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A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
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Remote Sens. 2019, 11(16), 1893; https://doi.org/10.3390/rs11161893
Received: 22 May 2019 / Revised: 3 July 2019 / Accepted: 8 August 2019 / Published: 13 August 2019
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Abstract

Precise modeling of tropospheric delay and weighted mean temperature (Tm) is critical for Global Navigation Satellite System (GNSS) positioning and meteorology. However, the model data in previous models cover a limited time span, which limits the accuracy of these models. Besides, the vertical variations of tropospheric delay and Tm are not perfectly modeled in previous studies, which affects the performance of height corrections. In this study, we used the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis from 1979 to 2017 to build a new empirical model. We first carefully modeled the lapse rates of tropospheric delay and Tm. Then we considered the temporal variations by linear trends, annual, and semi-annual variations and the spatial variations by grids. This new model can provide zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and Tm worldwide with a spatial resolution of 1° × 1°. We used the ECMWF ERA-Interim data and the radiosonde data in 2018 to validate this new model in comparison with the canonical GPT2w model. The results show that the new model has higher accuracies than the GPT2w model in all parameters. Particularly, this new model largely improves the accuracy in estimating ZHD and Tm at high-altitude (relative to the grid point height) regions. View Full-Text
Keywords: GNSS meteorology; tropospheric delay; weighted mean temperature; ECMWF ERA-Interim GNSS meteorology; tropospheric delay; weighted mean temperature; ECMWF ERA-Interim
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Sun, Z.; Zhang, B.; Yao, Y. A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017. Remote Sens. 2019, 11, 1893.

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