澎湖位於台灣海峽中段，是三道洋流的交會處，在各種冷暖流的交會下，加上島嶼本身多變的地形，使得周圍生物種類繁多，漁業資源豐富，讓澎湖漁業得以蓬勃發展，是台灣的漁業重鎮之一。
澎湖分別在2008年與2011年發生寒害，除了對周圍海洋生態系統造成傷害，也發生漁獲減產的現象，加上當地許多居民採用箱網養殖，寒害也對當地的養殖業者造成嚴重的損失，在生態與經濟上都造成嚴重災害，為提早預測到低溫事件並進行防寒措施，發展海溫預報機制有其必要性。
本研究採用多元迴歸模式建立預報公式，並以2011年之實測資料對公式進行訓練，採用前兩日海溫、氣溫、風速及雲遮量為因子，預測後兩日的海溫，對2008年、2015年、2017年及2018年做事後預測，結果顯示該預報公式在海溫的全年預報上相當準確，平均誤差小於0.4度，相關係數也在0.9以上，因此使用此公式來預測兩日後的海溫趨勢是可行的，在觀察到連續低溫後可提出預警，提前進行防災措施，減少寒害損失。

Peng Hu is located at the center of Taiwan Strait. The ecosystem and aquaculture here is plentiful, because of being effected by China Coast Current, South China Sea Current and branch of Kuroshio. Above these, Peng Hu becomes an important fishery area in Taiwan.
In 2008 and 2011, cold disaster happened around the sea area of Peng Hu, and resulted in a lot of fish died. Furthermore, marine ecosystem was destroyed and the fishery catch was reducing. In order to preventing the low sea temperature to make these kind of disaster, the technique of sea temperature forecasting is important and must develop to apply around Taiwan sea area.
In this study, we use multiple regression model to build sea surface temperature forecasting model. The model is trained by the observations in 2011 to predict the two-days-after sea surface temperature. The observations include sea surface temperature, air temperature, wind speed and cloud-cover-rate from Peng Hu and nearby Peng Hu atmosphere stations. After that, the model is validated by the data in 2008, 2015, 2017 and 2018. The result has a well-done and similar trend between the model and observation. The RMSE is about 0.4 and the relation coefficient is above 0.9. In conclusion, the Peng Hu multiple regression model is reliable.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
圖目錄 vii
表目錄 x
第一章　緒論 1
1-1 前言 1
1-2 研究動機與目的 2
1-3 研究架構 5
第二章　文獻回顧 6
2-1 研究區域說明 6
2-1-1台灣海峽海流分布 7
2-1-2 台灣海峽季風變化 13
2-2 海象與氣象的交互影響 13
2-2-1 氣溫 14
2-2-2 風速 15
2-2-3 雲遮量 17
2-2-4 聖嬰現象 18
2-3 澎湖歷年寒害事件 19
2-4 小結 21
第三章　研究方法 22
3-1 資料補遺與擬合 22
3-1-1 誤差與相關性係數 22
3-1-2 傅立葉擬合與移動平均 24
3-1-3 小結 31
3-2 預報因子篩選 32
3-2-1 海溫自相關性 32
3-2-2 海溫氣溫相關性 34
3-2-3 海溫風速相關性 38
3-2-4 海溫與雲遮量之相關性 40
3-3 多元迴歸模式簡介 42
3-4 小結 43
第四章　迴歸公式建置與驗證 44
4-1 預報使用因子 44
4-2 以鄰近測站預測澎湖海溫 46
4-3 澎湖當地海溫模擬 50
4-3-1澎湖當地因子測試 50
4-3-2 澎湖每6小時平均海溫模擬 57
4-3-3 澎湖全年海溫模擬 59
4-4 小結 65
第五章　結論與建議 66
5-1 總結 66
5-2 建議 67
參考文獻 68
附錄 72
附錄一 多階傅立葉擬合結果1 72
附錄二 多階傅立葉擬合結果2 73
附錄三 歷年各測站冬季海溫時序列圖(無補遺) 74
附錄四 2011年各測站海溫與氣溫時序列圖 77
附錄五 2008年各測站海溫與風速時序列圖 79
附錄六 2011年各測站海溫與風速時序列圖 81

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