| 基于Gauss-Markov卡尔曼滤波的电离层数值同化现报预报系统的构建-以中国及周边地区为例的观测系统模拟试验 |
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| 引用本文： | 乐新安,万卫星,刘立波,宁百齐,赵必强,李国主,熊波.基于Gauss-Markov卡尔曼滤波的电离层数值同化现报预报系统的构建-以中国及周边地区为例的观测系统模拟试验[J].地球物理学报,2010,53(4):787-795. |
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| 作者姓名： | 乐新安 万卫星 刘立波 宁百齐 赵必强 李国主 熊波 |
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| 作者单位： | 1.中国科学院地质与地球物理研究所, 北京 100029;2.中国科学院空间天气学国家重点实验室, 北京 100190 |
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| 基金项目： | 中国博士后科学基金项目(20080440066)、国家自然科学基金（40904037）和空间天气学国家重点实验室开放课题资助. |
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| 摘 要： | 本文给出了一个基于Gauss-Markov卡尔曼滤波的电离层数据同化系统的初步构建和试验结果.我们选择中国及周边地区部分涉及电离层观测的台站(包括子午工程台站、中国地壳形变网和部分IGS台站)作为观测系统进行模拟试验，背景场利用IRI模式，观测值则由NeQuick模式计算得到.我们的同化结果表明，采用Kalman滤波算法，把部分斜TEC同化到背景模式当中，能够获得较好的同化结果，说明我们设计的算法可行、所选择的各种参数比较合理，采用Gauss-Markov假设进行短期预报也取得了较合理的结果.本项研究经过进一步的改进和完善，可以用来对中国地区的电离层进行现报和短期预报，一方面满足相关空间工程应用，另一方面可以提升现有观测系统的科学意义.
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| 关 键 词： | 电离层 数据同化 卡尔曼滤波 误差协方差 |
| 收稿时间： | 2009-04-15 |
| 修稿时间： | 2010-03-03 |
Development of an ionospheric numerical assimilation nowcast and forecast system based on Gauss-Markov Kalman filter-An observation system simulation experiment taking example for China and its surrounding area |
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| LE Xin-An,MO Wei-Xing,LIU Li-Bei,NING Bai-Ji,DIAO Bi-Jiang,LI Guo-Zhu,XIONG Bei.Development of an ionospheric numerical assimilation nowcast and forecast system based on Gauss-Markov Kalman filter-An observation system simulation experiment taking example for China and its surrounding area[J].Chinese Journal of Geophysics,2010,53(4):787-795. |
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| Authors: | LE Xin-An MO Wei-Xing LIU Li-Bei NING Bai-Ji DIAO Bi-Jiang LI Guo-Zhu XIONG Bei |
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| Institution: | 1.Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;2.State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | In this paper, we constructed an ionospheric data assimilation system based on Gauss-Markov Kalman filter and gave some test results. We chose some ionosphere stations (including meridional project stations, China lithosphere deformation GPS network, part of IGS stations) in China and its surrounding area as observation system to do the simulation experiment. International Reference Ionosphere (IRI) is chosen to be the background model, while NeQuick model output is taken to be the observations. Our assimilation results show that it can get good estimation of ionosphere electron density by ingesting the observed slant TEC data into the model by Kalman filter. It illustrates that our assimilation algorithm is feasible and the selected parameters are reasonable. We also obtained reasonable short time forecast results by Gauss-Markov assumption. |
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| Keywords: | Ionosphere Data assimilation Kalman filter Error covariance |
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