11A.1 Reaching for 20 Years with the IMERG Multi-Satellite Products (Invited Presentation)

Thursday, 10 January 2019: 8:30 AM
North 126BC (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, E. J. Nelkin, S. Sorooshian, J. Tan, and P. Xie

The latest releases of Global Precipitation Measurement (GPM) mission products cap five years of vigorous development cycle since the launch of the GPM Core Observatory, and these now provide data sets that are relatively homogeneous across the joint Tropical Rainfall Measuring Mission (TRMM) and GPM eras. Version 06 of the U.S. GPM team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product enforces a consistent intercalibration for all precipitation products computed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- and GPM-era calibrators, respectively, and incorporates monthly surface gauge data. The basic IMERG algorithm now features precipitation motion vectors (used to drive the Lagrangian interpolation, or “morphing”) that are computed by tracking vertically integrated vapor fields analyzed in MERRA2 and GEOS5. This innovation provides globally complete coverage, expanding IMERG’s coverage beyond the 60°N-S latitude band provided by IR-based vectors, although we continue to mask out precipitation over snowy/icy surfaces as unreliable. A second innovation is the Quality Index (QI) data field. The half-hourly QI is taken as the approximate Kalman Filter correlation computed in the morphing calculation. It depends (non-linearly) on the time offset of each propagated passive microwave overpass, and infrared contribution (as necessary) in each grid box. The monthly QI (computed for Final Run IMERG) is based on the Huffman et al. (1997) Equivalent Gauge concept, which inverts the equation for estimating monthly random error in each grid box. We will summarize the processing status for Version 06 IMERG and provide examples of performance. For example, the TQV motion vectors are typically slightly better than the IR-based vectors at all latitudes. The transition across the TRMM/GPM data boundary will be discussed, together with the implications for the utility of IMERG data for long-record calculations. As well, we will review the eventual retirement of the predecessor TMPA multi-satellite dataset.
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