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GES DISC DAAC Data Guide: GPCP Version 1a Combined Precipitation Data & Intermediate Products

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(The technical information contained in this document are taken from the Data Producer's official released readme document "GPCP Version 1a Combined Precipitation Data Set" dated June 5, 1996)

Summary:

This document contains information on the structure of a global precipitation data set which is formally referred as the "GPCP Version 1a Combined Precipitation Data Set" also referred as the "GPCP Combined Data Set" and "Version 1a Data Set." The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit satellite infrared data, and rain gauge observations. The dataset also contains as "Intermediate Products" the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. All products are provided on a 2.5 degree by 2.5 degree latitude and longitude grids for the period covering July 1987 to December 1995.

Table of Contents:

1. Data Set Overview:

Data Set Identification:

GPCP Version 1a Combined Precipitation Data & Intermediate Products

Data Set Introduction:

The GPCP Version 1a Combined Precipitation Data Set is based on the sequential combination of microwave, infrared estimates and rain gauge data. This dataset contains the final merged product as well as the intermediate products (the individual input fields such as IR, SSM/I, and rain gauge estimates, their combinations and error estimates) as supporting information on a 2.5 degree by 2.5 degree grid for the period July 1987 to December 1995.

The input fields for producing the GPCP Version 1a Combined Product has been provided by the following GPCP participating institutions:

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

These individual data sets, as well as the combinations based on them are contained in the Version 1a Data Set and referred to here as "Intermediate Products". The intermediate and final data products are listed in Table 1, categorized by the technique used for estimating the variables. The "technique" name tells what algorithm was used to generate the product. There are eight such techniques in the Version 1a 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 1a Data Set: Precipitation Rate, Sampling Error, Source, and Number of Samples.

                                 Table 1 GPCP Version 1a Combined Precipitation Data Set Product List
       
  
           \  Variable |  Precip  | Sampling  |        |                         
            \          | 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  | 0.5 deg 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      |        |     |

In the above table "[ }" give the abbreviation by which the technique or variable is known, while units are given in "( )", except for number of samples. In the table there 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.

Objective/Purpose:

This dataset has been produced for the Global Precipitation Center Project (GPCP), an international effort organized by GEWEX/WCRP/WMO to provide an improved long-term precipitation record over the globe(for details see WCRP, 1990; WMO,1985; WMO/ICSU,1990) with the purpose of evaluating and providing global gridded data sets of monthly precipitation based on all suitable observation techniques.

The main applicatations of the global precipitation data set will be the verification of monthly satellite based precipitation estimates , large-scale hydrological studies and verification of global climate models.

Summary of Parameters:

The main parameter for this data set is monthly average rainfall reported in the units of mm/day. In addition to this there are three other variables sampling Error, Number of samples and Source.

Discussion:

For a discussion, please refer to Huffman et al. 1997.

Related Data Sets:

The following related data sets are available from the Goddard Space Flight Center (GSFC)) Distributed Active Archive Center (DAAC):

Chang SSM/I Derived Ocean Monthly Rain Indices
SSM/I Pathfinder Precipitation Monthly Product
Arkin & Janowiak GPCP Satellite Derived Monthly Rainfall
Jaeger Monthly Mean Global Precipitation
Legates Surface and Ship Observation of Precipitation

** The combination data set by Arkin and Xie (1996) uses similar input data and has similar temporal and spatial coverage, but is carried out with a much different technique. This dataset will be available soon.

2. Investigator(s):

t

Investigator(s) Name and Title:

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

and

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

Title of Investigation:

The Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set

Contact Information:

Please direct all queries to Goddard Space Flight Center (GSFC) DAAC Help Desk.

DAAC Help Desk:

The DAAC help desk provides additional information on the Goddard DAAC sytem capabilities, and other supported datasets. The Help Desk can be reached at:
EOS Distributed Active Archive Center (DAAC)
Code 610.2
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet:daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)

3. Theory of Measurements:

The GPCP Global Precipitation estimates are based on the sequential combination of microwave, infrared and gauge data. The input fields for producing this GPCP Version 1a Combined Product has been provided by : GPCP Polar Satellite Precipitation Data Center (SSM/I emission estimates); NOAA Office of Research and Application (SSM/I scattering estimates) ; GPCP Geostationary Satellite Precipitation Data Center (GPI estimates) ; GPCP Global Precipitation Climatology Centre (rain gauge analyses). The details of the the theory involved in producing the intermediate and final products are given below:

Intermediate Data Sets

SSM/I Emission Products

The SSM/I emission precipitation product is produced by the Polar Satellite Precipitation Data Centre 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 data are recorded by selected Defense Meteorological Satellite Program(DMSP) satellites, and are provided in packed form by Remote Sensing Systems (Santa Clara, CA). 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 are generated for "months" that are rounded to the nearest pentad boundary, and an approximate correction to calendar months has been applied.

