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研究生:曾品涵
研究生(外文):Pin-Han Tseng
論文名稱:旬到月尺度統計預報模式的發展
論文名稱(外文):A development of statistic forecast system for pentad to monthly scales prediction
指導教授:李永安李永安引用關係
指導教授(外文):Yung-An Lee
學位類別:碩士
校院名稱:國立中央大學
系所名稱:大氣物理研究所
學門:自然科學學門
學類:大氣科學學類
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:58
中文關鍵詞:持續中和法
外文關鍵詞:persistence neutralization method
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摘 要

本篇研究主要目的是嘗試發展出一套旬到月尺度統計預報系統。此系統的基本架構是由持續中和法(persistence neutralization)和線性迴規模式所建構出的一套統計預報系統。持續中和法是一種利用變數轉換方式把原始變數的持續性濾除,此方法不僅可以更清楚釐清變數間的超前-延遲關係,也可以使所有建構於此方法上的預報模式具有比持續預報更好的預報能力。因此我們首先利用持續中和法將被預報變數先進行變數轉換以中和其持續性,再利用線性迴歸模式來尋找對於轉換過後的被預報變數具有預報能力的預報因子。進而發展出有最佳旬到月尺度預報能力的統計預報系統。
本研究使用的資料包括外逸長波輻射(OLR),海溫,絕對渦度、海平面氣壓、水平風場等,總共有60種不同的氣候變數,時間長度為20年(1982-2001)侯平均(pentad average)的資料。因為影響季節的氣候變數可能不同,所以將一年的資料分成六段,一段為兩個月,建構出每個季節的預報系統。首先,將60種氣候變數,使用持續中和法中和其持續性後,再利用線性迴歸模式除去原始變數的持續性,建構出經轉換過後在無偏差情況下具有預報能力的預報因子,並挑選OLR為被預報因子而其他氣候變數則為預報因子,在不同的季節,可顯示出不同預報能力的預報因子,再計算出OLR(被預報因子)和不同季節所挑選出不同預報因子的相關性和最小均方根誤差,再由相關性空間的分佈來觀察出被預報因子和預報因子在南北緯40度分布的情形。
結果顯示1月和2月所挑選出11個變數,在熱帶東太平洋約西經140度有很高的相關性,相關係數達0.8以上,在赤道太平洋約東經150度到西經80度相關係數仍有0.6以上,其次澳洲以北,約印尼、菲律賓到南海一帶其相關系數也達0.6以上。3月和4月其在熱帶東太平洋約換日線到西經70度變數間相關係數最高達0.8以上,東經120度、北緯10度到赤道地區其相關系數約為0.6。5月和6月可看出在熱帶太平洋西經120度其相關系數為0.7,而太平洋東經160度到西經70度其相關係數有0.7以上,南美洲約西經50度、赤道到南緯20度其相關性有0.7以上,澳洲上方和澳洲地區相關係數有0.6,在南海地區則有0.7,孟加拉灣右側的相關係數則高達0.8,在南非也有0.8的高度相關。7月和8月則在東經0度到60度、北緯40度到南緯20度有較高的相關性。9月和10月為東太平洋、東經120度到0度、北緯40度到南緯20度有0.7以上,此月份特別在南美洲約南緯20度有0.6的相關性。11月和12月其相關性高的位置和9月、10月類似,但可降水和絕對渦度850hPa則和被預報因子OLR相關性最好。不同月份其被預報因子與預報因子相關性高的區域不同,但是在同一月份則大部分挑選出來的預報因子則和被預報因子OLR有很高的一致性。
ABSTRACT

The main purpose of this research is to develop a statistic forecast system for pentad to monthly scales prediction. The basic structure of this system was built by the persistence neutralization method and the linear regressive model. The persistence neutralization method filtered out the persistence of variables to distinguish the relationship between lead time and lag time. It had better performance than the persistence forecast. At first, the persistence neutralization method was used to transform the variables of predictand for neutralizing the persistence effect in climate data. Then, the predictive predictors were picked out by using the linear regressive model to develop a statistic forecast system for pentad to monthly scales prediction.
60 climate variables were used, including the outgoing longwave radiation (OLR), sea surface temperature (SST), estimated precipitation version1 (Precip), and mean sea level pressure (mslp), etc. Because each variable had different seasonal influence, the annual data were divided into six periods to construct the prediction system. First, we used the persistence neutralization method and the linear regressive model to neutralize and filter out of the persistence effect in 60 kinds of climate variables. The OLR field was used to be predictand and all 60 climate variables were used to be predictors. Each predictors had different predictive skill in different periods. We calculated the correlation coefficient and root mean square errors between OLR (predictand) and all climate variables. The spacial distribution of correlation coefficient between 40oS and 40oN was exhibited the relationship between predictand and predictors.
11 variables were selected in January and February. The correlation coefficient was more than 0.8 over the tropical Eastern Pacific and exceeded 0.6 in the north of Australia, Indonesia, Philippine, and South China Sea. In March and April, the correlation coefficient was more than 0.8 from the date line to 70oW on tropical Eastern Pacific and was about 0.6 near 120oE from 10oN to the Equator. In May and June, the correlation coefficient was 0.7 near 120oW on tropical Pacific Ocean, from 160oE to 70oW in Pacific Ocean, South America, and Australia. There was more than 0.8 in South Africa. High correlation exited from 0oE to 60oE and 40oN to 20oS in July and August. In September and October, the correlation coefficient was more than 0.7 from 120oE to 0oE and 40oN to 20oS and was 0.6 near 20oS in South America. The correlation coefficient in November and December were similar to September and October. But the atmos column precipitation water and absolute vorticity on 850hPa showed the best predictive skill to predict OLR. The high correlation areas between predictand and each predictor were dissimilar in different periods, but displayed consistency in same period.
目 錄
摘要⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯Ⅰ
ABSTRACT⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯Ⅳ
致謝 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ VI
目錄 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯VIII
表目錄 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯IX
圖目錄 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯XI
一、前言 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 1
二、資料來源與前置處理 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 5
三、研究方法 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 9
3.1 持續中和法 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 9
3.2 線性迴歸模式⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 11
3.3 持續中和法,持續參數(α)選擇方法 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 12
3.4 預報因子的選擇方法 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 13
四、統計模式建立 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 17
4.1 統計模式設計 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 17
4.2 持續中和法,持續參數(α)的特性⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 19
4.3 被預報因子和預報因子的γ值關係⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 22
五、結果討論與未來展望 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 26
5.1 預報因子的挑選 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 26
5.2 被預報因子和預報因子的相關性⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 30
5.3 相關係數之空間分佈 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 32
5.4各變數之γ關係⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 35
六、結論與未來展望 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 37
6.1結論⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 37
6.2未來展望⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 39
參考文獻 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 40
附表 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 42
附圖 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 44
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