Abstract: The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. TRMM was successfully launched on November 27, at 4:27 PM (EST) from the Tanegashima Space Center in Japan.
The rainfall measuring instruments on the TRMM satellite include the Precipitation Radar (PR), an ... electronically scanning radar operating at 13.8 GHz; TRMM Microwave Image (TMI), a nine-channel passive microwave radiometer; and Visible and Infrared Scanner (VIRS), a five-channel visible/infrared radiometer.
The purpose of 3B43 algorithm is to produce the best-estimate precipitation rate (in mm/hr) and root-mean-square (RMS) precipitation-error estimates from TRMM and other data sources. The algorithm combines multiple independent precipitation estimates from the TMI, Advanced Microwave Scanning Radiometer for Earth Observing Systems (AMSR-E), Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS), microwave-adjusted merged geo-infrared (IR), and monthly accumulated Global Precipitation Climatology Centre (GPCC) rain gauge analysis. All input microwave data are intercalibrated to TRMM Combined Instrument (TCI) precipitation estimates (TRMM product 3B31); the iIR estimates are computed using monthly matched microwave-IR histogram matching; then missing data in individual 3-hourly merged-microwave fields are filled with the IR estimates. After the preprocessing is complete, the 3-hourly multi-satellite fields are summed for the month and combined with the monthly gauge analysis using inverse-error-variance weighting to form the best-estimate precipitation rate and RMS precipitation-error estimates. These gridded estimates have a calendar month temporal resolution and a 0.25-degree by 0.25-degree spatial resolution. Spatial coverage extends from 50 degrees south to 50 degrees north latitude.
The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata. The file size is about 5 MB.
Important Changes: After the initial Version 7 processing, it was discovered that AMSU data were neglected in the first retrospective processing of both the Version 7 TMPA (3B42/43) and TMPA-RT (3B40/41/42RT) data series, which created an important shortcoming in the inventory of microwave precipitation estimates used during 2000-2010. In addition, a coding error in the TMPA-RT replaced the occasional missings in product 3B42RT with zeros. Accordingly, both product series were retrospectively processed again. The main impact in both series was to improve the fine-scale patterns of precipitation during 2000-2010 (and for 3B4xRT into late 2012). Averages over progressively larger time/space scales should be progressively less affected. [This is the reason the lack of AMSU went undiscovered; the merger system copes very reasonably with missing data.] Nonetheless, users are urged to switch to the newest Version 7 data sets. The newest runs may be identified by the file names: V.7 3B42/43 suffix of "7A.HDF" for January 2000 - September 2010; V.7 3B4xRT suffix of "7R2.bin" for 1 March 2000 - 6 November 2012. It continues to be the case that the Version 7 3B42/43 is some 4% higher than the calibrating data set (2B31) over oceans, which is still under study. However, the initial conclusion is that it results from the sampling mismatch between the (very sparse) TCI and the (much denser) microwave constellation. At the large scales this offset seems to be nearly a proportional constant.
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The Mirador Earth Science Data Search Tool includes TRMM 3B43, TRMM Best-estimate precipitation rate (mm/hr) and root-mean-square (RMS) precipitation-error estimates. Mirador features include location and event gazetteers.
This interface is designed for quick analyses of the TRMM Level-3 monthly rainfall product, 3B43. Users can plot area averages (area plot) and time series (time plot) for selected areas and time periods.
This URL contains information and data access for all of the Goddard DAAC's hydrology data holdings, including TRMM data from the TMI, Precipitation Radar (PR) and Visible and Infrared Scanner (VIRS) instruments, the Ground Validation (GV) radar data, and ancillary and correlative data products.
Satellite/gauge relative error estimates are provided for each 0.25 x 0.25 degree box. The estimates are derived in two steps: (1) the error estimates of each input data are computed separately, mostly reflecting the sampling-induced uncertainty of each field and (2) a linear combination of these field errors is computed, in which each field error estimate is weighted by the inverse of its ... error-variance. There are currently no known deficiencies in the 3B43 algorithm. The quality of the 3B43 product is highly sensitive to the quality of the input data: TRMM adjusted merged-IR (product 3B42) and the monthly gridded precipitation gauge analysis (courtesy of the Global Precipitation Climatology Centre). If the quality of the input data is less than anticipated, then the quality of 3B43 will also be less.
Huffman, G.J., 1997: "Estimates of ... Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation," J. Appl. Meteor., 1191-1201.
Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi- Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1), 38-55. PDF available at ftp://meso.gsfc.nasa.gov/agnes/huffman/papers/ TMPA_jhm_07.pdf.gz
Huffman, G.J., R.F. Adler, D.T. Bolvin, E.J. Nelkin, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1 in Satellite Rainfall Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds. Springer Verlag, ISBN: 978-90-481-2914-0, 3-22.