The SSM/I emission number of samples product originates 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 1a 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 (under)estimate of the number of independent samples contributing to the average.

SSM/I Scattering Products

The SSM/I scattering precipitation product is produced 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 data are recorded by selected DMSP 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 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 scattering number of samples product originates 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 1a 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 (under)estimate of the number of independent samples contributing to the average.

SSM/I Composite Products

The SSM/I composite precipitation product is produced as part of the GPCP Version 1a Combined Precipitation Data Set. 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 SSM/I composite number of samples product is produced as part of the GPCP Version 1a Combined Precipitation Data Set. 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 it is ignored 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) = | ------------------------------------------------------     (2)
               |                       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 source variable is produced as part of the GPCP Version 1a Combined Precipitation Data Set. 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 variable source may be expressed as

         | 0 ;                         N(emiss) >= 0.75 * N(scat)
         |
SOURCE = | ( N(scat) - N(emiss) )
         | ---------------------- ;    N(emiss) < 0.75 * N(scat)            (3)
         |       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.

GPI Products

The GPI precipitation product is produced by the Geostationary Satellite Precipitation Data Centre (GSPDC) 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. The GPI data are accumulated on 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.

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 in each pentad (5-day period) of the year. 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.

AGPI Products

The AGPI precipitation product is produced as part of the GPCP Version 1a Combined Precipitation Data Set, following the Adjusted GPI (AGPI) technique 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. Alternatively, in regions of light precipitation an additive adjustment is computed as the difference between smoothed SSM/I and GPI values when the SSM/I is greater, and zero otherwise. In regions lacking geo-IR data, estimates of adjustment coefficients are constructed with low-orbit IR data. The spatially varying arrays of adjustment coefficients are then applied to the full set of GPI estimates, producing the AGPI.

Multi-Satellite Products

The multi-satellite precipitation product is produced as part of the GPCP Version 1a Combined Precipitation Data Set following Huffman et. al. 1995). AGPI estimates are taken where available (latitudes 40 deg N-S), the weighted combination of the SSM/I composite estimate and the microwave-adjusted low-orbit IR elsewhere in the 40 deg N-S belt, and the SSM/I composite outside of that zone. The combination weights are the inverse (estimated) error variances of the respective estimates. Such weighted combination of microwave and microwave-adjusted low-orbit IR is done because the low-orbit IR lacks the sampling to warrant the AGPI adjustment scheme.

Rain Gauge Products

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 regional or national data collections. An extensive quality-control system is run, featuring an automated step and then a manual step designed to retain legitimate extreme events that typify precipitation. A variant of the SPHEREMAP spatial interpolation routine (Willmott et al. 1985) is used to analyze station values to area averages. The analyzed values have been corrected for systematic error following Legates (1987).

The 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 Combined Precipitation Data Set:

Merged Multi-Satellite and Gauge Products

The satellite-gauge precipitation product is produced as part of the GPCP Version 1a Combined Precipitation Data Set 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.

4. Equipment:

Sensor/Instrument Description:

Collection Environment:

The collection environment for this data set include: satellite & ground.

Source/Platform:

The sources for this data set include: DMSP, GOES, GMS, Meteosat and NOAA Satellites, Meteorological Ground Stations and Ship.

Source/Platform Mission Objectives:

The mission of the DMSP is to provide global visual and infrared meteorological and oceanographic data required to support worldwide Department of Defense operations and high-priority programs. Timely data are supplied to Air Force Global Weather Central, the Navy Fleet Numerical Meteorology and Oceanography Center (FNMOC) and to deployed tactical receiving terminals worldwide. The mission of NOAA polar satellites ND Geostaionary satellites (GOES, METEOSAT, GMS) is to provide near continuous visible and near infrared observation of the earth and its atmosphere for weather informations. The mission of collecting rain gauge observations fall under the objectives of Global Precipitation Project initiated by world climate research program, in an effort to provide long-term global datasets of precipitation records for verification of climate models and investigation of global hydrological cycles.

Key Variables:



                
The following table provides the primary quantities measured by the instruments which provide input for this data set.

