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An Improved Non-Regression-Coefficients Method in Finding Sea Surface Temperature with AVHRR Data

非迴歸係數方法於AVHRR資料求取海溫之改進

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摘要


Yu and Barton曾提出一個非迴歸係數方法用以應用衛星資料求取海面溫度,該方法也普遍地推廣到多種衛星反演海溫的模式,如MCSST與CPSST模式。在他們的方法中,需計算兩個紅外窗區頻道相鄰視場溫度變化值的比值,即R54(△T5/△T4),其中使用到統計的運算,因此有時並不適用於真實的海氣情況。本研究旨在保有該方法的物理考慮及推導優點,並改進的計算方法。並使用空間相干法及三點法瀘雲與標定晴空區以獲得更精確的海溫分佈。應用本研究所改良的非迴歸係數方法反演出台灣附近10^0×10^0範圍的海溫分佈圖,並與船測海溫及其他三種方法(Chen, MCSST, CPSST)所反演的海溫比較,顯示本方法可獲致0.8K RMSE及0.1K bias的精度,且在雲污染區有較其他方法更好的表現。

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並列摘要


Yu and Barton proposed a non-regression-coefficient method in improving the accuracy of routine sea surface temperature retrievals through satellite data, which are then applied to sea surface temperature estimation models such as MCSST or CPSST algorithms. Their method involves a parameter called R54(=△T5/△T4), which is derived from the ratio of neighborhood temperature variances from two window channels in the thermal IR region. Due to the fact that statistical calculations were used in the derivation for the value of R54, the method is not applicable in various real cases. The aim of this study is to retain the advantage of their method in terms of its physical considerations and derivations, while providing an alternative in improving the calculations of R54. In this study, a spatial coherence method is also used to mask cloudy areas, and an improved three-point method was brought in to determine the clear-sky brightness temperature. A NOAA-12 AVHRR data set partially covered with clouds was used to estimate a 10^0 ×10^0 longitude-latitude SST map of the Taiwan area, which were compared with 61 ship-measured SSTs, as well as SST retrievals estimated by Chen, MCSST and CPSST. The results reveal improvements in the 0.8K RMSE and 0.1K bias SST map. The accuracy levels also grew higher, especially for partially cloud-contaminated cases. Moreover, this study can further produce maps regarding the amount of clouds present, or the temperature distribution of the cloud-tops for related research.

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