Precipition Simulation Associated with Typhoon Sinlaku (2002) in Taiwan Area Using the LAPS Diabatic Initialization for MM5

Abstract

A precipitation simulation associated with Typhoon Sinlaku using the fifth-generation Pennsylvania State University – National Center for Atmospheric Research Mesoscale Model (MM5) initialized diabatically with the Local Analysis and Prediction System (LAPS) is evaluated over the Taiwan area. Two purposes of this paper are to test the performance of the LAPS diabatic data assimilation technique and investigate the impact of the Doppler radar data on the short-range quantitative precipitation forecasts for the typhoon. Typhoon Sinlaku was selected because Dopper radar is one of the most important data sources for an accurate analysis of typhoons and Sinlaku was located close to the Wu-Fen-Shan (WSR-88D) Doppler radar station at north tip of Taiwan during part of its lifetime on 6-7 September 2002. The observed rainfall distribution associated with Sinlaku was closely related to the topography in northern Taiwan. Simulation results show that the MM5 initialized diabatically with LAPS has higher skill for precipitation simulation than the nnon-LAPS cold start experiment, especiallyfor the higher thresholds in the early portion of model integration (≥10 mm in 0-6 h). The assimilation of the Wu-Fen-Shan Doppler radar data played a key role in the improvement of precipitation simulation owing to improvement in the presentation of typhoon hydrometeoro-logical features, such as clouds and the outer rainband, in the model initial conditions. The presence of these initial hydrometeor species had a beneficial impact on reduced precipitation spin-up time. The use of Doppler radar data also can enhance the forecast definition of typhoon structure and rainband simulations. However, these radar data only play a minor role on the typhoon track simulation in this case study. Overall, the mesoscale model initialized diabtically with LAPS data assimilation shows improved capability on the typhoon short-range quantitative precipitation forecasts, especially when the Doppler radar data is included.

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