Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather
forecasting model that provides accurate short to medium-range global predictions at …

It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …

The abrupt rise in extreme weather events (floods, heat waves, droughts, etc.) due to
changing climate in the last decades has increased the level of threats to various sectors …

We present a significantly improved data‐driven global weather forecasting framework using
a deep convolutional neural network (CNN) to forecast several basic atmospheric variables …

We develop elementary weather prediction models using deep convolutional neural
networks (CNNs) trained on past weather data to forecast one or two fundamental …

It is shown that it is possible to emulate the dynamics of a simple general circulation model
with a deep neural network. After being trained on the model, the network can predict the …

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …

Recently, there has been growing interest in the possibility of using neural networks for both
weather forecasting and the generation of climate datasets. We use a bottom–up approach …