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This paper introduces Nested-EAGLE, a novel machine learning model that integrates short- and medium-range weather forecasting into a unified system, enhancing the predictive capabilities for the public. By employing a 0.25掳 global model with a 6 km refinement over the Contiguous United States (CONUS), it achieves lower mean-squared error in near-surface and low-level quantities compared to existing NOAA systems. The model's superior performance in storm location forecasting at longer leads highlights the potential for improved weather prediction through the incorporation of high-resolution regional analysis data.
Combining short- and medium-range weather forecasts into a single model significantly enhances storm location accuracy, challenging traditional forecasting paradigms.
The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weather into a single prediction system would provide the public with a useful distillation of global weather and its impacts. To this end, we present Nested-EAGLE (Experimental Artificial intelligence Global and Limited-area Ensemble): a 0.25{\deg} global weather model with a 6 km refinement over the Contiguous United States (CONUS). The model achieves significantly lower mean-squared error in near-surface and low-level quantities over CONUS compared to NOAA's Global Forecast System and High-Resolution Rapid Refresh (HRRR), while remaining competitive throughout the rest of the global atmosphere. We show that the skill gains for near-surface fields stem from incorporating high-resolution regional analysis data into training through the nesting process. Forecasts of precipitation amounts are less skillful than those from HRRR, owing to deterministic training. However, we show that Nested-EAGLE provides the most accurate forecasts of storm locations at longer leads, despite blurred extrema. Our results motivate future work to extend the skill gains beyond CONUS and improve precipitation representation.
