Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving ...

We've been watching temperatures climb, extreme weather events intensify, and ice sheets shrink. Every weather forecast and climate projection relies on incredibly complex computer simulations that ...

Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving models while retaining their accuracy. The approach retains physics-based ...

Deep learning is increasingly being used to emulate cloud and convection processes in climate models, offering a faster ...

Pan's research focuses on regional climate changes, mesoscale phenomena, land surface processes, and ecosystem modeling. Specifically, he is interested in: Mesoscale weather and regional climate ...

The algorithms behind generative AI tools like DallE, when combined with physics-based data, can be used to develop better ways to model the Earth's climate. Computer scientists have now used this ...

Our project is designed to unravel the complexities of climate change impacts within the Neponset River Watershed, employing sophisticated methods tailored specifically to this study domain. Through ...

The Gordon Bell Climate Prize-winning team reached a landmark this year by being the first team ever to develop a Full Earth Simulation at 1 km (extremely high) Resolution. St. Louis, MO, November 20, ...

An agreement on the third implementation phase of Destination Earth (DestinE), the European Commission's initiative to ...

Description of the method that learns a map between the attractor of the coarsely-resolved equations and the attractor of the reference trajectory. Left: the red dashed curve represents the reference ...