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.
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.
Name | Description | Gee:unit |
---|---|---|
precipitation | Merged microwave/IR precipitation estimate | mm/hr |
relativeError | Merged microwave/IR precipitation random error estimate | mm/hr |
gaugeRelativeWeighting | Relative weighting of the rain gauges used in calibration | % |
Providers | |
---|---|
NASA GSFC (producer, licensor) | |
Google Earth Engine (host) | |
STAC Version | 0.6.2 |
Keywords | climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather |
License | proprietary |
Temporal Extent | 12/31/1997, 4:00:00 PM - now |
Citation | 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. |
Type | image_collection |
GSD | arc degreesm |
Platform | TRMM |
Instrument | TMI |
Cadence | month |
cube:dimensions | {"x":{"type":"spatial","axis":"x","extent":[-180,180]},"y":{"type":"spatial","axis":"y","extent":[-50,50]},"temporal":{"type":"temporal","extent":["1998-01-01T00:00:00Z",null]},"bands":{"type":"bands","values":["precipitation","relativeError","gaugeRelativeWeighting"]}} |