GPCP VERSION 2 COMBINED PRECIPITATION
DATA SET DOCUMENTATION
George J. Huffman
David T. Bolvin
SSAI and Laboratory for Atmospheres,
NASA Goddard Space Flight Center
26 September 2002
i. CONTENTS
1. DATA SET NAMES AND GENERAL CONTENT
2. RELATED PROJECTS, DATA NETWORKS, AND DATA SETS
3. STORAGE AND DISTRIBUTION MEDIA
4. READING THE DATA
5. DEFINITIONS AND DEFINING ALGORITHMS
6. TEMPORAL AND SPATIAL COVERAGE AND RESOLUTION
7. PRODUCTION AND UPDATES
8. SENSORS
9. ERROR DETECTION AND CORRECTION
10. MISSING VALUE ESTIMATION AND CODES
11. QUALITY AND CONFIDENCE ESTIMATES
12. DATA ARCHIVES
13. DOCUMENTATION
14. INVENTORIES
15. HOW TO ORDER AND OBTAIN INFORMATION ABOUT THE DATA
ii. KEYWORDS
absolute random error variable
accuracy
AGPI coefficients with missing data
AGPI precipitation product
algorithm intercomparison projects
archive and distribution sites
contributing centers
data access policy
data file access technique
data set
data set archive
data set creators
data set curator
data set inventory
data set revisions
date
documentation curator
documentation revision history
estimate missing values
GPCP
GPI number of samples product
GPI precipitation product
grid
intercomparison results
IR
IR data correction
known anomalies
known data set issues
known errors
merged SSM/I/TOVS precipitation product
missing months
multi-satellite precipitation product
number of samples variable
obtaining data
OLR
OPI precipitation product
OPI quality control
OPI revisions in 1979 - 1981
originating machine
pentads
period of record
precipitation variable
production and updates
products
quality index
rain gauge
rain gauge number of samples product
rain gauge precipitation product
rain gauge quality control
read a month of a product
read the header record
references
satellite-gauge precipitation product
similar data sets
source variable
spatial coverage
spatial resolution
SSM/I
SSM/I composite number of samples product
SSM/I composite precipitation product
SSM/I emission number of samples product
SSM/I emission precipitation product
SSM/I error detection/correction
SSM/I scattering number of samples product
SSM/I scattering precipitation product
standard missing value
technique
temporal resolution
TOVS
TOVS precipitation product
TOVS quality control
units of the variables
variable
iii. ACRONYMNS
1DD One Degree Daily
AGPI Adjusted GPI
AIP Algorithm Intercomparison Project
AVHRR Advanced Very High Resolution Radiometer
CPC Climate Prediction Center
CMAP CPC Merged Analysis of Precipitation
DMSP Defense Meteorological Satellite Program
DWD Deutscher Wetterdienst
GARP Global Atmospheric Research Programme
GATE GARP Atlantic Tropical Experiment
Geo Geosynchronous
GEWEX Global Energy and Water Cycle Experiment
GHCN Global Historical Climate Network
GMDC GPCP Merge Development Centre
GMS Geosynchronous Meteorological Satellite
GOES Geosynchronous Operational Environmental Satellites
GPCC Global Precipitation Climatology Centre
GPCP Global Precipitation Climatology Project
GPI Global Precipitation Index
GSFC Goddard Space Flight Center
GSPDC Geostationary Satellite Precipitation Data Centre
HIRS2 High-Resolution Infrared Sounder 2
IR Infrared
lat/lon latitude/longitude
Leo Low-Earth-orbit
MB megabytes
MSU Microwave Sounding Unit
NASA National Aeronautics and Space Administration
NCDC National Climatic Data Center
NCEP National Centers for Environmental Prediction
NESDIS National Environmental Satellite Data and Information Service
NOAA National Oceanic and Atmospheric Administration
OLR Outgoing Longwave Radiation
OPI OLR Precipitation Index
SRDC Surface Reference Data Center
SSM/I Special Sensor Microwave/Imager
Ta Antenna Temperature
Tb Brightness Temperature
TIROS Television Infrared Operational Satellite
TOVS TIROS Operational Vertical Sounder
UTC Universal Coordinated Time (same as GMT, Z)
WCRP World Climate Research Programme
WMO World Meteorological Organization
1. DATA SET NAMES AND GENERAL CONTENT
The *data set* is formally referred to as the "GPCP Version 2 Combined
Precipitation Data Set." It is also referred to as the "Version 2 Data Set."
The Version 2 data set supercedes the previous Version 1c data set, which is
now considered obsolete.
The current data set provides two final products, the combined satellite-gauge
precipitation estimate and the combined satellite-gauge precipitation error
estimate. The complete data set, which includes the input and intermediate
data files, contains a suite of 27 products providing monthly, global gridded
values of precipitation totals and supporting information for the 22-year
period January 1979 - June 2002.
Since no single satellite data source spans the entire data record, the product
draws upon many different sources covering different times within the entire
data record. The three periods of differing data coverage are January 1979 -
December 1985, January 1986 - June 1987 (and December 1987), and July 1987 -
present (excluding December 1987). The data contributing to the resulting
precipitation estimates for each of these three periods is discussed in section
5. Substantial attempts have been made to ensure consistency among the
different available input sources.
A formal refereed citation for the data set is in preparation. The earlier
Version 1 is documented in Huffman et al. (1997) (all references are listed
in section 13), which also appears in Huffman (1997b).
...........................................................................
2. RELATED PROJECTS, DATA NETWORKS, AND DATA SETS
The *data set creators* are G.J. Huffman, D.T. Bolvin, and R.F. Adler,
working in the Laboratory for Atmospheres, NASA Goddard Space Flight
Center, Code 912, Greenbelt, Maryland, 20771 USA, as the GPCP Merge
Development Centre.
...........................................................................
The work is being carried out as part of the Global Precipitation
Climatology Project (*GPCP*), an international project of the
WMO/WCRP/GEWEX designed to provide improved long-record estimates of
precipitation over the globe. The GPCP home page is located at
http://orbit-net.nesdis.noaa.gov/arad/gpcp/
...........................................................................
The Version 2 Data Set contains data from several *contributing
centers*:
1. GPCP Polar Satellite Precipitation Data Centre - Emission (SSM/I
emission estimates),
2. GPCP Polar Satellite Precipitation Data Centre - Scattering (SSM/I
scattering estimates),
3. GPCP Geostationary Satellite Precipitation Data Centre (GPI and OPI
estimates and rain gauge analyses),
4. NASA/GSFC Satellite Applications Office (TOVS estimates), and
5. GPCP Global Precipitation Climatology Centre (rain gauge analyses),
The final satellite-gauge combination, the single-source input data and the
intermediate satellite-only combination products are currently being
distributed. Some single-source data sets extend beyond the periods for which
they're used in Version 2 in their original archival locations. The latter two
are only posted for months in which they contribute to the final product.
...........................................................................
The GPCP has 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.
...........................................................................
Only a few *similar data sets* are available. The GPCP Version 1c Data Set was
produced at GMDC. It has gaps in polar regions and it is believed that the
estimates for the higher latitude oceans are systematically low. Also, it only
provides data for months with SSM/I data (starting July 1987 and missing
December 1987). Consequently, it is considered obsolete and it is recommended
that Version 2 be used instead. The Climate Prediction Center Merged Analysis
of Precipitation (CMAP) 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. Numerous single-source data sets exist that provide
quasi-global coverage; several are used in this release and are described in
section 5.
...........................................................................
3. STORAGE AND DISTRIBUTION MEDIA
The current *data set archive* consists of unformatted binary files with ASCII
headers. It is distributed by FTP over the Internet and on Exabyte 8mm tape
media. Each file occupies almost 0.5 MB, and the collection of final
precipitation and error estimates is currently about 22 MB (gzip'ed). The user
may also choose to download the single source input data and the intermediate
satellite-only combinations which occupy about 100 MB (gzip'ed).
...........................................................................
4. READING THE DATA
The *data file access technique* is the same for all files, regardless of
which variable and estimation technique are related to the file. These
files are accessible by standard third-generation computer languages
(FORTRAN, C, etc.).
Each file consists of a 576-byte header record containing ASCII characters
(which is the same size as one row of data), then 12 grids of size 144x72
containing REAL*4 values. The header line makes the file nearly
self-documenting, in particular spelling out the variable and technique
names, and giving the units of the variable. The header line may be read
with standard text editor tools or dumped under program control. All 12
months of data in the year are present, even if some have no valid data.
Grid boxes without valid data are filled with the (REAL*4) missing
value -99999. The data may be read with standard data-display tools
(after skipping the 576-byte header) or dumped under program control.
...........................................................................
The *originating machine* on which the data files where written is a Silicon
Graphics, Inc. Unix workstation, which uses the "big-endian" IEEE 754-1985
representation of REAL*4 unformatted binary words. Some CPUs might require a
change of representation before using the data. In some cases, the gunzip
routine, used to compress the data, will change representations automatically.
...........................................................................
It is possible to *read the header record* with most text editor tools,
although the size (576 bytes) may be longer than some tools will support.
Alternatively, the header record may be dumped out under program control,
as demonstrated in the following programming segment. The header is
written in a KEYWORD=VALUE format, where KEYWORD is a string without
embedded blanks that gives the parameter name, VALUE is a string
(potentially) containing blanks that gives the value of the parameter,
and blanks separate each KEYWORD=VALUE unit. To prevent ambiguity, "="
is not permitted as a character in either KEYWORD or VALUE.
