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系統識別號 U0026-2408201517292900
論文名稱(中文) 分析及預估熱帶太平洋衛星測高海水面異常
論文名稱(英文) Analysis and prediction of satellite altimetric sea level anomalies in the tropical Pacific Ocean
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 103
學期 2
出版年 104
研究生(中文) 陳怡靜
研究生(英文) Yi-Ching Chen
學號 P66024031
學位類別 碩士
語文別 英文
論文頁數 96頁
口試委員 指導教授-郭重言
口試委員-曾國欣
口試委員-江凱偉
中文關鍵字 衛星測高  經驗正交法  ARIMA  支持向量回歸 
英文關鍵字 Satellite Altimetry  Empirical Orthogonal Function  Autoregressive Integrated Moving Average Models  Support Vector Regression 
學科別分類
中文摘要 隨著全球暖化,海水面上升對人類生活環境與經濟造成極大的影響,嚴重可能危及人類生命及財產,因此,持續監測與分析海水面是相當重要議題,可便於瞭解海洋特徵與訊號,另外準確預估海水面未來變化對沿岸經營管理亦是不可或缺的資訊。本研究使用多顆測高衛星觀測得海水面異常資料,利用傳統經驗正交法(Empirical Orthogonal Functions)、複數經驗正交法(Complex EOF)以及斜率經驗正交法(Trend EOF)分析熱帶太平洋海水面異常資料並萃取出主要海洋變化訊號,透過分解後的空間特徵與對應的時間序列推斷代表之海洋現象。分析結果顯示,傳統經驗正交法與複數經驗正交法皆顯示出在熱帶太平洋地區的主要特徵訊號為聖嬰現象,與多重聖嬰現象指數(Multivariate ENSO Index, MEI)比較,其相關係數高達0.9,而複數經驗正交法更能顯示出模式(modes)中海水面訊號的傳遞訊息。此外,海水面上升速率可由斜率經驗正交法直接分解測高海水面異常資料求得,其第一個模態顯現海水面上升的特徵,估算1993年至2013年熱帶太平洋海水面上升速率為2.6 mm/yr。除分析海水面訊號,本研究亦利用傳統經驗正交法分解結果,透過Autoregressive Integrated Moving Average (ARIMA) model以及Support Vector Regression (SVR)對前五個分解得到的時間序列(PCs)做一年預估,結合前五個對應空間模式重建每月海水面異常,並進行預估結果評估。結果顯示在熱帶太平洋SVR比ARIMA預估能力好,評估一年預估值與觀測值差值之均方根誤差,SVR預估之均方根誤差為1 mm,而ARIMA預估則為1.5 mm。另外,利用決定係數(coefficient of determination)評估空間精度,SVR預估結果顯示將近有62%的研究區之決定係數大於0.9,而ARIMA結果僅有42%的區域能有較好的預估表現。
英文摘要 Sea level rise, one of the consequences of global warming, has a substantial impact on economic and living environment to even damage human life and property. Therefore, continuous monitoring and analyzing sea level signals are extremely important for understanding ocean characteristics while accurate predicting of sea level changes is also indispensable for coastal management to prevent or reduce disaster resulting from sea level rise. In this research, three kinds of Empirical Orthogonal Functions (EOFs), including conventional EOF, Complex EOF and Trend EOF, were applied to decompose monthly gridded sea level anomalies data derived from satellite altimetry to extract dominant signals in spatial and corresponding temporal domains for finding out the ocean phenomena. After examining the leading corresponding principle components (PCs) derived from the conventional EOF or Complex EOF with ENSO index, we discover that ENSO signal is the dominant phenomena in the Tropical Pacific Ocean and the correlation coefficients of the PCs and Multivariate ENSO Index (MEI) are up to roughly 0.9. Moreover, complex EOF can show information about the signal propagation in the decomposed modes. Trend EOF can directly extract the trend, which is presented in first mode, from altimetric sea level anomalies with the estimated rate of 2.6 mm/yr during 1993-2013 in tropical Pacific Ocean. Not only focus on analyzing the ocean signal but also interested in sea level prediction, Autoregressive Integrated Moving Average (ARIMA) model and Support Vector Regression (SVR) were used to predict one-year time series of the first five PCs derived from the conventional EOF decomposition. Afterwards, we reconstructed gridded sea level anomalies using the combination of predicted PCs and spatial modes. The one-year predicted results in the tropical Pacific Ocean indicate that SVM demonstrates a better performance in predicting with the root mean square error (RMSE) of differences between the SVR predicted and observed mean sea level anomalies at 1 mm, when RMSE differences of ARIMA predicted and observed sea level is 1.5 mm. Sixty-two percent of the study areas can be perfectly predicted by SVR with the coefficient of determination larger than 0.9 while ARIMA just can predict well in the 42% of the area.
論文目次 中文摘要 i
Abstract iii
誌謝 v
Contents vi
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Outlines 8
Chapter 2 Satellite Altimetry 9
2.1 Introduction to Satellite Altimetry 9
2.2 Principle of Satellite Altimetry 13
2.3 Corrections for Altimetry Measurements 15
2.3.1 Orbit error 16
2.3.2 Instrument corrections 16
2.3.3 Geophysical corrections 17
2.3.4 Propagation corrections 18
2.3.5 Surface corrections 19
2.4 Altimatry data 21
Chapter 3 Methods 24
3.1 Flow chart 24
3.2 Methods of Spatial–Temporal Data Analysis 25
3.2.1 Conventional Empirical Orthogonal Function (EOF) 25
3.2.2 Complex Hilbert Empirical Orthogonal Function (Complex EOF) 27
3.2.3 Trend Empirical Orthogonal Function (Trend EOF) 30
3.3 Autoregressive Integrated Moving Average Models 33
3.4 Support Vector Regression 38
Chapter 4 Results and discussion 44
4.1 Experimental area 44
4.2 EOF Analysis in the Tropical Pacific Ocean 46
4.2.1 Analysis of conventional EOF 46
4.2.2 Analysis of complex EOF 51
4.2.3 Analysis of trend EOF 58
4.3 Performance of ARIMA Prediction 64
4.4 Prediction Performance of SVR 77
4.5 Reconstructed 1-year Prediction Results 82
Chapter 5 Conclusions and Recommendations 88
REFERENCE 91
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