Model Predictability of Hail Precipitation with a Moderate Hailstorm Case. Part I: Impact of Improved Initial Conditions by Assimilating High-Density Observations

Abstract

Hailstones have large damage potential; however, their explicit prediction remains quite challenging. The uncertainty in a model’s initial condition and microphysics are two of the significant contributors to the challenge. This two-part study aims to investigate the impacts of improved initial condition and microphysics on hail prediction for a moderate hailstorm that occurred in Beijing on 10 June 2016. In the first part, the role of initial conditions on hail prediction is explored by assimilating high-density observations into a numerical model with a recently developed explicit hail microphysics scheme. High-resolution and high-frequency observations from radar and surface networks are assimilated using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. The role of the initial conditions in improving explicit hail prediction with two different planetary boundary layer (PBL) schemes, the Yonsei University (YSU) scheme and the Mellor–Yamada–Janjić (MYJ) scheme, is then examined. Results indicate that the data assimilation significantly improves the hail size and location prediction for both PBL schemes by reducing errors in surface wind, temperature, and moisture fields. It is also shown that the improved analyses of low-level and midlevel vertical wind shear, resulting mainly from radar data assimilation, are pivotal to the improvement of hailstorm prediction with the YSU scheme, while the improved analysis of thermodynamic field resulting from the assimilation of both radar and surface data plays a more important role with the MYJ scheme. The results of this work shed light on the influence of data assimilation and provide insights on explicit hail predictability with respect to model initial conditions.