基于Holt指数平滑模型的Klobuchar模型精化

刘立龙, 陈军, 黄良珂, 吴丕团, 秦旭元, 蔡成辉

刘立龙, 陈军, 黄良珂, 吴丕团, 秦旭元, 蔡成辉. 基于Holt指数平滑模型的Klobuchar模型精化[J]. 武汉大学学报 ( 信息科学版), 2018, 43(4): 599-604. DOI: 10.13203/j.whugis20150751
引用本文: 刘立龙, 陈军, 黄良珂, 吴丕团, 秦旭元, 蔡成辉. 基于Holt指数平滑模型的Klobuchar模型精化[J]. 武汉大学学报 ( 信息科学版), 2018, 43(4): 599-604. DOI: 10.13203/j.whugis20150751
LIU Lilong, CHEN Jun, HUANG Liangke, WU Pituan, QIN Xuyuan, CAI Chenghui. A Sophisticated Klobuchar Model Based on the Holt Exponential Smoothing Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(4): 599-604. DOI: 10.13203/j.whugis20150751
Citation: LIU Lilong, CHEN Jun, HUANG Liangke, WU Pituan, QIN Xuyuan, CAI Chenghui. A Sophisticated Klobuchar Model Based on the Holt Exponential Smoothing Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(4): 599-604. DOI: 10.13203/j.whugis20150751

基于Holt指数平滑模型的Klobuchar模型精化

基金项目: 

国家自然科学基金 41664002

国家自然科学基金 41704027

广西自然科学基金 2017GXNSFBA198139

广西自然科学基金 2017GXNSFDA198016

广西空间信息与测绘重点实验室 16-380-25-01

广西空间信息与测绘重点实验室 15-140-07-19

广西“八桂学者”岗位专项经费 

详细信息
    作者简介:

    刘立龙, 博士, 教授, 研究方向为GNSS空间环境。hn_liulilong@163.com

    通讯作者:

    黄良珂, 硕士, 讲师。lkhuang666@163.com

  • 中图分类号: P228

A Sophisticated Klobuchar Model Based on the Holt Exponential Smoothing Model

Funds: 

The National Natural Science Foundation of China 41664002

The National Natural Science Foundation of China 41704027

Guangxi Natural Science Foundation of China 2017GXNSFBA198139

Guangxi Natural Science Foundation of China 2017GXNSFDA198016

the Guangxi Key Laboratory of Spatial Information and Geomatics 16-380-25-01

the Guangxi Key Laboratory of Spatial Information and Geomatics 15-140-07-19

the " Ba Gui Scholars" Program of the Provincial Government of Guangxi 

More Information
    Author Bio:

    LIU Lilong, PhD, professor, specializes in GNSS space environment. E-mail: hn_liulilong@163.com

    Corresponding author:

    HUANG Liangke, master, lecturer. E-mail: lkhuang666@163.com

  • 摘要: 利用IGS(International GNSS Service)中心提供的中、低纬度地区平静期、活跃期观测数据,通过Klobuchar模型与双频观测模型解算电离层总电子含量(total electron content,TEC)值。采用Holt指数平滑模型对每个历元前6 d两种模型差值进行1 d预测,利用预测所得差值对Klobuchar模型第7 d的TEC值进行改进。实验结果表明,无论在电离层活跃期还是平静期,改进模型改正效果比基本模型有显著提升,改进模型能更好地反映电离层变化特性,尤其是夜间电离层变化特性。
    Abstract: We use Klobuchar model and Dual-frequency observation model to calculate TEC values with the data provided by IGS center over low-latitude and mid-latitude areas in ionospheric quiet period and active period. Then improve 7th day's TEC values calculate by Klobuchar model with the difference values come from the forecast difference values in each epoch of the preceeding 6 days between Klobuchar model and Dual-frequency observation model by the Holt exponential smoothing model. The experiment results show that the correction effect of improved model has a significant improvement than the basic model no matter whether it is in in ionospheric quiet period or active period. In addition, the improved model can better reflect the ionospheric changing characteristics, especially the night ionospheric changing characteristics.
  • 致谢: 感谢IGS中心提供的数据。
  • 图  1   活跃期低纬度与中纬度3种模型对比

