Analysis of ocean diurnal variations from the Korean Geostationary Ocean Color Imager measurements using the DINEOF method

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• 作者：Xiaoming Liu^a ; ^b ; Menghua Wang^a ; ^{Menghua.Wang@noaa.gov" class="auth_mail" title="E-mail the corresponding author}
• 关键词：GOCI ; Ocean color ; EOF ; DINEOF ; Kd(490) ; Tide
• 刊名：Estuarine, Coastal and Shelf Science
• 出版年：2016
• 出版时间：5 October 2016
• 年：2016
• 卷：180
• 期：Complete
• 页码：230-241
• 全文大小：3707 K

文摘

High-frequency images of the water diffuse attenuation coefficient at the wavelength of 490 nm (K_d(490)) derived from the Korean Geostationary Ocean Color Imager (GOCI) provide a unique opportunity to study diurnal variation of water turbidity in coastal regions of the Bohai Sea, Yellow Sea, and East China Sea. However, there are many missing pixels in the original GOCI-derived K_d(490) images due to clouds and various other reasons. Data Interpolating Empirical Orthogonal Function (DINEOF) is a method to reconstruct missing data in geophysical datasets based on the Empirical Orthogonal Function (EOF). It utilizes both temporal and spatial coherencies of data to infer a solution at the missing locations. In this study, the DINEOF is applied to GOCI-derived K_d(490) data in the Yangtze River mouth and the Yellow River mouth regions, and the DINEOF reconstructed K_d(490) data are used to fill in the missing pixels. In fact, DINEOF has been used to fill in gaps in ocean color chlorophyll-a and turbidity data from the Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) in previous studies. Our GOCI validation results show that the bias between the reconstructed data and the original K_d(490) value is quite small (<∼5%). The standard deviation of the reconstructed/original ratio is ∼0.25 and ∼0.30 for the mouths in the Yangtze River and Yellow River, respectively. In addition, GOCI high temporal resolution measurements in K_d(490) can capture sub-diurnal variation due to the tidal forcing. The spatial patterns and temporal functions of the first three EOF modes are also examined. The first EOF mode characterizes the general mean spatial distribution of the region, while the second and third EOF modes represent the variations due to the tidal forcing in the region.