43 Principal Modes of Precipitation Variability from Preliminary Series of IMERG Data

Monday, 23 January 2017
Andrey Savtchenko, NASA GSFC, Greenbelt, MD; and G. J. Huffman and B. Vollmer

Handout (2.1 MB)

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission, “IMERG”, is the unified U.S. algorithm that provides merged Microwave/Infrared (IR) satellite precipitation product for the U.S. GPM team.

IMERG data are output from a complex processing system, where the precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed, then gridded and intercalibrated to the GPM Combined (Radiometer-Radar) product, into half-hourly 0.1 x 0.1 degree grid. In parallel, NOAA Climate Prediction Center assembles the IR fields, and thus prepared MW and IR data are merged in an intricate processing system based on artificial neural networks and Kalman filtering.

As part of the NASA/GSFC Global Change Data Center, the Goddard Earth Sciences Data and Information Services Center collaborates with the Precipitation Measurement Missions in providing long term storage, services, and expertise on data usage in climate studies. Our analysis tests the information content in the monthly IMERG time series which at this point are limited in length, and to +-60 latitudes band. We compile monthly series by combining the Final and Late IMERG output to extend the record, and downscale the resolution to a global climate-relevant 1 x 1 degree. After applying Singular Value Decomposition, we have a preliminary look at the distribution of precipitation variability among the resulting Principal Components. Our goal is to assess how IMERG responds to internal climate forcings, and in particular the El-Nino Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Using well-established climate indices for ENSO and NAO, we estimate which principal components give promise to be exploited in future studies of impacts of ENSO and NAO on precipitation variability.

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