局地误差子空间变换卡尔曼滤波方法的最优参数选取-海洋预报

局地误差子空间变换卡尔曼滤波方法的最优参数选取

单位：1. 国家海洋环境预报中心, 北京 100081;
2. 中国海洋大学海洋环境与生态教育部重点实验室, 山东青岛 266100;
3. 国家海洋局海洋灾害预报技术研究重点实验室, 北京 100081

分类号：P456.7

出版年·卷·期（页码）：2020·37·第五期（42-51）

摘要：

基于PDAF同化框架，通过Lorenz96模型的孪生实验，探讨了LESTKF同化方案中的两个重要参数局地化半径和遗忘因子对于同化结果的影响。实验结果表明：局地化半径对分析结果的空间分布影响明显。局地化半径过大，不能很好地滤去背景误差协方差矩阵中的虚假相关；局地化半径过小则分析太细节化使得物理量场不符合实际。遗忘因子的选取对于同化效果影响显著：孪生实验证明遗忘因子（取值为0~1）选取适当能够明显提高同化效果，但如果选得太小则会使同化结果过于接近模式，观测信息对模式的调整将减弱。当二者同时作为自变量影响Lorenz96模式的同化效果时，存在一个最优的参数选择区域，但该最优区域紧邻滤波发散的区域，因此在实际同化应用中应格外重视。

Based on the ensemble assimilation method LESTKF within PDAF, this paper analyzes the impact of local radius and forgetting factor on assimilation results using twin experiments with Lorenz96 model. The results show that the localization radius significantly impacts the spatial distribution of the analysis results. The spurious correlations in the background error covariance can't be well filtered if the localization radius is too large, while the physical quantities field doesn't conform with the reality due to over-detailed analysis if the localization radius is too small. The twin experiments reveal that the assimilation performance could be significantly improved by choosing proper forget factor (0~1). The smaller the forget factor is, the closer the assimilation results will resemble the model simulation, which indicates the weakening impacts of observations in adjusting the model. Therefore, special attention should be made in choosing the localization radius and forget factor in data assimilation applications.

参考文献：

[1] Evensen G. Advanced data assimilation for strongly nonlinear dynamics[J]. Monthly Weather Review, 1997, 125(6):1342-1354.
[2] Evensen G, Van Leeuwen P J. An ensemble Kalman smoother for nonlinear dynamics[J]. Monthly Weather Review, 2000, 128(6):1852-1867.
[3] Evensen G. The ensemble Kalman filter:theoretical formulation and practical implementation[J]. Ocean Dynamics, 2003, 53(4):343-367.
[4] Evensen G. Data assimilation:the ensemble Kalman filter[M]. Berlin, Heidelberg:Springer, 2007.
[5] Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. Journal of Geophysical Research:Oceans, 1994, 99(C5):10143-10162.
[6] Houtekamer P L, Mitchell H L. Data assimilation using an ensemble Kalman filter technique[J]. Monthly Weather Review, 1998, 126(3):796-811.
[7] Pham D T, Verron J, Gourdeau L. Singular evolutive Kalman filters for data assimilation in oceanography[J]. Comptes Rendus de l'Academie des Sciences Series II A Earth and Planetary Science, 1998, 326(4):255-260.
[8] Pham D T. Stochastic methods for sequential data assimilation in strongly nonlinear systems[J]. Monthly Weather Review, 2001, 129(5):1194-1207.
[9] Nerger L, Danilov S, Hille W, et al. Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter[J]. Ocean Dynamics, 2006, 56(5-6):634-649.
[10] Losa S N, Danilov S, Schröter J, et al. Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas:Part 2. Sensitivity of the forecast's skill to the prior model error statistics[J]. Journal of Marine Systems, 2014, 129:259-270.
[11] Bell M J, Lefèbvre M, Le Traon P Y, et al. GODAE:The global ocean data assimilation experiment[J]. Oceanography, 2009, 22(3):14-21.
[12] Whitaker J S, Hamill T M. Ensemble data assimilation without perturbed observations[J]. Monthly Weather Review, 2002, 130(7):1913-1924.
[13] Bishop C H, Etherton B J, Majumdar S J. Adaptive sampling with the ensemble transform Kalman filter. Part I:Theoretical aspects[J]. Monthly Weather Review, 2001, 129(3):420-436.
[14] Courtier P, Thépaut J N, Hollingsworth A. A strategy for operational implementation of 4D-Var, using an incremental approach[J]. Quarterly Journal of the Royal Meteorological Society, 1994, 120(519):1367-1387.
[15] Hamill T M, Whitaker J S, Snyder C. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter[J]. Monthly Weather Review, 2001, 129(11):2776-2790.
[16] Houtekamer P L, Mitchell H L. A sequential ensemble Kalman filter for atmospheric data assimilation[J]. Monthly Weather Review, 2001, 129(1):123-137.
[17] Gaspari G, Cohn S E. Construction of correlation functions in two and three dimensions[J]. Quarterly Journal of the Royal Meteorological Society, 1999, 125(554):723-757.
[18] Nerger L, Janjić T, Schröter J, et al. A regulated localization scheme for ensemble-based Kalman filters[J]. Quarterly Journal of the Royal Meteorological Society, 2012, 138(664):802-812.
[19] Nerger L, Hiller W, Schröter J. A comparison of error subspace Kalman filters[J]. Tellus A, 2005, 57(5):715-735.
[20] Nerger L, Gregg W W. Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK filter[J]. Journal of Marine Systems, 2007, 68(1-2):237-254.
[21] Nerger L, Hiller W, Schröter J. PDAF-the parallel data assimilation framework:experiences with Kalman filtering[M]//Zwieflhofer W, Mozdzynski G. Use of High Performance Computing in Meteorology. Reading:World Scientific, 2005:63-83.
[22] Nerger L, Hiller W. Software for ensemble-based data assimilation systems-Implementation strategies and scalability[J]. Computers & Geosciences, 2013, 55:110-118.
[23] Sanchez S, Wicht J, Bärenzung J, et al. Sequential assimilation of geomagnetic observations:perspectives for the reconstruction and prediction of core dynamics[J]. Geophysical Journal International, 2019, 217(2):1434-1450.
[24] Lorenz E N. Predictability-a problem partly solved[C]//Proceedings Seminar on Predictability. Reading, UK:ECMWF, 1996:1-18.

服务与反馈：

【文章下载】【发表评论】【查看评论】【加入收藏】