用物理约束提升台风强度长期预测准确率
VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints
- 将台风强度映射到离散潜空间，保留空间信息差异
- 结合气象模型预报与理论极限强度，误差降低35.65%~42.51%
- 适合灾害预警与应急决策人员参考
台风强度预测对早期灾情预警和应急决策至关重要。尽管已有研究尝试用深度学习解决业务预报中的计算与后处理问题，但其长期预测能力仍不足。本文提出VQLTI框架，通过增强台风强度与空间信息的匹配，并引入物理知识与约束来缓解误差累积。该方法将台风强度信息转入离散潜空间，同时保留空间差异，以大规模气象数据为条件。进一步利用天气预报模型FengWu的输出提供额外物理信息，并通过计算潜在强度（PI）对潜变量施加物理约束。在全球长期台风强度预测中，VQLTI在24至120小时预测上达到当前最优性能，最大持续风速（MSW）预测误差相比ECMWF-IFS降低35.65%至42.51%。
原文摘要 · Abstract (English)
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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