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  • 2020-2024  (4)
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  • 1
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-06-01
    Description: Atmospheric Precipitable Water Vapor (PWV) information from Global Navigation Satellite Systems (GNSS) has been proven to be of a high value for meteorology due to its distinguished characteristics such as high accuracy, high temporal and spatial resolution, global coverage and 24/7 availability. In this study, real-time PWVs were retrieved from GNSS observations over 66 GNSS stations in USA for a period of 10 years from 2010 to 2020. The IGS ultra-fast orbit products were used to estimate Zenith Total Delay (ZTD), and a global Zenith Hydrostatic Delay (ZHD) model and a global weighted mean temperature (T〈sub〉m〈/sub〉) model, both of which do not require meteorological parameters (surface pressure or surface temperature), were used to calculated ZHD and T〈sub〉m〈/sub〉. Then, the PWV together with rainfall data for the period from 2010 to 2018 were used to construct a simulated real-time rainfall forecast model, which included three components: PWV value, PWV increase, and PWV maximum increase rate. Finally, the accuracy of the model was evaluated using data for the period from 2019 to 2020 and compared with a model that was constructed based on the high-accuracy PWV derived from GNSS. The results showed that the accuracy of the real-time model is consistent with that of the high-accuracy model, and both of them can achieve good forecast effect.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 2
    Publication Date: 2023-09-12
    Description: As one of the most important parameters, water vapor is very valuable for the studies of extreme weather events and climatic phenomena. The GNSS have been proven to be a relatively new technique for sounding atmospheric water vapor which has experienced unprecedented developments over the past decade due to the rapid deployment of wide-spread space- and ground-based infrastructures. This significant development and the long-term accumulation of GNSS data, has offered a strong data support and new opportunity to advance our understanding of climate change and extreme weather events. In the meanwhile, the fifth resurgence of artificial intelligence (AI) technology has empowered us in the ability of big geodetic data analytics and data mining. All these have helped us in the understanding the atmospheric dynamic process, and mining the detailed information of the formation, evolution, development and dissolution of extreme weather events and complicated atmospheric process. This contribution presents a summary of our 10-year relevant research in the area that involves a full spatial domain of ground-, air- and space-based systems and a comprehensive technological domain of GNSS/geodesy, remote sensing, meteorology and atmosphere. First, the background, current status and recent international frontier developments of the GNSS tropospheric sounding technique are summarized. Then, the theory, technical features and major advancement of the GNSS-derived atmospheric products in the applications of climate analyses and extreme weather forecasting are presented. Finally, the challenges, opportunities and future prospectives in terms of the technique and its innovative applications of national and international significance are provided.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 3
    Publication Date: 2024-01-17
    Description: Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.
    Language: English
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 4
    Publication Date: 2021-09-01
    Print ISSN: 0168-1923
    Electronic ISSN: 1873-2240
    Topics: Geography , Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Physics
    Published by Elsevier
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