本研究之目的在利用波譜資料同化技術提昇波浪數值現報與預報模式之準確度。資料同化技術應用於數值預報與傳統預報方法最大的不同點在於：傳統的作法利用波浪數值模式進行推算(hindcast)，並與現場觀測結果作比對，藉以調整波浪數值模式參數的設定，從中調校出最佳的參數組合，其後，波浪數值模式便被用來進行海況預報；相對於上述作法，波譜資料同化則是將波浪模式與即時觀測資料進行整合，利用即時資料對海況進行即時校正，以提昇波浪數值模式現報與預報的準確度。
早期前人利用衛星波高或資料浮標波高進行資料同化，發現在距離實測資料較遠的格點會產生極端的修正參數。又本研究對於波浪數值模式的研究以作業化波浪預報為目標，故本研究擬採用連續資料同化進行資料同化，其中的方法擬引用Voorrips等人提出的分割最佳內插法(Optimal interpolation of Partition)進行修正，結合衛星波高資料與資料浮標波譜資料進行研究。經本研究可建立波譜資料同化技術，並探討波譜資料同化應用於颱風波浪數值模式效果，藉以驗證波譜資料同化模式的正確性及了解波譜資料同化模式應用於台灣海域之特性。由於波譜資料可以即時獲得，相反的，衛星資料即使可以即時取得，但不具有時間上的優勢，因此本研究探討波譜資料同化與衛星資料同化的差異性。

The purpose of this study is to enhance the accuracy of numerical wave forecast with data assimilation. Traditional forecast is consisting in: uses numerical wave model to do hindcast, and then adjusting the parameters by measured data. Use numerical wave model to forecast wave after finding the best parameters. A technique – spectral data assimilation combine model results with measured data. Using measured data to correct model results immediately and to do wave forecast.
Review past research, we find that the extreme parameters were produced at grid point in the distance of measured station by using data of wave height to do data assimilation. In order to make this research look forward to do operating numerical wave model, we adopt sequential data assimilation to do data assimilation. In the process of data assimilation, the optimal interpolation of partition from Voorrips, etc. is adopted and improved. And the analytic data is from satellites and data buoy. Expect to set up the technique of spectral data assimilation. And then discuss the result which using spectral data assimilation to typhoon numerical wave model in order to verify the accuracy of spectral data assimilation model and analyze the wave character Taiwan water. The spectral data is near real time, on the contrary, the satellite even near real time, but there is no advantageous in time domain. Therefore, this study discuss the difference between spectral data assimilation and satellite data assimilation.

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