C**********************************************************************
C FORTRAN program segment to read the header record and file
C arrays of KEYWORD and VALUE.
C
C The header is written in a KEYWORD=VALUE format, where KEYWORD
C is a string without embedded blanks that gives the parameter
C name, VALUE is a string (potentially) containing blanks that
C gives the value of the parameter, and blanks separate each
C KEYWORD=VALUE unit. To prevent ambiguity, "=" is not permitted
C as a character in either KEYWORD or VALUE.
C
C The data arrays are dimensioned large enough that we don't have
C to be careful about overflows; they could be reduced if space
C is short.
C**********************************************************************
C
IMPLICIT NONE
CHARACTER*576 header
CHARACTER*80 keywd (50), value (50)
INTEGER neq (50), kstrt (50), nvend (50)
INTEGER iret, i, l_header, ipt, in, numkey, j
C
C Open the data file (using the 1987 satellite-gauge precip as
C an example) with a RECL of 1 data row.
C ==>> WARNING WARNING WARNING <<==
C The RECL is defined differently on different machines; it isn't
C specified in the FORTRAN77 standard. On SGI it's in 4-B words.
C If you find that you only get 36 good values and then garbage
C (either all zeros or random values) in the last 108 elements of
C the row, your machine wants RECL in bytes, and you should say
C RECL=576 in the following OPEN.
C
OPEN ( UNIT=10, FILE='gpcp_v2_psg.1987', ACCESS='DIRECT',
+ FORM='UNFORMATTED', STATUS='OLD', RECL=144,
+ IOSTAT=iret )
IF ( iret .NE. 0 ) THEN
WRITE (*, *) 'Error: open error', iret,
+ ' on file gpcp_v2_psg.1987'
STOP
END IF
C
C Read the header (the first record) and close the file.
C
READ ( UNIT=10, REC=1, IOSTAT=iret ) header
IF ( iret .NE. 0 ) THEN
WRITE (*, *) 'Error: read error', iret,
+ ' on file gpcp_v2_psg.1987'
STOP
END IF
CLOSE ( UNIT=10 )
C
C Find the actual length of the header (as opposed to the
C declared FORTRAN size) by parsing back from the end for the
C first non-blank character (it was written blank-filled).
C
DO 10 i = 1, 576
IF ( header (577-i:577-i) .NE. ' ' ) GO TO 20
10 CONTINUE
WRITE (*, *) 'Error: found no non-blanks in the header'
STOP
20 l_header = 577 - i
C
C Parse for "=".
C
ipt = 1
DO 30 i = 1, l_header
in = INDEX ( header (ipt:l_header), '=' )
IF ( in .EQ. 0 ) THEN
GO TO 40
ELSE
neq (i) = ipt + in - 1
ipt = ipt + in
END IF
30 CONTINUE
WRITE (*, *) 'Error: ran through header without ending parsing'
STOP
40 CONTINUE
numkey = i - 1
C
C Now find corresponding beginning of each keyword by parsing
C backwards for " ". The first automatically starts at 1. We
C assume that there are at least 2 keywords!
C
kstrt (1) = 1
DO 60 i = 2, numkey
DO 50 j = 1, neq (i) - 1
IF ( header (neq(i)-j:neq(i)-j) .EQ. ' ' ) GO TO 55
50 CONTINUE
55 kstrt (i) = neq (i) - j + 1
60 CONTINUE
C
C The end of the value string is the 2nd character before the start
C of the next keyword, except the last is at l_header.
C
DO 70 i = 1, numkey - 1
nvend (i) = kstrt (i+1) - 2
70 CONTINUE
nvend (numkey) = l_header
C
C Now use these indices to load the arrays. We assume that null
C strings will not be encountered.
C
DO 80 i = 1, numkey
keywd (i) = header (kstrt(i):neq(i)-1)
value (i) = header (neq(i)+1:nvend(i))
80 CONTINUE
C
C Now there are "numkey" keywords with corresponding values ready
C to be manipulated, printed, etc. For example, print them:
C
DO 85 i = 1, numkey
WRITE (*, *) '"', keywd (i) (1:neq(i)-kstrt(i)), '" = "',
+ value (i) (1:nvend(i)-neq(i)), '"'
85 CONTINUE
STOP
END
...........................................................................
It is possible to *read a month of a product*, i.e., one grid of data,
with many standard data-display tools. By design, the 576-byte header is
exactly the size of one row of data, so the header may be bypassed by
skipping 576 bytes or 144 REAL*4 data points or one row. Alternatively,
the data may be dumped out under program control as demonstrated in the
following programming segment. Once past the header, there are always 12
grids of size 144x72 containing REAL*4 values. All months of data in the
year are present, even if some have no valid data. Grid boxes without
valid data are filled with the (REAL*4) "missing" value -99999. Months
in a year that lack data are entirely filled with "missing."
C**********************************************************************
C FORTRAN program segment to read a month of data.
C
C Once the header of size 576 B (one data row) is skipped, there
C are always 12 grids of size 144x72 containing REAL*4 values.
C All months of data in the year are present, even if some have
C no valid data. Grid boxes without valid data are filled with
C the (REAL*4) "missing" value -99999.
C**********************************************************************
C
IMPLICIT NONE
REAL*4 data (144, 72)
INTEGER month, nskip, iret, i, j
C
C Set the user input for month number (using August, the 8th
C month, as an example).
C
month = 8
C
C Open the data file (using the 1987 satellite-gauge precip as
C an example) with a RECL of 1 data row.
C ==>> WARNING WARNING WARNING <<==
C The RECL is defined differently on different machines; it isn't
C specified in the FORTRAN77 standard. On SGI it's in 4-B words.
C If you find that you only get 36 good values and then garbage
C (either all zeros or random values) in the last 108 of the row,
C your machine wants RECL in bytes, and you should say RECL=576
C in the following OPEN.
C
OPEN ( UNIT=10, FILE='gpcp_v2_psg.1987', ACCESS='DIRECT',
+ FORM='UNFORMATTED', STATUS='OLD', RECL=144,
+ IOSTAT=iret )
IF ( iret .NE. 0 ) THEN
WRITE (*, *) 'Error: open error', iret,
+ ' on file gpcp_v2_psg.1987'
STOP
END IF
C
C Compute the number of records to skip, namely 1 for the header
C and 72 for each intervening month.
C
nskip = 1 + ( month - 1 ) * 72
C
C Read the 72 rows of data and close the file.
C
DO 10 j = 1, 72
READ ( UNIT=10, REC=j+nskip, IOSTAT=iret )
+ ( data (i, j), i = 1, 144 )
IF ( iret .NE. 0 ) THEN
WRITE (*, *) 'Error: read error', iret,
+ ' on file gpcp_v2_psg.1987'
STOP
END IF
10 END DO
CLOSE ( UNIT=10 )
C
C Now array "data" is ready to be manipulated, printed, etc.
C For example, dump the single month as unformatted direct:
C
OPEN ( UNIT=10, FILE='junk', ACCESS='DIRECT',
+ FORM='UNFORMATTED', RECL=144, IOSTAT=iret )
IF ( iret .NE. 0 ) THEN
WRITE (*, *) 'Error: open error', iret,
+ ' on file junk'
STOP
END IF
DO 20 j = 1, 72
WRITE ( UNIT=10, REC=j, IOSTAT=iret )
+ ( data (i, j), i = 1, 144 )
IF ( iret .NE. 0 ) THEN
WRITE (*, *) 'Error: write error', iret,
+ ' on file junk'
STOP
END IF
20 END DO
CLOSE ( UNIT=10 )
STOP
END
...........................................................................
5. DEFINITIONS AND DEFINING ALGORITHMS
The GPI estimates originally reported on a 2.5x2.5-deg lat/lon grid (2.5-deg
GPI) used for the period January 1986 - December 1996 are provided as
accumulations over *pentads*, which are 5-day periods starting Jan. 1 of each
year. That is, pentad 1 covers Jan. 1-5, pentad 2 covers Jan. 6-10, and pentad
73 covers Dec. 27-31. Leap Day (Feb. 29) is included in pentad 12, which then
covers 6 days. The pentad accumulation period prevents an exact computation of
monthly average for the 2.5-deg GPI and subsequent products. We assume that a
pentad crossing a month boundary contributes to the statistics in proportion to
the fraction of the pentad in the month. For example, a pentad with 40 images
that starts the last day of the month is assumed to contribute 8 images
(one-fifth of the full pentad) of rainfall information. The 1x1-deg GPI
estimates used for the period January 1997 - present are reported as individual
3-hrly images, and all other input single-source data fields are provided to
GPCP in monthly form.
...........................................................................
The distributed data set contains 27 *products*, each of which is named by
concatenating a technique name with a variable name. As shown in Table 1,
there are 12 precipitation estimation techniques and four variables, but only
27 of the 35 possible products are considered useful and archived. Besides
product availability, Table 1 displays the abbreviations used for coding the
technique and variable in the file names, the units of the various products,
and the currently distributed products.
