Investigating Potential Biases in TRMM Measurements of Precipitation Systems with Extreme Rain Rates and Extreme Intensities

Tuesday, 19 April 2016
Plaza Grand Ballroom (The Condado Hilton Plaza)
Alexandria C. Gingrey, Univ. of Utah, Salt Lake City, UT; and E. Zipser, A. C. Varble, and S. Bang

Convective systems exhibit a variety of precipitation structures that are associated with severe weather and strongly impact society. Some have both extremely high rain rates and very intense convection, but upon occasion, either can occur without the other. This study uses the University of Utah TRMM Precipitation Database to identify the most extreme 1% of tropical and subtropical precipitation features (PFs) in each 2.5-degree grid box. These PFs are separated into three categories: intense & rainy (PF is in top 1% of both rain rates and 40 dBZ echo top heights), intense only (PF is in top 1% of 40 dBZ echo top heights only), and rainy only (PF is in top 1% of rain rates only). The global tropical and subtropical distribution of these three PF types is presented together with their occurrence in specific regions, e.g., Argentina, the Amazon, the Congo, the Southeast United States, and the Central Pacific Ocean. The variability in the overlap between extreme rain and extremely intense PFs is examined within each region, as well as each region's individual variability in 40 dBZ echo top heights and rain rates.

A known issue in using TRMM to examine extreme precipitation is that the precipitation radar suffers from attenuation in intense storms. Thus the accuracy of rain rate retrievals is sensitive to both the attenuation correction and the Z-R relationship used. To examine potential TRMM biases, we compare NEXRAD dual-polarimetric rain rates and non-attenuated reflectivities over the southern United States to the respective TRMM radar estimates. This region contains both shallow PFs with high rain rates, deep PFs with smaller rain rates for a given reflectivity, and PFs that are a combination of the two, thus making it a good region to establish biases for these types of events. Our goal is to learn how to compensate for these biases in these types of extreme events, and apply this information to TRMM observations throughout the tropics and subtropics.

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