高级搜索

一种用于中国地区的对流层天顶延迟实时修正模型

杜晓燕 乔江 卫佩佩

引用本文: 杜晓燕, 乔江, 卫佩佩. 一种用于中国地区的对流层天顶延迟实时修正模型[J]. 电子与信息学报, 2019, 41(1): 156-164. doi: 10.11999/JEIT180353 shu
Citation:  Xiaoyan DU, Jiang QIAO, Peipei WEI. Real-time Correction Model for Zenith Tropospheric Delay Applied to the Chinese Region[J]. Journal of Electronics and Information Technology, 2019, 41(1): 156-164. doi: 10.11999/JEIT180353 shu

一种用于中国地区的对流层天顶延迟实时修正模型

    作者简介: 杜晓燕: 女,1975年生,副教授,研究方向为电磁场、微波技术与天线等;
    乔江: 男,1995年生,硕士生,研究方向为对流层电波传播;
    卫佩佩: 女,1990年生,博士生,研究方向为电波传播、电磁计算及反演问题等
    通讯作者: 乔江,qj951213@163.com
摘要: 针对目前对流层延迟修正受限于探空数据不足导致修正效率低的问题,该文结合Saastamoinen和GPT2w模型构建形成组合模型Sa+GPT2w模型,通过利用GPT2w模型提供的高精度气象数据,实现中国地区对流层天顶延迟(ZTD)的实时修正,克服对探空数据的依赖,并用实测数据对计算结果进行验证。以IGS提供的中国地区2015至2017年ZTD时间序列为评估标准时,Sa+GPT2w模型(bias: 1.661 cm, RMS: 4.711 cm)的精度较同等条件下的Sa+EGNOS, Sa+UNB3m和Hop+GPT2w模型分别提升50.5%, 41.9%和37.1%;以GGOS Atmosphere 2017年ZTD数据为标准时,Sa+GPT2w模型(bias: 1.551 cm, RMS: 4.859 cm)的精度相对同等条件下的另3种模型分别提升49.5%, 38.5%和46.8%;最后对Sa+EGNOS, Sa+UNB3m和Sa+GPT2w模型在ZTD修正中误差结果的时空分布特征进行分析。研究结果可为在中国地区的导航定位、大气折射研究中,应用不同气象参数模型进行ZTD修正的有效性和可能达到的精度提供参考。

English

    1. [1]

      赵静旸, 宋淑丽, 陈钦明, 等. 基于垂直剖面函数式的全球对流层天顶延迟模型的建立[J]. 地球物理学报, 2014, 57(10): 3140–3153. doi: 10.6038/cjg20141005
      ZHAO Jingyang, SONG Shuli, CHEN Qinming, et al. Establishment of a new global model for zenith tropospheric delay based on functional expression for its vertical profile[J]. Chinese Journal of Geophysics, 2014, 57(10): 3140–3153. doi: 10.6038/cjg20141005

    2. [2]

      姚宜斌, 何畅勇, 张豹, 等. 一种新的全球对流层天顶延迟模型GZTD[J]. 地球物理学报, 2013, 56(7): 2218–2227. doi: 10.6038/cjg20130709
      YAO Yibin, HE Changyong, ZHANG Bao, et al. A new global zenith tropospheric delay model GZTD[J]. Chinese Journal of Geophysics, 2013, 56(7): 2218–2227. doi: 10.6038/cjg20130709

    3. [3]

      HOPFIELD H S. Troposphere effect on electromagnetic measured range: Prediction from surface weather data[J]. Radio Science, 1971, 6(3): 357–367. doi: 10.1029/RS006i003p00357

    4. [4]

      SAASTAMOINEN J. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites[J]. Use of Artificial Satellites for Geodesy, 1972, 15(6): 247–251. doi: 10.1029/GM015p0247

    5. [5]

      杨徉, 喻国荣, 潘树国, 等. 一种综合的对流层延迟模型算法[J]. 东南大学学报(自然科学版), 2013, 43(S2): 418–422. doi: 10.3969/j.issn.1001-0505.2013.S2.043
      YANG Yang, YU Guorong, PAN Shuguo, et al. A comprehensive algorithm using fusion of tropospheric delay models[J]. Journal of Southeast University(Natural Science Edition), 2013, 43(S2): 418–422. doi: 10.3969/j.issn.1001-0505.2013.S2.043

    6. [6]

      姚宜斌, 张豹, 严凤, 等. 两种精化的对流层延迟改正模型[J]. 地球物理学报, 2015, 58(5): 1492–1501. doi: 10.6038/cjg20150503
      YAO Yibin, ZHANG Bao, YAN Feng, et al. Two new sophisticated models for tropospheric delay corrections[J]. Chinese Journal of Geophysics, 2015, 58(5): 1492–1501. doi: 10.6038/cjg20150503

    7. [7]

