| 统计权重矩阵法在雷达估测降水集成中的应用 |
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| 引用本文： | 邵月红,张万昌.统计权重矩阵法在雷达估测降水集成中的应用[J].水科学进展,2009,20(6):755-762. |
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| 作者姓名： | 邵月红 张万昌 |
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| 作者单位： | 1.南京大学国际地球系统科学研究所, 江苏, 南京, 210093; |
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| 基金项目： | 国家重点基础研究发展计划(973)资助项目，中国科学院"百人计划"择优支持项目，国家自然科学基金资助项目 |
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| 摘 要： | Z～R关系法和6种雷达雨量计联合法反演的区域降水量与雨量计观测得到的降水场存在较大的误差,将这7种降水估测结果作为信息源,采用统计权重矩阵法对上述7种反演结果进行集成分析,提出了一种改进雷达估测降水的方法。结果表明:在被集成资料中,Z～R关系法估测的降雨场具有明显的偏低现象,精度最差,6种雷达雨量计联合法的估测精度明显优于Z～R关系法。通过统计权重矩阵集成后,精度比集成前所有方法均有明显提高,尤其是降水场分布形势和降水中心的强度都与雨量计场非常吻合。集成得到的降水空间分布场能够较真实地反映地面的降水情况,可以在估测区域降水量中进行业务试用。
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| 关 键 词： | 统计权重矩阵法 集成分析 多元统计分析 降水估测 多普勒雷达 |
| 收稿时间： | 2008-11-17 |
Application of statistical weight matrix method in estimation of regional rainfall |
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| SHAO Yue-hong,ZHANG Wan-chang.Application of statistical weight matrix method in estimation of regional rainfall[J].Advances in Water Science,2009,20(6):755-762. |
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| Authors: | SHAO Yue-hong ZHANG Wan-chang |
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| Institution: | 1.International Institute for Earth System Science, Nanjing University, Nanjing 210093, China;2.Center for Hydro-Science Research, Nanjing University, Nanjing 210093, China |
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| Abstract: | In terms of the Doppler radar rainfall estimation algorithms, such as the Z-R relation, the average calibration, the Kalman filter, the optimum interpolation, the variation, the optimum Kalman filter and the variation Kalman, the regional rainfall are estimated and compared with those interpolated from the observations in the automatic precipitation observatories. The results suggest that the performance of each algorithms mentioned above is not very satisfied. The statistical analyses are applied to the estimated results, and the statistical weight matrix approach is employed to improve the accuracy of the regional rainfall estimations. The results reveal that the precision of the regional rainfall estimation from the average calibration, the Kalman filter, the optimum interpolation, the variation, the optimum Kalman filter and the variation Kalman are evidently superior to those from the Z-R relation, and the regional rainfall estimation from the Z-R relation shows the evident underestimation. What's more, the results further show that the accuracy of the estimated regional rainfall derived from the statistical weight matrix approach by integrating all individual algorithm mentioned above is evidently higher than that obtained by any individual ones. The quantitative rainfall estimations with the statistical weight matrix approach are very close to the automatic rain-gage network observed either in the spatial distribution or in the location of the intense precipitation centers. The regional precipitation estimation of the statistic weight matrix approach can truly reflect the precipitation status over the ground surface and might be served as a promising conventional method for estimation of the regional rainfall for the studied region. |
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| Keywords: | statistical weight matrix method integrated analysis multivariate statistical analysis precipitation estimation Doppler radar |
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