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

• Main Authors: P. Sun, S. Wu, K. Zhang, M. Wan, R. Wang
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-03-01
• Series: Atmospheric Measurement Techniques
• Online Access: https://amt.copernicus.org/articles/14/2529/2021/amt-14-2529-2021.pdf

<p>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 (<span class="inline-formula"><i>T</i>_m</span>) along the vertical direction in the atmosphere over the site. Thus, the accuracy of <span class="inline-formula"><i>T</i>_m</span> directly affects the quality of the GNSS-derived PWV. Currently, the <span class="inline-formula"><i>T</i>_m</span> value at a target height level is commonly modeled using the <span class="inline-formula"><i>T</i>_m</span> value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in <span class="inline-formula"><i>T</i>_m</span> is neglected. This may result in large errors in the <span class="inline-formula"><i>T</i>_m</span> result for the target height level, as the variation trend in the vertical direction of <span class="inline-formula"><i>T</i>_m</span> may not be linear. In this research, a new global grid-based <span class="inline-formula"><i>T</i>_m</span> empirical model with a horizontal resolution of 1<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 1<span class="inline-formula">^∘</span> , 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 <span class="inline-formula"><i>T</i>_m</span> at the grid points, and the temporal variation in each of the four coefficients in the <span class="inline-formula"><i>T</i>_m</span> fitting function was also modeled 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 <span class="inline-formula"><i>T</i>_m</span> values in 2018 to compare with the following two references in the same year: (1) <span class="inline-formula"><i>T</i>_m</span> from ERA5 hourly reanalysis with the horizontal resolution of 5<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 5<span class="inline-formula">^∘</span>; (2) <span class="inline-formula"><i>T</i>_m</span> from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted <span class="inline-formula"><i>T</i>_m</span> values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted <span class="inline-formula"><i>T</i>_m</span> values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model's <span class="inline-formula"><i>T</i>_m</span> values over all pressure levels in the height range from the surface to 10 km altitude were <span class="inline-formula">−0.16</span> and 4.20 K, respectively. The new model results were also compared with that of the 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 improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in <span class="inline-formula"><i>T</i>_m</span> and the temporal variation in the coefficients of the <span class="inline-formula"><i>T</i>_m</span> model can significantly improve the accuracy of model-predicted <span class="inline-formula"><i>T</i>_m</span> for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals.</p>