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Assimilation of ice concentration in a coupled ice-ocean model, using the Ensemble Kalman filter

机译：使用Ensemble Kalman滤波器对冰海耦合模型中的冰浓度进行同化

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An implementation of the Ensemble Kalman filter (EnKF) with a coupled ice-ocean model is presented. The model system consists of a dynamic-thermodynamic ice model using the elastic-viscous-plastic (EVP) rheology coupled with the HYbrid Coordinate Ocean Model (HYCOM). The observed variable is ice concentration from passive microwave sensor data (SSM/I). The assimilation of ice concentration has the desired effect of reducing the difference between observations and model. Comparison of the assimilation experiment with a free-run experiment shows that there are large differences, especially in summer. In winter the differences are relatively small, partly because the atmospheric forcing used to run the model depends upon SSM/I data. The assimilation has the strongest impact close to the ice edge, where it ensures a correct location of the ice edge throughout the simulation. An inspection of the model ensemble statistics reveals that the error estimates of the model are too small in winter, partly a result of too low model ice-concentration variance in the central ice pack. It is found that the ensemble covariance between ice concentration and sea-surface temperature in the same grid cell is of the same sign (negative) throughout the year. The ensemble covariance between ice concentration and salinity is more dependent upon the physical mechanisms involved, with ice transport and freeze/melt giving different signs of the covariances. The ice-transport and ice-melt mechanisms also impact the ice-concentration variance and the covariance between ice concentration and ice thickness. The ensemble statistics show a high degree of complexity, which to some extent merits the use of computationally expensive assimilation methods, such as the Ensemble Kalman filter. The present study focuses on the assimilation of ice concentration, but it is understood that assimilation of other datasets, such as sea-surface temperature, would be beneficial.
机译：提出了具有耦合冰海模型的Ensemble Kalman滤波器（EnKF）的实现。该模型系统由动态热力学冰模型组成，该模型使用了弹性粘塑性（EVP）流变学和混合坐标海洋模型（HYCOM）。观察到的变量是来自被动微波传感器数据（SSM / I）的冰浓度。冰浓度的同化具有减小观测值与模型之间差异的理想效果。同化实验与自由运行实验的比较表明，差异很大，尤其是在夏天。在冬季，差异相对较小，部分原因是用于运行模型的大气强迫取决于SSM / I数据。在冰边缘附近，同化影响最大，在整个模拟过程中，同化可确保冰边缘的正确位置。对模型集合统计数据的检查显示，模型的误差估计在冬季过小，部分原因是中央冰袋中的模型冰浓度变化过低。结果发现，在同一网格中，全年冰浓度与海表温度之间的整体协方差具有相同的符号（负）。冰浓度和盐度之间的整体协方差更多地取决于所涉及的物理机制，冰的运输和冻结/融化给出了协方差的不同符号。冰的运输和融冰机制也会影响冰浓度的变化以及冰浓度和冰厚度之间的协方差。整体统计数据显示出高度的复杂性，在某种程度上值得使用计算上昂贵的同化方法，例如Ensemble Kalman滤波器。本研究着重于冰浓度的同化，但是可以理解的是，对其他数据集（如海面温度）的同化将是有益的。

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