  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. Specifically, we employ a carefully designed PDE kernel to simulate
physical evolution on a small time scale (e.g., 300 seconds) and use a parallel
neural networks with a learnable router for bias correction. Furthermore, we
introduce a lead time-aware training framework to promote the generalization of
the model at different lead times. The weight analysis of physics-AI modules
indicates that physics conducts major evolution while AI performs corrections
adaptively. Extensive experiments show that WeatherGFT trained on an hourly
dataset, achieves state-of-the-art performance across multiple lead times and
exhibits the capability to generalize 30-minute forecasts.
