Thursday, 26 January 2017: 4:30 PM
Conference Center: Skagit 2 (Washington State Convention Center )
Thunderstorms impose a large economic burden on the United States, significantly impacting weather-sensitive industries such as aviation. For example, unexpected thunderstorm development over a busy airspace can lead to delayed or cancelled flights, cascading across the National Airspace System. These issues necessitate improved radar algorithms that capitalize on the added microphysical information provided with polarimetric radar. One unique polarimetric signature that often highlights new convective growth is the size sorting of raindrops, consisting of high differential reflectivity (ZDR
) collocated with relatively low reflectivity (Z). Recent research has confirmed the ubiquitous nature of this signature (e.g., Kumjian and Ryzhkov 2012), but limited operationally focused work exists to leverage its valuable ability to identify developing convection.
This work details the development of a novel size sorting identification algorithm that can be utilized to improve the near-term prediction of thunderstorms. At the core of the algorithm, a unique Z-ZDR relationship is created for each elevation scan and positive ZDR outliers (indicative of size sorting and potential updrafts) are identified. By building unique Z-ZDR relationships for each elevation scan of each radar, this algorithm is immune to calibration issues that can hinder traditional algorithm performance over time and space. Initial testing over a variety of convective modes and geographic regions shows good skill in predicting thunderstorm propagation trends on the scale of 5-30 minutes. Our work will present some of these test cases, as well as outline targets of opportunity for further refinement of the algorithm.
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