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Ocean Observation: From Microfluidics to Global Scale
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Improved Multi-Fractal Fusion Method to Blend SMOS Sea Surface Salinity based on Semi-Parametric Weight Function P-OB-07-S Hengqian Yan* , College of Meteorology and Oceanography, National University of Defense Technology Ren Zhang, College of Meteorology and Oceanography, National University of Defense Technology Gongjie Wang, College of Meteorology and Oceanography, National University of Defense Technology Huizan Wang, College of Meteorology and Oceanography, National University of Defense Technology Jian Chen, Beijing Institute of Applied Meteorology Senliang Bao, College of Meteorology and Oceanography, National University of Defense Technology Presenter Email: brainholeqian@163.com |
The multi-fractal fusion method has proved to be an effective algorithm to mitigate the noise of the sea surface salinity (SSS) of Soil Moisture and Ocean Salinity (SMOS) mission. However, the traditional non-parametric weight function used in this method is unable to capture the dynamic evolution of oceanic environment. Considering the multi-scale, non-uniform, anisotropic and flow-dependent nature of the ocean, the improved method with so-called “flexible circle” weight function and “flexible ellipse” weight function with a set of pre-defined parameters is proposed in this paper. The improved weight function can draw dynamic information from the sea surface temperature, Rossby radius of deformation and surface geostrophic flow to complement the poor remote sensing SSS. The validation against the in-situ data indicates that the improved weight function performs better than the traditional one with reduced root mean squared error (RMSE) and higher correlation coefficient. What’s more, the salinity map based on “flexible ellipse” weight function can reflect more mesoscale signals without the sacrifice of computational efficiency. |
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