使用氣象雷達改善對流尺度定量降水預報研究－理想和真實個案之分析結果

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邱健倫(Jian-Luen Chiou) 查詢紙本館藏

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大氣物理研究所

論文名稱

使用氣象雷達改善對流尺度定量降水預報研究－理想和真實個案之分析結果

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摘要(中)

本文為針對都卜勒雷達觀測所設計之資料同化方法，並探討此方法將雷達資料反演的大氣狀態變數以及置入數值模式後，對改善短期劇烈天氣降雨預報可行性。此方法主要包含三大部分 : 多都卜勒雷達風場反演、熱動力反演、及水氣調整。為了先探討此方法反演的能力以及對模式預報的影響，因此在研究中使用 Observation System Simulation Experiment (OSSE) 進行初步的探討，其中實驗包括: (1) 研究方法完整的流程 (2) 敏感度測試。
研究著重於熱動力反演及水氣調整的影響，因此在OSSE架構下假設多都卜勒雷達風場反演與虛擬真實大氣相同，進行熱動力反演和水氣調整。反演結果顯示擾動壓力及擾動位溫場與natural run(虛擬真實大氣)結果極為一致，擾動水汽場的結構也有充分掌握到。反演後的降雨預報結果，的確可以降低初始場中因擾動太過微弱所導致降水延遲且強度不足的現象。
敏感度測試中，探討熱動力反演及水氣調整對反演結果及預報結果的影響，共分為四大部分。第一部分測試在沒有探空資料的情況下反演大氣狀態變數，除了反演的壓力有很明顯的偏差存在，但是空間分布以及溫度、水氣並沒有太大的影響，置入模式後，降雨在模擬後期有明顯高估的情形，但是透過第二次無探空同化可以將降雨高估的情況改善。第二部份測試水氣調整對降水預報的影響程度，結果顯示水氣調整有其必要性，若沒有水氣調整此步驟，降雨趨勢相較於natural run會有很大的差異存在。第三部分測試不同門檻條件的水氣調整，但降雨結果並沒有太明顯改善，顯示出水氣調整的方法未來還有再改進的可能，需要更細節的雲微物理概念，才可能將完整地將水氣結構解析出來。第四部份為作業化雷達掃描間隔測試，結果顯示若雷達掃描時間拉長，會使得反演結果變的較差一些，但是從降雨結果來看，回波和降雨的強度與natural run的趨勢則有很明顯不一致的情況，顯示出對於降水預報仍有蠻明顯的影響。
在真實個案上選取了2008西南氣流實驗計畫(SoWMEX IOP8個案)，利用本研究所使用之方法，反演大氣狀態變數並且置入數值模式進行預報。模式經由同化雷達後，可以很明顯改善降雨預報維持將近三個小時，儘管雨量有高估的現象，但對比於沒有同化雷達資料的實驗來說仍然表現較為出色。除此之外，若沒有探空之同化結果，仍能改善定量降雨預報。

摘要(英)

In this study an algorithm is developed for improving the model short-term quantitative precipitation forecasts (QPF). In methodology, the complete algorithm is composed of three main part : a newly designed multiple-Doppler radar synthesis technique, a modified thermodynamic retrieval method originally proposed by Gal-Chen, and a moisture adjustment schene. The performance of the proposed method is first investigated under the framework of OSSE (Observing System Simulation Experiment) including different situations.
Experiments results show that retrieved perturbation pressure and temperature have a good agreement with natural run (truth), but the main feature of perturbation water vapor mixing ratio also can be captured. Also, by assimilating the retrieved three-dimensional winds, thermodynamic, and microphysical parameters into a numerical model, the model QPF can significantly improve the accuracy of forecast for almost 1 hour long.
In sensitivity test, it is feasible to use the model outputs to replace the role played by a sounding for estimating the unknown constant at each altitude, which is a required quantity in the Gal-Chen method for deducing the thermodynamic field, and if one conducts a second assimilation even without sounding, additional improvements can be achieved. Moisture adjustment is found to provide a better rainfall precipitation forecast at the early stage of the model integration after the data assimilation. Different saturated condition in water vapor adjustment, the result seems to have less improvements compared to control run. The last part is time interval test for operational radar scanning, retrieval and forecast results shows a slightly different from natural run.
In real case study, we chose case 2008 SoWMEX IOP8 applied on this data assimilation method to retrieve atmospheric state variables and insert in numerical model for following precipitation forecast. The model after assimilating radar data significantly improves the accuracy of forecast for at least 3 hours compared with one without data assimilation in spite of over-estimated precipitation . In addition, the one without sounding data also can improve QPF as much as the one with sounding. Therefore, this data assimilation method is practicable to be implemented on real case study.

關鍵字(中)

★ 3DVAR
★ 氣象雷達
★ 風場合成
★ 熱動力反演
★ 水氣調整

關鍵字(英)

★ weather radar
★ velocity synthesis
★ thermodynamic retrieval
★ moisture adjustment

論文目次

中文摘要 ………………………………………………………………………………I
英文摘要 ………………………………………………………………………………III
致謝 ………………………………………………………………………………IV
目錄 ………………………………………………………………………………V
圖表說明 ………………………………………………………………………………VII
第一章緒論 ………………………………………………………………………………1
1-1 前言…………………………………………………………………………1
1-2 前人研究……………………………………………………………………1
1-3 論文架構……………………………………………………………………3
第二章研究方法…………………………………………………………………………4
2-1 都卜勒雷達風場反演………………………………………………………4
2-2 熱動力反演…………………………………………………………………5
2-3 熱力場與水氣調整…………………………………………………………7
第三章 OSSE實驗設計……………………………………………………………………10
3-1 數值模式介紹與模式設定…………………………………………………10
3-1-1 模式設定………………………………………………………………10
3-2 實驗設計與流程介紹………………………………………………………10
3-3 檢驗方法……………………………………………………………………12
3-3-1 反演結果檢驗方法……………………………………………………12
3-3-2 定量降水檢驗方法……………………………………………………13
第四章實驗結果…………………………………………………………………………14
4-1 F、G、H檢驗………………………………………………………………14
4-2 反演結果……………………………………………………………………14
4-3 降雨預報結果………………………………………………………………16
第五章敏感度測試………………………………………………………………………18
5-1 無探空資料同化策略………………………………………………………18
5-2 無水氣調整測試……………………………………………………………21
5-3 水氣調整門檻設定測試……………………………………………………22
5-4 作業化雷達掃描時間測試………………………………………………………22
第六章 SoWMEX IOP#8 個案研究………………………………………………………24
第七章結論與未來展望…………………………………………………………………26
7-1 結論………………………………………………………………………………26
7-2 未來展望…………………………………………………………………………29
參考文獻 …………………………………………………………………………………30
附表與附圖…………………………………………………………………………………34

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指導教授

廖宇慶(Yu-Chieng Liou)

審核日期

2013-8-9

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