Time-expanded sampling approach for Ensemble Kalman Filter: Experiment assimilation of simulated soundings
Time-expanded sampling approach for Ensemble Kalman Filter: Experiment assimilation of simulated soundings
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集成卡尔曼滤波器的时间扩展采样方法：模拟探测的实验同化
DOI：
10.1007/s13351-011-0502-0
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发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Gong Zhongqiang
中科院分区：
文献类型：
--
作者：
Yang Yi;Wang Jinyan;Liu Xinhua;Gong Zhongqiang
在集成卡尔曼滤波（EnKF）数据同化预测系统中，大部分计算时间花在集成成员的预测运行上。有限或较小的集合尺寸确实可以减少计算成本，但是过小的集合尺寸通常会导致滤波器发散，特别是当存在模型误差时。为了提高EnKF数据同化预报系统的效率，防止滤波发散，采用基于WRF （Weather Research and Forecasting）模式的EnKF时间扩展采样方法对模拟探空数据进行同化。该方法从nbmember预测中采样一系列扰动状态向量，不仅在分析时间运行（如传统方法所做的那样），而且在mtimes分析时间之前和之后的等间隔时间级别（时间间隔为Δt）运行。使用上述所有采样状态向量构建集成并计算背景协方差进行分析，因此在不增加预测运行次数的情况下，从nbtonb +2M×Nb=(1+2M)×Nb增加集成规模（仍然是nb）。这降低了计算成本。通过一系列实验研究了Δt（时间扩展采样的时间间隔）和m（最大采样次数）对分析结果的影响。结果表明，如果ΔtandMare选择得当，时间扩展采样方法与集合大小为（1+2M）×Nb的常规方法效果相当，但预测运行次数大大减少。
In the Ensemble Kalman Filter (EnKF) data assimilation-prediction system, most of the computation time is spent on the prediction runs of ensemble members. A limited or small ensemble size does reduce the computational cost, but an excessively small ensemble size usually leads to filter divergence, especially when there are model errors. In order to improve the efficiency of the EnKF data assimilation-prediction system and prevent it against filter divergence, a time-expanded sampling approach for EnKF based on the WRF (Weather Research and Forecasting) model is used to assimilate simulated sounding data. The approach samples a series of perturbed state vectors fromNbmember prediction runs not only at the analysis time (as the conventional approach does) but also at equally separated time levels (time interval is Δt) before and after the analysis time withMtimes. All the above sampled state vectors are used to construct the ensemble and compute the background covariance for the analysis, so the ensemble size is increased fromNbtoNb+2M×Nb=(1+2M)×Nb) without increasing the number of prediction runs (it is stillNb). This reduces the computational cost. A series of experiments are conducted to investigate the impact of Δt(the time interval of time-expanded sampling) andM(the maximum sampling times) on the analysis. The results show that if ΔtandMare properly selected, the time-expanded sampling approach achieves the similar effect to that from the conventional approach with an ensemble size of (1+2M)×Nb, but the number of prediction runs is greatly reduced.
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