VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints

• URL: http://arxiv.org/abs/2501.18122v1
• Date: Thu, 30 Jan 2025 03:52:37 GMT
• Title: VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints
• Authors: Xinyu Wang, Lei Liu, Kang Chen, Tao Han, Bin Li, Lei Bai,
• Abstract summary: Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. We propose the VQLTI framework to enhance long-term forecasting performance. In the global long-term TC intensity forecasting, VQLTI achieves state-of-the-art results for the 24h to 120h, with the (Maximum Sustained Wind) forecast error reduced by 35.65%-42.51% compared to ECMWF-IFS.
• Score: 21.437159129186664
• License:
• Abstract: Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of forecasting errors. To achieve the above strategies, we propose the VQLTI framework. VQLTI transfers the TC intensity information to a discrete latent space while retaining the spatial information differences, using large-scale spatial meteorological data as conditions. Furthermore, we leverage the forecast from the weather prediction model FengWu to provide additional physical knowledge for VQLTI. Additionally, we calculate the potential intensity (PI) to impose physical constraints on the latent variables. In the global long-term TC intensity forecasting, VQLTI achieves state-of-the-art results for the 24h to 120h, with the MSW (Maximum Sustained Wind) forecast error reduced by 35.65%-42.51% compared to ECMWF-IFS.

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