Mesoscale convective systems (MCSs) describe organized groupings of thunderstorms in
the tropics and mid-latitudes that span thousands of square kilometres. While recognized for …

Recent years have witnessed considerable developments in multiple fields with the potential
to enhance our capability of forecasting pluvial flash floods, one of the most costly …

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 …

Numerical weather prediction models require ever‐growing computing time and resources
but, still, have sometimes difficulties with predicting weather extremes. We introduce a data …

Deep learning techniques such as convolutional neural networks (CNNs) can potentially
provide powerful tools for classifying, identifying, and predicting patterns in climate and …

Recently, the use of machine learning in meteorology has increased greatly. While many
machine learning methods are not new, university classes on machine learning are largely …

Raw forecasts from numerical weather prediction models suffer from systematic bias and
cannot be directly used in applications such as hydrological forecasting. Statistical post …

A deep learning objective forecasting solution for severe convective weather (SCW)
including short-duration heavy rain (HR), hail, convective gusts (CG), and thunderstorms …

Spatially interpolated temperature and precipitation are hydrological variables that are
widely applied in models of ecology, hydrology, agronomy, and other environmental …