--> NOTE: In general, users wishing to use the "final" combined <--
--> product should use the "psg" data files (satellite-gauge <--
--> combined precipitation product). <--
Table 1. GPCP Version 2 Combined Precipitation Data Set Product
List, where * denotes a distributed product, [] gives the abbreviation used
for coding the technique or variable in the file names, and () gives the
units of the various products, except Number of Samples, whose units are
displayed in the last column.
\ Variable | Precip | Absolute | |
\ | Rate [p] | Error [e] | Source | Number of Samples
Technique \ | (mm/d) | (mm/d) | [s] | [n] | (Units)
---------------------+----------+-----------+--------+---------------------
| | | | |
SSMI Emission [se] | * | | | * | 55 km images
| | | | |
SSMI Scattering [ss] | * | | | * | overpass days
| | | | |
SSMI Composite [sc] | * | | * | * | 55 km images
| | | | |
TOVS [tv] | * | | | |
| | | | |
SSMI/TOVS Composite | | | | |
[st] | * | * | * | |
| | | | |
OPI [op] | * | * | | |
| | | | |
GPI [gp] | * | | | * | 2.5 deg images
| | | | |
AGPI [ag] | * | * | | |
| | | | |
Multi-Satellite [ms] | * | * | | |
| | | | |
GHCN+CAMS Gauge [g1] | * | * | | * | gauges
| | | | |
GPCC Gauge [g2] | * | * | | * | gauges
| | | | |
Satellite-Gauge [sg] | * | * | | |
For example, the absolute error variable for the multi-satellite
technique may be found in files with "ems" in the name, but there is no
product giving the number-of-samples variable for the multi-satellite
technique.
...........................................................................
The *technique* name tells what algorithm was used to generate the product.
There are 12 such techniques in the Version 2 Data Set: SSMI Emission, SSMI
Scattering, SSMI Composite, TOVS, SSMI/TOVS Composite, OPI, GPI, AGPI,
Multi-Satellite, GHCN+CAMS Rain Gauge, GPCC Rain Gauge, and Satellite-Gauge.
...........................................................................
The *variable* name tells what parameter is in the product. There are
four such variables in the Version 2 Data Set: Precipitation Rate,
Absolute Error, Source, and Number of Samples.
...........................................................................
The *precipitation variable* is computed as described under the individual
product headings. All precipitation products have been converted from
their original units to mm/d.
..........................................................................
The *SSM/I emission precipitation product* is produced by the Polar
Satellite Precipitation Data Centre - Emission of the GPCP under the
direction of A. Chang, located in the Laboratory for Hydrospheric
Processes, NASA Goddard Space Flight Center, Code 971, Greenbelt,
Maryland, 20771 USA. The Special Sensor Microwave/Imager (SSM/I) data
are recorded by selected Defense Meteorological Satellite Program
satellites, and are provided in packed form by Remote Sensing Systems
(Santa Clara, CA) for 1987-1998 and National Climatic Data Center
(Asheville, NC) starting in 1999. The algorithm applied is the Wilheit
et al. (1991) iterative histogram approach to retrieving precipitation
from emission signals in the 19-GHz SSM/I channel. It assumes a
log-normal precipitation histogram and estimates the freezing level from
the 19- and 22-GHz channels. The fit is applied to the full month of
data. Individual estimates on the 2.5x2.5-deg grid occasionally fail to
converge. In that case the estimate is set to the simple average of
the 5-degree precipitation estimates available in the box for the month.
The microwave emission technique infers the quantity of liquid water in a
column from the increased low-frequency observed microwave brightness
temperatures. Greater amounts of liquid water in the the column tend to
correlate with greater surface precipitation. The algorithm takes the
additional step of fitting a log-normal curve to the month of observations to
control sampling-induced noise. This technique works well over ocean where the
surface emissivity is low and uniform. Over land, however, the emissivity is
near one and extremely heterogeneous, making the scattering algorithm the only
choice.
The available products related to the SSM/I emission precipitation data are
provided in Table 1.
...........................................................................
The *SSM/I scattering precipitation product* is produced by the GPCP
Polar Satellite Precipitation Data Centre - Scattering under the
direction of R. Ferraro, located in the Office of Research and
Application of the NOAA National Environmental Satellite Data and
Information Service (NESDIS), Washington, DC, 20233 USA. The SSM/I
(Special Sensor Microwave/Imager) data are recorded by selected Defense
Meteorological Satellite Program satellites, and are transmitted to
NESDIS through the Shared Processing System. The algorithm applied is
based on the Grody (1991) Scattering Index (SI), supplemented by the
Weng and Grody (1994) emission technique in oceanic areas. A similar
fall-back approach was used during the period June 1990 - December 1991
when the 85.5-GHz channels were unusable. Pixel-by-pixel retrievals are
accumulated onto separate daily ascending and descending 0.333x0.333 deg
lat/long grids, then all the grids are accumulated for the month on the
2.5 deg grid.
The microwave scattering technique infers the quantity of hydrometeor ice in a
column from the depressions in the high-frequency 85GHz channel brightness
temperatures. More ice aloft typically implies more surface precipitation.
This relationship is physically less direct than in the emission technique, but
it works equally well over land and ocean whenever deep convection is
important.
The available products related to the SSM/I scattering precipitation data are
provided in Table 1.
...........................................................................
The *SSM/I composite precipitation product* is produced as part of the
GPCP Version 2 Combined Precipitation Data Set by the GPCP Merge
Development Centre (see section 2). The concept is to take 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. The merger may be expressed as
| R(emiss) ; N(emiss) >= 0.75 * N(scat)
|
| N(emiss) * R(emiss) + ( N(scat) - N(emiss) ) * R(scat)
R(compos) = | ------------------------------------------------------; (1)
| N(scat)
| N(emiss) < 0.75 * N(scat)
where R is the precipitation rate; N is the number of samples; composite,
emiss, and scat denote composite, emission, and scattering, respectively;
and the 0.75 threshold allows for fluctuations in the methods of counting
samples in the emission and scattering techniques. Note that the second
expression reduces to R(scat) when N(emiss) is zero.
Important Note: The emission and scattering fields used in this merger
have been edited to remove known and suspected artifacts, such as high
values in polar regions. These edited fields may be approximated by
using the source variable to mask the emission and scattering fields
contained in this data set. That is, the user may infer that editing
must have occurred for points where the source variable indicates that
the scattering or emission (or both) are not used, but the scattering or
emission (or both) values are non-missing.
The available products related to the SSM/I composite precipitation data are
provided in Table 1.
............................................................................
The *TOVS precipitation product* is produced by the Satellite Applications
Office under the direction of Dr. Joel Susskind, located at he NASA Goddard
Space Flight Center's Laboratory for Atmospheres, Greenbelt, MD, 20771 USA.
Data from the Television Infrared Operational Satellite (TIROS) Operational
Vertical Sounder (TOVS) instruments aboard the NOAA series of polar-orbiting
platforms are processed to provide a host of meteorological statistics.
Susskind and Pfaendtner (1989) and Susskind et al. (1997) describe the TOVS
data processing.
The TOVS precipitation estimates infer precipitation from deep, extensive
clouds. The technique uses a multiple regression relationship between
collocated rain gauge measurements and several TOVS-based parameters that
relate to cloud volume: cloud-top pressure, fractional cloud cover, and
relative humidity profile. This relationship is allowed to vary seasonally and
latitudinally. Furthermore, separate relationships are developed for ocean and
land.
The TOVS data are used for the SSM/I period July 1987 - present and are
provided at the 1-degree spatial resolution and at the monthly temporal
resolution. The data covering the span July 1987 - February 1999 are based on
information from two satellites. For the period March 1999 - present, the TOVS
estimates are based on information from one satellite due to changes in
satellite data format. A future release should include data from both NOAA
satellites.
During the SSM/I period, the TOVS estimates are used for filling in the polar
and cold-land regions in the SSM/I data. The end result is a globally
complete "high-quality" precipitation field for use in adjusting the GPI
data.
The available products related to the TOVS precipitation data are provided in
Table 1.
............................................................................
The *merged SSM/I/TOVS precipitation product* is produced as part of the GPCP
Version 2 Combined Precipitation Data Set by the GPCP Merge Development Centre
(see section 2). The coverage of the SSM/I precipitation estimates is limited
by the orbit of the DMSP satellites as well as shortcomings in the microwave
technique over cold land. These holes are filled using the globally complete
TOVS data. In the span 40N - 40S, the SSM/I data are used as is. Where there
are holes as the result of cold land, the TOVS data are adjusted to the
zonally averaged mean bias of the SSM/I data and inserted. Just outside of the
zone 40N - 40S, the SSM/I and TOVS data are averaged using equal weighting.
Moving further towards the poles where the SSM/I data become less reliable, the
SSM/I-TOVS average is replaced with zonally-averaged, bias-adjusted TOVS data.
The bias adjustment is anchored on the equator side by the SSM/I-TOVS average
and on the polar side by climatological rain gauge estimates. From 70N to the
North Pole, TOVS data are adjusted to the bias of the available monthly rain
gauge data. From 70S to the South Pole, TOVS data are adjusted to the bias of
the annual average climatology of the rain gauge data. The monthly
climatological values are not used in the Antarctic as the lack of sufficient
land coverage there yields unstable results. The available products related
to the merged SSM/I/TOVS precipitation data are provided in Table 1.
............................................................................
