Abstract
With the development and deployment of observation systems in the ocean, more precise passive and active microwave data are becoming available for the weather forecasting and the climate monitoring. Due to the complicated variability of the sea ice concentration (SIC) in the marginal ice zone and the scarcity of high-precision sea ice data, how to use less data to accurately reconstruct the sea ice field has become an urgent problem to be solved. A reconstruction method for gridding observations using the variational optimization technique, called the multi-scale high-order recursive filter (MHRF), which is a combination of Van Vliet fourth-order recursive filter and the three-dimensional variational (3D-VAR) analysis, has been designed in this study to reproduce the refined structure of sea ice field. Compared with the existing spatial multi-scale first-order recursive filter (SMRF) in which left and right filter iterative processes are executed many times, the MHRF scheme only executes the same filter process once to reduce the analysis errors caused by multiple filters and improve the filter precision. Furthermore, the series connected transfer function in the high-order recursive filter is equivalently replaced by the paralleled one, which can carry out the independent filter process in every direction in order to improve the filter efficiency. Experimental results demonstrate that this method possesses a good potential in extracting the observation information to successfully reconstruct the SIC field in computational efficiency.
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Funding
The National Key Research and Development Program of China under contract Nos 2018YFC1407402 and 2017YFC1404103; the National Programme on Global Change and Air-Sea Interaction (GASI-IPOVAI-04) of China; the Open Fund Project of Key Laboratory of Marine Environmental Information Technology, Ministry of Natural Resources.
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Yang, L., Li, D., Zhang, X. et al. A multi-scale high-order recursive filter approach for the sea ice concentration analysis. Acta Oceanol. Sin. 41, 103–115 (2022). https://doi.org/10.1007/s13131-021-1940-x
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DOI: https://doi.org/10.1007/s13131-021-1940-x