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姓名	葉浩倫(Hao-Lun Yeh)  查詢紙本館藏	畢業系所	大氣科學學系
論文名稱	(Impact of including observation error correlation for assimilating radar radial wind and its impact on heavy rainfall prediction)
相關論文	★ 利用WRF-LETKF同化系統探討掩星折射率觀測對於強降水事件預報之影響 ★ 改善區域系集卡爾曼濾波器在颱風同化及預報中的spin-up問題-2008年颱風辛樂克個案研究 ★ LETKF加速就位法於颱風同化預報之應用 ★ 利用系集重新定位法改善颱風路徑預報-2011年南瑪都颱風個案研究 ★ 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善定量降水即時預報：莫拉克颱風（2009） ★ 利用系集資料同化系統估算區域大氣化學耦合模式中trace物種之排放與吸收：以CO2為例 ★ OSSE實驗架構下利用系集預報敏感度工具探討觀測對於颱風路徑預報及結構之影響 ★ 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善短期定量降雨預報: SoWMEX IOP8 個案分析 ★ 利用系集重新定位法改善對流尺度定量降水即時預報：2009年莫拉克颱風個案研究 ★ LAPS 短時(0-6小時)系集降水機率預報之評估與應用 ★ 利用辛樂克颱風(2008)建立的觀測系統模擬實驗評估系集奇異向量在颱風系集預報之應用 ★ 雷達資料同化於多重尺度天氣系統(梅雨)的強降雨預報影響：SoWMEX IOP#8 個案研究 ★ 基於高解析度系集卡爾曼濾波器之渦旋初始化及其對於颱風強度預報之影響：2010年梅姬颱風個案研究 ★ 系集轉換卡爾曼漸進式平滑器在資料同化之應用 ★ 不同微物理方案在雲可解析模式的系集預報分析: SoWMEX-IOP8 個案 ★ 利用正交向量改善系集卡爾曼濾波器之系集空間及其對同化與預報之影響
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摘要(中)	在資料同化中，以往會假設觀測誤差彼此間是無相關的。為了避免觀測誤差相關性，在進行同化前，高解析資料通常會經過如超級觀測化(superobbing)或資料稀化(data thinning)策略來降低資料解析度。然而，這些策略犧牲了高時空解析度觀測在提供密集天氣資訊上的優勢。除此之外，在不加入觀測誤差相關性下，同化高解析觀測(例如：雷達徑向風)會低估分析誤差而產生過度擬合，進而降低分析及預報場的準確度。為了能更有效的使用高解析雷達資料以避免過度擬合，本篇研究提出在WRF雷達資料同化系統(WRF-LETKF Radar Assimilation System, WLRAS)中加入都卜勒雷達徑向風誤差相關性，並評估其對短期降水預報的影響。所利用的個案為2017年6月2日台灣北部的梅雨強降水事件，並同時同化雷達徑向風及回波資料。 本研究分為兩階段，第一部分利用觀測增量樣本估計徑向風水平方向上的觀測誤差相關性。結果顯示即便使用不同解析度之超級觀測，徑向風的誤差尺度皆約為25公里。接著，第二部分實驗將徑向風誤差相關性的估計結果加入至雷達資料同化系統中。由同化實驗結果可知，相較於不考慮觀測誤差相關性的實驗，同化徑向風觀測時加入觀測誤差相關性可產生空間上較小尺度的風場修正，而此風場修正會使得分析場在梅雨鋒面區域擁有較強的輻合、激發較多的局部對流，並伴隨較多的水氣量以及較強的西南風，進而提供對流良好的發展條件。風場與水氣場的修正隨後改善了回波及降水的預報。除了更好的單一預報表現之外，在機率定量降水預報(PQPF)當中，可以得知在同化系統中加入徑向風觀測誤差相關性後，系集能有更高機率預報出較為接近觀測的降水強度以及空間分布。另外，由每小時累積雨量預報FSS (Fractions Skill Score)得分之校驗結果得知，加入徑向風觀測誤差相關性之改善的效果可以維持約六小時。
摘要(英)	An assumption of uncorrelated observation errors is commonly adopted in conventional data assimilation. For this reason, high-resolution data is re-sampled with strategies like superobbing or data thinning. This also sacrifices the advantage of high temporal and spatial resolution observations that can provide essential detailed structures. However, assimilating the high-resolution data, such as radar radial wind, without considering the observation error correlation can lead to overfitting and thus degrade the performance of data assimilation and forecast. This study uses the radar ensemble data assimilation system, which couples the Weather Research and Forecasting model and Local Ensemble Transform Kalman Filter (WRF-LETKF), to assimilate the radar radial velocity and reflectivity data. We present a strategy to include the error correlation of the Doppler radar radial velocity in the WRF-LETKF radar assimilation system and examine their impact on short-term precipitation prediction based on the heavy rainfall case on 2nd June 2017 in Taiwan. We first estimate the horizontal error correlation scale for radial velocity based on the innovation statistics. The error correlation scale is approximately 25 km. The introduction of correlated observation error for Doppler radar radial winds exhibits more small-scale features in the wind analysis corrections compared to the experiment using the independent observation assumption. Consequently, the modification on wind corrections leads to stronger convergence accompanied by higher water vapor content, and induces subsequent local convections, resulting in more accurate simulated reflectivity. For probability quantitative precipitation forecast (PQPF), our results show that the experiment using the correlated observation error has higher probability to generate heavy rainfall and agrees better with the observation.
關鍵字(中)	★ 資料同化 ★ WRF-LETKF雷達資料同化系統 ★ 觀測誤差相關性 ★ 都卜勒雷達徑向風	關鍵字(英)	★ Data Assimilation ★ WRF-LETKF Radar Assimilation System ★ Observation Error Correlation ★ Doppler Radar Radial Wind
論文目次	摘要 i Abstract ii Acknowledgement iii Table of Contents iv List of Tables vi List of Figures vi 1. Introduction 1 1.1 Background and Literature Review 1 1.2 Motivation and Objectives 4 2. Methodology 6 2.1 WRF-LETKF Radar Data Assimilation System 6 2.1.1 WRF Model 6 2.1.2 Local Ensemble Transform Kalman Filter (LETKF) 7 2.2 Radar data processing 9 2.3 Superobservation and observation operator 10 2.4 Estimation of error correlations for radial wind 12 2.5 Accounting for correlated observation error in LETKF 13 2.5.1 Adjusted Gradient Updating Method 15 2.5.2 Reconditioning 16 2.5.3 Eigen-decomposition Method 17 3. Estimating Error Correlation Length Scale of Radial Wind 19 3.1 Overview of the heavy rainfall event on 2nd June 2017 19 3.2 Experimental setup 20 3.3 Results of estimation 22 4. Including Error Correlation of Radial Wind in WLRAS 25 4.1 Experimental setup 25 4.2 General performance of radar data assimilation 26 4.3 Impact of using a correlated observation error covariance on analysis and forecast 29 4.4 Sensitivity test 34 4.5 Including error correlation with Eigen method 36 4.6 Summary 38 5. Conclusions and Future Work 40 5.1 Conclusions 40 5.2 Future work 42 References 44 Appendix I 53 Appendix II 54 Tables 55 Figures 57
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指導教授	楊舒芝(Shu-Chih Yang)	審核日期	2021-1-5
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