The *OPI precipitation product* is produced by the Geostationary Satellite
Precipitation Data Centre of the GPCP under the direction of J. Janowiak,
located in the Climate Prediction Center, NOAA National Centers for
Environmental Prediction, Washington, DC, 20233 USA. The OPI technique is
based on the use of low-Earth orbit satellite outgoing longwave radiation (OLR)
observations. Colder OLR radiances are directly related to higher cloud tops,
which are related to increased precipitation rates. It is necessary to define
"cold" locally, so OLR and precipitation climatologies are computed and a
regression relationship is developed for OLR and precipitation anomalies. In
use, the total precipitation inferred is the estimated anomaly plus the local
climatological value. A backup direct OLR-precipitation regression is used
when the anomaly approach yields unphysical values. This spatially and
temporally varying climatological calibration is then applied to the
independent OPI data covering the span 1979 - 1987 to fill all months lacking
SSM/I data. This adjusted OPI data provides a globally complete proxy for the
SSM/I data. The available products related to the OPI precipitation data are
provided in Table 1.
............................................................................
The *GPI precipitation product* is produced by the Geostationary
Satellite Precipitation Data Centre of the GPCP under the direction of J.
Janowiak, located in the Climate Prediction Center, NOAA National Centers
for Environmental Prediction, Washington, DC, 20233 USA. Each
cooperating geostationary satellite operator (the Geosynchronous
Operational Environmental Satellites, or GOES, United States; the
Geosynchronous Meteorological Satellite, or GMS, Japan; and the
Meteorological Satellite, or Meteosat, European Community) accumulates
three-hourly infrared (IR) imagery which are forwarded to GSPDC. The
global IR rainfall estimates are then generated from a merger of these
data using the GOES Precipitation Index (GPI; Arkin and Meisner, 1987)
technique, which relates cold cloud-top area to rain rate.
The GPI technique is based on the use of geostationary satellite IR
observations. Colder IR brightness temperatures are directly related to
higher cloud tops, which are loosely related to increased precipitation
rates. From the GATE data, an empirical relationship between brightness
temperature and precipitation rate was developed. For a brightness
temperature <= 235K, a rain rate of 3 mm/hour is assigned. For a
brightness temperature > 235K, a rain rate of 0 mm/hour is assigned. The
GPI works best over space and time averages of at least 250 km and 6
hours, respectively, in oceanic regions with deep convection.
For the period 1986-March 1998 the GPI data are accumulated on a 2.5x2.5-
deg lat/lon grid for pentads (5-day periods), preventing an exact
computation of the monthly average. We assume that a pentad crossing a
month boundary contributes to the statistics in proportion to the
fraction of the pentad in the month. For example, given a pentad that
starts the last day of the month, 0.2 (one-fifth) of its samples are
assigned to the month in question and and 0.8 (four-fifths) of its
samples are assigned to the following month.
Starting with October 1996 the GPI data are accumulated on a 1x1-deg
lat/lon grid for individual 3-hrly images. In this case monthly totals
are computed as the sum of all available hours in the month.
In both data sets gaps in geo-IR are filled with low-earth-orbit IR
(leo-IR) data from the NOAA series of polar orbiting meteorological
satellites. However, the 2.5x2.5-deg data only contain the leo-IR used
for fill-in, while the 1x1-deg data contain the full leo-IR. The latter
allows a more accurate AGPI (see "AGPI precipitation product"). The
Indian Ocean sector routinely lacked geo-IR coverage until Meteosat-5
was repositioned in June 1998.
See the "IR data correction" and "known data set issues" sections for some
additional details on the GPI data record.
The Version 2 GPI product is based on the 2.5x2.5-deg IR data for the
period 1988-1996, and the 1x1-deg beginning in 1997. The boundary is set
at January 1997 to avoid making the change during the 1997-1998 ENSO event.
The available products related to the GPI precipitation data are provided in
Table 1.
...........................................................................
The *AGPI precipitation product* is produced as part of the GPCP Version
2 Combined Precipitation Data Set by the GPCP Merge Development Centre
(see section 2). The technique follows the Adjusted GPI (AGPI) of Adler
et al. (1994).
During the SSM/I period (starting July 1987), separate monthly averages
of approximately coincident GPI and merged SSM/I/TOVS precipitation
estimates are formed by taking cut-outs of the 3-hourly GPI values that
correspond most closely in time to the local overpass time of the DMSP
platform. The ratio of merged SSM/I/TOVS to GPI averages is computed
and controlled to prevent unstable answers. In regions of light
precipitation an additive adjustment is computed as the difference
between smoothed merged SSM/I/TOVS and ratio-adjusted GPI values when
the merged SSM/I/TOVS is greater, and zero otherwise. The spatially
varying arrays of adjustment coefficients are then applied to the full
set of GPI estimates. In regions lacking geo-IR data, leo-GPI data are
calibrated to the merged SSM/I/TOVS, then these calibrated leo-GPI are
calibrated to the geo-AGPI. This two-step process tries to mimic the
information contained in the AGPI, namely the local bias of the SSM/I and
possible diurnal cycle biases in the geo-AGPI. The second step can only
be done in regions with both geo- and leo-IR data, and then smooth-filled
across the leo-IR fill-in. In the case of the 2.5x2.5-deg IR, which
lacks leo-IR in geo-IR regions, the missing calibrated leo-GPI is
approximated by smoothed merged SSM/I/TOVS for doing the calibration to
geo-AGPI.
During the pre-SSM/I period January 1986 - June 1987 and December 1987, the
OPI data, calibrated by the GPCP satellite-gauge estimates for the SSM/I
period, are used as a proxy for the merged SSM/I-TOVS field in the AGPI
procedure described for the SSM/I period. Because the overpass times of the
calibrated OPI data are not available, a controlled ratio between the full
monthly calibrated OPI estimates and the full monthly GPI data is computed.
These ratios are then applied to the GPI data to form the AGPI. The additive
constant is computed and applied, when necessary, for light-precipitation
regions.
During the pre-SSM/I period January 1979 - December 1985 there is no geo-IR
GPI, and therefore no AGPI. The OPI data, calibrated by the GPCP
satellite-gauge estimates during the SSM/I period, are used "as is" for the
multi-satellite estimates.
The available products related to the AGPI precipitation data are provided in
Table 1.
...........................................................................
The *multi-satellite precipitation product* is produced as part of the
GPCP Version 2 Combined Precipitation Data Set by the GPCP Merge
Development Centre (see section 2) following Huffman et al. (1995).
During the SSM/I period, the multi-satellite field consists of a
combination of Geo-AGPI estimates where available (latitudes 40 deg N-S),
the weighted combination of the merged SSM/I-TOVS estimates and the leo-
AGPI elsewhere in the 40 deg N-S belt, and the merged SSM/I-TOVS data
outside of that zone. The combination weights are the inverse
(estimated) error variances of the respective estimates. Such weighted
combination of SSM/I-TOVS and leo-AGPI is done because the leo-IR lacks
the sampling to support the full AGPI adjustment scheme.
During the pre-SSM/I January 1986 - June 1987 and December 1987, the
multi-satellite field consists of a combination of geo-AGPI estimates where
available (latitudes 40 deg N-S) and the calibrated OPI estimates elsewhere.
The combination weights are the inverse (estimated) error variances of the
respective estimates.
During the pre-SSM/I period January 1979 - December 1985, the OPI data,
calibrated by the GPCP satellite-gauge estimates, are used "as is" for
the multi-satellite estimates.
The available products related to the multi-satellite precipitation data are
provided in Table 1.
...........................................................................
The *rain gauge precipitation product* for the period January 1986 - present
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). Rain gauge reports are archived from about 6700
stations around the globe, both from Global Telecommunications Network
reports, and from other world-wide 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 characterize
precipitation. A variant of the SPHEREMAP spatial interpolation routine
(Willmott et al. 1985) is used to analyze station values to area averages.
During the pre-GPCC period, January 1979 - December 1985, the rain gauge
precipitation product is produced by the GPCP Geostationary Satellite
Precipitation Data Centre of the GPCP under the direction of J. Janowiak,
located in the Climate Prediction Center, NOAA National Centers for
Environmental Prediction, Washington, DC, 20233 USA. The data set
consists of a combination of Global Historical Climate Network (GHCN) and
Climate Assessment and Monitoring System (CAMS) rain gauge data with
analysis using SPHEREMAP - the GHCN+CAMS analysis. This analysis has
error-checking based on station availability.
The analyzed values over the entire period 1979 - present have been corrected
for climatological estimates of systematic error due to wind effects,
side-wetting, evaporation, etc., following Legates (1987).
The available products related to the rain gauge precipitation data are
provided in Table 1.
...........................................................................
The *satellite-gauge precipitation product* is produced as part of the
GPCP Version 2 Combined Precipitation Data Set by the GPCP Merge
Development Centre (see section 2) 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 available products related to the satellite-gauge precipitation data are
provided in Table 1.
...........................................................................
The *absolute random error variable* is produced as part of the GPCP Version 2
Combined Precipitation Data Set by the GPCP Merge Development Centre (see
section 2). 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) * [ 1 + 10 * SQRT ( rbar ) ]
VAR = ----------------------------------------------- (2)
Ni
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 (2) 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. Table
2 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 2. Numerical values of H and S constants used to estimate
absolute error for various precipitation estimates.
| S |
Technique | (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, 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.
