Ensemble covariances adaptively localized with ECO-RAP. Part 2: a strategy for the atmosphere
Ensemble covariances adaptively localized with ECO-RAP. Part 2: a strategy for the atmosphere
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DOI：
10.1111/j.1600-0870.2008.00372.x
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发表时间：
2009-01-01
影响因子：
2
通讯作者：
Hodyss, Daniel
中科院分区：
文献类型：
--
作者：
Bishop, Craig H.;Hodyss, Daniel
第1部分的定位方法，集成相关性提升到一个功率（ECO-RAP），被纳入到一个局部集成变换卡尔曼滤波器（LETKF）。由于蛮力合并的代价太大，因此我们演示了第1部分的协方差自适应定位与ECO-rap （CALECO）预测误差协方差矩阵的分解特性，该特性与其他简化一起降低了成本。该特性廉价地提供了一个大的CALECO集合，其协方差是CALECO矩阵。CALECO集成的每个成员都是一个原始集成成员与ECO-RAP矩阵平方根的一列之间的元素积。LETKF应用于CALECO集成而不是原始集成。该方法能够以很少的额外计算成本更新每个观测域中的大量变量。在合理的假设下，这使得CALECO和标准LETKF的成本相似。CALECO LETKF不需要人工观测误差膨胀或垂直受限的观测量，这两种情况都会干扰诸如卫星观测等非局部观测的同化。利用来自全球数值天气预报（NWP）系统的27个成员集合，我们描述了四维（4-D）流动自适应误差协方差定位，并测试了CALECO LETKF减少分析误差的能力。
Part 1's localization method, Ensemble COrrelations Raised to A Power (ECO-RAP), is incorporated into a Local Ensemble Transform Kalman Filter (LETKF). Because brute force incorporation would be too expensive, we demonstrate a factorization property for Part 1's Covariances Adaptively Localized with ECO-rap (CALECO) forecast error covariance matrix that, together with other simplifications, reduces the cost. The property inexpensively provides a large CALECO ensemble whose covariance is the CALECO matrix. Each member of the CALECO ensemble is an element-wise product between one raw ensemble member and one column of the square root of the ECO-RAP matrix. The LETKF is applied to the CALECO ensemble rather than the raw ensemble. The approach enables the update of large numbers of variables within each observation volume at little additional computational cost. Under plausible assumptions, this makes the CALECO and standard LETKF costs similar. The CALECO LETKF does not require artificial observation error inflation or vertically confined observation volumes both of which confound the assimilation of non-local observations such as satellite observations. Using a 27 member ensemble from a global Numerical Weather Prediction (NWP) system, we depict four-dimensional (4-D) flow-adaptive error covariance localization and test the ability of the CALECO LETKF to reduce analysis error.