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MPA accumulated rainfall map

Project Information



The U.S. Department of Agriculture’s Foreign Agricultural Service (USDA-FAS), in cooperation with the National Aeronautics and Space Administration’s (NASA) Goddard Earth Sciences Data and Information Services Center (GES DISC), has been routinely using satellite-derived data to monitor precipitation around the world. A key feature of this project is its use of near-real time global satellite precipitation data in an operational manner. Satellite precipitation products are produced by NASA via a semi-automated process and made accessible from this Web site for USDA and public viewing. Monitoring precipitation for agriculturally important areas around the world greatly assists the USDA-FAS to quickly locate regional weather events, as well as improve crop production estimates.

Data Processing Top
The NASA Goddard Space Flight Center (GSFC) system to produce the "TRMM and Other Data" estimates in real time was developed to apply new concepts in merging quasi-global precipitation estimates and to take advantage of the increasing availability of input data sets in near real time. The overall system is referred to as the "Version 6 TRMM Real-Time Multi-Satellite Precipitation Analysis." For convenience, it is referred to here as the "TMPA-RT."

The TMPA-RT is run quasi-operationally on a best-effort basis at the NASA Precipitation Processing System (PPS, formerly the TRMM Science Data and Information System, TSDIS), with on-going scientific development by the research team led by Drs. Robert Adler and George Huffman in the GSFC Laboratory for Atmospheres. Estimates are posted to the Web about six hours after observation time, although processing issues may delay or prevent this schedule. Due to the experimental nature of these estimates, users are encouraged to report their experiences with the data, and they should expect episodic upgrades or outages as the system develops.

There are three "TRMM and Other Data" products: (1) 3B40RT (High Quality or HQ), which is a combination of all available TMI, SSM/I, AMSR-E, and AMSU-B microwave precipitation estimates; (2) 3B41RT (Variable Rain-rate Infrared, or VAR) precipitation estimates from geostationary infrared (IR) observations using spatially and temporally varying calibration by the HQ; and (3) 3B42RT (HQ + VAR), which is a combination of 3B40RT (HQ) and 3B41RT (VAR). The current combination scheme is simple replacement, i.e., for each gridbox, the HQ value is used if available; otherwise, the VAR value is used. As a final step for the real-time system, the 3B42RT estimates are multiplied by simple calibration coefficients that are climatological, and are designed to make the product more closely follow the bias of the more-accurate research version, 3B42. The specific product available from Crop Explorer is 3B42RT. For the latter, all fields are 1440x480 gridboxes (0-360° E, 60° N-S). The first grid box center is at 0.125° E and 59.875° N. Files are produced every three hours on synoptic observation hours (00 UTC, 03 UTC, ..., 21 UTC). Each file is considered to represent the three-hour period centered on the file time, so, e.g., 00 UTC nominally represents the period from 2230 UTC of the previous day to 0130 UTC of the current day. Valid estimates are only provided in the band 50° N-S.

Data Access Top
The 3B42RT is the specific product available from the NASA GES-DISC Interactive Online Visualization and Analysis Infrastructure-Agriculture (Giovanni-Ag; Giovanni is the underlying infrastructure for a growing family of Web interfaces that allows users to easily view and analyze gridded data interactively online without having to download any data. As implemented in this project, Giovanni output plots of the 3B42RT precipitation data are accessed seamlessly from within the context of FAS’ Crop Explorer (i.e., a retrieved Giovanni plot shows precipitation for the same geographical area and time period as those of current interest to the Crop Explorer user).

Access to the digital data is via Under subdirectory pub/merged, there are three directories: (1) combinedMicro (3B40RT), (2) calibratedIR (3B41RT), and (3) mergeIRMicro (3B42RT).

