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[1] Long Fengyang, Hu Wusheng, Dong Yanfeng, Yu Longfei, et al. A neural network method for estimating weighted meantemperature over China and adjacent areas [J]. Journal of Southeast University (English Edition), 2021, 37 (1): 84-90. [doi:10.3969/j.issn.1003-7985.2021.01.011]
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A neural network method for estimating weighted meantemperature over China and adjacent areas()
一种用于估计中国及毗邻区域加权平均温度的神经网络方法
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
37
Issue:
2021 1
Page:
84-90
Research Field:
Other Disciplines
Publishing date:
2021-03-20

Info

Title:
A neural network method for estimating weighted meantemperature over China and adjacent areas
一种用于估计中国及毗邻区域加权平均温度的神经网络方法
Author(s):
Long Fengyang Hu Wusheng Dong Yanfeng Yu Longfei
School of Transportation, Southeast University, Nanjing 211189, China
龙凤阳 胡伍生 董彦锋 余龙飞
东南大学交通学院, 南京 211189
Keywords:
weighted mean temperature GPT2w model neural network error compensation GNSS meteorology
加权平均温度 GPT2w模型 神经网络 误差补偿 GNSS气象学
PACS:
P412.2
DOI:
10.3969/j.issn.1003-7985.2021.01.011
Abstract:
To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas, the error compensation technology based on the neural network was proposed, and a total of 374 800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region. The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model. Results show that the proposed model is about 14.9% better than the GPT2w model and about 7.6% better than the Bevis model with measured surface temperature in accuracy. The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges, but also in different months throughout the year. Moreover, the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed. The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.
为了提高全球温压湿模型(GPT2w)在估计中国及毗邻区域加权平均温度中的适用性, 采用了基于神经网络的模型误差补偿技术, 以分布在中国及毗邻区域的100个探空站2006—2015年的374 800条大气垂直廓线资料为数据源, 建立了适用于该地区加权平均温度估计的增强模型. 利用分布在该地区的其余92个探空站2016—2018年的数据测试模型性能.结果表明, 该模型的精度比GPT2w模型提高了约14.9%, 比基于实测气象参数的Bevis模型提高了约7.6%.该模型的性能无论是在各个高度区间, 还是在不同季节都比GPT2w模型有明显改进, 并且在探空站分布十分稀少的我国西北部地区, 加权平均温度的估计精度也得到显著的改善.该模型在开展全国范围内的地基GNSS实时水汽反演中具有巨大的应用潜力.

References:

