The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble Kalman Filter algorithm seems to contain implicit assumptions: the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized.While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent.On the above basis, we present two modified Kalman gain algorithms. Compared to the current Kalman gain algorithm,the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified Kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated.
利用1958—2007年间的月平均SODA(Simple Ocean Data Assimilation/海洋同化数据)资料,较系统地研究了北太平洋经向盐量输送的基本特征和季节变化,并探讨了盐量输送季节变化的可能原因。结果表明,北太平洋净经向盐量输送的季节变化具有明显的区域性特征,在14°N以南海域盐量输送的季节变化较显著,而在14°N以北海域则较小;北太平洋净经向盐量输送的季节变化在很大程度上是由同一纬度上Ekman盐量输送和中东太平洋经向盐量输送的季节变化引起的。
