Making the Black Box More Transparent: Understanding the Physical Implications of Machine
Learning in: Bulletin of the American Meteorological Society Volume 100 Issue 11 (2019) Jump …

Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

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

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 …

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

While global warming has generally increased the occurrence of extreme precipitation, the
physical mechanisms by which climate change alters regional and local precipitation …

We present an overview of recent work on using artificial intelligence (AI)/machine learning
(ML) techniques for forecasting convective weather and its associated hazards, including …

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme
weather events. This is typically accomplished with ensemble prediction systems, which …

Correcting the forecast bias of numerical weather prediction models is important for severe
weather warnings. The refined grid forecast requires direct correction on gridded forecast …

Machine learning (ML) algorithms are powerful tools to build models of clouds and climate
that are more faithful to the rapidly increasing volumes of Earth system data than commonly …