[1] Rocken C, Ware R, Van Hove T, et al. Sensing atmospheric water vapor with the global positioning system[J].Geophysical Research Letters, 1993, 20(23): 2631-2634. DOI:10.1029/93GL02935.
[2] Suparta W, Afar J, Alauddin M, et al. Monitoring of GPS precipitable water vapor during the severe flood in Kelantan[J].American Journal of Applied Sciences, 2012, 9(6): 825-831. DOI:10.3844/ajassp.2012.825.831.
[3] Adams D K, Gutman S I, Holub K L, et al. GNSS observations of deep convective time scales in the Amazon[J].Geophysical Research Letters, 2013, 40(11): 2818-2823. DOI:10.1002/grl.50573.
[4] Suparta W, Rahman R. Spatial interpolation of GPS PWV and meteorological variables over the west coast of Peninsular Malaysia during 2013 Klang Valley Flash Flood[J].Atmospheric Research, 2016, 168: 205-219. DOI:10.1016/j.atmosres.2015.09.023.
[5] Askne J, Nordius H. Estimation of tropospheric delay for microwaves from surface weather data[J].Radio Science, 1987, 22(3): 379-386. DOI:10.1029/RS022i003p00379.
[6] Durre I, Vose R S, Wuertz D B. Overview of the integrated global radiosonde archive[J].Journal of Climate, 2006, 19(1): 53-68. DOI:10.1175/jcli3594.1.
[7] Huang L K, Liu L L, Chen H, et al. An improved atmospheric weighted mean temperature model and its impact on GNSS precipitable water vapor estimates for China[J].GPS Solutions, 2019, 23(2): 1-16. DOI:10.1007/s10291-019-0843-1.
[8] Businger S, Chiswell S R, Bevis M, et al. The promise of GPS in atmospheric monitoring[J].Bulletin of the American Meteorological Society, 1996, 77(1): 5-18. DOI: 10.1175/1520-0477(1996)077<0005:TPOGIA>2.0.CO;2.
[9] Bevis M, Businger S, Herring T A, et al. GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system[J].Journal of Geophysical Research: Atmospheres, 1992, 97(D14): 15787-15801. DOI:10.1029/92JD01517.
[10] Li Q Z, Yuan L G, Chen P, et al. Global grid-based Tm model with vertical adjustment for GNSS precipitable water retrieval[J].GPS Solutions, 2020, 24(3): 73. DOI:10.1007/s10291-020-00988-x.
[11] Yao Y B, Zhu S, Yue S Q. A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere[J].Journal of Geodesy, 2012, 86(12): 1125-1135. DOI:10.1007/s00190-012-0568-1.
[12] Yao Y B, Xu C Q, Zhang B, et al. GTm-Ⅲ:A new global empirical model for mapping zenith wet delays onto precipitable water vapour[J]. Geophysical Journal International, 2014, 197(1): 202-212. DOI:10.1093/gji/ggu008.
[13] He C Y, Wu S Q, Wang X M, et al. A new voxel-based model for the determination of atmospheric weighted mean temperature in GPS atmospheric sounding[J].Atmospheric Measurement Techniques, 2017, 10(6): 2045-2060. DOI:10.5194/amt-10-2045-2017.
[14] B�0;F6;hm J, M�0;F6;ller 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.
[15] Xu C Q, Yao Y B, Zhang B, et al. Accuracy analysis and test on the weighted mean temperature of the atmosphere grid data offered by GGOS atmosphere[J]. Journal of Geomatics, 2014, 39(4): 13-16. DOI:10.14188/j.2095-6045.2014.04.017. (in Chinese)
[16] Bevis M, Businger S, Chiswell S, et al. GPS meteorology: Mapping zenith wet delays onto precipitable water[J].Journal of Applied Meteorology, 1994, 33(3): 379-386. DOI: 10.1175/1520-0450(1994)0332.0.CO;2.
[17] Ding M H. A neural network model for predicting weighted mean temperature[J].Journal of Geodesy, 2018, 92(10): 1187-1198. DOI:10.1007/s00190-018-1114-6.
[18] Hu W S, Sun L. Neural network based method for compensating model error[J].Journal of Southeast University(English Edition), 2009, 25(3): 400-403.
[19] Zhu M C, Hu W S, Wang L S. Accuracy test and analysis for GPT2w model in China[J].Geomatics and Information Science of Wuhan University, 2019, 44(9): 1304-1311. DOI:10.13203/j.whugis20170387. (in Chinese)
[20] Huang L K, Jiang W P, Liu L L, et al. A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor[J].Journal of Geodesy, 2019, 93(2): 159-176. DOI:10.1007/s00190-018-1148-9.

Memo

Memo:
Biography: Long Fengyang(1990—), male, Ph.D. candidate, fengyang_win@163.com.
Foundation items: The National Natural Science Foundation of China(No.41574022), the Postgraduate Research & Practice Innovation Program of Jiangsu Province(No.KYCX17_0150).
Citation: Long Fengyang, Hu Wusheng, Dong Yanfeng, et al.A neural network method for estimating weighted mean temperature over China and adjacent areas[J].Journal of Southeast University(English Edition), 2021, 37(1):84-90.DOI:10.3969/j.issn.1003-7985.2021.01.011.
Last Update: 2021-03-20