Instrument Key Variable Rain Gauge Precipitation SSM/I Radiance VISSR Infrared Radiation

Principles of Operation:

For detail information on the principles of operation for the SSM/I instruments, see the Special Sensor Microwave Imager (SSM/I) Sensor Document. For information on the principles of operation for the VISSR instrument, see the Visible Infrared Spin-Scan Radiometer Instrument Document. And for information on the principles of operation for the Rain Gauge instrument, see the Rain Gauge Document

Sensor/Instrument Measurement Geometry:

This information is not available at this time.

Manufacturer of Sensor/Instrument:

This information is not available at this time.

Calibration:

Specifications:

This information is not available at this time.

Tolerance:

This information is not available at this time.

Frequency of Calibration:

This information is not available at this time.

Other Calibration Information:

This information is not available at this time.

5. Data Acquisition Methods:

The combined dataset is based on multi_satellite and gauge precipitation estimates. The Geostationary Satellite Precipitation Data Centre at the Climate Analysis Center, Washington, D.C. operationally provides the precipitation estimates from satellite observations. The Polar Satellite Precipitation Data Centre at Goddard Space Flight Center, Greenbelt MD provides SSM/I derived precipitation estimates. The Global Precipitation Climatology Center in Germany provides quality controlled rain-gauge analysis based on observations from 6700 meterological stations.

6. Observations:

Data Notes:

This information is not available at this time.

Field Notes:

This information is not available at this time.

7. Data Description:

Spatial Characteristics:

Spatial Coverage:

The combined data set contains global gridded rainfall estimates. The data progresses from West to East from the Prime Meridian, and then in latitude from North to South. The first grid is centered at (88.75N,1.25E), the second grid is ceneterd at (88.75N,3.75E) and the last grid is centered at (88.75S,1.25W).

Spatial Coverage Map:

The data coverage is global. However, for this combined global precipitation data set, grids with no data are set to a missing value code -99999.

Spatial Resolution:

The spatial resolution of the data is 2.5 degree latitude by 2.5 degree longitude grid boxes.

Projection:

Cylindrical Equidistant.

Grid Description:

The data are provided on a 2.5 degree latitude by 2.5 degree longitude Cylindric Equidistant grid. Data runs in the longitude West to East starting at the Prime Meridian for each latitude grid from North to South. Whole and half-degree values are at grid edges.
  • Image orientation: North to South
  • First Grid Position: (88.75N, 1.25E)
  • Second Grid Position: (88.75N,3.75E)
  • Last Grid Position: (88.75S,1.25W)

Temporal Characteristics:

Temporal Coverage:

For this GPCP combined data set, the temporal coverage is from July, 1987 through December, 1995. The start and gap are based on the availability of SSM/I data. The end is based on the availability of rain gauge analyses, and will be extended in future releases.

Temporal Coverage Map:

No maps are available at this time

Temporal Resolution:

The data set contains monthly rainfall estimates.

Data Characteristics:

  • Parameters: Accumulated surface precipitation
  • Units: mm/day
  • Typical Range: 0-50
  • Missing Code: -99999.

Sample Data Record:

The data record in binary format is preceeded by the ASCII header. The following is a sample of the Ascii header record :
size=(char*576) header + (real*4)x144x72x12 data file=gpcp_v1a_psg.87 title=GPCP Version 1a Combined Data Sets version=1a creation_date=960605 variable=precip technique=satellite/gauge units=mm/day year=87 months=1-12 grid=2.5x2.5 deg lon/l at 1st_box_center=(88.75N,1.25E) 2nd_box_center=(88.75N,3.75E) last_box_center=(88.75S,358.75E)
 missing_value=-99999. creation_machine=Silicon Graphics, Inc. 
contact=Mr. A. McNab, NCDC, Rm. 514, 151 Patton Ave, Ashville, NC 28801-5001 USA telephone=704-271-4592 facsimile=704-271-4328 internet=amcnab@ncdc.noaa.gov

8. Data Organization:

Data Granularity:

A general description of data granularity as it applies to the IMS appears in the EOSDIS Glossary.
  • Number of Files per Granule: One yearly file per
  • Variable (precipitation, sampling error, number of samples, source) & per
    Technique (SSM/I Emission,...,Gauge, Satellite Gauge)
  • File Size: 0.5 Mega Bytes per file
  • File Name: gpcp_v1a_VTT.YY.Z
    V is variable (p,e,s,n),
    TT is technique (se,ss,sc,gp,ag,ms,ga,sg),
    YY is year number,
    V and TT codes are explained in Table 1 (see Section 1)
    The table also provides the units & different files of the products available

Data Format:

The format of the data file is the same for all files, regardless of the variable and estimation technique. Each file covers one year of data. The data runs from July 1987 through December 1995, with December 1987 unavailable. Within a year file, missing months are filled entirely with the standard missing value, so that the month number and the position of the specific months in the files are always same and is easy to read.