...........................................................................
The *source variable* is produced as part of the GPCP Version 2 Combined
Precipitation Data Set by the GPCP Merge Development Centre (see section 2).
It is available for the SSM/I composite and the SSM/I/TOVS composite
techniques and gives the fractional contribution to the composite by the
SSM/I scattering estimate. Referring to (1) in the "SSM/I composite
precipitation product" description, the source SOURCE may be expressed as
| 0 ; N(emiss) >= 0.75 * N(scat)
|
| ( N(scat) - N(emiss) )
| ---------------------- ; N(emiss) < 0.75 * N(scat)
SOURCE = | N(scat) (3)
|
| N(SSM/I) + 2 ; SSM/I / TOVS combined
|
| 4 ; TOVS
where N is the number of samples, emiss and scat denote SSM/I emission
and scattering, respectively, N(SSM/I) is the SSM/I source determined
from the emission and scattering components, and the 0.75 threshold
allows for fluctuations in the methods of counting samples in the
emission and scattering techniques. Note that the second expression
reduces to 1 when N(emiss) is zero.
...........................................................................
The *number of samples variable* is produced in a variety of units as
described under the individual product headings.
...........................................................................
The *SSM/I emission number of samples product* is provided to the GPCP as the
number of pixels contributing to the grid box average for the month (i.e., the
number of "good" pixels). As part of the Version 2 Data Set processing, this
number is converted to the number of 55x55 km boxes that the number of pixels
can evenly and completely cover. This conversion provides a very approximate
(over)estimate of the number of independent samples contributing to the
average. The available products related to the SSM/I emission number of
samples are provided in Table 1.
...........................................................................
The *SSM/I scattering number of samples product* is provided to the GPCP
as the number of "overpass days," the count of days in the month that had
at least one ascending pass plus days that had at least one descending
pass. As part of the Version 2 Data Set processing, this number is
converted to the number of 55x55 km boxes that the number of pixels can
evenly and completely cover. This conversion provides a very approximate
(over)estimate of the number of independent samples contributing to the
average. The available products related to the SSM/I scattering number of
samples are provided in Table 1.
...........................................................................
The *SSM/I composite number of samples product* is produced as part of
the GPCP Version 2 Combined Precipitation Data Set by the GPCP Merge
Development Centre (see section 2). Due to the different units for the
SSM/I emission and scattering numbers of samples, it is necessary to
convert at least one before doing the merger. We have chosen to convert
overpass days (SSM/I scattering estimates) to an estimate of complete
55x55 km boxes (our modified units for the SSM/I emission). In the
latitude belt 60 deg N-S, orbits in the same direction don't overlap on a
single day, and there is an approximate linear relationship between
overpass days and 55 km boxes. Outside that belt the overlaps cause
non-linearity, but we ignore it because the general lack of reliable
SSM/I at higher latitudes overwhelms details about the numbers of
samples. The separate numbers of samples for each technique, measured in
55 km boxes, are merged according to the same formula as the rainfall:
| N(emiss) ; N(emiss) >= 0.75 * N(scat)
|
| N(emiss) * N(emiss) + ( N(scat) - N(emiss) ) * N(scat)
N(compos) = | ------------------------------------------------------; (4)
| N(scat)
| N(emiss) < 0.75 * N(scat)
where N is the number of samples; composite, emiss, and scat denote composite,
emission, and scattering, respectively; and the 0.75 threshold allows for
fluctuations in the methods of counting samples in the emission and scattering
techniques. Note that the second expression reduces to N(scat) when N(emiss)
is zero. The available products related to the SSM/I composite number of
samples are provided in Table 1.
...........................................................................
The *GPI number of samples product* is provided to the GPCP as the number of
IR images that contribute to the 2.5x2.5-deg grid box. For the 2.5x2.5-deg IR
data it is provided as the number of images per pentad (5-day period), while
for the 1x1-deg IR data each 3-hrly image is a separate dataset. For the
2.5x2.5-deg IR data the contribution by pentads that cross month boundaries
are taken to be proportional to the fraction of the pentad in the month.to the
fraction of the pentad in the month. For example, given a pentad that starts
the last day of the month, 0.2 (one-fifth) of its samples are assigned to the
month in question and and 0.8 (four-fifths) of its samples are assigned to
the following month. The available products related to the GPI number of
samples are provided in Table 1.
..........................................................................
The *rain gauge number of samples product* is provided to the GPCP as the
number of stations providing gauge reports for the month in the 2.5x2.5-
deg grid box. The available products related to the rain gauge number of
samples are provided in Table 1.
..........................................................................
The *units of the variables* are given in Table 1 (Section 5) under the
entry "Products." In particular, the precipitation estimates are in
mm/day.
..........................................................................
6. TEMPORAL AND SPATIAL COVERAGE AND RESOLUTION
The *date* for a file is the year in which the months it contains
occurred. The date for a grid is the year/month over which the
observations were accumulated to form the averages and estimates.
All dates are UTC.
...........................................................................
The *temporal resolution* of the products is one calendar month. The
temporal resolution of the original single-source data sets is also one
month, except the GPI data source has pentad (five-day) or 3-hrly
temporal resolution for the 2.5x2.5-deg and 1x1-deg IR data sets,
respectively. Some of the single-source data sets are available from
other archives at a finer resolution.
...........................................................................
The *period of record* for the GPCP Version 2 Combined Precipitation is
January 1979 through June 2002. The start is based on the availability of
the gauge and OLR data. The end is based on the availability of input
analyses, and will be extended in future releases. Some of the single-source
data sets have longer periods of record in their original archival sites. The
data span for each product available in the distributed data set is provided in
Table 3. Some products are available for longer timespans, but only the data
used in the GPCP V2 processing is distributed. Data available but not used in
the GPCP V2 processing is available upon request from the *data set creators*.
Table 3. GPCP Version 2 Combined Precipitation Data Set Product
List with data span coverage in the distributed data set.
\ Variable |
\ |
Technique \ | Availability in Distribution
---------------------+-----------------------------------------------------
|
SSMI Emission [se] | 07/1987 - 11/1987, 01/1988 - present
|
SSMI Scattering [ss] | 07/1987 - 11/1987, 01/1988 - present
|
SSMI Composite [sc] | 07/1987 - 11/1987, 01/1988 - present
|
TOVS [tv] | 07/1987 - 11/1987, 01/1988 - present
|
SSMI/TOVS Composite |
[st] | 07/1987 - 11/1987, 01/1988 - present
|
OPI [op] | 01/1979 - 06/1987, 12/1987
|
GPI [gp] | 01/1986 - present
|
AGPI [ag] | 01/1986 - present
|
Multi-Satellite [ms] | 01/1979 - present
|
GHCN+CAMS Gauge [g1] | 01/1979 - 12/1985
|
GPCC Gauge [g2] | 01/1986 - present
|
Satellite-Gauge [sg] | 01/1979 - present
...........................................................................
The *grid* on which each field of values is presented is a 2.5x2.5 deg
latitude--longitude (Cylindrical Equal Distance) global array of points.
It is size 144x72, with X (longitude) incrementing most rapidly West to
East from the Prime Meridian, and then Y (latitude) incrementing North
to South. Whole- and half-degree values are at grid edges:
First point center = (88.75N,1.25E)
Second point center = (88.75N,3.75E)
Last point center = (88.75S,1.25W)
...........................................................................
The *spatial resolution* of the products is 2.5x2.5 deg lat/long, as it
was for the original single-source data sets, except the 1x1-deg IR (used
starting January 1997). Some of the single-source data sets are
available from other archives at a finer resolution.
...........................................................................
The *spatial coverage* of the products is global in the sense that they
are provided on a global grid. However, most of the products have
meaningful values only on a subset of the grid points. The single-source
products have the largest holes, and the combination products cover
successively more of the globe. See the sensor descriptions (section 8)
for additional discussion of coverage by the single-source products.
...........................................................................
7. PRODUCTION AND UPDATES
The GPCP is responsible for managing *production and updates* of the
GPCP Combined Precipitation Data Set (WCRP 1986). Version 2 is
produced by the GPCP Merge Development Centre (GMDC), located at NASA
Goddard Space Flight Center in the Laboratory for Atmospheres.
Various groups in the international science community are given the tasks of
preparing precipitation estimates from individual data sources, then the GMDC
is charged with combining these into a "best" global product. This activity
takes place after real time, at a pace governed by agreements about forwarding
data to the individual centers and activities designed to ensure the quality
in each processing step and usually within three months. The techniques used
to compute the individual and combination estimates are described in section 5.
Updates will be released to (1) extend the data record, (2) take
advantage of improved combination techniques, or (3) correct errors.
Updates resulting from the last two cases will be given new version
numbers.
==> NOTE: The changes described in this section are typical of the <==
==> changes that are required to keep the GPCP Combined Precipitation <==
==> Data Set abreast of current requirements and science. Users are <==
==> strongly encouraged to check back routinely for additional <==
==> upgrades, and to refer other users to this site rather than <==
==> redistributing data that are potentially out of date. <==
..........................................................................
To date, two *data set revisions* have been implemented from Version 1c to
Version 2.
1. Version 2 uses the current version of the Chang SSM/I data for the
entire span July 1987 - present. The reprocessed Chang SSM/I estimates
are systematically lower than the Chang data used in Version 1c by about
5%.
