May 27 - August 1
Evaluating Multi-Radar, Multi-Sensor Hail Diagnosis with High Resolution Hail Reports
Christopher Wilson, Kiel Ortega, and Valliappa Lakshmanan ("Lak")
Abstract:
Low resolution verification data, as available from the Storm Data database, has hindered the development and evaluation of high resolution hail algorithms as well as the assessment of hail forecasting techniques. Previous studies have highlighted the inadequacies and inaccuracies associated with this verification data. This study uses high resolution ground-truth hail verification data from the Severe Hazards Analysis and Verification Experiment (SHAVE) to evaluate gridded synthetic hail verification and different radar derived parameters used in predicting severe hail.
MESH is found to have limited skill as a synthetic verification tool due to a high probability of false detection and a wide distribution of MESH values for each reported hail size range. In addition, radar-derived parameters are found to provide little skill in the prediction of severe hail as the probability of false detection associated with these parameters leads to low skill scores. The predictive skill of these parameters is also found to decrease with time, limiting the lead time in which surface hail fall is possible using radar derived parameters.