Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) conducted with state-of-the-art atmospheric models. To deal with model biases, a modification of the standard 4D-Var algorithm, called weak-constraint 4D-Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the ECMWF operational forecasting system.
Despite different origins and applications, Data Assimilation and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from Radio Occultation (RO) measurements. Neural Networks require a large number of samples to properly capture the relationship between the temperature first-guess trajectory and the model bias. As running the IFS data assimilation system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial Neural Networks are trained using the ERA5 reanalysis before using transfer learning on one year of the current IFS model. Preliminary results show that convolutional neural networks are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D-Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.