2. The version 1c Chang SSM/I precipitation values were considered too low
in higher latitudes (> 40 degrees). To fix this, the Chang SSM/I
estimates are combined with the globally complete TOVS data at the
higher latitudes in Version 2 to eliminate the unrealistic roll-off.
The blending of the Chang SSM/I and TOVS estimates is a combination of
averaging the two, where appropriate, and then adjusting the bias of
the TOVS estimates to the bias of the SSM/I-TOVS average at polar
latitudes where SSM/I estimates are believed unreliable.
..........................................................................
A number of *known data set issues* exist:
1. The present GPI contains no intersatellite calibration. This is not a
serious issue in the AGPI and combination, although having the
intersatellite calibration would provide a better GPI and at second
order refine the AGPI at satellite data boundaries. By contrast, the
"official" NCEP GPI time series has intersatellite calibration for Jan.
1986 - March 1998, then none thereafter. Tests show that the 40 deg N-S
oceanic average is about 3% higher for the intercalibrated data,
compared to the non-intercalibrated data.
2. The present GPI has a 3x3-gridbox smoother applied for non-SSM/I months
(Jan. 1986 - June 1987, Dec. 1987). Locally, values are different than
the non-smoothed version, but large-area averages should be accurate.
3. The present GPI lacks leo-GPI data during the 1x1-deg era (Jan. 1997 -
present). This is mostly a problem in the Indian Ocean sector before
July 1998, when full months of METEOSAT-5 data started.
4. Presently the choice of IR satellite source is strictly by the
number of images in the 2.5x2.5-deg 3-hrly pentad IR (used to compute
adjustment coefficients), but in the 2.5x2.5-deg pentad IR the
distance to the satellite is also considered (used to compute the
AGPI). So, at some locations nearly equidistant between the two
satellites the AGPI is derived for one satellite, but applied to the
other. GSPDC will produce 2.5x2.5-deg 3-hrly pentad IR for a future
release.
NOTE: In the 1x1-deg 3-hrly GPI it is possible for the two satellites
to cut in and out on successive hours. As long as the relative
contribution of each is in the same proportion for both the SSM/I-
matched subset and the full data set this is not too important. Using
intersatellite calibrated data would overcome this issue, although it
is likely a second-order effect.
5. The 1x1-deg IR dataset provides comprehensive leo-IR data while the
2.5x2.5-deg IR only provides leo-IR in regions lacking geo-IR. The
additional data in the 1x1-deg IR allows more accuracy in estimating
the calibration of the SSM/I-calibrated leo-GPI to the geo-AGPI,
causing biases between the 1x1- and 2.5x2.5-deg AGPI in leo regions
(the Indian Ocean being the prime case) of up to 15% for Version 1c.
NOTE: Alternatively, a whole different 2.5x2.5-deg pentad low-orbit
GPI dataset could be generated, and then integrated into the system.
The improvement over the fix should be only second-order.
6. The GMS 2.5x2.5-deg histograms were collected with temperature bin
boundaries at half-degree values, but the 1x1-deg histograms are
being collected on whole-degree temperature boundaries; this causes
GPI differences in excess of 10% at 30-40 deg latitude, and everywhere
the 1x1-deg GPI is smaller. The AGPI largely calibrates out this
problem, but if the GPI itself needs to be consistent, the 235K class
could be split in the 1x1-deg histograms in a future release.
7. The TOVS precipitation estimates for the SSM/I period July 1987 - February
1999 are based on two satellites. After February 1999, the TOVS estimates
are based on only one satellite. It is expected that the TOVS data after
February 1999 will be reprocessed using two satellites once the new
operational stream has been developed.
8. Every effort has been made to preserve the homogeneity of the Version 2 data
record. However, the regional variances inherent in the OPI data are
typically smaller than those encountered in the SSM/I data, so the visual
nature of the Version 2 fields will be different for the pre- and post-SSM/I
period. Future efforts will be directed at minimalizing these differences.
9. The rain gauge data used in the Version 2 analysis consists of GHCN+CAMS for
the period January 1979 - December 1985 and GPCC for the period January 1986
- present. Though there is overlap in the input data for both analyses,
there exists a minimal possibility of a discernible boundary at the
cross-over month for the land precipitation.
10.Every attempt has been made to create an observation-only based
precipitation data set. However, the TOVS estimates currently rely on
numerical model data to initialize the estimation technique. Though it is
believed that the impact of the numerical model data is negligible on the
final precipitation estimates, analysis is currently underway to objectively
assess the impact of the model data on the TOVS precipitation estimates.
11.Some polar-orbiting satellites can experienced significant drifting of the
equator-crossing time during their period of service. There is no direct
effect on the accuracy of the data, but it is possible that the systematic
change in sampling time could introduce biases in the resulting
precipitation estimates.
12.Questions haved been raised about the sufficiency of the SPHEREMAP gauge
analysis scheme in regions of complex terrain. Streamflow comparisons
indicate a low bias in regions of complex terrain.
...........................................................................
8. SENSORS
The Special Sensor Microwave/Imager (*SSM/I*) is a multi-channel passive
microwave radiometer that has flown on selected Defense Meteorological
Satellite Program (DMSP) platforms since mid-1987. The DMSP is placed
in a sun-synchronous polar orbit with a period of about 102 min. The
SSM/I provides vertical and horizontal polarization values for 19, 22,
37, and 85.5 GHz frequencies (except only vertical at 22) with conical
scanning. Pixels and scans are spaced 25 km apart at the suborbital
point, except the 85.5-GHz channels are collected at 12.5 km spacing.
Every other high-frequency pixel is co-located with the low-frequency
pixels, starting with the first pixel in the scan and the first scan in
a pair of scans. The channels have resolutions that vary from 12.5x15
km for the 85.5 GHz (oval due to the slanted viewing angle) to 60x75 km
for the 19 GHz.
The polar orbit provides nominal coverage over the latitudes 85 deg N-S,
although limitations in retrieval techniques prevent useful precipitation
estimates in cases of cold land (scattering), land (emission), or sea ice
(both scattering and emission).
The SSM/I is an operational sensor, so the data record suffers the usual
gaps in the record due to processing errors, down time on receivers, etc.
Over time the coverage has improved as the operational system has
matured. As well, the first 85.5 GHz sensor to fly degraded quickly due
to inadequate solar shielding. After launch in mid-1987, the 85.5 GHz
vertical- and horizontal-polarization channels became unusable in 1989
and 1990, respectively.
Further details are available in Hollinger et al. (1990).
The SSM/I emission estimates are based on data from the F8 instrument
from mid-1987 through 1991, with the F11 being used for 1992 through April
1995, and the F13 thereafter.
...........................................................................
The TIROS Operational Vertical Sounder (*TOVS*) dataset of surface and
atmospheric parameters are derived from analysis of High-Resolution
Infrared Sounder 2 (HIRS2) and Microwave Sounding Unit (MSU) data aboard
the NOAA series of polar-orbiting operational meteorological satellites.
The retrieved fields include land and ocean surface skin temperature,
atmospheric temperature and water vapor profiles, total atmospheric ozone
burden, cloud-top pressure and radiatively effective fractional cloud
cover, outgoing longwave radiation and longwave cloud radiative forcing,
and precipitation estimate.
For the period January 1979 - present (used July 1987 - present), the TOVS
precipitation estimates are accumulated on a 1x1-deg lat/lon grid at the
monthly temporal resolution. Due to the estimation technique and the polar
orbit of the NOAA satellites, TOVS provides a globally complete estimate of
precipitation.
For the period January 1979 - February 1999 (used July 1987 - February 1999),
the TOVS estimates are based on two NOAA satellites orbiting in quadrature.
Beginning in March 1999, the TOVS estimates are based on a single NOAA
satellite. This occurred as the result of the failure of NOAA-11. Data will
become available from the next-generation NOAA satellites when the responsible
data center can implement the operational stream.
The various instruments are operational sensors, so the data record
suffers the usual gaps in the record due to processing errors, down time
on receivers, sensor failures, etc.
More information can be found in Susskind et al. (1997)
...........................................................................
The *OLR* estimates of broadband outgoing longwave radiation are based on
an algorithm applied to the narrow-band IR channels on the Advanced Very
High Resolution Radiometer (AVHRR) aboard the polar-orbiting NOAA series
of satellites. Typically two satellites are available, but occasionally
the OLR is based on only one satellite.
The various IR instruments are operational sensors, so the data record
suffers the usual gaps in the record due to processing errors, down time
on receivers, sensor failures, etc.
More information can be found in Xie et al. (2000), and Xie and Arkin (1998).
...........................................................................
The infrared (*IR*) data are collected from a variety of sensors. The
primary source of IR data is the international constellation of
geosynchronous-orbit meteorological satellites -- the Geosynchronous
Operational Environmental Satellites (GOES, United States), the
Geosynchronous Meteorological Satellite (GMS, Japan), and the
Meteorological Satellite (Meteosat, European Community). There are
usually two GOES platforms active, GOES-EAST and -WEST, which cover the
eastern and western United States, respectively. Gaps in geosynchronous
coverage (most notably over the Indian Ocean before METEOSAT-5 began
imaging there in June 1998) are filled with IR data from the NOAA-series
polar-orbiting meteorological satellites. The geosynchronous data are
collected by scanning (parts of) the earth's disk, while the polar-orbit
data are collected by cross-track scanning. The data are accumulated
for processing from full-resolution (4x8 km) images.
