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

arXiv:2405.13796v1 Announce Type: new
Abstract: 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 …

abstract artificial artificial intelligence arxiv box cs.ai cs.lg data data-driven evolution fine-grained focus forecast forecasting however hybrid intelligence limitations mapping medium modeling nowcasting physics systems temporal type via weather weather forecasting