Development of an offline and online hybrid model for the Integrated Forecasting System

Systematic model errors significantly limit the predictability horizon and practical utility of the current state-of-the-art forecasting systems. Even though accounting for these systematic model errors is increasingly viewed as a fundamental challenge in the field of numerical weather prediction, estimation and correction of the predictable component of the model error has received relatively little attention. Modern implementations of weak-constraint 4D-Var are an exception here and a promising avenue within the variational data assimilation framework, showing encouraging results. Weak-constraint 4D-Var can be viewed as an online hybrid data assimilation and machine learning approach which gradually learns about model errors from partial and imperfect observations, allowing to improve the state estimation. We propose a natural extension of this approach by applying deep learning techniques to further develop the concept of online model error estimation and correction.

In this talk, we will present recent progress in developing a hybrid model for the ECMWF Integrated Forecasting System (IFS). This system augments the state-of-the-art physics-based model with a statistical model implemented via a neural network, providing flow dependent model error corrections. While the statistical model can be pre-trained offline, we demonstrate that by extending the 4D-Var control vector to include the parameters of the neural network, i.e. the model of model error, we can further improve its predictive capability. We will discuss the impact of applying the flow dependent model error corrections in the medium range forecasts on the forecast quality.