TRMM 3B43: Monthly Precipitation Estimates

TRMM/3B43V7
Dataset Availability
1998-01-01T00:00:00 - Present
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("TRMM/3B43V7")
Tags
precipitation geophysical weather climate rainfall trmm nasa jaxa

Description

This dataset algorithmically merges microwave data from multiple satellites, including SSMI, SSMIS, MHS, AMSU-B and AMSR-E, each inter-calibrated to the TRMM Combined Instrument.

Algorithm 3B43 is executed once per calendar month to produce the single, best-estimate precipitation rate and RMS precipitation-error estimate field (3B43) by combining the 3-hourly merged high-quality/IR estimates (3B42) with the monthly accumulated Global Precipitation Climatology Centre (GPCC) rain gauge analysis.

All of the global precipitation datasets have some calibrating data source, which is necessary to control bias differences between contributing satellites. The multi-satellite data are averaged to the monthly scale and combined with the Global Precipitation Climatology Centre's (GPCC) monthly surface precipitation gauge analysis. In each case the multi-satellite data are adjusted to the large-area mean of the gauge analysis, where available (mostly over land), and then combined with the gauge analysis using a simple inverse estimated-random-error variance weighting. Regions with poor gauge coverage, like central Africa and the oceans, have a higher weighting on the satellite input.

See the algorithm description and the file specification for details.

Bands

Resolution
0.25 arc degrees

Bands

Name Units Min Max Description
precipitation mm/hr 0* 6.73*

Merged microwave/IR precipitation estimate

relativeError mm/hr 0.001* 16.36*

Merged microwave/IR precipitation random error estimate

gaugeRelativeWeighting % 0* 100*

Relative weighting of the rain gauges used in calibration

* estimated min or max value

Image Properties

Image Properties

Name Type Description
StartGranuleDateTime STRING

Granule start date and time

StopGranuleDateTime STRING

Granule end date and time

Terms of Use

Terms of Use

This dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.

Citations

Citations:
  • 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. Hydrometeor., 4(6), 1147-1167.

  • 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., 2012: Algorithm Theoretical Basis Document (ATBD) Version 3.0 for the NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (I-MERG). GPM Project, Greenbelt, MD, 29 pp.

  • Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolph, and 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.

  • 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(1), 36-50.

  • Huffman, G.J., R.F. Adler, B. Rudolph, U. Schneider, and 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.

Explore in Earth Engine

var dataset = ee.ImageCollection('TRMM/3B43V7')
                  .filter(ee.Filter.date('2018-04-01', '2018-05-01'));
var precipitation = dataset.select('precipitation');
var precipitationVis = {
  min: 0.1,
  max: 1.2,
  palette: ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
};
Map.setCenter(6.746, 46.529, 3);
Map.addLayer(precipitation, precipitationVis, 'Precipitation');