 
NASA GPCP V1B satellite-gauge precipitation |
George J. Huffman
SSAI and Laboratory for Atmospheres,
NASA Goddard Space Flight Center
March 8, 1999
CONTENTS
1. DATA SET NAMES AND GENERAL CONTENT
2. RELATED PROJECTS, DATA NETWORKS, AND DATA SETS
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
KEYWORDS
absolute error variable
accuracy
AGPI coefficients with missing data
AGPI precipitation product
algorithm intercomparison projects
archive and distribution sites
data file access technique
data set
data set archive
data set creators
data set inventory
data set revisions
date
documentation creator
documentation revision history
estimate missing values
GPCP
GPCP components
GPI number of samples product
GPI precipitation product
grid
intercomparison results
IR
IR data correction
known errors
missing months
multi-satellite precipitation product
number of samples variable
obtaining data
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
variable
1. DATA SET NAMES AND GENERAL CONTENT
The *data set* is formally referred to as the "GPCP Version 1b Combined
Precipitation Data Set." It is also referred to as the "GPCP Combined
Data Set" or the "Version 1b Data Set."
The data set currently contains a suite of 19 products providing monthly,
global gridded values of precipitation totals and supporting information
for the 11-year period July 1987 through September 1998.
The products include precipitation estimates based on several
"single-source" data sets (IR, SSM/I, and rain gauge), precipitation
estimates based on the combination of multiple satellite data sets, and
precipitation estimates based on the combination of the satellites with
the rain gauge analysis.
The main refereed citation for the data set is 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 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 1b Data Set contains data from several *GPCP components*:
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 estimates),
and
4. GPCP Global Precipitation Climatology Centre (rain gauge analyses).
These single-source data sets, as well as combinations based on them are
contained in the Version 1b Data Set for the period of SSM/I data
availability. Some single-source data sets extend beyone the SSM/I period
in their original archival locations.
...........................................................................
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 earlier combined
precipitation data set produced by the GPCC, the original "Version 1 Data
Set" produced at NASA/GSFC, and the Version 1a Data Set produced at GMDC
are all superseded by the Version 1b Data Set. The combination data set
by Xie and Arkin (1996) uses similar input data and has similar temporal
and spatial coverage, but is carried out with a much different technique.
Numerous single-source data sets exist that provide quasi-global coverage;
several are included in this release and are described in section 5.
...........................................................................
5. DEFINITIONS AND DEFINING ALGORITHMS
The GPI estimates originally reported on a 2.5x2.5-deg lat/lon grid
(2.5-deg GPI) 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 are reported as individual
3-hrly images, and all other input single-source data fields are
provided to GPCP in monthly form.
...........................................................................
The data set contains 19 *products*, each of which is named by
concatenating a technique name with a variable name. As shown in Table 1,
here are eight precipitation estimation techniques and four variables, but
only 19 of the 32 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, and the units of the
various 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 1b Combined Precipitation Data Set Product List,
where X denotes an available 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] | X | | | X | 55 km images
| | | | |
SSMI Scattering [ss] | X | | | X | overpass days
| | | | |
SSMI Composite [sc] | X | X | X | X | 55 km images
| | | | |
GPI [gp] | X | | | X | 2.5 deg images
| | | | |
AGPI [ag] | X | X | | |
| | | | |
Multi-Satellite [ms] | X | X | | |
| | | | |
Rain Gauge [ga] | X | X | | X | gauges
| | | | |
Satellite-Gauge [sg] | X | X | | |
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 eight such techniques in the Version 1b Data Set:
SSMI Emission, SSMI Scattering, SSMI Composite, GPI, AGPI, Multi-Satellite,
Rain Gauge, and Satellite-Gauge.
...........................................................................
