Abstract
In global navigation satellite system (GNSS) meteorology, the weighted mean temperature (Tm) is a variable parameter in the conversion between zenith wet delay errors of GNSS and precipitable water vapor. The combined models of Tm, which are modeled with a combination of Tm seasonal variations and relationships between Tm and site meteorological measurements (mainly site measured temperature), have been proven to be of relatively higher accuracy. In this study, an improved combined model for Tm called the NN-II model was developed and is the second generation of the NN model. Similar to the NN model, NN-II is a combined model and is modeled by using the neural network model. The NN model was only designed for Tm estimates near the surface, while NN-II was designed for Tm estimates from the surface to almost the top of the troposphere. Compared with the NN model, the NN-II model shows some advanced features in terms of model design: modeled Tm data cover from the surface to almost the top of the troposphere, a more accurate seasonal Tm from the GTrop-Tm model is used, and the input variables are refined. Due to these refinements, the bias and RMSE of NN-II for global Tm from the surface to almost the top of the troposphere are 0.08 K and 3.34 K, respectively, and this new model shows 29.1% and 40.6% improved accuracies compared to those of the GTrop-Tm model and the NN model, respectively. The accuracy advantage is maintained over different heights of the troposphere on a global scale.
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Data availability
The radiosonde data used in this study are available at ftp://ftp.ncdc.noaa.gov/pub/data/igra/v1/derived-v2/data-por/. All data and results in this paper can be provided to readers by contacting the corresponding author.
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Acknowledgements
The author would like to thank Dr. Ming Shangguan and Dr. Zhen Li because this study was supported by their funding, i.e., the Natural Science Foundation of Jiangsu Province (BK20170665) and the National Natural Science Foundation of China (41601488).
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Ding, M. A second generation of the neural network model for predicting weighted mean temperature. GPS Solut 24, 61 (2020). https://doi.org/10.1007/s10291-020-0975-3
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DOI: https://doi.org/10.1007/s10291-020-0975-3