Algorithm Overview

The purpose of Algorithm 3B-43 is to produce the "Tropical Rainfall Measuring Mission (TRMM) and Other Data" best-estimate precipitation rate and root-mean-square (RMS) precipitation-error estimates. These gridded estimates are on a calendar month temporal resolution and a 0.25-degree by 0.25-degree spatial resolution global band extending from 50 degrees South to 50 degrees North latitude.

Algorithm 3B-43 is executed once per calendar month to produce the single, best-estimate precipitation rate and RMS precipitation-error estimate field (3B-43) by combining the 3-hourly merged high-quality/IR estimates with the monthly accumulated Climate Assessment and Monitoring System (CAMS) or Global Precipitation Climatology Centre (GPCC) rain gauge analysis (3A-45).

The 3-hourly merged high quality/IR estimates are summed for the calendar month, and then the rain gauge data are used to apply a large-scale bias adjustment to the 3B-42 estimates over land. The monthly gauge-adjusted merged estimate is then combined directly with the rain gauge estimates using inverse error variance weighting.

File Format

The file content description for Product 3B-43 can be obtained from the Volume 4 - Level 2 and Level 3 File Specifications provided by TSDIS. It is available at: http://tsdis02.nascom.nasa.gov/tsdis/Documents/ICSVol4.pdf.

Known Deficiencies

The IR data prior to February 2000 covers the span 40 degrees North to 40 degrees South. After and including February 2000, the data cover 50 degrees North to 50 degrees South. This results in a minor discontinuity in the data record. Also, HQ data sources are introduced at different points in the data record. Therefore, variations in HQ coverage will occur throughout the record, increasing as time progresses. Most critically, the introduction of AMSU-B over 2001-2003 gradually introduced a low bias of almost 10% globally; then a change in the AMSU-B algorithm in late May 2007 relaxes the low bias to about 5%.

Planned Improvements

Efforts are currently focusing on the validation of the Product 3B-43 precipitation estimates with selected rain gauge data, ground-based radar data, and data from the Global Precipitation Climatology Project (GPCP). Any shortcomings of the algorithm identified during the validation efforts, such as the low bias induced by AMSU-B, will be addressed as associated enhancements to the input algorithms unimplemented.

References

Huffman, G.J., R.F. Adler, B. Rudolph, U. Schneider, P. Keehn, 1995: "Global Precipitation Estimates Based on a Technique for Combining Satellite-Based Estimates, Rain Gauge Analysis, and NWP Model Precipitation Information", J. Clim., 8, 1284-1295.

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, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolph, U. Schneider, 1997: "The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset", Bul. Amer. Meteor. Soc., 78, 5-20.

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.

Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, E.J. Nelkin, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hdrometeor., 4(6), 1147-1167.