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SW-LSTM-Enhanced GNSS-IR for Monitoring Storm-Surge-Induced Sea-Level Variations
编号：344 访问权限：仅限参会人 更新：2026-08-31 15:51:04 浏览：0次 口头报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

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摘要
Conventional tide gauges have limitations in extreme weather due to their susceptibility to damage from high waves and storm surges, as well as potential inaccuracies caused by rapid changes in wind speed and atmospheric pressure. To overcome these potential issues, Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) is adopted for detecting storm surges as a supplement part of observation systems. The current GNSS-IR technique has insufficient accuracy and temporal resolution to capture high storm surge. In this study, we develop a sliding-window-based Long Short Term Memory (SW-LSTM) framework to post-process GNSS-IR retrieval results. Three scenarios of the SW-LSTM model are proposed in terms of the prior information involved as input features into LSTM networks. The models are validated in the inversion of sea levels in Quarry Bay of Hong Kong during typhoons Hato, Khanun, Mangkhut, Wipha, and Kompasu. The RMSEs in the GNSS-IR retrieval results are between 15.0 cm and 18.8 cm and the skill scores vary between 0.963 and 0.979. The data during Mangkhut, Wipha and Kompasu are adopted to train the SW-LSTM models. After post-processing with the SW-LSTM models, the RMSE in the obtained sea levels is reduced to 6.8 cm for the Super Typhoon Hato and 6.5 cm for the Severe Typhoon Khanun. The skill scores are all above 0.990. Moreover, a comparison analysis shows that the SW-LSTM models outperform the least squares method and the cubic spline interpolation for GNSS-IR retrieval improvement. The SW-LSTM models show potential to extend GNSS-IR altimetry to real-time monitoring and synchronous predictions.
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报告人
Kailun Hu
University of Macau

稿件作者
Kailun Hu University of Macau
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重要日期

  • 会议日期

    01月12日

    2027

    至

    01月15日

    2027

  • 07月21日 2026

    初稿截稿日期

  • 01月15日 2027

    注册截止日期

主办单位
State Key Laboratory of Marine Environmental Science, Xiamen University (MEL)
Department of Earth Sciences, National Natural Science Foundation of China (NSFC)
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