International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 271–277, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-271-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 271–277, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-271-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

WATER VAPOR CONVERSION FACTOR OVER QINGHAI-TIBET PLATEAU REGION CONSIDERING ELEVATION, LATITUDE AND FINE SEASONAL VARIATION IS CONSTRUCTED

H. Peng1,2, L. K. Huang1,2,3, C. Li1,2, L. L. Liu1,2, S. Wang1,2, and S. Wang1,2 H. Peng et al.
  • 1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
  • 2Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China
  • 3GNSS Research Center, Wuhan Universtiy, Wuhan 430079, China

Keywords: QTm model, Conversion factor, GGOS atmosphere data, GPS Precipitable Water Vapor, weighted mean temperature, water vapor

Abstract. In this paper, the conversion factor K model of Qinghai-Tibet plateau region was established based on the QTm model which is established using high-precision the Global Geodetic Observing System (GGOS) Atmosphere grid data from 2007 to 2014. The model took into account the influence of elevation fluctuation and latitude change on the model, and analyzed the relevant characteristics with seasonal changes. The 2015 GGOS grid data and radiosonde data were used as the reference value for accuracy assess. The established QTm model was compared with GPT2w model in bias and RMS. Compared with GGOS grid data, the average annual bias and RMS of QTm model were -0.28K and 2.70k respectively. The RMS of GPT2w-5 and GPT2w-1 were 58.16% and 28.84% higher, respectively. Compared with radiosonde data, QTm model has 1.13k average annual bias and the RMS error of 2.92k. Compared with GPT2w-5 and GPT2w-1, the RMS value of QTm model was improved by 25.08% and 29.43%, respectively. The value of atmospheric water vapor conversion coefficient was calculated by the integral method calculated by radio sounding data in the Qinghai-Tibet region in 2015 was used as the reference value for assess the performance of conversion factor K, and compared and analyzed the conversion coefficient K which provided by QTm and GPT2w. The results show that the value of Tm provided by QTm model has the highest accuracy, which is 25.07% higher than that of GPT2w-5 and 29.42% higher than that of GPT2w-1. QTm models can achieve GPS-PWV retrieval precision of better than 2 mm. Which has potential application for high-precision real-time GNSS-PWV retrieving in Qinghai-Tibet region.