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- W4220958069 abstract "The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021)." @default.
- W4220958069 created "2022-04-03" @default.
- W4220958069 date "2022-03-16" @default.
- W4220958069 modified "2023-10-14" @default.
- W4220958069 title "Comment on essd-2021-432" @default.
- W4220958069 doi "https://doi.org/10.5194/essd-2021-432-rc3" @default.
- W4220958069 hasPublicationYear "2022" @default.
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