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A coalescent grid model of weighted mean temperature for China region based on feedforward neural network algorithm

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Abstract

Weighted mean temperature (Tm) acts as a vital parameter in an extensive range of applications, such as atmospheric Precipitation Water Vapor (PWV) prediction based on the tropospheric delays from Global Navigation Satellite System (GNSS) measurements. However, few high-accuracy Tm models for the region of China were established in high temporal and high spatial resolution due to the complex topography in China. We utilize the latest version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re-Analysis 5 (ERA5) data to establish a Tm model for the China region, named the CTm-FNN model. This new coalescent model is independent of meteorological parameters but based on combining the idea of the traditional grid model and the feedforward neural network (FNN) algorithm. Tm values can be obtained by inputting the 3-D coordinates of the station, day of year, and hour in UT to the CTm-FNN model. When validated by ERA5 and radiosonde data in 2019, the new model shows that the root mean square (RMS) error is 3.54 K and 4.72 K, respectively. Compared with the Chinese Tropospheric Model (CTrop) model, the RMS error of CTm-FNN is reduced by 29% and 8.5% with respect to ERA5 and radiosonde data, respectively. Compared with the global pressure and temperature 3 (GPT3) model, the reduction is 86% and 83%. The standard deviation (STD) of the CTm-FNN models is 3.54 K and 4.15 K, which are reduced by 26% and 20% compared with the CTrop model, and 82% and 79% compared with the GPT3 model when validated by the ERA5 and radiosonde data. This new model manifests its ability to capture high-accuracy Tm from the surface to almost the tropopause with high temporal and high spatial resolution, which can expand the application of GNSS-PWV inversion technique in China.

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Data availability

The ERA5 data used in this study are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels. And the radiosonde data are available at https://www1.ncdc.noaa.gov/pub/data/igra. The MATLAB source code of C \(T_{{\text{m}}}\)-FNN model is available at https://doi.org/10.6084/m9.figshare.14473335.v1.

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Acknowledgements

The authors acknowledge the support of the National Natural Science Foundation of China (NNSFC), Grant Number 41974030, and the key project of the college of natural science funding of Anhui Provincial Department of Education and Postgraduate Research & Practice Innovation Program of Jiangsu Province, Grant Numbers KJ2018A0480 and KYCX18_0145. The authors would like to thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing ERA5 reanalysis data and the Integrated Global Radiosonde Archive (IGRA) for providing radiosonde data.

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Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, Jiangsu, China

    Mingchen Zhu & Xianwen Yu

  2. School of Civil Engineering and Architecture, Tongling University, Tongling, Anhui, China

    Mingchen Zhu & Wei Sun

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  1. Mingchen Zhu
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  2. Xianwen Yu
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  3. Wei Sun
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Correspondence to Xianwen Yu.

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Zhu, M., Yu, X. & Sun, W. A coalescent grid model of weighted mean temperature for China region based on feedforward neural network algorithm. GPS Solut 26, 70 (2022). https://doi.org/10.1007/s10291-022-01254-y

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