Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling

Understand

Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting.

• However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension.
• Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales.
• This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset.