Subseasonal to Seasonal Forecast Using Neural Ordinary Differential Equations

While short-term weather forecasting has benefited from extensive data and research, leading to high predictive accuracy, long-term forecasts, particularly medium-range predictions, lag significantly due to data scarcity. This research aims to bridge this gap by leveraging the advancements in Artificial Intelligence (AI), particularly Deep Learning. We propose a novel approach using Neural Ordinary Differential Equations (NODEs), which represents a transformative step in dynamic systems modeling. Neural ODEs offer a flexible and powerful framework for continuous-time models, which is particularly beneficial for handling sparse or irregularly sampled data prevalent in climate studies. Our methodology utilizes the Empirical Orthogonal Function (EOF) to extract principal component time series from limited climate data. These components serve as inputs for NODEs to predict future climatic conditions. This approach is innovative in its ability to handle non-linearities and temporal dependencies in climatic data, making it highly suitable for medium-range weather forecasting. The potential of NODEs in this context is significant, as they provide a means to accurately predict weather patterns with less data, a common limitation in long-term forecasting. By enhancing the precision of medium-range forecasts, this research contributes to more effective climate change adaptation and mitigation strategies, ultimately aiding in the safeguarding of ecosystems and human societies against the adverse effects of extreme weather conditions.