瞭解海洋環境與我們的生活息息相關，正確地海洋環境資訊是從事安全的海岸工程設計與海域活動的依據，”波浪”是海洋環境中主要的參數，為本文的研究對象。波浪受到氣象、地形等因素的影響，是相當複雜的紛紜現象，研究波浪的方法除了從事水工試驗與數值模式分析之外，”觀測(observation)”是研究波浪重要的手段。

　　波浪觀測區分為現場觀測(in-situ measurement)和遙測(remote sensing technique)兩種。現場觀測是感測器直接與海面接觸來進行量測的工作，被視為是最正確的量測結果；遙測方法具有探測廣大海面的波浪狀態的能力，是未來可以用來測波的工具之一，在波浪遙測的方式中，利用雷達(radar)從事波浪觀測的研究是最有效的方法。雖然遙測是一種很有潛力的測波方式，在正式應用這個方式之前，必須瞭解幾個疑問，這包含了波浪遙測結果的正確性與準確程度是如何。

　　所有的量測工作都被假設是有量測誤差的，不確定性(uncertainty)是度量誤差的一種方式，也是衡量觀測資料品質的一個定量指標。為了瞭解波浪遙測結果的正確性與準確性，可以從研究波浪遙測結果的不確定性著手，不確定性的分析一方面可以瞭解誤差程度，增加使用波浪遙測資料的信心，另一方面，不確定性分析結果可以做為波浪遙測系統改進的參考。雷達測波的不確定來源可能從雷達訊號擷取與影像分析等過程產生，由於雷達是一種間接的波浪觀測方式，為了獲得絕對的波浪觀測值，雷達測波結果必須與鄰近的現場觀測資料從事率定的工作，這是雷達測波作業中很重要的一個步驟，本研究目的即是在評估在雷達測波的率定過程中所產生的不確定性有多少。

　　為了達到上述研究目的，本文包含三個研究內容，首先是建立率定模式，第二部份是雷達測波的不確定性分析，包含了模式中參數的不確定性與模式本身的不確定性分析，本研究最後則探討消減雷達測波不確定性可能的方法，做為未來雷達測波作業的參考。

　　為了建立雷達測波率定模式，必須計算雷達影像譜來分析雷達回波訊號的訊噪比(Signal to Noise Ratio, SNR)，分析訊噪比與海面波高之間存在的關係，由於近岸海域的雷達影像為非均勻性(non-homogeneous)，利用傳統的傅立葉譜分析方法並不合適，因此本研究基於小波理論發展了一套非均勻影像譜分析方法，驗證結果顯示可以正確地分析近岸雷達影像譜，本文研究發現，雷達回波的訊噪比與海面波高之間存在著線性關係。為了獲得高品質的現場資料從事雷達測波的率定研究，同時為了要評估率定模式中參數的不確定性，本文建立一個資料品管方法，包含了原始時序列資料的品管與統計資料的品管方式，藉由品管前後現場資料的品質變化來研究參數的不確定程度。在本文第六章則計算波浪遙測率定模式的不確定性，根據不確定性的傳遞理論，計算整體雷達波浪遙測的不確定性與各種不確定來源所佔的比率，此結果有助於未來改善雷達測波系統準確性的參考。

　　透過本研究得知雷達測波所產生的不確定性主要的來自於現場的率定資料，其次則是雷達影像譜的方析方法，至於率定模式的選擇所產生的不確定性並不顯著。為了要減低雷達測波結果的不確定性，提昇雷達測波結果的正確性，在率定過程中，收集具有代表性的現場波浪資料是最重要的工作，同時對於現場資料從事品管檢測，不但獲得高品質的現場資料，也消減了波浪遙測的不確定性，也就是說要有好的雷達測波結果必須採用高品質的現場觀測資料從事率定工作，因此現場觀測與遙測是互相依賴缺一不可的。

　Ocean wave, among the natural phenomena, is one of the most complex factors prevalent in coastal and ocean engineering. Its features are extremely random, affected by meteorological factors, topological conditions and currents, which cannot be fully understood by numerical or physical models alone. Field measurement must be performed to increase the knowledge of waves.

　The most common approaches to wave measurement can be categorized as direct and indirect methods represented by In-Situ measurement and Remote Sensing technique respectively. The data measured from the direct in-situ method is viewed as ground truth data. The remote sensing technique provides extensive sets of ocean surface data. It points to a developing trend of coastal and ocean monitoring methods. Among all the sensors, RADAR (Radio Detection And Ranging) is potentially the key instrument for wave measurement purposes due to by its properties of high resolution and fewer environmental limitations. However, although the remote sensing technique is such a promising tool, with it exist some problems such as confidence in the correctness and accuracy of remotely sensed data.

　Any measurement is subject to imperfections and potential errors. This uncertainty is defined as the measure of measurement error and serves as the quantitative indication of the quality of measurement data. Assessment of uncertainty increases the confidence of data users and provides information for technical improvements in radar wave observation systems. The uncertainty of wave remote sensing is multiplied by variables such as data acquisition, image processing, correction and so on. Due to the remote sensing technique being an in-direct observation method, significant parameters need to be calibrated with in-situ data which demonstrates that the calibration process is the vital procedure in setting a radar wave observation system. The purpose of this study is to assess the uncertainty of remotely sensed wave heights produced by the calibration processes.

　To achieve this, several tasks need to be performed beginning with the establishment of the calibration model. Using the idea that the radar wave height is correlated with the image SNR (Signal to Noise Ratio), a linear regression model can be identified. Second comes the estimation of uncertainties, including both uncertainty from the parameters within the model and uncertainty from the model itself. In the final part of this study, the uncertainty reduction approaches are presented and discussed.

　In order to establish the calibration model, the spectrum has to be extracted from radar images. The non-homogeneity property is identified in the nearshore area in chapter 4 leading to the development of a non-homogeneous image spectrum analysis method used to extract the spectrum from radar images in chapter 5. This algorithm is derived and developed by this study and validated as a proper tool for non-homogeneous image spectrum analysis. In order to provide high quality in-situ data for calibration and assess the parameter uncertainty, a data quality-check program is developed in chapter 3. This data quality-check program contains both time series raw data checks and statistical parameters checks. Variations in data after applying the quality-check program are regarded as uncertainties of in-situ data and are used to assess the parameter uncertainty in the calibration process. In chapter 6, the uncertainties of wave remote sensing are evaluated by the uncertainty propagation method.

　This study will show that the dominant uncertainty in remotely sensed wave heights results from the quality of in-situ data and that the minor uncertainty comes from the image spectrum analysis method. The formation of the calibration model produces yet less uncertainty. Thus, in order to improve the accuracy of remotely sensed data, the work of collecting high quality representative in-situ data for calibration must be emphasized alongside the choice of calibration model.

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