辛樂克颱風(2008)降雨系集模擬與分析-卡爾曼濾波器資料同化

Abstract

Forecasting the typhoon-induced rainfall near Taiwan has long been a very challenging issue due to the complex interaction between typhoon circulation and topography of Taiwan. Typhoon Sinlaku (2008), a case in point under The Observation System Research and Predictability Experiment ( THORPEX ) Pacific Asian Regional Campaign ( T-PARC ), with the most abundant aircraft observations taken and with the potential to address major TC issues, produced heavy rainfall over the mountain-area of Taiwan. A newly-proposed vortex initialization method based on the ensemble Kalman filter (EnKF) and the Weather Research and Forecasting (WRF) model is adopted in this study. Taking advantages from the T-PARC data and the new vortex initialization method, this study constructs different ensemble simulations of Sinlaku to investigate the associated precipitation during its landfall period. The simulated ensemble mean well captures the movement of Sinlaku while there is a wide spread of tracks among the ensemble members. By analyzing all 28 members, the predictability of the rainfall associated with Sinlaku is examined. It is found that members with tracks close to the best track from the CWB are able to present more realistic rainfall distributions ( both temporally and spatially ) though the total rainfall amounts and their cumulative frequencies are underestimated. For members with southward-biased tracks, despite the cumulative frequencies are more similar to the observation, the simulated rainfall pattern is less consistent with the observation and overestimate the maximum of the rainfall amount. When a finer resolution is applied to the simulation, the maximum rainfall amount becomes slightly greater, but the cumulative frequency stays nearly unchanged. For landfalling typhoons in Taiwan, the track is a critical factor in affecting rainfall simulation. It is important to note that the uncertainties which cause different rainfall patterns or amounts can be addressed from variations among the ensemble members, thus providing more insights into the rainfall predictability associated with typhoons near Taiwan.