Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model
Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model
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局部系综变换卡尔曼滤波器的权重结构：以中间大气环流模型为例
DOI：
10.1002/qj.3852
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发表时间：
2020
影响因子：
8.9
通讯作者：
Miyoshi Takemasa
中科院分区：
文献类型：
--
作者：
Kotsuki Shunji;Pensoneault Andrew;Okazaki Atsushi;Miyoshi Takemasa
局部集合变换卡尔曼滤波(LETKF)通过使用第一次猜测集合与定位截止半径内的周围观测的加权平均来计算分析。由于重叠的观测在相邻的网格点被同化，LETKF产生了空间平滑的权重。本研究利用中间大气模式SPEIDE(简化的参数化法，原始方程动力学)探索权重的空间结构。根据权值结构的特点，我们还改进了权值插值法，该方法用于在较粗的参考点计算权值，并将权值内插到更高分辨率的模型网格点。结果表明，更大的局部化和更稀疏的观测导致空间上更平滑的权重。当体重模式在空间上更平稳时，Wi的危害较小。与均匀分布的参考点相比，具有观测密度依赖参考点的改进的WI方法具有更好的预测效果。这一改进可能是由于具有观测密度相关参考点的WI方法实现的空间不均匀定位功能。最优局部化尺度的空间分布表明，较大(较小)的局部化有利于稀疏(密集)观测区域。对于较大的集合，WI方法的计算效率更高，因为WI方法的额外计算成本低于LETKF方法。
The Local Ensemble Transform Kalman Filter (LETKF) computes analysis by using a weighted average of the first‐guess ensemble with surrounding observations within a localization cut‐off radius. Since overlapped observations are assimilated at neighbouring grid points, the LETKF results in spatially smooth weights. This study explores the spatial structure of the weights with the intermediate atmospheric model SPEEDY (Simplified Parameterizations, Primitive Equation Dynamics). Based on the characteristics of the weight structure, we also aim to improve the weight interpolation (WI) method, which we use to compute the weights at coarser reference points and interpolate the weights into higher‐resolution model grid points. The results show that larger localization and sparser observations result in spatially smoother weights. WI is less detrimental when weight patterns are spatially smoother. An advanced WI method with observation‐density‐dependent reference points results in better forecasts than those with uniformly distributed reference points. This improvement may be due to the spatially inhomogeneous localization function realized by the WI method with observation‐density‐dependent reference points. The spatial distribution of the optimal localization scales shows that larger (smaller) localization is beneficial in sparsely (densely) observed regions. The WI method is computationally more efficient with larger ensembles since the additional computational cost for the WI is lower than that for the LETKF.