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

We have developed a convolutional neural network (CNN) to predict tornadoes at lead times up to one hour in a storm-centered framework. We trained the CNN with data similar to those used in operations – namely, a radar image of the storm and a sounding of the near-storm environment. However, CNNs and other ML methods are often distrusted by users, who view them as opaque “black boxes” whose...

Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear...

Weather forecasting systems have not fundamentally changed since they were first operationalised nearly 50 years ago. They use traditional finite-element methods to solve the fluid dynamical flow of the atmosphere and include as much sub-grid physics as they can computationally afford. Given the huge amounts of data currently available from both models and observations new opportunities exist...

The rise of machine learning offers many exciting avenues for improving weather forecasting. Possibly the lowest hanging fruit is the acceleration of parameterisation schemes through machine learning emulation. Parameterisation schemes are highly uncertain closure schemes necessitated by the finite grid-spacing of weather forecasting models. Here we assess the challenges and benefits of...