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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-274
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-274
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal AMT.

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Peng Sun1, Suqin Wu1,2, Kefei Zhang1,2, Moufeng Wan1, and Ren Wang1 Peng Sun et al.
  • 1School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
  • 2SPACE Research Center, School of Science, RMIT University, Melbourne 3001, Australia

Abstract. Global Navigation Satellite Systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modelled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1°×1°, named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modelled with the variables of the mean, annual and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year 1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5°×5°; 2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model predicted Tm values over all global grid points at the 950 hPa and 500 hPa pressure levels were 3.35 K and 3.94 K respectively. Compared to the second reference, the mean bias and mean RMSE of the model predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 K and 3.89 K respectively; the mean bias and mean RMSE of the model’s Tm values at all pressure levels in the height range from the surface to 10 km altitude were −0.16 K and 4.20 K respectively. The new model results were also compared with that of the GPT3, GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The impact of Tm on GNSS-PWV was evaluated in terms of relative error, and significant improvement was found compared to the widely used GPT3 model. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located in anywhere below the tropopause (assumed to be 10 km), which has significance for applications needing real-time or near real-time PWV converted from GNSS signals.

Peng Sun et al.

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Short summary
In GPS/GNSS meteorology, precipitable water vapor (PWV) at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals using a conversion factor which is a function of weighted mean temperature (Tm) over the site. In this research, a new global grid-based empirical Tm model was developed using ERA5 reanalysis data. The model-predicted Tm value has significance for applications needing real-time or near real-time PWV converted from GNSS signals.
In GPS/GNSS meteorology, precipitable water vapor (PWV) at a station is obtained from a...
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