Sensitivity analysis of the WRF-HAILCAST model to estimate hail size
Presentation 1600-1630. Hailstorms are a severe weather phenomenon which cause the losses of billions of dollars in the United States each year. The simulated dynamical evolution of hailstorms remains a challenge due to a number of factors such as physical parameterization and initial and boundary conditions. In this study the Weather research forecast (WRF) coupled with HAILCAST which is a one-dimensional model is used in this study to produce hail size forecast at fine spatial scale. This work focus to evaluate the ability WRF-HAILCAST model using GFS reanalysis data as the initial and boundary conditions to test different microphysics parameterization (MP) and to examine the stochastically kinetic energy backscatter (SKEB) and Stochastic perturbed parametrization tendency (SPPT) schemes to skillfully predict plowable hail that occurred over Minnesota on 11 June 2017. A thunderstorm that caused plowable hail accumulations of hail up to 2.5 inches and damaging winds in localized areas of Minnesota. Results suggests that Thompson or Goddard scheme are recommended to be used, although further testing needs to be done, to determine which microphysics parameterization options are the most skillful.