A simulation study using a local ensemble transform Kalman filter for data assimilation in New York Harbor
A simulation study using a local ensemble transform Kalman filter for data assimilation in New York Harbor
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DOI：
10.1175/2008jtecho565.1
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发表时间：
2008-09-01
影响因子：
2.2
通讯作者：
Henderson, John M.
中科院分区：
文献类型：
--
作者：
Hoffman, Ross N.;Ponte, Rui M.;Henderson, John M.
使用集合来近似卡尔曼滤波器的数据同化方法在海洋学应用中具有许多潜在的优点。为了探索这种情况下，河口海岸海洋模式（ECOM）耦合的现代数据同化方法的基础上的本地集合变换卡尔曼滤波（LETKF），并进行了一系列的模拟实验。在这些实验中，长时间的ECOM“自然”运行被认为是“真理”。“观测是在分析时通过扰动随机选择的模型网格点上的自然运行而产生的，这些网格点具有已知统计数据的误差。一个不同的收集模型状态用于初始合奏。所有的实验使用相同的侧边界条件和外部强迫场在自然运行。在数据同化中，分析步骤使用卡尔曼滤波方程将观测值和ECOM预报相结合。作为控制，自由运行的预测（FIRE）是由初始集合平均检查外部强迫与资料同化的分析技巧的相对重要性。同化周期和FRF的结果进行比较，以量化的技能。LETKF表现良好的情况下，在这里研究。经过几个同化周期后，分析误差小于观测误差，并且远小于FRF中的误差。同化很快消除了初始集合的域平均偏差。该过滤器可以准确地跟踪所有数据密度下的真实情况，从50%的模型网格点到2%的模型网格点。随着数据密度的增加，系综扩展、偏差和误差标准差减小。随着系综大小的增加，系综扩展增加，误差标准差减小。观测误差的大小的增加导致更大的系综扩展，但对分析精度的影响很小。
Data assimilation approaches that use ensembles to approximate a Kalman filter have many potential advantages for oceanographic applications. To explore the extent to which this holds, the Estuarine and Coastal Ocean Model (ECOM) is coupled with a modern data assimilation method based on the local ensemble transform Kalman filter (LETKF), and a series of simulation experiments is conducted. In these experiments, a long ECOM "nature" run is taken to be the "truth." Observations are generated at analysis times by perturbing the nature run at randomly chosen model grid points with errors of known statistics. A diverse collection of model states is used for the initial ensemble. All experiments use the same lateral boundary conditions and external forcing fields as in the nature run. In the data assimilation, the analysis step combines the observations and the ECOM forecasts using the Kalman filter equations. As a control, a free-running forecast (FIRE) is made from the initial ensemble mean to check the relative importance of external forcing versus data assimilation on the analysis skill. Results of the assimilation cycle and the FRF are compared to truth to quantify the skill of each.The LETKF performs well for the cases studied here. After just a few assimilation cycles, the analysis errors are smaller than the observation errors and are much smaller than the errors in the FRF. The assimilation quickly eliminates the domain-averaged bias of the initial ensemble. The filter accurately tracks the truth at all data densities examined, from observations at 50% of the model grid points down to 2% of the model grid points. As the data density increases, the ensemble spread, bias, and error standard deviation decrease. As the ensemble size increases, the ensemble spread increases and the error standard deviation decreases. Increases in the size of the observation error lead to a larger ensemble spread but have a small impact on the analysis accuracy.