正则化变分同化AIRS红外高光谱水汽资料研究

Research on Regularization Variational Assimilation of Water Vapour Data from Atmospheric Infrared Sounder

结合经典变分同化和正则化约束两者的优点,对多正则化参数约束变分同化方法进行了研究。与经典变分同化中背景和观测项对目标泛函等权重不同,正则化约束对观测项权重进行调节,并在正则化参数优化时基于Huber—估计法给定权重函数。高光谱大气红外探测器(Atmospheric Infrared Sounder,AIRS)水汽通道模拟亮温试验表明,本文的变分法同化AIRS亮温资料比经典变分同化法的效果更好。基于信号自由度诊断了观测资料对分析场的影响,结果表明本文的方法能够有效挖掘水汽通道的亮温信息。

In combination with the advantages of classical variational assimilation and regularization constraint, a multi-parameter regularization constraint assimilation method is studied. Unlike the weight of background and observation to the objective functional of classical variational assimilation, the regularization constraint can adjust the weight of observation term and give the weight function on the basis of Huber-estimator weight function during regularization parameter optimization. Simulation brightness temperature experiment is made for the water vapor channel of the Hyper-spectral Atmospheric Infrared Sounder（AIRS）. The result shows that the variation method studied is better than the classical variational assimilation method. The influence of observation data on analysis fields is diagnosed on the basis of the degree of freedom for signals. The result shows that the method studied can effectively extract the brightness temperature information from the water channel of AIRS.