DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models

Numerical weather prediction (NWP) centers around the world operate a variety of NWP models, and recent advances in AI-driven NWP models have increased the availability of diverse NWP outputs. While this expansion holds the potential to improve forecast accuracy, it also raises a critical challenge of identifying the most reliable predictions for specific forecast scenarios. Traditional approaches, such as ensemble or weighted averaging, combine multiple NWP outputs but often generate unrealistic atmospheric fields, complicating the production of reliable and consistent forecasts in operational settings. In this study, we introduce DeepMedcast, a deep learning method that generates intermediate forecast, or "medcast", between two or more NWP outputs. Unlike ensemble averaging, DeepMedcast can provide consistent and explainable medcast without distorting meteorological fields. This paper details the methodology and case studies of DeepMedcast, discussing its advantages and potential contributions to operational forecasting.