利用WRF 3DVAR Hybrid資料同化系統探討GPS掩星觀測對颱風海燕及梅姬模擬之影響

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Title:	利用WRF 3DVAR Hybrid資料同化系統探討GPS掩星觀測對颱風海燕及梅姬模擬之影響
Authors:	趙竑;Zhao,Hung
Contributors:	大氣物理研究所
Keywords:	混合資料同化系統;全球定位系統掩星觀測
Date:	2015-08-04
Issue Date:	2015-09-23 10:11:20 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究主要探討兩個個案，分別為2013年11月3日到11日的颱風海燕(Haiyan)和2010年10月12日到24日的颱風梅姬(Megi)，兩個颱風都造成重大災情，因此颱風預報儼然是一個重要的議題。由於傳統觀測資料(GTS)大部分集中陸地，海面上的觀測資料並不多，所以海上觀測通常使用遙測的方式取得，GPSRO掩星觀測就是其中例子。若能將GPSRO觀測資料與數值天氣預報做整合，就有機會得到更準確的預報颱風路徑和強度，就渴望能降低颱風災害。本篇使用WRF Hybrid資料同化系統將觀測資料和數值天氣預報模式做結合，所以透過Hybrid資料同化系統來測試GPSRO掩星觀測和權重選取對颱風預報的影響。結果顯示，同化GPSRO掩星觀測在模式預報上優於沒同化GPSRO，在同化期間和預報上都有改善，在路徑預報上優勢可持續至少96小時；方均根誤差和空間相關係數驗證上，顯示GPSRO對水氣、熱力和動力場上預報有顯著影響，特別是在高對流層區域，影響力持續72小時。Hybrid資料同化方法對兩個颱風個案預報也有明顯改善，當加入系集背景誤差協方差後，路徑上有明顯接近JTWC最佳路經，使用50%系集背景誤差協方差表現最好；在方均根誤差和空間相關係數的校驗中，當加上系集背景誤差協方差在熱力場預報上比沒加系集背景誤差協方差表現還優異，測試中50 %到75%的系集背景誤差協方差表現最好。另外針對二個個案進行spin-up時間測試，但結果不是非常理想。最後在同化半徑測試裡面，顯示使用800公里的同化半徑在個案預報上表現最佳。 ;This study focuses on two cases. The first case is Typhoon Haiyan on November 3, 2013, the second one is Typhoon Megi on October 12, 2010. These two typhoons lead to the significant disaster situation, so it is an important subject for the typhoon forecast. Because the traditional observations (Global Telecommunication System) mainly centralize on land, they are not numerous upon the sea. The observations upon the sea is usually obtained by using telemetering. The Global Positioning System Radio Occultation (GPSRO) observation is one of the examples. If we can combine the GPSRO observation with the numerical weather prediction (NWP), we will get the more accurate typhoon track forecast and the intensity, and might reduce more damage. In this study, WRF Hybrid Data Assimilation will be used to combine observations with the numerical weather prediction (NWP). We will use WRF Hybrid Data Assimilation to test GPSRO observation sensitivity and hybrid weighting sensitivity to the typhoon forecasting influence. The result shows that assimilating GPSRO observation is better results than no assimilating GPSRO observation for these two cases during assimilation. Typhoon track forecast will be available within 96 hrs or so. On verification of the RMSE (Root mean squared error) and SCC (spatial correlation coefficients), the result shows that GPSRO observation significantly influence the water vapor field, the thermodynamic field and the dynamic field about 72 hrs, specially in the upper troposphere. WRF Hybrid Data Assimilation is also significant improvement in these two cases. When we add in ensemble background error covariance, typhoon track forecast will be closer to the JTWC best track, and using 50% ensemble background error covariance is the best for these two cases. After verifying the Rmse (Root mean squared error) and Scc (spatial correlation coefficients) and comparing with and without the ensemble background error covariance, we found that the thermodynamic field with te ensemble background error covariance is better. The result shows that 50% to 75% ensemble background error covariance is better. After the spin-up time testing of other two cases, we found that results are not satisfactory. Last, we did the localization experiment that 800 km radius case showed the best result in forecast performance.
Appears in Collections:	[Department of Atmospheric Sciences and Graduate Institute of Atmospheric Physics ] Department of Earth Sciences

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