Using GPM Data to Understand Hurricanes
The 2019 Atlantic "hurricane season" is officially upon us and runs through November 30th. Did you know that GPM data play a fundamental role in the ability to monitor existing storm activity such as capturing the location and intensity of rainfall inside a storm, as well as improving weather and precipitation forecasts through assimilation of instantaneous precipitation information? Here are a few applications of GPM data used to study hurricanes and how the data was then used for decision-making. Monitoring Irma with GPM This image shows rainfall analysis that was derived from GPM's GMI data...
Date Last Updated
July 1st, 2024
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This document describes the basic idea of DPR data processing. It was originally written for the algorithm used in the at-launch version (V03). The algorithm has been modified and improved since then. Although the basic idea of data processing remains the same, the actual flow of processing, in particular that in the solver module, has changed substantially. As a result, some part of description in Section 3.1 may not be relevant any more.

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Date Last Updated
October 6th, 2020
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The Integrated Multi-satelliE Retrievals for GPM (*IMERG*) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.  The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Radar Radiometer Analysis product (with GPCP climatological calibration), and combined into half-hourly 0.1°x0.1° fields.

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Date Last Updated
January 3rd, 2020
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This document describes the algorithm and processing sequence for the Integrated Multi-satellitE Retrievals for GPM (IMERG).  This algorithm is intended to intercalibrate, merge, and interpolate “all” satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe.

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Date Last Updated
October 6th, 2020
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In IMERG up through V05, the cloud motion vector computation approach used is that pioneered in CMORPH (Joyce et al. 2011), in which motion vectors are computed from 4-km geosynchronous infrared (GEO-IR) brightness temperatures. Hence, the motion vectors reflect cloud top motions. However, there are two main limitations in using GEO-IR. The first limitation is that cloud top motions may not match precipitation motions due to both wind shear and the growth and decay of precipitation systems.