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

Deep learning is increasingly being used to emulate cloud and convection processes in climate models, offering a faster alternative to computationally intensive cloud-resolving simulations. However, ...

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 ...

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 ...

Developed by a team led by Daniel Klocke at the Max Planck Institute for Meteorology in Germany, the model reaches a spatial resolution of 1.25 kilometers -- a level many atmospheric scientists have ...

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 ...

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, ...

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 ...