臺灣博碩士論文加值系統

氣象領域常採用weather research and forecasting (WRF)模式進行雨量預報，惟其預報結果會隨模式參數而變動，對真實雨量常有不準確的估計。若能有效修正WRF系集預報資料，則可減少預報誤差以提高模式準確度。為此，本研究以雷達估計雨量為觀測值，計算WRF系集預報資料與觀測值之間的雨量誤差。並採用二種不同的機器學習法，支撐向量機與隨機森林，學習WRF系集預報中大量網格預報雨量資料之誤差結構，建立即時誤差修正模式，以修正WRF系集預報在前置時間1至6小時之雨量預報。其中，本研究採二種建模方式配合機器學習法以建置即時誤差修正模式如下：(1)所有系集成員合併建立一個誤差修正模式(以下簡稱合併建模)，其係直接針對系集平均值進行誤差修正；(2)每個系集成員獨自建立誤差修正模式(以下簡稱獨立建模)，其係先針對個別WRF系集成員進行誤差修正，再取修正後預報值之平均。因此，本研究中即時誤差修正模式共有四種，分別為：支撐向量機搭配合併建模、支撐向量機搭配獨立建模、隨機森林搭配合併建模、隨機森林搭配獨立建模。分析結果顯示：合併建模與獨立建模有相似的表現，不同的建模方式於即時雨量預報修正之表現並沒有明顯差異，故於模式結構建議直接採用合併建模，不僅可降低模式複雜性，同時亦能縮減計算時間。此外，本研究所採用之二種機器學習法於大部分颱風事件中表現相近，且以評鑑指標(相關係數與均方根誤差)來看，二種方法皆可顯著地改善WRF系集預報雨量。其中，誤差修正模式於前置時間1至3小時之改善效果較佳，而前置時間4至6小時於部分颱風事件之改善能力則較不明顯。整體而言，本研究提出之即時誤差修正模式可有效提升WRF系集預報之準確度，改善雨量預報值低估之情況，但對於不同機器學習法間的差異則有待進一步地分析與比較。

The main purpose of this study is to correct the errors of weather research and forecasting (WRF) model in precipitation forecasting. The study area is Kaoping River Basin located in southern Taiwan where usually suffers from floods during typhoon season. In this study, the radar precipitation data are regard as the reference (i.e., true values) to eliminate WRF forecasting error. Two methods, random forests (RF) and support vector machines (SVM), are used to correct the discrepancies between the forecasted precipitation data from WRF model and the radar precipitation data. The correction is based on a real-time updating procedure where two error correction models will be updated every 6 hours (i.e., WRF model provides 6-hour forecast in each run). For example, the error correction models applied to correct the 6-hour ahead precipitation forecasts from WRF model which were set up based on the precipitation data (WRF and the radar precipitation data) from the past 6 hours. The results reveal that the error correction models can further improve WRF model rainfall forecasting by using the real-time updating procedure, however, the accuracy of forecasting decreases with lead time increasing. The results also show that either SVM-based model or RF-based model performs well on two performance indexes (i.e., correlation coefficient and root mean squared error) which can improve the accuracy of precipitation forecasting by 5% to 300% when it compares to original WRF model.

目錄
摘要 I
Extended Abstract II
致謝 VI
目錄 VII
表目錄 IX
圖目錄 XI
第一章 緒論 1
1-1研究動機與目的 1
1-2文獻回顧 3
1-2-1 系集預報之發展 3
1-2-2 WRF模式誤差修正 4
1-2-3 支撐向量機之應用 6
1-2-4 隨機森林之應用 7
1-3論文架構 9
第二章 研究區域及資料處理 11
2-1研究區域 11
2-2資料說明與處理 12
2-2-1 雷達雨量估計資訊 12
2-2-2 WRF模式及各WRF系集成員簡介 14
第三章 研究方法 19
3-1即時誤差修正模式 19
3-1-1 模式建置流程 20
3-1-2 模式架構 21
3-2雨量修正方法 24
3-2-1 支撐向量機 24
3-2-2 隨機森林 29
3-3時雨量預報評鑑指標 33
第四章 結果與討論 34
4-1資料分析與模式建置 34
4-1-1合併建模 35
4-1-2獨立建模 42
4-2雨量預報修正結果 47
4-2-1模式建置方式比較 47
4-2-2機器學習法比較 50
第五章 結論與建議 66
5-1結論 66
5-2建議 67
參考文獻 68

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