Abstract
Water vapor plays an essential role in regulating the earth’s weather and climate, and the tropospheric delays caused by water vapor are one of the error sources in space geodetic techniques. Attributing to the enhancement of the spatiotemporal resolution of satellite observations and the availability of model simulation data, the fusion of the two datasets provides a promising opportunity to improve the performance of tropospheric delay modeling and forecasting. In this contribution, a tropospheric delay network (TropNet) model is developed based on deep learning method to forecast the zenith wet delays (ZWD) by combining information provided by the Geostationary Operational Environmental Satellite-R series and the global forecast system (GFS). The performance of the tropospheric delays predicted from TropNet is assessed with tropospheric products derived from GNSS. The results demonstrate that the TropNet predicted ZWD agree well with the GNSS-derived ZWD, and an accuracy of better than 11 mm is achieved for all the forecast lead times, showing an overall improvement of 15.5% when compared to the GFS ZWD. Moreover, intercomparisons with ZWD derived from radiosondes and Vienna Mapping Functions 3 (VMF3) are performed to further evaluate the performance of the TropNet model. Averaged RMS values equal to 14.9 mm and 13.9 mm are obtained when compared to radiosondes and VMF3. Furthermore, the TropNet model is able to forecast high-quality ZWD up to 6 h at a spatial resolution of 2 km and a temporal resolution of 1 h, which indicates a prospective potential for time-critical applications.
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Data availability
The GOES-R CMI products are available from https://www.ncdc.noaa.gov/airs-web/search, the GFS datasets are available from https://rda.ucar.edu/datasets/ds084.1/, the GNSS tropospheric products are available from https://www.unavco.org/data/tropospheric/tropo-products/tropo-products.html, the SRTM topography data are available from https://srtm.csi.cgiar.org/srtmdata/, the VMF3 products are available from https://vmf.geo.tuwien.ac.at/, and the radiosonde products are available from https://www.ncei.noaa.gov/products/weather-balloon/integrated-global-radiosonde-archive.
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Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (Grant 41974029) and the Fellowship of China Postdoctoral Science Foundation (2020M682481). The numerical calculations in this study have been carried out based on the supercomputing system in the Supercomputing Center of Wuhan University. We gratefully acknowledge the NOAA for offering the radiosonde data, the UNAVCO data center for supplying the GNSS tropospheric products, and the NCEP for offering the GFS products. We also would like to thank TU Vienna for providing the VMF3 tropospheric products.
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CXL and YXZ proposed the idea of this manuscript, designed and performed the experiments, and wrote the paper. ZLW and YSZ helped to perform the experiments and write the paper. QYW, ZW, and YXL contributed to the data pre-processing and analyzed the results. YXZ reviewed the manuscript and provided suggestions for improvements. All authors have reviewed the final submitted version of the manuscript.
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Lu, C., Zheng, Y., Wu, Z. et al. TropNet: a deep spatiotemporal neural network for tropospheric delay modeling and forecasting. J Geod 97, 34 (2023). https://doi.org/10.1007/s00190-023-01722-4
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DOI: https://doi.org/10.1007/s00190-023-01722-4