For the period 1986-March 1998 the GPI data are accumulated on a 2.5x2.5-
deg lat/lon grid for pentads (5-day periods). Starting with October 1996
the GPI data are accumulated on a 1x1-deg lat/lon grid for individual
3-hrly images. In both data sets gaps in geo-IR are filled with low
earth orbit IR (leo-IR) data from the NOAA series of polar orbiting
meteorological satellites. However, the 2.5x2.5-deg data only contain the
leo-IR used for fill-in, while the 1x1-deg data contain the full leo-IR.
The GPI product is based on the 2.5x2.5-deg data for the period 1987-1996,
and the 1x1-deg beginning in 1997. The boundary is set at January 1997 to
avoid placing the boundary during the 1997-1998 ENSO event.
The combination of IR satellites provides near-global coverage, but
limitations in retrieval techniques prevent useful precipitation
estimates poleward of about latitude 40 deg in the summer hemisphere, and
about latitude 30 deg in the winter hemisphere.
The various IR instruments are operational sensors, so the data record
suffers the usual gaps in the record due to processing errors, down time
on receivers, sensor failures, etc. Most notably, the GOES series
experienced successive failures and replacement over the whole period of
record.
Further details are available in Janowiak and Arkin (1991).
...........................................................................
The *rain gauge* data are quite heterogeneous. Unlike the fairly uniform
preparation of satellite data sets, gauge data sources and qualities are
extremely variable. Choice of instrumentation, including wind-shielding
(if any), siting, observing practices, error detection/correction, and
data transmission techniques are all governed by national or regional
rules. Typical rain-gauge models include simple 8-inch cylinders (read
manually), weighing (ink trace on graph paper), or tipping bucket
(digital or analog record) devices located in an open area. Reports are
usually generated manually and transmitted to a central regional or
national site. Most of the rain gauge reports contributing to the GPCP
Version 2 Combined Precipitation Data Set were transmitted as SYNOP or
CLIMAT reports on the Global Telecommunications System, although these
were supplemented by national and regional collections retrieved after
real time.
There are about 6700 stations in the current data set, mostly in land
areas and concentrated in developed countries. Version 2 uses the
March 1999 version of the GHCN+CAMS data for the period January 1979 -
December 1985, and uses the January 1999 version of the GPCC "monitoring
analysis" for 1986-September 1998, together with real-time pulls the GPCC
of analyses for subsequent months. [See "rain gauge precipitation product"]
Further details on the GPCC gauge data are available in Rudolf (1993).
Details concerning the GHCN+CAMS gauge data can be found in Xie and Arkin
(1997).
...........................................................................
9. ERROR DETECTION AND CORRECTION
*SSM/I error detection/correction* has several parts. Built-in hot- and
cold-load calibration checks are used to convert counts to Antenna
Temperature (Ta). An algorithm has been developed to convert Ta to
Brightness Temperature (Tb) for the various channels (eliminating
cross-channel leakage). As well, systematic navigation corrections are
performed. All pixels with non-physical Tb and local calibration errors
are deleted.
Accuracies in the Tb's are within the uncertainties of the precipitation
estimation techniques. For the most part, tests show only small
differences among the SSM/I sensors flying on different platforms.
Some leo-IR/OPI/TOVS satellites experienced significant drifting of the
equator-crossing time during their period of service. There is no direct
effect on the accuracy of the leo-IR/OPI/TOVS data, but it is possible that the
systematic change in sampling time could introduce biases in the resulting
precipitation estimates.
...........................................................................
The dominant *IR data correction* is for slanted paths through the
atmosphere. Referred to as "limb darkening correction" in polar-orbit
data, or "zenith-angle correction" in geosynchronous-orbit data (Joyce et
al., 2001), this correction accounts for the fact that a slanted path
through the atmosphere increases the chances that (cold) cloud sides will
be viewed, rather than (warm) surface, and raises the altitude dominating
the atmospheric emission signal (almost always lowering the equivalent
Tb). In addition, the various sensors have a variety of sensitivities
to the IR spectrum, usually including the 10-11 micron band. Inter-
satellite calibration differences are documented, but they are not
implemented in the current version. They are planned for a future
release. The AGPI largely corrects intersatellite calibration, except
for small effects at boundaries between satellites. The satellite
operators are responsible for detecting and eliminating navigation and
telemetry errors.
Some IR satellites experienced significant drifting of the equator-crossing
time during their period of service. There is no direct effect on the accuracy
of the IR data, but it is possible that the systematic change in sampling time
could introduce biases in the resulting precipitation estimates.
...........................................................................
The *OPI quality control* scheme consists of visual inspection of OLR and
OLR anomalies for egregious errors. If errors are detected, the source
of the problem is identified and corrected.
...........................................................................
*OPI revisions in 1979 - 1981* were made to correct apparent
calibration-induced biases in the OLR records of TIROS-N (January 1979 -
January 1980) and NOAA-6 (February 1980 - August 1981). Though the biases in
the OLR are small (less than 1%), the resulting biases in the OPI data are
estimated to be 13% for TIROS-N and 3% for NOAA-6. These bias estimates are
based on averaging the precipitation over all gridboxes having OPI where there
are at least 2 GHCN+CAMS gauges/gridbox and a GHCN+CAMS gauge estimate of at
least 50 mm/month, for all months of TIROS-N (January 1979 - January 1980),
NOAA-6 (February 1980 - August 1981), and NOAA-7 (September 1981 - February
1985). The same averaging is done for the corresponding GHCN+CAMS estimates
and compared with the 3 satellite estimates. The ratios of the averages for
each satellite versus the gauge data were computed. Using the NOAA-7 OPI-gauge
ratio as representative since it is believed to be minimally biased, a ratio
correction was applied to the TIROS-N and NOAA-6 data to match the ratio of the
NOAA-7 period. Work continues on finding and correcting the errors in the
original OLR data.
Some OPI satellites experienced significant drifting of the equator-crossing
time during their period of service. There is no direct effect on the accuracy
of the OPI data, but it is possible that the systematic change in sampling time
could introduce biases in the resulting precipitation estimates.
...........................................................................
The *TOVS quality control* scheme consists of inspection of TOVS
precipitation fields for egregious errors. If errors are detected, the
source of the problem is identified and corrected.
Some TOVS satellites experienced significant drifting of the equator-crossing
time during their period of service. There is no direct effect on the accuracy
of the TOVS data, but it is possible that the systematic change in sampling
time could introduce biases in the resulting precipitation estimates.
...........................................................................
The *rain gauge quality control* scheme for the GPCC gauge data is discussed in
Rudolf (1993) and section 13. For the most part, quality-control errors are
deleted. The largest correctable error for individual reports is the
systematic bias. The use of the Legates (1987) climatological correction is
only an approximate solution, since the correction ought to be applied to the
gauges before averaging. The GPCC is researching an event-by-event correction
for a future release. The availability of rain-gauge reports is extremely
variable in space and time, and within a box the coverage by gauges is often
not uniform. As a result, even the "ground truth" of rain gauge data has
non-trivial errors. Analysis values are omitted if the gridbox and all
adjacent gridboxes totally lack gauge sites. The GHCN+CAMS gauge data are
quality controlled in a similar manner.
...........................................................................
Seven 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
continue to be refined as additional cases are uncovered.
2. The climatological bias correction to the gauge data have artifacts
in a few areas, 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 and are replaced
with the average of the surrounding points.
5. Some leo-IR satellites experience noticeable drift in their equator crossing
time, which can lead to (diurnal) sampling-induced biases of up to 15% in
the resulting single-sensor precipitation estimate.
6. The AGPI calibration coefficients for the 2.5x2.5-deg IR input (1987-
1996) are sometimes derived on one choice of satellites in regions of overlap
between geo satellites, and applied to another.
7. There is no inter-satellite calibration applied to the GPI.
...........................................................................
Some *known anomalies* in the data set are documented and left intact
at the discretion of the data producers. The current list of anomalies is:
1. January 2000: The extreme southwestern portion of Greenland the GPCC
precipitation values are unusually high, resulting in correspondingly
high values in the combined satellite-gauge field. According to the
GPCC, the high values were the result of near-continuous precipitation
at Nuuk, Greenland (validated by corresponding synoptic reports). The
GPCC believe that the Nuuk gauge precipitation reports are correct in
providing greater than normal precipitation, but perhaps
unrealistically so. Eliminating the Nuuk station from the gauge
analysis would produce unrealistically low precipitation values, so it
was decided to leave the station in the analysis. The February 2000
GPCC data shows a similar pattern, but the precipitation amount at
Nuuk is much lower and more in line with surrounding values.
2. Various Winter Months 1987-1999: Persistent large "blocks" of high
precipitation appear over continental Antarctica in several winter
months throughout the data record. This is a result of unusually high
GPCC precipitation values from the sparse gauge network, compounded
by the Legates (1987) climatological bias correction. These values
are considered unrealistically high compared to expected values and
the corresponding multisatellite estimates. Users should take care
when analyzing precipitation estimates over Antarctica.
