Abstract A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.

中文翻译：

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用 EnKF-SQ 同化半定性海冰厚度数据：双实验

摘要 一种新引入的随机数据同化方法，Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) 在双实验框架中应用于北极的现实耦合冰海模型，即 TOPAZ4 配置。该方法被证明可以为从被动微波遥感获得的范围有限的薄冰厚度测量增加价值，相对于更琐碎的解决方案，例如忽略超出范围的值或同化气候学。评估了 EnKF-SQ 固有的一些已知属性：使解更接近厚度阈值的趋势、所得分析集合的偏度以及异常值的潜在外观。实验表明，鉴于这里使用的海冰数据同化系统的其他次优特征（非线性、非高斯变量、缺乏强耦合），这些特性都没有证明是有害的。EnKF-SQ 有一个单一的调谐参数，该参数被调整以获得手头系统的最佳性能。灵敏度测试表明，调谐参数不会严重影响结果。EnKF-SQ 总体上是一种将半定性观察同化为高维非线性系统的有效方法。