用深度学习提升季节预测精度，成本更低且可定制区域变量
DeepSeasons: a Deep Learning scale-selecting approach to Seasonal Forecasts
- 基于先进神经网络与历史气候数据，捕捉复杂非线性气候模式
- 在关键区域和变量上表现优于传统模型，计算成本显著降低
- 支持直接预测异常值与时间均值，适合气候敏感行业应用
季节性预测因大气动力学的固有混沌性仍具挑战。本文提出DeepSeasons，一种新型深度学习方法，旨在提升季节预测的准确性和可靠性。该方法利用先进的神经网络架构和广泛的历史气候数据集，识别气候变量中的复杂非线性模式与依赖关系，在关键区域和变量上的预测性能与或优于基于全球气候模型（GCM）的方法，同时计算成本显著降低。框架支持针对特定区域或变量的定制化应用，而非对整个大气/海洋系统进行整体预测。所提方法还可直接输出异常值和时间均值，为长期预测提供新路径，并展现出在气候敏感领域实际部署的巨大潜力。该创新方法有望显著提升气候风险管理和决策水平。
原文摘要 · Abstract (English)
Seasonal forecasting remains challenging due to the inherent chaotic nature of atmospheric dynamics. This paper introduces DeepSeasons, a novel deep learning approach designed to enhance the accuracy and reliability of seasonal forecasts. Leveraging advanced neural network architectures and extensive historical climatic datasets, DeepSeasons identifies complex, nonlinear patterns and dependencies in climate variables with similar or improved skill respcet GCM-based forecasting methods, at a significant lower cost. The framework also allow tailored application to specific regions or variables, rather than the overall problem of predicting the entire atmosphere/ocean system. The proposed methods also allow for direct predictions of anomalies and time-means, opening a new approach to long-term forecasting and highlighting its potential for operational deployment in climate-sensitive sectors. This innovative methodology promises substantial improvements in managing climate-related risks and decision-making processes.
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