Each file consists of a 576-Byte header record containing ASCII characters (which is the same size as one row of data), then 12 monthly grids with each month of data of size 144x72 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. Note! 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.

9. Data Manipulations:

Formulae:

Derivation Techniques and Algorithms:

See Section 3

Data Processing Sequence:

Processing Steps:

See Section 3

Processing Changes:

This information is not available at this time.

Calculations:

Special Corrections/Adjustments:

This information is not available at this time.

Calculated Variables:

(1) Monthly area-mean precipitation totals on grid

(2) Number of samples per grid related to (1)

(3) sampling error of (1)

Graphs and Plots:

No graphs or plots are available.

10. Errors:

Sources of Error:

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). 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 "sampling error" is assumed to be dominant, and estimates are computed as discussed for the "absolute error variable". Note that the rain gauge analysis' sampling error is just as real as that of the satellite data, even if somewhat smaller. Sampling error cannot be corrected. The "bias error" is not corrected in the SSM/I emission, SSM/I scattering, SSM/I composite, and GPI precipitation estimates. In the AGPI the GPI is adjusted to the large-scale bias of the SSM/I, which is assumed lower than the GPI's. As noted in the "satellite-gauge precipitation product" discussion, the Multi-Satellite product is adjusted to the large-scale bias of the Gauge analysis before the combination is computed. It continues to be the case that biases over ocean can not be corrected by gauges in the Multi-Satellite and Satellite-Gauge products.

The sampling error variable is produced as part of the GPCP Version 1a Combined Precipitation Data Set (Huffman 1997). Bias error is neglected compared to sampling-induced error (both physical and algorithmic), then simple theoretical and practical considerations lead to the functional form

      H * ( rbar + S) * [ 720 + 268 * SQRT ( rbar ) ]
VAR = -----------------------------------------------                       (4)              
                         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 (4) 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 which are used to estimate Sampling Error for various precipitation estimates. All absolute error fields have been converted from their original units of mm/mo to mm/d.

                                   Table 2.

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

Quality Assessment:

Data Validation by Source:

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.


                  |  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. (1996) 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 (4), 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 (4) 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.

Confidence Level/Accuracy Judgement:

This information is not available at this time.

Measurement Error for Parameters:

This information is not available at this time.

Additional Quality Assessments:

This information is not available at this time.

Data Verification by Data Center:

The GSFC DAAC completes visual inspection of the data files that comprise the data set to ensure that the data set is complete and that the files were not corrupted during the file transfer from the data producer.

11. Notes:

Limitations of the Data:

This information is not available at this time.

Known Problems with the Data:

This information is not available at this time.

Usage Guidance:

This information is not available at this time.

Any Other Relevant Information about the Study:


MISSING VALUE ESTIMATION

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

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

12. Application of the Data Set:

The main applicatations of the land surface data set will be the verification of monthly satellite based precipitation estimates and large-scale hydrological studies.

13. Future Modifications and Plans:

The data producer provides periodic updates to the data set with increased temporal coverage. The GSFC DAAC will work with the data producer to include the additional data in the archive.

14. Software:

Software Description:

SAMPLE SOFTWARE

(The code was provided by data producer George Huffman)
EXTRACTION OF THE HEADER RECORD DATA:
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          (CHARACTER*576)          ASCII header C           (REAL*4)x144x72x12 Data C 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.  The RECL might differ C on different machines; it isn't specified in the FORTRAN77 C standard.  On SGI it's in 4-B words.
C OPEN  ( UNIT=10, FILE='gpcp_v1a_psg.87', ACCESS='DIRECT', 
     +          FORM='UNFORMATTED', STATUS='OLD', RECL=144, 
     +      IOSTAT=iret )
        IF  ( iret .NE. 0 )  THEN WRITE (*, *) 'Error: open error', iret, 
     +                   ' on file gpcp_v1a_psg.87'
            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_v1a_psg.87'
            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
EXTRACTION OF A SPECIFIC MONTH OF DATA

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 Byte 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 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 Byte (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.  The RECL might differ C on different machines; it isn't specified in the FORTRAN77 C standard.  On SGI it's in 4-B words.
C OPEN  ( UNIT=10, FILE='gpcp_v1a_psg.87', ACCESS='DIRECT', 
     +          FORM='UNFORMATTED', STATUS='OLD', RECL=144, 
     +      IOSTAT=iret )
        IF  ( iret .NE. 0 )  THEN WRITE (*, *) 'Error: open error', iret, 
     +                   ' on file gpcp_v1a_psg.87'
            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_v1a_psg.87'
                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

15. Data Access:

Contact Information:

Goddard Space Flight Center DAAC User Services.

