Learn the best way to implement the variational data assimilation, with mathematical details and PyTorch for efficient implementation
Weather forecasting models are chaotic dynamical systems, where forecasts turn into unstable on account of small perturbations in model states, making blind trust on the forecasts dangerous. While current forecasting services, reminiscent of 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 great forecasts lies within the four-dimensional variational data assimilation (4D-Var), used since 1997 in ECMWF. This algorithm incorporates real-time observations to enhance forecasts. Because the most important technique to reduce the butterfly effect — the high sensitivity to initial conditions — 4D-Var can also be widely utilized in operational time-series forecasting systems across other fields.
