Learn how to implement the 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 on the forecasts risky. While current forecasting services, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), achieve high accuracy in predicting mid-range (15 days) to seasonal weather. The hack behind the good forecasts lies in the 4-dimensional variational data assimilation (4D-Var), used since 1997 in ECMWF. This algorithm incorporates real-time observations to improve forecasts. As the main technique to minimize the butterfly effect — the high sensitivity to initial conditions — 4D-Var is also widely used in operational time-series forecasting systems across other fields.