The *variable* name tells what parameter is in the product. There are
four such variables in the Version 1b 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 of mm/mo or mm/hr 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 SSM/I (Special Sensor Microwave/Imager) 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 corresponding estimate on the 5x5-deg grid
is substituted for precipitation, and the number of samples is estimated
by smooth-filling from surrounding boxes with data. In the current
version the SSM/I emission products for 1987-1995 are generated for
"months" that are rounded to the nearest pentad boundary. As a result,
the period and number of samples will fluctuate compared to the other
estimates. Starting with 1996 the products are generated for calendar
months, in agreement with the other estimates. In a future release all
emission estimates will be produced on calendar months.
...........................................................................
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 theShared 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 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 *SSM/I composite precipitation product* is produced as part of the
GPCP Version 1b 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(composite) = | ------------------------------------------------------ ; (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 *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 GPI (GOES Precipitation
Index; Arkin and Meisner, 1987) technique, which relates cold cloud-top
area to rain rate.
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 GPI product is based on the 2.5x2.5-deg data for the period 1987-96,
and the 1x1-deg beginning in 1997. The boundary is set at January 1997
to avoid the 1997-1998 ENSO event.
...........................................................................
The *AGPI precipitation product* is produced as part of the GPCP Version
1b 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). Separate monthly averages of approximately coincident GPI and
SSM/I 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 SSM/I to GPI averages is computed,
controlled to prevent unstable answers, and smoothly filled in regions
where the SSM/I is missing but the GPI is available. In regions of light
precipitation an additive adjustment is computed as the difference between
smoothed SSM/I and ratio-adjusted GPI values when the SSM/I 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 SSM/I, then
these SSM/I-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 SSM/I-calibrated leo-GPI is approximated by smoothed SSM/I
for doing the calibration to geo-AGPI.
...........................................................................
The *multi-satellite precipitation product* is produced as part of the GPCP
Version 1b Combined Precipitation Data Set by the GPCP Merge Development
Centre (see section 2) following Huffman et al. (1995). Geo-AGPI estimates
are taken where available (latitudes 40 deg N-S), the weighted combination
of the SSM/I combined estimate and the leo-AGPI elsewhere in the 40 deg N-S
belt, and the SSM/I combined outside of that zone. The combination weights
are the inverse (estimated) error variances of the respective estimates.
Such weighted combination of microwave and leo-AGPI is done because the
leo-IR lacks the sampling to support the full AGPI adjustment scheme.
...........................................................................
The *rain gauge precipitation product* is produced by the Global
Precipitation Climatology Centre (GPCC) under the direction of B. Rudolf,
located in the Deutscher Wetterdienst, Offenbach a.M., Germany (Rudolf
1993). 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.
The analyzed values have been corrected for climatological estimates of
systematic error due to wind effects, side-wetting, evaporation, etc.,
following Legates (1987).
...........................................................................
The *satellite-gauge precipitation product* is produced as part of the
GPCP Version 1b 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 *absolute error variable* is produced as part of the GPCP Version 1b
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) * [ 720 + 268 * SQRT ( rbar ) ]
VAR = ----------------------------------------------- (2)
Ni
for absolute error, where VAR is the estimated error variance of an average
over a finite set of observations, H is taken as constant (actually
slightly dependent on the shape of the precipitation rate histogram),
rbar is the average precipitation rate in mm/mo, S is taken as constant
(approximately SQRT(VAR) for rbar=0), Ni is the number of INDEPENDENT
samples in the set of observations, and the expression in square brackets
is a parameterization of the conditional precipitation rate based on work
with the Goddard Scattering Algorithm, Version 2 (Adler et al. 1994) and
fitting of (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 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/mo) | H
---------------------+---------+-----------------------
| |
SSMI Emission [se] | 30 | 3.25 (55 km images)
| |
SSMI Scattering [ss] | 30 | 4.5 (55 km images)
| |
AGPI [ag] | 20 | 0.6 (2.5 deg images)
| |
Rain Gauge [ga] | 6 | 0.005 (gauges)
For the indenpendent 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
error.
