Assimilation Of Radar Data For The Identification Of Downslope Windstorm Using The 3DVAR Assimilation Method

Abstract

Assimilation of radar data is highly important for improving the accuracy in simulating atmospheric conditions. Radar data are used to refine initial data to align with the actual atmospheric conditions. This technique can be applied to detect complex atmospheric dynamics involving mountainous topography. Mountain waves and downslope windstorms are two phenomena typically interconnected, where intense downslope winds in deep cross-mountain flow trigger strong mountain waves in the stable lower atmospheric layer. The research was conducted from October 8 to 10, 2024, with simulation data covering October 7 to 11, 2024, for the spin-up process. The objectives of this study are to understand the atmospheric dynamics conditions, identify the types of mountain waves and downslope windstorms, and simulate mountain wave and downslope windstorm phenomena using WRF-CTL and WRF-DA. The research findings indicate that the atmospheric stability before the event showed strong static stability with speeds reaching 2.4 m/s, while after the event, there was an increase in dynamic turbulence. The parameters used include the Brunt-Väisälä Frequency with values ranging from 0.09001948 N² to 0.134224952 N², the Richardson Number with values ranging from 2.05 to 10.00, the Scorer Parameter with values from 0.1 1/m to 0.3 1/m, and the Froude Number with values indicating 0.2 to 1.4. The research results show that radar data assimilation successfully enhanced the simulation accuracy compared to WRF-CTL. Mountain waves were identified as vertical propagating waves during the day with vertical propagation up to 400 mb and trapped lee waves at night with horizontal oscillations spanning 18-25 km. The downslope windstorm was classified as a shallow cross-mountain flow with a flow depth of less than 1 km.