Day 1 for the Integrated Multi-satellitE Retrievals for GPM (IMERG) Data Sets

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Wednesday, 7 January 2015: 9:15 AM
231ABC (Phoenix Convention Center - West and North Buildings)
George J. Huffman, NASA/GSFC, Greenbelt, MD; and D. T. Bolvin, D. Braithwaite, K. Hsu, R. J. Joyce, C. Kidd, S. Sorooshian, and P. Xie

The Integrated Multi-satellitE Retrievals for GPM (IMERG) is a U.S. GPM algorithm system that is designed to estimate the best time series of global precipitation from the international constellation of precipitation-relevant satellites and a surface precipitation gauge analysis (from Global Precipitation Climatology Centre). IMERG was developed to use GPM Core Observatory data as a reference for the satellites of opportunity that constitute the GPM virtual constellation. Computationally, IMERG is a unified U.S. algorithm drawing on strengths in the three contributing groups, whose previous work includes:

1) the TRMM Multi-satellite Precipitation Analysis (TMPA); 2) the CPC Morphing algorithm with Kalman Filtering (K-CMORPH); and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS).

We review the design, development, testing, and status for IMERG, which provides 0.1x0.1 half-hourly data for successive runs at 4 hours, 8 hours, and 2 months after observation time. Initially, the spatial extent is 60N-S, for the period March 2014 to the present. Later, it will be extended to fully global, covering the period 1998 to the present. Both the set of input data set retrievals and the IMERG system are substantially different than those used in the previous U.S. products mentioned above. The input passive microwave data are all being produced with GPROF2014, which is substantially upgraded compared to previous versions, and including microwave sounder for the first time. Such new algorithms require careful check of the initial test data sets for performance. IMERG output will be illustrated using newly released operational test data (as possible). This includes a variety of diagnostic fields that developers and users might find useful, including precipitation estimate, time of observation, and source of the current half hour's microwave input data, and the current half hour's IR precipitation estimate. Finally, we will summarize the expected release of various output products, and the subsequent reprocessing sequence.