Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

The intensifying climate crisis poses a severe threat to ecosystems and human well-being, particularly to ageing populations 1,2,3. Stroke is a major contributor to the global burden of cardiovascular ...

Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrate global observations, data assimilation (DA), and physics-based models. However, further advances ...

Because small changes in atmospheric and surface conditions can have large, difficult-to-predict effects on future weather, traditional weather forecasts are released only about 10 days in advance. A ...

PG&E has begun using artificial intelligence to stay ahead of potential fires. The power company's machine learning model ...

Meteorologists and other environmental scientists rely on numerical forecast models to aid in developing a weather outlook. These models, such as the American GFS model and European ECMWF model, use ...

UK to fund AI weather forecasting as ‘super’ El Niño threatens wave of climate shocks - Exclusive: ‘Our new partnership with ...

When supply chain practitioners think about forecasting, they focus on demand forecasting. Demand forecasting is essential, but the number of different forecasts that an effective organization should ...

Successful test results of a new machine learning (ML) technique developed at Georgia Tech could help communities prepare for extreme weather and coastal flooding. The approach could also be applied ...