GPCP 1-Degree Daily Combination (Version 1.2)

This data set is a companion to the GPCP Version 2.2 SG Combination (which the reader should see for descriptions, including references). Many users need precipitation estimates at finer space and time scales than the Version 2.2 SG set provides. The 1DD V1.2 is computed by the GPCP Global Merge Development Centre, led by Dr. George J. Huffman in the NASA/GSFC Mesoscale Atmospheric Processes Laboratory. This takes place 2 months after the end of the month, once all input data sets become available.


The 1DD uses the "best" quasi-global observational estimators of underlying statistics to adjust quasi-global observational datasets that have desirable time/space coverage. Specifically,


  1. Special Sensor Microwave Imager, then Special Sensor Microwave Imager Sounder (SSMI, SSMIS; 0.5°x0.5° by orbit, GPROF algorithm) provides fractional occurrence of precipitation, and
  2. GPCP Version 2.2 Satellite-Gauge (SG) combination (2.5°x2.5° monthly) provides monthly accumulation of precipitation to algorithms applied to
  3. geosynchronous-orbit IR (geo-IR) Tb histograms (1°x1° grid in the band 40°N-40°S, 3-hourly),
  4. low-orbit IR (leo-IR) GOES Precipitation Index (GPI; same time/space grid as geo-IR), and
  5. TIROS Operational Vertical Sounder, then Atmospheric Infrared Sounder (TOVS, AIRS; 1°x1° on daily nodes, Susskind algorithm).


Although microwave precipitation estimates and gauge analyses are not explicitly used due to sampling limitations, the calibration of the 1DD to the monthly Version 2.2 SG ensures that they do have a strong influence on the overall scaling. The differences between the IR and TOVS (AIRS) datasets required that the 1DD be formulated in two parts, with smoothing over the latitude band 40° to 50° in each hemisphere to patch the data boundary.


In the latitude band 40°N-S the Threshold-Matched Precipitation Index (TMPI) produces approximate instantaneous precipitation from the geo-IR Tb with fill-in by rescaled leo-IR GPI. It is a GPI-type algorithm with locally-calibrated Tb threshold and rainrate. To do this, time/space-matched geo-IR Tb and GPROF-SSMI(SSMIS) estimates of fractional coverage by precipitation are used to set the Tb threshold such that instantaneous geo-IR fractional coverage equals that of the GPROF-SSMI(SSMIS) estimation. Then a single rainrate for "raining" geo-IR pixels is computed for each grid box that makes the full month of TMPI sum to the local SG (monthly) value. Mismatches in geo-IR and GPROF-SSMI(SSMIS) precipitation cause some unrealistic TMPI conditional rain rates, so an "auditing" technique was developed to fill in reasonable values and re-estimate the geo-IR Tb threshold. A less tractable problem is that the warmest geo-IR histogram bin starts at Tb=270K, which prevents correctly setting the threshold in regions with warm-top clouds. In parallel, individual leo-IR GPI values used for fill-in are scaled by the (local) ratio of the SG monthly value to the monthly sum of all available leo-IR GPI estimations.


The original TOVS and AIRS datasets tend to exhibit a very high number of rain days and a correspondingly low conditional rain rate. To overcome this, each month in each hemisphere the local number of TOVS (AIRS) rain days was reduced by the ratio of the total number of TMPI and TOVS (AIRS) rain days at latitude 40°. The remaining non-zero daily rain amounts are rescaled to start at zero and sum over the month to the (local) SG value.


A study for the period October 1996 -- 2002 indicated the instantaneous TMPI estimations showed good consistency from one time to the next and with the daily TMPI, GPI, and rescaled TOVS fields. A month of 1DD estimates correctly sum to the monthly SG, except in the subtropical highs where geo-IR threshold saturation becomes a problem. Even before smoothing, there is good continuity across the 40° N and S data boundary, perhaps in part because the IR and TOVS datasets both largely represent clouds. This dependence on scaled cloud information implies that users should expect larger errors in the individual daily values, and preliminary validation results support this view. Space and/or time averages should be more reliable.

It is expected that the 1DD will see extensive development work. This may include: diurnally varying calibrations; extension back in time; additional sensors; direct use of microwave estimates; and refined combination approaches.


The current data set extends from October 1996--present, with some delay to allow input fields to be computed. The primary product in the 1DD dataset is a combined observation-only dataset. That is, a gridded analysis based on satellite estimates of rainfall is constrained by a monthly analysis that is based on gauge and satellite observations. The product suite consists of the "final" estimates of precipitation.


The data set archive consists of unformatted REAL*4 binary files with ASCII headers. Each file holds 28-31 daily fields. Each file occupies about 8 MB. The grid on which each field of values is presented is a 1°x1° latitude--longitude (Cylindrical Equal Distance) global array of points. It is size 360x180, 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 = (89.5°N,0.5°E)
Second point center = (89.5°N,1.5°E)
Last point center = (89.5°S,0.5°W)

Missing values are denoted by the value -99999., and the units are mm/day.

The standard reference is:


Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. J. Hydrometeor., 2, 36-50.

The dataset curator is:


David T. Bolvin
Code 612
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Phone: +1 301-614-6323
Fax: +1 301-614-5492
Internet:  precip data set curator


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