1069 Enhancing Specific Attenuation Rain Rates in Stratiform and Convective Rain Regimes

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Stephen B. Cocks, CIMMS/Univ. of Oklahoma, Norman, OK; and L. Tang, J. Zhang, A. Ryzhkov, P. Zhang, and K. W. Howard

A new multi-radar multi-sensor (MRMS) Dual Pol (Q3DP) quantitative precipitation estimate (QPE) algorithm has been developed that utilizes a combination of specific attenuation (A), reflectivity (Z) and specific differential phase (Kdp). Extensive verification showed Q3DP performed significantly better than the current operational MRMS QPE utilizing multiple precipitation rate (R) and Z relationships. Key to the Q3DP QPE success is the usage of R(A) relationship in areas where radar observations are below the melting layer. A is more linearly related to R than other radar variables and is insensitive to systematic errors in Z and Zdr (differential reflectivity). As a result, R(A) provides a QPE with less systematic and random errors than does other radar variables, especially in moderate to heavy rain where substantial attenuation of electromagnetic energy exists.

Although successful, Q3DP exhibited a dry bias in light to moderate stratiform rainfall and a wet bias in some convective rainfall events. Subsequent analyses indicated the major contributing factor to these biases was the use of a spatially uniform parameter α, defined as the ratio A/Kdp, for the radar field of view when multiple DSDs were present. To address this challenge, the Q3DP QPE was refined to better capture spatial variations of precipitation regimes. Test results utilizing 29 predominantly stratiform, 12 predominantly convective, and 34 mixed regime rainfall events indicated the enhanced QPE algorithm utilizing the new technique significantly reduced variability and exhibited over 20% reduction in the Mean Absolute Errors for stratiform and convective events. While the error reduction for mixed regime events was smaller, ~5%, there was a significant reduction in the variability resulting in more robust estimates.

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