Data Center Identification:

GSFC Distributed Active Archive Center

Procedures for Obtaining Data:

This data set is available via several ways: by contacting the GSFC DAAC User Services, by accessing the DAAC/EOSDIS Archive Search and Order Utilities, the WWW interface, or Anonymous FTP.

GSFC DAAC User Services:

The DAAC User Services or Help Desk also provides additional information on the Goddard DAAC system capabilities, and other supported datasets. The Help Desk can be reached at:
     EOS Distributed Active Archive Center (DAAC)
     Code 610.2 NASA Goddard Space Flight Center Greenbelt, Maryland 20771 Internet:daacuso@daac.gsfc.nasa.gov
     301-614-5224 (voice)
     301-614-5268 (fax)
       
     

DAAC SEARCH AND ORDER SERVICES:

The data stored in the Goddard DAAC archives can be ordered from:

ANONYMOUS FTP:

The Precipitation Global Data Set also resides on-line at the Goddard DAAC anonymous FTP and may be accessed either directly from this document,
FTP GIF GPCP v1a Combined Precipitation

or can be acessed via FTP at
login: anonymous
password: < your internet address >
cd http://disc.sci.gsfc.nasa.gov/data/hydrology/precip/gpcp/gpcp_v1a_combined
mget *

Data Center Status/Plans:

The GSFC DAAC plans to periodically check with the data producer regarding updates to the data set.

16. Output Products and Availability:

Data sets are provided on 8 mm tapes, 4 mm tapes, or ftp .

17. References:

Adler, R.F., G.J. Huffman, and P.R. Keehn 1994: Global rain estimates from microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11, 125-152.

Arkin, P.A., 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.

Arkin, P.A., and P. Xie, 1996: Analysis of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. J. Climate, 9 (to appear).

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

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

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

Huffman, G.J., 1997: Simple estimates of sampling error for precipitation. J. Appl. Meteor. (to appear).

Huffman, G.J., R.F. Adler, B. Rudolf, U. Schneider, and P.R. Keehn, 1995: Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information. J. Climate, 8, 1284-1295.

Huffman, G.J., R.F. Adler, P.A. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, R.J. Joyce, A. McNab, B. Rudolf, U. Schneider, and P. Xie, 1996: The Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set. Bull. Amer. Meteor. Soc.,78, (to appear).

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

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

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

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

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

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

Willmott, C.J., Rowe, C.M., Philpot, W.D. (1985): Small-Scale Climate Maps: A Sensitivity Analysis of Some Common Assumptions Associated with Grid-Point Interpolation and Contouring. Amer. Cartographer, 12, 5-16.

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

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

WCRP, 1990. The Global Precipitation Climatology Project - Implementation and Data Management Plan. WMO/TD-No. 367, Geneva, June 1990, 47 pp. and appendices.

18. Glossary of Terms:

EOSDIS glossary

19. List of Acronyms:

EOSDIS acronyms

DAAC

Distributed Active Archive Center

DMSP

Defense Meteorological Satellite Program

GPCP

Global Precipitation Climatology Project GPCC

Global Precipitation Climatology Center

GMS

Geostationary Meteorological Satellite

GOES

Geostationary Operational Environmental Satellites

IR

Infrared

GSFC

Goddard Space Flight Center

Meteosat

Meteorological Satellite

SSM/I

Special Sensor Microwave/Imager

TB

Brightness Temperatures

URL

Uniform Resource Locator

20. Document Information:

Document Revision Date:Fri May 10 11:52:41 EDT 2002

September 5, 1996

Document Review Date:

September 5, 1996

Document ID:

This information is not available at this time.

Citation:

This information is not available at this time.

Document Curator:

Suraiya Ahmad
ahmad@gsfc.nasa.gov

Document URL:

/guides/GSFC/guide/gpcp_v1a_combined_dataset.html
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