In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network‐based models capture the large‐scale dynamics of ...

Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest ...

To understand and predict large, complex, and chaotic systems, Earth scientists build simulators from physical laws. Simulators generalize better to new scenarios, require fewer tunable parameters, and are more interpretable ...

The characterization of uncertainties in geophysical quantities is an important task with widespread applications for time series prediction, numerical modeling, and data assimilation. In this context, machine learning is ...

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic ...

Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal ...

Wheat production plays an important role in Morocco. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, ...

We present a machine learning approach to statistically derive geothermal heat flow (GHF) for Antarctica. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially ...

Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could also be used to predict global weather ...