颱風渦漩初始化與資料同化對颱風預報的影響

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陳致穎(Chih-Ying Chen) 查詢紙本館藏

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大氣物理研究所

論文名稱

颱風渦漩初始化與資料同化對颱風預報的影響

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摘要(中)

本研究探討使用三維變分（3DVAR）和系集調整卡爾曼濾波器（EAKF）同化全球電信觀測系統（GTS）以及GPS無線電掩星（GPSRO）資料對莫拉克颱風預報的影響。從模式預報降雨與地面降雨資料的統計校驗中發現，使用WRF-EAKF同化GTS或GPSRO明顯提高了WRF 模式在24 - 48小時間的降雨預報。主要因為颱風初始渦旋經由2天的EAKF同化方法循環同化後強度增加。然而，當模式預報時間超過48小時後，同化後的降雨預報並沒有顯著改善。綜合而言同化GPSRO資料，對於莫拉克颱風0-24小時降雨預報明顯高估，而48-72小時則是明顯低估。我們發現，使用EAKF同化方式作為低解析度WRF模式的初始化方法可以產生較好的降雨預報。相較之下，3DVAR資料同化的整體表現則較差。這主要是由於不同的循環資料同化是否能產生接近實際觀測的颱風初始渦旋的緣故。
其次，我們利用18公里解析度模式預報資料對莫拉克颱風進行2公里高解析度降尺度預報。我們發現使用NCEP GFS作為對流尺度高解析度模式的側邊界條件，將會使模式明顯改善24小時以後的降雨預報，而且提供較佳的預報結果。然而使用EAKF或者冷起動模式的預報資料作為側邊界條件，其預報結果並不如預期的好。由此結果顯示，側邊界條件對小區域的雲解析度模式預報之影響相當重要。因為，模式預報24-48小時颱風的路徑以及降雨是受環境流場影響為主，而初始渦旋強度以及結構主要是影響模式在最初0-24小時內（CTRL，EAKF和GFS）的降雨量預測，這結果相當明顯呈現在我們的敏感度實驗中。
由於，前兩個部分針對莫拉克颱風的同化與敏感度實驗已證實初始渦旋以及側邊界條件對模式預報的重要性。基於資料同化對於颱風初始渦旋掌握的不確定性，以及無法在模式初始場提供正確的颱風渦旋雨帶結構。因此，我們利用了Nguyen and Chen (2011)的颱風初始化方法針對2004 – 2013年18個西北太平洋的颱風進行颱風初始化對颱風結構的影響以及分析，主要目的是要瞭解這個初始化方法能否初始化出接近實際衛星觀測的雨帶結構。初步結果顯示，透過這個方法可以正確的呈現環境場與颱風渦旋間的交互作用的過程，而且可以有效提供模式初始條件較合理的颱風渦旋結構。因此，最後我們將NC2011以及3DVAR資料同化方法進行結合對2012年颱風杰拉華進行實驗性預報，並討論部分預報結果。初步結果顯示，結合NC2011以及3DVAR資料同化方法是可行的，不過由於杰拉華颱風的路徑預報受到大尺度環流的影響過於顯著，透過使用Nataional Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP CFSR)作為側邊界條件可以證明模式中LBCs的重要性。雖然預報路徑與實際最佳路徑相比仍有相當差距，不過颱風結構仍舊呈現出與衛星觀測接近的特徵。

摘要(英)

