Machine learning weather prediction model development for global ensemble forecasts at NCEP

Data driven machine learning-based weather prediction (MLWP) models have been under rapid development in recent years. These models leverage autoregressive neural network architectures and are trained using reanalysis data generated by operational centers, demonstrating proficient forecasting abilities. One remarkable advantage of these MLWP models is that, once trained, they take significantly less amount of computational resources to produce forecasts compared to traditional numerical weather prediction (NWP) models while maintaining or surpassing the NWP performance.NCEP has started machine learning development collaborating with the research community for several years. This presentation will provide an overview of the development of MLWP models for the global ensemble forecast system at NCEP Environment Modeling Center (EMC). The model adopts state-of-the-art MLWP models such as GraphCast and leverages the methodologies from FuXi global ensemble system. The development includes developing cascade MLWP models, training the model with GEFSv12 reanalysis data and producing forecasts with the operational GEFSv12 initial states. The model will be validated using two years of GEFSv12 operational forecast data. The ultimate objective is to deliver 15-day forecasts with skill levels comparable to the operational GEFSv12.