      刘继业, 陈西宏, 刘赞. 对流层散射双向时间比对中对流层斜延迟实时估计[J]. 电子与信息学报, 2018, 40(3): 587–593. doi: 10.11999/JEIT170581
      LIU Jiye, CHEN Xihong, and LIU Zan. Real-time estimation of tropospheric slant delay in two-way troposphere time transfer[J]. Journal of Electronics &Information Technology, 2018, 40(3): 587–593. doi: 10.11999/JEIT170581

    8. [8]

      滑中豪, 柳林涛, 梁星辉. GPT2w模型检验以及对流层模型的参数互融[J]. 武汉大学学报:信息科学版, 2017, 42(10): 1468–1473. doi: 10.13203/j.whugis20150758
      HUA Zhonghao, LIU Lintao, and LIANG Xinghui. An assessment of GPT2w model and fusion of a troposphere model with in situ data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1468–1473. doi: 10.13203/j.whugis20150758

    9. [9]

      施宏凯, 何秀凤, 王俊杰. 全球气压气温模型在中国地区的精度分析[J]. 大地测量与地球动力学, 2017, 37(8): 841–844. doi: 10.14075/j.jgg.2017.08.014
      SHI Hongkai, HE Xiufeng, and WANG Junjie. Accuracy analyses of global pressure and temperature model in China[J]. Journal of Geodesy and Geodynamics, 2017, 37(8): 841–844. doi: 10.14075/j.jgg.2017.08.014

    10. [10]

      LAGLER K, SCHINDELEGGER M, and NILSSON T. GPT2: Empirical slant delay model for radio space geodetic tech-niques[J]. Geophysical Research Letters, 2013, 40(6): 1069–1073. doi: 10.1002/grl.50288

    11. [11]

      BÖHM J, MÖLLER G, SCHINDELEGGER M, et al. Development of an improved empirical model for slant delays in the troposphere (GPT2w)[J]. GPS Solutions, 2015, 19(3): 433–441. doi: 10.1007/s10291-014-0403-7

    12. [12]

      BRAUN J, ROCKEN C, and WARE R. Validation of line-of-sight water vapor measurements with GPS[J]. Radio Science, 2001, 36(3): 459–472. doi: 10.1029/2000RS002353

    13. [13]

      姚宜斌, 徐星宇, 胡羽丰. GGOS对流层延迟产品精度分析及在PPP中的应用[J]. 测绘学报, 2017, 46(3): 278–287. doi: 10.11947/j.AGCS.2017.20160383
      YAO Yibin, XU Xingyu, and HU Yufeng. Precision analysis of GGOS tropospheric delay product and its application in PPP[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 278–287. doi: 10.11947/j.AGCS.2017.20160383

    14. [14]

      ASKNE J and NORDIUS H. Estimation of tropospheric delay for microwaves from surface weather data[J]. Radio Science, 1987, 22(3): 379–386. doi: 10.1029/RS022i003p00379

    15. [15]

      NIGEL P and ALAN D. Assessment of EGNOS tropospheric correction model[J]. Journal of Navigation, 1999, 54(1): 37–55.

    16. [16]

      LEANDRO R F, LANGLEY R B, and SANTOS M C. UNB3m_pack: A neutral atmosphere delay package for radiometric space techniques[J]. GPS Solutions, 2008, 12(1): 65–70. doi: 10.1007/s10291-007-0077-5

    17. [17]

      QU Weijing, ZHU Wenyao, SONG Shuli, et al. Evaluation of the precision of three tropospheric delay correction models[J]. Chinese Astronomy and Astrophysics, 2008, 32(4): 429–438. doi: 10.1016/j.chinastron.2008.10.010

    18. [18]

      中国天气网. 2016年中国十大天气气候事件评选结果[OL]. http://news.weather.com.cn/2016/12/2638475.shtml. 2016.12.

    1. [1]

      殷志祥, 唐震, 张强, 崔建中, 杨静, 王日晟, 赵寿为, 张居丽. 基于DNA折纸基底的与非门计算模型. 电子与信息学报, 2020, 42(6): 1355-1364.

    2. [2]

      李骜, 刘鑫, 陈德运, 张英涛, 孙广路. 基于低秩表示的鲁棒判别特征子空间学习模型. 电子与信息学报, 2020, 42(5): 1223-1230.

    3. [3]

      夏晓峰, 向宏, 肖震宇, 蔡挺. 基于国产密码算法的数控网络AUTH-VRF模型研究及安全评估. 电子与信息学报, 2020, 42(0): 1-7.

    4. [4]

      王璐慧, 王越, 钱梦瑶, 董亚非. 基于氧化石墨烯与金属离子的逻辑模型设计与可控性验证. 电子与信息学报, 2020, 42(6): 1410-1419.

    5. [5]

      张文明, 姚振飞, 高雅昆, 李海滨. 一种平衡准确性以及高效性的显著性目标检测深度卷积网络模型. 电子与信息学报, 2020, 42(5): 1201-1208.

    6. [6]

      晋守博, 魏章志, 李耀红. 基于大通讯时滞的2阶多智能体系统的一致性分析. 电子与信息学报, 2020, 42(0): 1-6.

