Vortex initialization through the high-resolution ensemble Kalman filter framework and its impact on intensity forecast: a case study of Typhoon Megi (2010)

Autor: Yi-Pin Chang, 張逸品
Rok vydání: 2018
Druh dokumentu: 學位論文 ; thesis
Popis: 106
One of the most challenging issues among tropical cyclone (TC) forecasting is the intensity prediction, especially for rapid intensification (RI) cases. Vortex initialization based on ensemble data assimilation has the advantage of dynamical consistency and makes good use of observations, potentially giving more realistic initial conditions and better intensity forecasts. In this study, initialization strategies based on EDA are applied to the case of Typhoon Megi (2010) under the framework of Weather Research and Forecasting model-TC Centered Local Ensemble Transform Kalman Filter (WRF-TCCLETKF) to explore the impact of assimilating inner-core observations, including dropsondes (DP) and the axisymmetric surface wind structure (VT). The dropsonde data were collected from the Impact of Typhoons on the Ocean in Pacific (ITOP) field campaign, and the assimilation of the axisymmetric surface wind structure follows the methodology proposed by Wu et al. (2010). The initial time of assimilation was 0000 UTC 14 October 2010, and the forecasts were carried out on 15 and 16 October 2010. Results show that the earlier forecast initial time gives better intensity prediction. The TC intensity in the VT analysis is close to the observation; however, the TC slowly intensifies with less convective bursts (CBs) during the forecast period. By contrast, although the TC intensity in the DP analysis is weaker than the observation during DA period, the TC rapidly intensifies with broader CB areas during the forecast period. Assimilating both types of observation shows positive impacts on not only the TC intensity but also the TC structure. In conclusion, assimilating the axisymmetric surface wind structure can enhance the mean wind structure, and assimilating dropsondes better represents the TC structure, leading to the process of RI.
Databáze: Networked Digital Library of Theses & Dissertations
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