We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

Nowcasting of convective storms is urgently needed yet rather challenging. Current
nowcasting methods are mostly based on radar echo extrapolation, which suffer from the …

Projecting climate change is a generalization problem: We extrapolate the recent past using
physical models across past, present, and future climates. Current climate models require …

There are different strategies for training neural networks (NNs) as subgrid‐scale
parameterizations. Here, we use a 1D model of the quasi‐biennial oscillation (QBO) and …

We present single‐column gravity wave parameterizations (GWPs) that use machine
learning to emulate non‐orographic gravity wave (GW) drag and demonstrate their ability to …

Atmospheric predictability from subseasonal to seasonal time scales and climate variability
are both influenced critically by gravity waves (GW). The quality of regional and global …

Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un‐
resolved and under‐resolved GWs have to be represented using a sub‐grid scale (SGS) …

Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale
parameterizations in weather and climate models. While NNs are powerful tools for learning …

In this study, we used convolutional neural networks (CNNs)—which are well-known deep
learning models suitable for image data processing—to estimate the temporal and spatial …

The drag due to breaking atmospheric gravity waves plays a leading order role in driving the
middle atmosphere circulation, but as their horizontal wavelength range from tens to …