  • 图 1  利用GPT2w模型获取测站气象参数示意图

    图 2  IGS测站处的误差月均值变化示意图

    图 3  以中国地区GGOS格点数据为标准的误差变化示意图

    图 4  以2017年GGOS数据为标准时不同年积日的bias和RMS空间变化示意图

    表 1  中国地区IGS测站信息(按纬度升序排列)

    ID测站纬度(°N)经度(°E)高程(m)
    ATCMS24.80120.9977.3
    BTWTF24.95121.16184.0
    CKUNM25.03102.802019.1
    DLHAZ29.6691.103622.0
    EWUHN30.53114.3642.6
    FSHAO31.10121.2022.1
    GXIAN34.37109.22498.5
    HBJFS39.61115.8998.3
    IGUAO43.4787.172049.2
    JURUM43.5987.63917.9
    KCHAN43.79125.44253.7
    下载: 导出CSV

    表 2  相对IGS测站数据的误差统计结果(cm)

    IDSa+EGNOS Sa+UNB3m Sa+GPT2w Hop+GPT2w
    biasRMSbiasRMSbiasRMSbiasRMS
    A20151.0487.969 2.5098.328 1.6155.883 0.9115.641
    20162.1598.585 3.6219.126 2.7295.969 2.0255.700
    20171.3128.3852.7738.7771.8795.811 1.1755.644
    B20150.2147.7362.6228.1481.4975.7681.9515.906
    20161.1008.2163.5088.8542.3865.7432.8395.956
    20170.3168.1022.7248.5191.6005.6492.0535.804
    C2015–6.9399.8173.6317.2510.5303.59510.54611.207
    2016–6.7289.7533.8397.5020.7423.81310.77111.466
    2017–6.7569.9773.8147.6340.7133.73210.73511.417
    D2015–10.19711.8391.4944.4270.3551.66712.81612.938
    2016–9.77811.9081.8995.3300.7652.03913.22113.347
    2017–10.00712.1171.6265.2240.4861.90612.9513.055
    E2015–2.21610.376–0.79910.1153.7397.0873.0576.748
    2016–1.25011.5350.16011.3814.7108.0614.0287.673
    2017–1.46811.776–0.06011.5774.4857.8433.8037.465
    F2015–2.81011.278–1.49211.0022.2637.4791.2857.237
    2016–1.59111.837–0.27111.6753.4797.8392.5027.432
    2017–2.53112.166–1.20111.8962.5456.9991.5676.692
    G2015–4.4779.843–0.0538.3541.7635.2573.9666.345
    2016–3.73810.0550.6788.8962.4935.3134.6966.648
    2017–4.06610.1060.3588.8162.1725.3144.3756.526
    H2015–3.9889.464–1.6378.7031.1364.2070.8754.152
    2016–3.57610.227–1.2289.6391.5464.9291.2864.822
    2017–4.08810.626–1.7369.8611.0384.8110.7774.743
    I2015–4.1567.3272.1224.8730.6562.25910.20610.429
    2016–3.7277.3342.5415.4081.0732.72710.61510.906
    2017–4.2027.4152.1004.9270.6342.18410.17710.385
    J2015–3.0516.9801.2905.5171.1153.3205.4376.261
    2016–2.4407.3081.8906.3241.7114.0696.0337.064
    2017–3.0717.1171.2695.6191.0953.2285.4176.202
    K2015–3.7808.453–1.4017.4740.6403.4471.2823.654
    2016–3.5318.846–1.1558.0080.8823.7121.5243.907
    2017–4.0929.337–1.7168.3230.3273.8150.9693.922
    平均–3.3979.5091.0218.1061.6614.7115.0267.494
    下载: 导出CSV

    表 3  相对GGOS格网数据的误差统计结果(cm)

    统计类型Sa+EGNOSSa+UNB3mSa+GPT2wHop+GPT2w
    biasMin–6.961 –0.812 –0.086 2.716
    Max1.9323.4613.4459.473
    Mean–3.605 1.3931.5516.581
    RMSMin7.7685.4802.5856.928
    Max11.428 10.010 7.28411.786
    Mean9.6317.8994.8599.135
    下载: 导出CSV

    表 4  相对IGS数据的误差统计结果(cm)

    ID年份Sa+EGNOS Sa+UNB3m Sa+GPT2w
    biasRMSbiasRMSbiasRMS
    B20120.2407.4352.6487.9001.7755.965
    2018–7.2189.611–4.8397.940–0.0585.082
    G2012–4.84910.676–0.4199.0801.3755.153
    2018–11.80113.128–7.3688.823–0.0692.302
    I2012–4.7177.8631.5644.8610.0862.005
    2018–8.4779.787–1.8773.303–0.3031.104
    下载: 导出CSV
  • 加载中
图(4)表(4)
计量
  • PDF下载量:  22
  • 文章访问数:  1316
  • HTML全文浏览量:  285
文章相关
  • 通讯作者:  乔江, qj951213@163.com
  • 收稿日期:  2018-04-17
  • 录用日期:  2018-09-26
  • 网络出版日期:  2018-10-23
  • 刊出日期:  2019-01-01
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章