Learn how to implement variational data assimilation, with mathematical details and PyTorch for efficient implementation.
Weather forecasting models are chaotic dynamical systems, where forecasts become unstable due to small perturbations in model states, making blind trust in forecasts risky. While current forecast services, such as the European Center for Medium-Range Weather Forecasts (ECMWF), achieve high accuracy in predicting mid-range (15 days) to seasonal weather. The trick behind good forecasts lies in 4-dimensional variational data assimilation (4D-Var), used since 1997 at ECMWF. This algorithm incorporates real-time observations to improve forecasts. As a primary technique for minimizing the butterfly effect (the high sensitivity to initial conditions), 4D-Var is also widely used in operational time series forecasting systems in other fields.
