Heat waves (HWs) can cause large socioeconomic and environmental impacts. The
observed increases in their frequency, intensity and duration are projected to continue with …

Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

Existing weather forecasting models are based on physics and use supercomputers to
evolve the atmosphere into the future. Better physics-based forecasts require improved …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

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 …

The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

Neural networks have become increasingly prevalent within the geosciences, although a
common limitation of their usage has been a lack of methods to interpret what the networks …