...........................................................................
The *source variable* is produced as part of the GPCP Version 1b Combined
Precipitation Data Set by the GPCP Merge Development Centre (see section
2). It is only available for the SSM/I composite technique 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)
|
SOURCE = | ( N(scat) - N(emiss) ) (3)
| ---------------------- ; N(emiss) < 0.75 * N(scat)
| N(scat)
where N is the number of samples, emiss and scat denote 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 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 1b 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 *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 1b 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 *SSM/I composite number of samples product* is produced as part of the
GPCP Version 1b 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(composite) = | ------------------------------------------------------ ; (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 *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 *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.
..........................................................................
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 1b Combined Precipitation is
July 1987 through September 1998, without December 1987. The start and
gap are based on the availability of SSM/I 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 *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, all 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 1b 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. 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.
At the present the GPCP is working to approve Version 2, which will
feature extension back to Jauary 1986 (before the beginning of SSM/I
coverage, and filling the December 1987 gap in SSM/I), as well as
extension of the observation-only product to the polar regions. A
second, more approximate product is being developed to provide globally
complete observation-only monthly estimates for 1979-1985. In parallel,
the GPCP is working to approve a Daily 1-Deg. product that
will provide daily estimates on a global 1x1-deg lat/lon grid for the
period January 1997 through the present (with a delay). It is based on
SSM/I and geo-IR data with calibration by the monthly GPCP Satellite-
Gauge Combination product. Experimental versions of both are available
from the GMDC, with formal release possible in Summer 1999.
..........................................................................
A number of *data set revisions* have been integrated into the GPCP
Version 1b Combined Precipitation, and additional enhancements are planned
for Version 2. In 1996 the GPCP started operational experiments with
collecting geosynchronous-orbit IR (geo-IR) data on a 1x1-deg lat/lon grid
in parallel with the operational 2.5x2.5-deg gridded data. In 1998 the GPCP
decided that the 1x1-deg data had sufficient similarity with the 2.5x2.5-deg
data that only the 1x1-deg data would be collected. Combinations computed
with the new dataset revealed certain processing problems with the old
dataset. None of the problems was considered so severe that immediate
reprocessing of the entire dataset was demanded. However, unless
reprocessing occurred (or unless the new data were processed with the same
mistakes) the combinations based on the 1x1-deg IR would show systematic
differences in climatology from the preceeding years of data based on the
2.5x2.5-deg IR.
1. ISSUE: In Version 1a intersatellite calibration was applied to
2.5x2.5-deg pentad IR, but not 2.5x2.5-deg 3-hrly pentad IR, so AGPI
adjustment coefficients were computed without the calibration, but
applied to calibrated data. Since the AGPI scheme acts to calibrate
all the IR to the SSM/I, the AGPI thus produced actually contained
approximately two times the actual intersatellite calibration.
FIX: Histograms without intersatellite calibration have been used for
Version 1b. Tests show the expected approximately neutral difference
between 2.5x2.5- and 1x1-deg results. Done.
The old intersatellite calibration scheme depended on ISCCP data
which ceased in 1994, so this needed work anyway. The Geostationary
Satellite Precipitation Data Centre (GSPDC) will pursue the
intersatellite calibration issue for Version 2. Pending.
NOTE: Having intersatellite calibration would provide a better GPI
and at second order refine the AGPI at satellite data boundaries.
2. ISSUE: In Version 1a the choice of 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.
FIX: GSPDC will produce 2.5x2.5-deg 3-hrly pentad IR for Version 2.
Pending.
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.
3. ISSUE: 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%.
FIX: First, smoothed SSM/I is used in Version 1b to estimate the
SSM/I-calibrated leo-GPI in regions where the 2.5x2.5-deg lacks leo-IR
data, rather than the Version 1a scheme of trying to estimate the
missing data from gradients in the available data. Second, the leo-GPI
is now not smoothed (in either the 1x1- or 2.5x2.5-deg case) before
calibration against the (smoothed) SSM/I. The GPI is already pretty
smooth, and this prevents differences between the 1x1-deg leo-GPI and
the smoothed SSM/I from bleeding into the leo-only regions due to
smoothing. Done.