Data set Validation Top
The TMPA-RT data sets should be considered experimental. Formal validation studies are underway, with the most relevant results available in Huffman et al. (2010). The primary limitations on the HQ (3B40RT) are the sparse sampling by the collection of passive-microwave satellites and algorithm drop-outs in regions with icy or frozen surface. The infrared results (3B41RT) are designed to emulate the microwave results as closely as possible, so known deficiencies in the microwave will likely be reflected in the infrared as well. In addition, it is well known that infrared algorithms of the kind used here have large random errors at the fine time and space scales provided. However, the infrared estimates are expected to match the histogram of the microwave estimates, so that user-specified averaging should yield approximately unbiased results. Finally, the combined microwave-IR fields (3B42RT) contain data boundaries between the regions of microwave and IR coverage. Instantaneously, the boundaries are usually subtle; but they are more noticeable in movie loops, because the regions of coverage change with each image. The TMPA-RT developers encourage users to report successes and problems in applying these data sets to their particular applications. Validation studies are being conducted under the auspices of the International Precipitation Working Group in Australia, the continental U.S., western Europe, and parts of South America.

Respectively, the following are the web sites for these activities:

Additional Documentation Top

Users should refer to the detailed documentation (3B4XRT_doc.pdf) at and programming examples at, for additional details.

References Top

Grody, N.C., 1991: Classification of snow cover and precipitation using the Special Sensor

Microwave/Imager (SSM/I). J. Geophys. Res., 96, 7423-7435.

Hollinger, J., R. Lo, G. Poe, and J. Peirce, 1987: Special Sensor Microwave/Imager User's Guide, Naval Res. Lab., Washington, DC.

Hollinger, J.P., J.L. Pierce, and G.A. Poe, 1990: SSM/I instrument evaluation. IEEE Trans. Geosci. Remote Sens., 28, 781-790.

Huffman, G.J., 1997: Estimates of root-mean-square random error contained in finite sets of estimated precipitation. J. Appl. Meteor., 36, 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

Huffman, G.J., R.F. Adler, D.T. Bolvin, E.J. Nelkin, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1 in Satellite Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds. Springer Verlag, ISBN: 978-90-481-2914-0, 3-22. PDF available at

Huffman, G.J., R.F. Adler, S. Curtis, D.T. Bolvin, E.J. Nelkin, 2007: Global rainfall analyses at monthly and 3-hr time scales. Chapter 23 of Measuring Precipitation from Space: TRMM 3B4XRT_doc 36 EURAINSAT and the Future, V. Levizzani, P. Bauer, and F.J. Turk, Eds., Springer Verlag (Kluwer Academic Pub. B.V.), Dordrecht, The Netherlands, 291-306. [Invited paper]

Huffman, G.J., R.F. Adler, E.F. Stocker, D.T. Bolvin, and E.J. Nelkin, 2003: Analysis of TRMM 3-hourly multi-satellite precipitation estimates computed in both real and post-real time. Combined Preprints CD-ROM, 83rd AMS Annual Meeting. Paper P4.11 in 12th Conf. on Sat. Meteor. and Oceanog., 9-13 Feb. 2003, Long Beach, CA, 6 pp.

Janowiak, J.E., and P.A. Arkin, 1991: Rainfall variations in the tropics during 1986-1989. J. Geophys. Res., 96, 3359-3373.

Jost, V., S. Bakan, K. Fennig, 2002: HOAPS - A new satellite-derived freshwater flux climatology. Meteorol. Zeit., 11, 61-70.

Joyce, R.J., J.E. Janowiak, P.A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at 8 km, hourly resolution. J. Climate, 5, 487-503.

Joyce, R.J., J.E. Janowiak, G.J. Huffman, 2001: Latitudinal and Seasonal Dependent Zenith Angle Corrections for Geostationary Satellite IR Brightness Temperatures. J. Appl. Meteor., 40(4), 689-703.

Kongoli, C., P. Pellegrino, R. Ferraro, 2007: The utilization of the AMSU high frequency measurements for improved coastal rain retrievals. Geophys. Res. Lett., 34, L17809, doi:10.1029/2007GL029940.

Kubota T., S. Shige, H. Hashizume, K. Aonashi, N. Takahashi, S. Seto, M. Hirose, Y.N. Takayabu, K. Nakagawa, K. Iwanami, T. Ushio, M. Kachi, K. Okamoto, 2007: Global precipitation map using satellite-borne microwave radiometers by the GSMaP Project: Production and validation. IEEE Trans. Geosci. Remote Sens. 45, 2259-2275.

Kummerow, C., W.S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 34, 1213-1232.

Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) Sensor Package. J. Atmos. and Oceanic Tech., 15(3), 809-817.

Smith, T.M., P.A. Arkin, J.J. Bates, G.J. Huffman, 2006: Estimating bias of satellite-based precipitation estimates. J. Hydrometeor., 7(5), 841-856.

Sorooshian, S., K.-L. Hsu, X. Gao, H.V. Gupta, B. Imam, and D. Braithwaite 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.

Spencer, R.W., 1993: Global oceanic precipitation from the MSU during 1979-92 and comparisons to other climatologies. J. Climate, 6, 1301-1326.

Tian, Y., C.D. Peters-Lidard, 2007: Systematic anomalies over inland water bodies in satellite-based precipitation estimates. Geophys. Res. Lett., 34, doi:1029/2007GL030787.

Turk, F.J., G.D. Rohaly, J. Hawkins, E.A. Smith, F.S. Marzano, A. Mugnai, and V. Levizzani, 1999: Meteorological applications of precipitation estimation from combined SSM/I, TRMM and infrared geostationary satellite data. Microwave Radiometry and Remote Sensing of the Earth's Surface and Atmosphere, P. Pampaloni and S. Paloscia Eds., VSP Int. Sci. Publ., 353-363.

Vila, D., R. Ferraro, R. Joyce, 2007: Evaluation and improvement of AMSU precipitation retrievals. J. Geophys. Res., 112, D20119, doi:10.1029/2007JD008617. TRMM 3B4XRT_doc 37

Villarini, G., W.F. Krajewski, 2007: Evaluation of the research-version TMPA three-hourly 0.25°x0.25° rainfall estimates over Oklahoma. Geophys. Res. Lett., 34, doi:10.1029/2006GL029147.

Weng, F., L. Zhao, R. Ferraro, G. Poe, X. Li, and N. Grody, 2003: Advanced Microwave Sounding Unit Cloud and Precipitation Algorithms. Radio Sci., 38, 8068Ð8079.

Wentz, F.J., and R.W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55(9), 1613-1627.

Zhao, L., and F. Weng, 2002: Retrieval of Ice Cloud Parameters Using the Advanced Microwave Sounding Unit. J. Appl. Meteor., 41, 384- 395.

For more information, contact: Top
  Dr. William Teng
Code 610.2
Greenbelt, MD 20771
Tel: (301)614-5164
Fax: (301)614-5268

Acknowledgment Top
The TMPA-RT data are "freely available." However, the following acknowledgment should be made if you use the data for a publication (research or otherwise), or for any other purpose: “TMPA-RT satellite precipitation data were provided by the NASA/Goddard Space Flight Center's Laboratory for Atmospheres and Precipitation Processing System, as a contribution to TRMM."

Disclaimer Top

Use of Data and Product

The information on government servers are in the public domain, unless specifically annotated otherwise, and may be used freely by the public. Before using information obtained from this server special attention should be given to the date & time of the data and products being displayed. This information shall not be modified in content and then presented as official government material.

The user assumes the entire risk related to its use of this data. USDA is providing this data "as is," and USDA disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose. In no event will USDA be liable to you or to any third party for any direct, indirect, incidental, consequential, special or exemplary damages or lost profit resulting from any use or misuse of this data.

As required by 17 U.S.C. 403, third parties producing copyrighted works consisting predominantly of the material appearing in USDA Web pages must provide notice with such work(s) identifying the USDA material incorporated and stating that such material is not subject to copyright protection.

Any reference from USDA Web server service to any non-government entity, product, service or information does not constitute an endorsement or recommendation by the USDA or any of its' field offices or employees. We are not responsible for the contents of any "off-site" web pages referenced from USDA servers.

Related Sites Top

Agriculture REASoN Project Portal:

AMSR instrument:

AMSU-B instrument: in the NOAA KLM User's Guide (September 2000 revision):


IPWG Validation for Australia:

IPWG Validation for U.S.:

IPWG Validation for Western Europe:

MAPB Precipitation Page:

MHS instrument: in the NOAA KLM User's Guide (September 2000 revision):

PR User Guide:

TMPA data:


TMPA-RT paper:

TMPA-RT update:


TRMM home:

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