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

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 …

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 …

Supercell storms are commonly responsible for severe hail, which is the costliest severe
storm hazard in the United States and elsewhere. Radar observations of such storms are …

Machine learning and deep learning methods have been widely explored in understanding
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …

Many studies have aimed to identify novel storm characteristics that are indicative of current
or future severe weather potential using a combination of ground-based radar observations …

Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and
heterogeneous biological environments where analytes often are interrelated. However …

Sea surface temperature (SST) is an important parameter for monitoring ocean phenomena.
Driven by ocean satellite Big Data, deep neural networks have achieved state-of-the-art …

This paper proposes a new quantitative precipitation estimation (QPE) technique to provide
accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The …