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首页> 外文期刊>Atmospheric environment >An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data
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An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data

机译：用于大气数据同化的集成卡尔曼滤波器：在风洞数据中的应用

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摘要

In the previous work (Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.
机译：在先前的工作（Zheng等，2007，2009）中，基于集合卡尔曼滤波器的数据同化方法已应用于蒙特卡洛弥散模型（MCDM）。当通过双生实验和短距离野外实验对该方法进行测试时，结果令人鼓舞。在本技术说明中，在风洞实验中收集的测量数据已被吸收到蒙特卡洛色散模型中。已经考虑了弥散模型中的不确定参数，包括源项，释放高度，湍流强度和风向。 3D参数，即湍流强度和风向，已被3D随机场干扰。为了找到可能影响同化结果的因素，进行了八种不同规格的试验。提出了两种构造风向3D摄动场的策略，结果表明，二级策略的性能优于一级策略。还发现适当的标准偏差和扰动场的相关半径对于数据同化结果起着重要作用。

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