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首页> 外文期刊>Radio Science >Data assimilation of incoherent scatter radar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter
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Data assimilation of incoherent scatter radar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter

机译:应用集合卡尔曼滤波将非相干散射雷达观测数据同化为一维中纬度电离层模型

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摘要

In this paper, electron densities during 25–28 September 2000 observed by the Millstone Hill incoherent scatter radar (ISR) are assimilated into a one-dimensional midlatitude ionospheric theoretical model by using an ensemble Kalman filter (EnKF) technique. It is found that (1) the derived vertical correlation coefficients of electron density show obvious altitude dependence. These variations are consistent with those from ISR observations. (2) The EnKF technique has a better performance than the 3DVAR technique especially in the data-gap regions, which indicates that the EnKF technique can extend the influences of observations from data-rich regions to data-gap regions more effectively. (3) Both the altitude and local time variations of the root mean square error (RMSE) of electron densities for the ensemble spread and ensemble mean from observation behave similarly. It is shown that the spread of the ensemble members can represent the deviations of ensemble mean from observations. (4) To achieve a better prediction performance, the external driving forces should also be adjusted simultaneously to the real weather conditions. For example, the performance of prediction can be improved by adjusting neutral meridional wind using equivalent wind method. (5) In the EnKF, there are often erroneous correlations over large distance because of the sampling error. This problem may be avoided by using a relative larger ensemble size.
机译:在本文中,通过使用集成卡尔曼滤波(EnKF)技术将Millstone Hill非相干散射雷达(ISR)在2000年9月25日至28日观察到的电子密度同化为一维中纬度电离层理论模型。发现(1)导出的电子密度垂直相关系数显示出明显的高度依赖性。这些变化与ISR观测结果一致。 (2)EnKF技术比3DVAR技术具有更好的性能,尤其是在数据空白区域,这表明EnKF技术可以更有效地将观测的影响从数据丰富的区域扩展到数据空白区域。 (3)整体扩展的电子密度的均方根误差(RMSE)的海拔高度和局部时间变化以及观测到的整体均值的行为类似。结果表明,集合成员的散布可以表示集合平均数与观测值的偏差。 (4)为了获得更好的预测性能,还应同时调整外部驱动力以适应实际天气情况。例如,可以通过使用等效风法调整中性子午风来提高预测性能。 (5)在EnKF中,由于采样误差,在较大距离上通常存在错误的相关性。通过使用相对较大的合奏大小可以避免此问题。

著录项

  • 来源
    《Radio Science》 |2007年第6期|1-20|共20页
  • 作者单位

    Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.;

    Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China., Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China., Graduate School, Chinese Academy of Sciences, Beijing, China.;

    Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China., Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China., Graduate School, Chinese Academy of Sciences, Beijing, China.;

    Graduate School, Chinese Academy of Sciences, Beijing, China., Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.;

    High Altitude Observatory, National Center for Atmospheric Research, Boulder, Colorado, USA.;

    Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China., Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China., Graduate School, Chinese Academy of Sciences, Beijing, China.;

    Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.;

    Haystack Observatory, Massachusetts Institute of Technology, Westford, Massachusetts, USA.;

    Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Mathematical model; Data models; Kalman filters; Data assimilation; Meteorology; Predictive models; Ions;

    机译:数学模型;数据模型;卡尔曼滤波器;数据同化;气象学;预测模型;离子;

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