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

In recent decades, numerical weather predictions (NWPs) and their post-processing have played a central role in issuing weather forecasts, warnings, and advisories [WMO, 2013, Vannitsem el al., 2021]. NWP centers around the world have developed and are operating a variety of NWP models for accurate weather predictions. For example, the European Centre for Medium-Range Weather Forecasts (ECMWF) operates the Integrated Forecasting System (IFS) and its ensemble prediction system [ECMWF, 2024]; the UK Met Office operates the Unified Model and the Met Office Global and Regional Ensemble Prediction System [Brown et al., 2012, Hagelin et al., 2017, Inverarity et al., 2023]. The National Centers for Environmental Prediction (NCEP) at the National Oceanic and Atmospheric Administration (NOAA) operates the Global Forecast System [NCEP, 2016], the High-Resolution Rapid Refresh [Dowell et al., 2022], and the Hurricane Weather Research and Forecasting model [Gopalakrishnan et al., 2011]. The Japan Meteorological Agency (JMA) operates three deterministic NWP models and two ensemble prediction systems for short-range to weekly forecasts: the Global Spectrum Model (GSM), the Meso-Scale Model (MSM), the Local Forecast Model, the Global Ensemble Prediction System, and the Mesoscale Ensemble Prediction System [JMA, 2024]. These models cover different areas with varying resolutions and processes. In addition to traditional physics-based NWP models, recent advancements in artificial intelligence (AI) have introduced new methods for producing weather predictions.