|Table of Contents|

[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, (1): 84-90. [doi:10.3969/j.issn.1003-7985.2021.01.011]
Copy

A neural network method for estimating weighted meantemperature over China and adjacent areas()
Share:

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
Issue:
2021年第1期
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
Author(s):
Long Fengyang Hu Wusheng Dong Yanfeng Yu Longfei
School of Transportation, Southeast University, Nanjing 211189, China
Keywords:
weighted mean temperature GPT2w model neural network error compensation GNSS meteorology
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.

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