This study focused on investigating the impacts of assimilating Global Telecommunication System (GTS) and/or GPS Radio Occultation (GPSRO) data using two assimilation systems 3-Dimension variational (3DVAR) and Ensemble Adjustment Kalman Filter (EAKF) on the realtime forecast for Typhoon Morakot (2009) using a 18-km grid. From statistical verifications of simulated rainfall with dense ground base rain guage data, we found that assimilation GTS and GPSRO has improved the 24-48 h rainfall forecast because the initial vortex is better resolved with data assimilation. However, beyond 48 h model runs, there is no significant improvement in rainfall forecast skill. We also found that model initialization using the EAKF assimilation system produces better rainfall forecast as compared with the 3DVAR data assimilation system simply because the EAKF data assimilation system provides better initial hurricane vortex as compared with the 3DVAR data assimilation scheme.
We conduct a series of sensitivity tests by nest down the initial vortex after data assimilation to a nested domain with a 2-km grid. We found that the lateral boundary conditions for the 2-km convection-allowing model provided by National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) produces better rainfall forecast after 24 h of model integration than the lateral boundary conditions from both the EAKF and 3DVAR runs with a 18-km grid. It is apparent that with different initial vortex (GFS, EAKF and GFS) rainfall forecast within the first 24 h is mainly affected by the strength and structure of the initial vortex. After 24-48 h model integration, the influences of lateral boundary conditions on model forecasts become important. It is apparent that the track and rainfall forecasts after 24-48 h model integration are dominated by environment flow.
We also examine 18 storms (2004-2013) over the western Pacific using TC method developed by Nguyen and Chen (2011). The preliminary results of this study show that the environment has a significant effect on the initial storm structure. During the early season, storms embedded within the southwesterly monsoon flow have a tendency to exhibit a “9” type asymmetric structure with an upper level outflow channel extending southwestward from the southeastern quadrate of the storm. At low levels, the convergence area between the storm circulation and the southwesterly flow is a favorable location for the development of spiral rainbands. Late season storms have a tendency to produce a “6” type storm structure with an outflow channel extending northeastward from the northwestern part of the eyewall, especially when an upper-level cold low or trough is present to the northwest of the storm. At low levels, the convergence of the northeasterly monsoon flow and the cyclonic circulation of the storm are favorable for the occurrences of spiral rainbands. For intense storms that underwent an eye-wall replacement cycle, the NC2011 scheme also shows considerable skill in reproducing the double eye-wall structure in the model initial conditions. So, finally we will combine NC2011 and 3DVAR data assimilation methods to study TC Jelawat (2012) and make some discussion about the forecasting results. Our preliminary results show that combining NC2011 and 3DVAR data assimilation method is feasible, however, because the track forecast affected by large-scale circulation is too significant, so the results did not fully achieve the desired effect, but still showing some characteristics close to satellite observations.

關鍵字(中)

★ 颱風渦漩初始化
★ 資料同化

關鍵字(英)

★ Tropical Cyclone Initialization
★ Data Assimilation

論文目次

Abstract I
中文摘要 IV
表目錄. VI
圖目錄 VII
一. 緒論..................................................1
1-1 前言...............................................1
1-2 研究回顧...........................................2
1-3 研究動機...........................................7
二. 研究方法與模式實驗設計................................10
2-1 18km 低解析度資料同化預報實驗設計.................10
2-2 2km 高解析對流降尺度預報實驗設計..................11
2-3 降雨預報校驗指數...................................13
2-4 NC2011熱帶氣旋初始化實驗設計......................14
2-5 NU-TCWRF(NC2011+3DVAR)實驗設計.....................15
三. GPSRO資料同化對颱風模擬之影響........................17
3-1 模式路徑以及降雨預報結果統計校驗與分析.............17
3-2 GPSRO資料同化對於模式預報的影響分析..............19
四. 2公里雲解析動力降尺度模式敏感度實驗.................22
4-1 高解析度颱風降尺度初始化以及預報誤差的討論........22
4-2 高解析度降雨預報能力結果討論......................24
五. NC2011颱風初始化以及結果討論........................29
5-1 “9”字型颱風初始結構分析.........................29
5-2 “6” 字型颱風初始結構分析........................30
5-3 雙眼牆颱風結構分析................................31
5-4 NC2011結合3DVAR資料同化預報結果.................34
六. 結論以及未來展望....................................38
參考文獻................................................43
附表....................................................53
附圖....................................................56

參考文獻

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指導教授

林沛練、陳景森、陳宇能
(Pay-Liam Lin、Chin-Sen Chen、Yi-Leng Chen)

審核日期

2014-6-20

推文