Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction

Model error is one of the main obstacles to improved accuracy and reliability in state-of-the-art analysis and forecasting applications, both in Numerical Weather Prediction and in climate prediction conducted with comprehensive high resolution general circulation models. In a data assimilation framework, recent advances in the context of weak constraint 4D-Var have shown that it is possible...

This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observations, possibly noisy and irregularly-sampled. Using automatic differentiation tools embedded in deep learning frameworks, we introduce end-to-end neural network (NN) architectures for variational data assimilation. It...

Data assimilation combines different information sources using a quantification of the uncertainty of each source to weight them. Therefore, a proper consideration of the uncertainty of observations and model states is crucial for the performance of the data assimilation system. Expert knowledge and expensive offline fine tuning experiments have been used in the past to determine the set of...