資料同化對台灣地區颱風和梅雨模擬之影響

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题名:	資料同化對台灣地區颱風和梅雨模擬之影響
作者:	迮嘉欣;Chia-hsin Tso
贡献者:	大氣物理研究所
关键词:	DOTSTAR;SSM/I;三維變分資料同化;GPS RO 折射率;SSM/I;DOTSTAR;GPS RO;3DVAR
日期:	2009-07-02
上传时间:	2009-09-22 09:44:02 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	本篇研究使用WRF模式和三維變分資料同化方法，選取2008年6月兩個梅雨事件與三個颱風個案進行模擬。模擬中分別同化FORMOSAT-3 GPS RO 折射率資料、追風計畫的投落送資料(dropsonde)、SSM/I、QuikSCAT衛星觀測資料和CWB提供的傳統觀測資料(GTS)，並探討五種不同的觀測資料對模式初始場與數值天氣預報的影響。觀測資料對模式初始場的修正結果顯示，同化SSM/I或GTS的濕度修正量比同化其他觀測資料大，溫度修正量方面則是同化GPS或GTS修正量較多，同化dropsondes或QuikSCAT在風速修正量值最為顯著。由梅雨與颱風個案模擬結果顯示，同化GTS或SSM/I資料對模擬結果改善最多，同化QuikSCAT資料對颱風預報也有正面的影響，同化GPS對颱風路徑改善並不明顯，而同化投落送資料模擬的天氣系統移速較快。降雨模擬方面，累積降雨預報中同化GPS或SSM/I結果最佳，沒有同化任何觀測資料的控制組降雨預報較差，以上結果顯示水氣和風場的修正對天氣預報的模擬很重要。 GPS和其他觀測資料結合模擬結果方面，同時同化GPS和dropsondes時，可發現同化dropsondes資料對於模式模擬的影響較大。GPS和其他觀測資料同化之結果顯示，有同化GPS會有明顯的改善。GPS cycling run實驗中，同時同化多種資料對模擬有顯著的改善。針對GPS資料點位置的敏感度實驗，結果顯示颱風環流附近的單一GPS資料點，對於模式模擬結果有很大的影響。 This study uses the Weather Research and Forecasting (WRF) model and three-dimensional data assimilation (3DVAR) system to ingest various observations, including FORMOSAT-3 GPS RO, DOTSTAR dropsondes, SSM/I, QuikSCAT, and conventional soundings, into the WRF model for understanding the impact of these data on improvement of numerical weather prediction. Five cases of 2008 weather systems, including two Mei-yu cases and three typhoon cases, are selected for simulation. The model initial increments show that assimilation of SSM/I or GTS gives humidity increments larger than those from the experiments assimilating other observations. Temperature increments are, however, larger for assimilation of GPS or GTS than other observations. Assimilation of dropsondes or QuikSCAT obtains the most significant wind increments. The results of Mei-yu and typhoon simulations show that assimilation of GTS or SSM/I data simulation has the best improvement, while QuikSCAT data also have some positive impact on typhoon forecasts. Assimilation of GPS RO data does not have a clear impact on typhoon track forecast. On the other hand, assimilation of dropsonde soundings tends to result in a faster moving system. For rainfall prediction, assimilation of GPS or SSM/I appears to give the best performance. The control experiment without assimilation of any observational data in general gives the worset rainfall prediction. The results indicate that consistent modifications on water vapor mixing ratio and wind fields are important to the weather forecasts. Among the impacts of combined observational data, the dropsonde soundings appear to be more dominant when the GPS data are also assimilated. When GPS data are also assimilated, the simulation with assimilation of other data will become improved. In GPS cycling run experiments, significant improvement of prediction is found in simulations with assimilation of other observations. From the sensitivity tests, single GPS sounding in the vicinity of typhoon circulation might have a considerable impact on typhoon prediction.
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