3. June 1990-December 1991: A fall-back scattering algorithm based on
37 GHz data was used for the NOAA scattering estimates when both
85.5 GHz channels were inoperable on F08. The algorithm's
sensitivity to precipitation is reduced, particularly light
precipitation rates.
...........................................................................
10. MISSING VALUE ESTIMATION AND CODES
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.
...........................................................................
We must compute the *AGPI coefficients with missing data* when leo-GPI
data are used to fill holes in he geo-GPI. In that case, the
calibration of the AGPI and SSM/I-calibrated leo-GPI is computed around
the edge of the hole, the calibration coefficients are smoothly filled
across the hole, and applied to the SSM/I-calibrated leo-GPI in the hole.
Because the 2.5x2.5-deg IR lacks leo-GPI in the geo-GPI region, smoothed
SSM/I is used to estimate SSM/I-calibrated leo-GPI in the geo-GPI region.
This is not necessary for the 1x1-degree IR because it has leo GPI
everywhere.
...........................................................................
All products in the GPCP Version 2 Data Set use the *standard missing
value* '-99999.' Some of the single-source data sets possess coded
missing values in other archives of the data set.
...........................................................................
Within a GPCP year file, *missing months* are filled entirely with the
standard missing value, so that the month number and the position of the
month in the file always agree.
...........................................................................
11. 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 discussed 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" (section 5).
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 (section 5), 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. The TOVS and OPI data, when used, are adjusted
to the bias of the corresponding SSM/I or rain gauge data, so they are
assumed to have only small bias error.
...........................................................................
The single-source estimates have shown reasonable *intercomparison
results* in various intercomparison projects (section 2).
Combinations are difficult to validate as they tend to include data that
would otherwise be independent. An early validation of the old Version
1a data set against the Surface Reference Data Center analysis yields the
statistics in Table 4. Overall, the combination appears to be working as
expected.
Table 4. Summary statistics for all cells and months comparing the
Version 1a SSM/I composite, Multi-satellite, Gauge, and Satellite-gauge
products to the SRDC analysis for July 1987 -- December 1991.
| Bias | Avg. 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
Krajewski et al. (2000) develop and apply 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.
..........................................................................
The *quality index* variable was 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 (2), 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 (2) 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) * [ 1 + 10 * SQRT ( rbarx ) ]
Neg = --------------------------------------------------- (5)
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.
..........................................................................
12. DATA ARCHIVES
The *archive and distribution sites* for the GPCP Version 2 Combined
Precipitation Data Set are as follows:
Mr. David Smith
World Data Center A (WDC-A)
National Climatic Data Center (NCDC)
Rm 120
151 Patton Ave.
Asheville, NC 28801-5001 USA
Phone: 828-271-4053
Fax: 828-271-4328
Internet: dsmith@ncdc.noaa.gov
WDC-A Home Page: http://www.ncdc.noaa.gov/wdcamet.html#GPCP
Dr. Bruno Rudolf
Global Precipitation Climatology Centre (GPCC)
Deutscher Wetterdienst (DWD)
Postfach 10 04 65
D-63004 Offenbach a.M., Germany
Phone: +49-69-8062-2765
Fax: +49-69-8062-2880
Internet: brudolf@dwd.d400.de
GPCC Home Page: http://www.dwd.de/research/gpcc
Dr. George J. Huffman
Code 912
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Phone: 301-614-6308
Fax: 301-614-5492
Internet: huffman@agnes.gsfc.nasa.gov
MAPB Precipitation Page: http://precip.gsfc.nasa.gov
Independent archive and distribution sites exist for the single-source
data sets, and a current list may be obtained by contacting Mr. Smith at
NCDC.
..........................................................................
13. DOCUMENTATION
The *documentation curator* is:
David T. Bolvin
Code 912
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Phone: 301-614-6323
Fax: 301-614-5492
Internet: bolvin@agnes.gsfc.nasa.gov
MAPB Precipitation Page: http://rsd.gsfc.nasa.gov/912/gpcp
..........................................................................
The *documentation revision history* is:
December 2, 1999 Draft 1 by GJH
January 23, 2000 Final by DTB
March 10, 2000 Rev.1 by DTB
April 28, 2000 Rev.2 by GJH
May 22, 2000 Rev.3 by DTB
August 8, 2000 Rev.4 by DTB
August 24, 2000 Rev.5 by DTB
October 5, 2000 Rev.6 by DTB
December 7, 2000 Rev.7 by DTB
February 8, 2001 Rev.8 by DTB
February 23, 2001 Rev.9 by DTB
March 14, 2001 Rev.10 by DTB
March 28, 2001 Rev.11 by DTB
April 18, 2001 Rev.12 by DTB
May 31, 2001 Rev.13 by DTB
June 4, 2001 Rev.14 by DTB
June 28, 2001 Rev.15 by DTB
August 1, 2001 Rev.16 by DTB
August 18, 2001 Rev.17 by DTB
September 4, 2001 Rev.18 by DTB
October 17, 2001 Rev.19 by DTB
November 2, 2001 Rev.20 by DTB
December 14, 2001 Rev.21 by DTB
February 5, 2002 Rev.22 by DTB
March 29, 2002 Rev.23 by DTB
April 4, 2002 Rev.24 by DTB
May 22, 2002 Rev.25 by DTB
July 31, 2002 Rev.26 by DTB
August 22, 2002 Rev.27 by DTB
August 30, 2002 Rev.28 by DTB
September 10, 2002 Rev.29 by DTB
September 11, 2002 Rev.30 by GJH
September 26, 2002 Rev.31 by DTB
The latest version includes data through June 2002.
..........................................................................
The list of *references* used in this documentation is:
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., 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.
Grody, N.C., 1991: Classification of snow cover and precipitation using
the Special Sensor Microwave/Imager (SSM/I). J. Geophys. Res., 96,
7423-7435.
Hollinger, J.P., J.L. Pierce, and G.A. Poe, 1990: SSM/I instrument
evaluation. IEEE Trans. Geosci. Remote Sens., 28, 781-790.
Huffman, G.J., 1997a: Estimates of root-mean-square random error
contained in finite sets of estimated precipitation. J. Appl.
Meteor., 36, 1191-1201.
__________, ed., 1997b: The Global Precipitation Climatology Project
monthly mean precipitation data set. WMO/TD No. 808, WMO, Geneva,
Switzerland. 37pp.
__________, 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.
__________, __________, 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.
Janowiak, J.E., and P.A. Arkin, 1991: Rainfall variations in the
tropics during 1986-1989. J. Geophys. Res., 96, 3359-3373.
Joyce, R.J., J.E. Janowiak, and G.J. Huffman, 2001: Latitudinal and
Seasonal Dependent Zenith Angle Corrections for Geostationary
Satellite IR Brightness Temperatures. J. Appl. Meteor., 40, 689-730.
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.
Legates, D.R, 1987: A climatology of global precipitation. Pub. in
Climatol., 40, U. of Delaware.
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.
Rudolf, B., 1993: Management and analysis of precipitation on a routine
basis. Proc. Internat. WMO/IAHS/ETH SYMP. on Precip. and Evap.,
Slovak Hydromet. Inst., Bratislava, Sept. 1993, 1, 69-76.
Susskind, J., and J. Pfaendtner, 1989: Impact of interactive
physical retrievals on NWP. Report on the Joint ECMWF/EUMETSAT
Workshop on the Use of Satellite Data iomn Operational Weather
Prediction: 1989-1993, Vol. 1, T. Hollingsworth, Ed., ECMWF,
Shinfield Park, Reading RG2 9AV, U.K., 245-270.
Susskind, J., P. Piraino, L. Rokke, L. Iredell, and A. Mehta, 1997:
Characteristics of the TOVS Pathfinder Path A Dataset. Bull.
Amer. Meteor. Soc., 78, 1449-1472.
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.
Xie, P., J.E. Janowiak, and P.A. Arkin, 2000: An improved global
precipitation index based on satellite-observed outgoing longwave
radiation. (to be submitted to J. Climate)
Xie, P., and P.A. Arkin, 1998: Global monthly precipitation estimates
from satellite-observed outgoing longwave radiation. J. Climate, 11,
137-164.
__________ and __________, 1997: Global precipitation: A 17-year monthly
analysis based on gauge observations, satellite estimates, and
numerical model outputs. BAMS, vol.78, 2539-2558.
__________ and __________, 1996: Analysis of global monthly precipitation
using gauge observations, satellite estimates, and numerical model
predictions. J. Climate, 9, 840-858.
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14. INVENTORIES
The *data set inventory* may be obtained by accessing the home pages or
contacting the representatives listed in section 12.
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15. HOW TO ORDER DATA AND OBTAIN INFORMATION ABOUT THE DATA
Users interested in *obtaining data* should access the home pages or
contact the representatives listed in section 12.
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The *data access policy* is "freely available" with three common-sense
caveats:
1. The data set source should be acknowledged when the data are used. [One
possible wording is: "The GPCP combined precipitation data were developed
and computed by the NASA/Goddard Space Flight Center's Laboratory for
Atmospheres as a contribution to the GEWEX Global Precipitation Climatology
Project."]
2. New users should obtain their own current, clean copy, rather than
taking a version from a third party that might be damaged or out of
date. Current users should check for updates and new versions to
avoid reliance on out-of-date data.
3. Errors and difficulties in the dataset should be reported to the
dataset creators.
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