    Figure  1.   Comparison of Three Model in Ionospheric Active Period over Low-Latitude and Mid-Latitude Areas

    图  2   平静期低纬度与中纬度3种模型对比

    Figure  2.   Comparison of Three Model in Ionospheric Quiet Period over Low-Latitude and Mid-Latitude Areas

    表  1   活跃期不同时段基本模型与改进模型改正率和RMSE统计表

    Table  1   Statistics of Relative Accuracy and RMSE Between Basic Model and Improved Model in Ionospheric Active Period

    站名 模型 [0, 4] [4, 8] [8, 12] [12, 16] [16, 20] [20, 24]
    Pimo 基本 81.74%/7.18 82.34%/8.59 84.19%/7.39 64.02%/7.56 59.84%/4.83 50.55%/4.70
    改进 97.52%/0.93 96.32%/1.83 90.43%/3.74 88.25%/2.46 79.55%/2.28 93.62%/0.33
    Guam 基本 73.98%/12.53 77.87%/11.63 78.47%/7.80 46.96%/11.86 57.71%/4.65 78.22%/5.33
    改进 98.37%/0.87 97.45%/1.36 82.87%/5.67 92.01%/2.37 94.87%/0.41 89.37%/1.94
    Crao 基本 -63.50%/5.69 66.31%/2.55 42.29%/9.56 43.71%/9.02 31.37%/5.14 -83.73%/5.95
    改进 87.14%/0.55 89.62%/1.14 91.19%/1.45 96.23%/0.72 91.95%/0.61 82.79%/0.57
    Chan 基本 62.83%/9.24 95.43%/1.62 84.32%/2.36 92.71%/0.76 92.26%/0.81 69.56%/3.48
    改进 91.80%/2.05 93.43%/1.88 91.16%/1.37 92.51%/0.74 86.97%/1.23 88.66%/1.81
    下载: 导出CSV

    表  2   平静期不同时段基本模型与改进模型改正率和RMSE统计表

    Table  2   Statistics of Relative Accuracy and RMSE Between Basic Model and Improved Model in Ionospheric Quiet Period

    站名 模型 [0, 4] [4, 8] [8, 12] [12, 16] [16, 20] [20, 24]
    Pimo 基本 86.30%/2.36 72.07%/4.95 69.22%/3.97 -160.64%/4.26 21.33%/6.58 -19.97%/4.70
    改进 76.90%/3.34 83.78%/2.98 83.83%/2.09 46.57%/2.92 61.54%/1.20 78.05%/1.90
    Guam 基本 92.98%/1.52 94.06%/1.79 84.38%/0.95 43.99%/0.83 -37.09% /0.47 73.73%/1.29
    改进 98.59%/0.55 92.02%/1.14 92.30%/1.45 89.76%/0.72 88.57%/0.62 89.28%/0.57
    Crao 基本 -221.59%/6.94 -35.77%/5.64 -87.48%/9.83 0.51%/7.95 77.96%/6.61 -287.77%/7.31
    改进 93.39%/0.16 92.24% /0.44 93.82%/0.38 88.59%/0.93 86.07%/0.72 84.96%/0.34
    Chan 基本 77.70%/2.38 80.09%/2.51 46.95%/4.44 92.24%/0.79 23.46%/3.94 73.61%/2.94
    改进 90.08%/1.03 95.45%/0.63 69.25%/2.52 90.08%/1.02 84.69%/0.96 86.19%/1.81
    下载: 导出CSV
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  • 收稿日期:  2016-09-19
  • 发布日期:  2018-04-04

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