1108 Recent Pre-Launch Improvements to the GOES-R Rainfall Rate Algorithm

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Yan Hao, IMSG, College Park, MD; and R. J. Kuligowski and Y. Li

The GOES-R Rainfall Rate algorithm, which is also called SCaMPR (Self-Calibrating Multivariate Precipitation Retrieval), is an effort to combine the relative strengths of infrared (IR)-based and microwave (MW)-based estimates of rain rate. It uses MW rain rate retrievals as its calibration standard for a GOES IR-based retrieval.  This allows MW rain rate to be used in an operational forecasting environment because the IR data that are used as inputs to the rain rate retrievals have  much shorter latency (minutes) relative to other products that use MW rain rate directly.

A simplified version of the algorithm has been run in real time on current-generation GOES since August 2011. Several improvements have been made since then including employing a relative humidity (RH) correction for sub-cloud evaporation that significantly reduced false alarm rainfall, and shrinking the calibration regions to make the calibration more temporally and spatially consistent and more accurate. Other potential improvements under investigation include incorporating available ground-based radar data into SCaMPR as a supplemental data set to MW-based rainfall rate estimates, and using rain rates derived from retrieved cloud-top properties to improve the detection of rainfall from warm cloud (using a static calibration in this case since the MW sensors frequently miss these events). An improved RH correction is also being pursued to reduce an excessively aggressive reduction of heavy rain rates in the current RH correction.

This presentation will describe the baseline GOES-R Rainfall Rate algorithm and its recent improvements and will compare its performance with the current version of the algorithm using rain gauges and radar-gauge blended fields for ground validation.  The evaluation will focus on the version of the algorithm that uses data from current GOES, but evaluation using the full GOES-R version of the algorithm may also be included if the data are available.

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