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.
4. ISSUE: The Version 1a floor of 0.2 on the SSM/I--IR calibration ratio
is too high to control cold surface (Himalayas, Rockies) and non-raining
cirrus (Sahara) in very dry regions, causing the AGPI to unphysically
exceed the calibrating SSM/I values in those areas.
FIX: The ratio floor is reduced to 0.02. Done.
NOTE: This ameliorates a long-standing artifact in the Himalayas.
5. ISSUE: Regions that lack SSM/I data, but have IR were treated
differently in the initial versions of the Version 1b 1x1- and
2.5x2.5-deg AGPI. Smoothfilling the coefficients leaves residual
artifacts, while smoothfilling the SSM/I underestimates precipitation
and provides better continuity between the 1x1- and 2.5x2.5-deg AGPI.
The third choice is to leave the no-SSM/I region blank as in Version 1a.
FIX: Since coverage is important, but we want to avoid noticeable
artifacts, smoothfilling the SSM/I is preferred. Done.
6. ISSUE: The use of individual average rainrates in the Version 1a
random error estimates causes the inverse error variance combination
scheme to tend toward the lower rainrate, causing the final combined
value to be systematically lower than expected.
FIX: The inverse error variances used for the combination in a gridbox
should be computed with a single average rainrate; in Version 1b this
has been implemented using the simple average of all rainrates that
contribute to the combination for the gridbox.
Done.
NOTE: This only affects combination values over land and in the
leo-IR fill-ins.
7. ISSUE: The data boundary between 1x1-deg and 2.5x2.5-deg input can be
put any time between Jan. 1997 and April 1998. An early start ensures
that the entire 1997-98 ENSO event is covered with homogeneous data,
while the latest date would allow the longest possible homogeneous
record (of 2.5x2.5-deg input).
FIX: Continuity through the ENSO seems most important, so the Jan.
1997 start is used. Done.
8. ISSUE: During the lifespan of Version 1a, incremental upgrades were
made to the GPCC analysis system. Similarly, incremental upgrades
were made to the software for controlling artifacts in the SSM/I data,
principally in coastal areas susceptible to ice-contamination
artifacts. The revisions were considered too minor to recompute the
entire data record.
FIX: Version 1b is a complete recomputation, so the latest GPCC
analysis has been pulled and the SSM/I artifact control is applied
throughout.
One additional issue has no action at this time:
9. ISSUE: 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 boundaries; this causes GPI differences
in excess of 10% at 30-40 deg latitude, and everywhere the 1x1-deg GPI
is smaller.
FIX: The AGPI largely calibrates out this problem. Done.
NOTE: The fix leaves the GPI somewhat damaged. If the GPI itself
needs to be consistent, one could split the 235K class in the 1x1-deg
histograms.
...........................................................................
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 1995,
and the F13 thereafter. In contrast, the SSM/I scattering estimates are
based on F13 starting in May 1995. In a future release the emission
estimates will also be based on F13 starting in May 1995.
...........................................................................
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) 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 1987-196, 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 1b 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 1b uses the
January 1999 version of the GPCC "monitoring analysis" for 1986-
September 1998, together with real-time pulls of analyses for subsequent
months.
Further details are available in Rudolf (1993).
...........................................................................
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.
...........................................................................
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, 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.
...........................................................................
The *rain gauge quality control* scheme 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 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.
...........................................................................
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. The emission estimates are not fully converged, resulting in a 13%
over-estimate, on average.
6. The AGPI calibration coefficients for the 2.5x2.5-deg IR input (1987-
1996) are 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.
...........................................................................
