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

Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to incomplete or inaccurate representations of the physical processes being modeled. Given rapid data growth due to advances in sensor technologies, there is a tremendous opportunity to systematically advance...

Atmospheric General Circulation Models (GCMs) contain computationally-demanding physical parameterization schemes, which approximate the unresolved subgrid-scale physics processes. This work explores whether a selection of machine learning (ML) techniques can serve as computationally-efficient emulators of physical parameterizations in GCMs, and what the pros and cons of the different...

In an Ensemble Kalman Filter (EnKF), many short-range forecasts are used to propagate error statistics. In the Canadian global EnKF system, different ensemble members use different configurations of the forecast model. The integrations with different versions of the model physics can be used to optimize the probability distributions for the model parameters.

Continuous parameters accept a...

Numerical weather prediction (NWP) models require ever-growing computing time and resources, but still, have sometimes difficulties with predicting weather extremes. We introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns), and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and an...

We present a deep convolutional neural network (CNN) to forecast four variables on spherical shells characterizing the dry global atmosphere: 1000-hPa height, 500-hPa height, 2-m surface temperature and 700-300 hPa thickness. The variables are carried on a cubed sphere, which is a natural architecture on which to evaluate CNN stencils. In addition to the forecast fields, three external fields...

We develop an ensemble prediction system (EPS) based on a purely data-driven global atmospheric model that uses convolutional neural networks (CNNs) on the cubed sphere. Mirroring practices in operational EPSs, we incorporate both initial-condition and model-physics perturbations; the former are sub-optimally drawn from the perturbed ECMWF ReAnalysis 5 members, while the latter are produced by...