Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …

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 …

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 …

Given the growing use of Artificial intelligence (AI) and machine learning (ML) methods
across all aspects of environmental sciences, it is imperative that we initiate a discussion …

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 …

Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in
Meteorological Applications in: Bulletin of the American Meteorological Society Volume 101 Issue …

Promising new opportunities to apply artificial intelligence (AI) to the Earth and
environmental sciences are identified, informed by an overview of current efforts in the …

Over the past decade the use of machine learning in meteorology has grown rapidly.
Specifically neural networks and deep learning have been used at an unprecedented rate …

This paper describes the development of U-net++ models, a type of neural network that
performs deep learning, to emulate the shortwave Rapid Radiative Transfer Model (RRTM) …

It remains difficult to disentangle the relative influences of aerosols and greenhouse gases
on regional surface temperature trends in the context of global climate change. To address …