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

Deep Learning has been shown to be efficient for many data-assimilation problems, and many deep learning methods have been used for this purpose. However, these applications typically focus on obtaining a best estimate of the atmospheric state, while providing a proper uncertainty estimate is as least as important. This is even more problematic as deep learning is prove to overfitting as the...

Weather forecasting has progressed from being a very human-intensive effort to now being highly enabled by computation. The first big advance was in terms of numerical weather prediction (NWP), i.e., integrating the equations of motion forward in time with good initial conditions. But the more recent improvements have come from applying machine-learning (ML) techniques to improve forecasting...

Despite the scientific consensus on climate change, drastic uncertainties remain. Crucial questions about regional climate trends, changes in extreme events, such as heat waves and mega-storms, and understanding how climate varied in the distant past, must be answered in order to improve predictions, assess impacts and vulnerability, and inform mitigation and sustainable adaptation strategies....

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to...