Blending satellite scatterometer data based on variational with multi-parameter regularization method
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摘要: 基于常规三维变分同化(3DVAR)思想和反问题中的正则化技术,提出了适用于风场融合的带正则化约束项的3DVAR方法,在南海海域开展数据融合试验,同时采用模型函数方法确定合理的正则化参数,针对一次台风个例进行了QuikSCAT散射计海面风场数据和华南中尺度模式海面风场数据的融合试验,结果表明采用带正则化约束的3DVAR融合方法,明显消除了常规3DVAR方法融合风场时带来的虚假信息,融合后分析风场以及涡度场和散度场分布均匀,结构清晰,气旋中心显著,且分析场中观测起主导作用;采用信号自由度(DFS)方法对融合方法进行定量评估,发现相对常规3DVAR方法,带正则化约束的3DVAR融合系统中观测数据提供的DFS较多,同时提高了观测场对分析场的影响;基于独立观测资料对融合结果进行检验发现相对华南中尺度模式和常规3DVAR方法的统计结果,带正则化约束的3DVAR方法得到的风场具有最小的均方根误差和最大的相关系数。Abstract: A 3DVAR method with regularization constraints is proposed to blend sea surface wind data in the South China Sea based on the traditional 3DVAR and regularization technology of the inverse problem, and the model function method which is used to determine the reasonable regularization parameters and then the blended experiments of the satellite scatterometer (QuikSCAT) and Guang Zhou Mesoscale Model (GZMM) sea surface wind field data are carried out for a typhoon case. Results show that when we use the regularization method for experiments, the false information caused by the traditional 3DVAR is eliminated obviously and the noise is almost disappeared, at the same time, the wind field and vorticity field as well as divergence field are distributed evenly, and the structure is clear, more importantly, it is clear that the cyclone center is remarkable, and observation is dramatic in the analysis field. Besides, the degrees of freedom for signal (DFS) method is used to evaluate blended systems quantitatively, it is found that the regularized constraint 3DVAR system has a higher DFS and observation influence related to traditional 3DVAR. The blended results are tested based on the independent observation data, it indicates that the result of regularized constraint 3DVAR method has the smallest root mean square error and maximum correlation coefficient, which is better than the statistical result of GZMM and the conventional 3DVAR method.
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Key words:
- 3DVAR /
- regularization /
- QuikSCAT /
- the degrees of freedom for signal /
- model function
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