During hurricanes, it’s not wind but water that poses the greatest risk – causing 86% of storm-related deaths in the past decade, mostly from inland flooding. FAU’s I-SENSE is revolutionizing storm forecasting through its leadership of the Southeast Atlantic Econet, a cutting-edge network of more 190 weather and flood monitoring stations. Spanning from Key West to South Carolina, it delivers real-time data that powers lifesaving forecasts from the National Weather Service.

Heatwaves, droughts and forest fires are some of the extreme climate-related events that are expected not only to become more frequent but also to increasingly strike at the same time. This finding emerges from a new study led by Uppsala University, in which researchers have mapped the impact of climate change in different regions of the world.

Tech sector carbon emissions continued their rise in recent years, fueled by rapid advances in artificial intelligence (AI) and data infrastructure, according to the ITU-WBA Greening Digital Companies 2025 report.

New research from scientists at Trinity College Dublin strongly implies that heatwaves have a major influence on the spread of many diseases – and that many existing predictive models have overlooked this complexity. Specifically, the scientists have discovered that differences in heatwaves – such as how much hotter they are than normal temperatures, and how long they last – can increase disease burden by up to 13 times in a commonly used experimental animal model

The University of Warwick and UCL astronomers, as part of a global collaboration including partners in Chile, USA, and Europe, have discovered the smallest known star to host a transiting giant planet, which should not exist under leading planet formation theories. 

New research from Colorado State University and Cornell University shows that the presence of solar panels in Colorado’s grasslands may reduce water stress, improve soil moisture levels and – particularly during dry years – increase plant growth by about 20% or more compared to open fields.

A team led by Virginia Tech graduate student Samuel Daramola developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal regions, to offer a faster, low-cost tool for flood forecasting.