Global Error Characterization for Satellite Precipitation Retrievals

Users of satellite precipitation retrievals need error estimates to appropriately utilize the retrieved precipitation information in data assimilation for numerical models and in forecasting. However, ground validation (GV) sites are sparse, particularly over the oceans. Over regions without GV sites, relative errors between independent estimates derived from passive microwave and precipitation radar are used to assess and diagnose errors in satellite precipitation retrievals. Local observations at GV sites can help guide the physical interpretation of the error characteristics. This work is in collaboration with Rob Wood of University of Washington and John Stout and John Kwiakowski of George Mason University.

Current work is focused on development of an algorithm prototype to estimate global errors in satellite precipitation retrievals. The prototype is being developed and tested using NASA Tropical Rainfall Measuring Mission (TRMM) data sets. A key design decision regarding the prototype was that no single error product will satisfy the diverse target users. Hence, the prototype is designed to yield a suite of products. Use of TRMM satellite data sets yields error information on the current TRMM products as well as the opportunity to prototype global error characterization methodologies for the TRMM follow-on program, Global Precipitation Measurement (GPM) mission to be launched sometime in the future.  Statistics are calculated describing relative error among precipitation retrievals from independent sensors, the Precipitation Radar (PR) and the TRMM Microwave Imager (TMI), at sensor native scales and PR data rescaled to spatial scales similar to TMI 10 GHz and 19 GHz pixels.

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