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 handle two cases of computing the *AGPI coefficients with missing
data*. First, when SSM/I data are missing in a region, but GPI data exist,
the SSM/I data are smoothly filled across the blank for the purpose of
computing the AGPI. Second, when leo-GPI data are used to fill holes in
the geo-GPI, the leo-GPI data are used to estimate a leo-AGPI. That is,
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.
...........................................................................
All products in the GPCP Version 1b 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 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.
...........................................................................
All four of the single-source estimates have shown strong *intercomparison
results* in various intercomparison projects (section 2), which was the
basis for selection for this project.
The concept of combination is relatively new, so there is no strong
comparison available. An early validation against the Surface Reference
Data Center analysis yields the statistics in Table 3. Overall, the
combination appears to be working as expected.
Table 3. Summary statistics for all cells and months comparing the
SSM/I composite, Multi-satellite, Gauge, and Satellite-gauge products
to the SRDC analysis for July 1987 -- December 1991.
| Bias | Avg. Diff. | RMS Error
Product | (mm/mo) | (mm/mo) | (mm/mo)
----------------+---------+------------+----------
| | |
SSM/I composite | 4.03 | 60.10 | 88.05
| | |
Multi-satellite | -5.80 | 44.20 | 62.47
| | |
Gauge (GPCC) | 6.77 | 18.85 | 35.11
| | |
Satellite-gauge | 3.70 | 20.29 | 32.98
..........................................................................
The *quality index* variable has recently been proposed by Huffman et
al. (1997) and developed in Huffman (1997a) as a way of comparing the
errors computed for different techniques. Absolute error tends to zero
as the average precipitation tends to zero, while relative error tends
to infinity. According to (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) * [ 720 + 268 * 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 major *archive and distribution sites* for the GPCP Version 1b
Combined Precipitation Data Set are as follows:
Dr. Alan McNab
World Data Center A (WDC-A)
National Climatic Data Center (NCDC)
Rm 514
151 Patton Ave.
Asheville, NC 28801-5001 USA
Phone: 704-271-4592
Fax: 704-271-4328
Internet: amcnab@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
Independent archive and distribution sites exist for the single-source
data sets, and a current list may be obtained by contacting Dr. McNab at
NCDC.
..........................................................................
13. DOCUMENTATION
The *documentation creator* is:
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
..........................................................................
The *documentation revision history* is:
April 24, 1996 Draft 1 by GJH
May 2, 1996 Draft 2 by GJH
May 3, 1996 Draft 3 by GJH
May 8, 1996 Draft 4 by GJH
May 10, 1996 Version 1 by GJH
June 5, 1996 Version 1a by GJH
August 13, 1996 Version 1a Rev. 1 by GJH
September 11, 1996 Version 1a Rev. 2 by GJH
June 20, 1997 Version 1a Rev. 3 by GJH
November 11, 1997 Version 1a Rev. 4 by GJH
January 18, 1998 Version 1a Rev. 5 by GJH
April 28, 1998 Version 1a Rev. 6 by GJH
May 5, 1998 Version 1a Rev. 7 by GJH
June 15, 1998 Version 1a Rev. 8 by GJH
August 10, 1998 Version 1a Rev. 9 by GJH
January 26, 1999 Version 1a Rev. 10 by GJH
March 8, 1999 Version 1b by GJH
The latest version has a new version number, 1b, and features new
combination errors, consistent lack of inter-satellite calibration,
a revised leo-AGPI scheme, and use of 1x1-deg IR data starting
January 1997.
..........................................................................
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.
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.
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.
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., and P.A. Arkin, 1996: Analysis of global monthly precipitation
using gauge observations, satellite estimates, and numerical model
predictions. J. Climate, 9, 840-858.
..........................................................................
14. INVENTORIES
The *data set inventory* may be obtained by accessing the home pages or
contacting the representatives listed in section 12.
..........................................................................
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.
..........................................................................