9. correction of the results from step 8 with regard to
systematic measuring errors,
10. 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:
1. 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
2. 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).
Contacts
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
References
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
Page Author: Dr. Suraiya Ahmad-- ahmad@daac.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
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