Investigator(s) Name and Title:

and

Title of Investigation:

Contact Information:

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)

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)

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)

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)

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.

Sensor/Instrument Description:

Collection Environment:

Source/Platform:

Source/Platform Mission Objectives:

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:

Sensor/Instrument Measurement Geometry:

Manufacturer of Sensor/Instrument:

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.

Data Notes:

Field Notes:

Spatial Characteristics:

Spatial Coverage:

Spatial Coverage Map:

Spatial Resolution:

Projection:

Grid Description:

• 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:

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

Data Granularity:

Data Format:

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.

Formulae:

Derivation Techniques and Algorithms:

Data Processing Sequence:

Processing Steps:

Processing Changes:

Calculations:

Special Corrections/Adjustments:

Calculated Variables:

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

(3) sampling error of (1)

Graphs and Plots:

Sources of Error:

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:

Measurement Error for Parameters:

Additional Quality Assessments:

Data Verification by Data Center:

Limitations of the Data:

Known Problems with the Data:

Usage Guidance:

Any Other Relevant Information about the Study:

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.

Software Description:

SAMPLE SOFTWARE

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

Contact Information:

Data Center Identification:

Procedures for Obtaining Data:

Data Center Status/Plans:

16. Output Products and Availability:

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

17. References:

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).

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18. Glossary of Terms:

19. List of Acronyms:

DAAC

DMSP

GPCP

Global Precipitation Climatology Project GPCC

Global Precipitation Climatology Center

GMS

GOES

IR

GSFC

Meteosat

SSM/I

TB

URL

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