Weather

Policymakers usually depend on coarse-resolution global climate models to assess a community's risk of extreme weather. By looking decades and even centuries into the future, these models can predict large-scale weather patterns but struggle to provide specific data for smaller locations. To estimate the risk of an area such as Boston experiencing extreme weather events…

Researchers at MIT have developed a method that improves the accuracy of predictions generated by climate models. The technique involves the use of machine learning and dynamical systems theory to make predictions from coarse climate models more accurate. These models, which are used to predict the impact of climate change including extreme weather events, work…

Scientists from MIT and the Pacific Northwest National Laboratory have developed a way to increase the accuracy of large-scale climate models, allowing for more precise predictions of extreme weather incidents in specific locations. Their process involves using machine learning in tandem with existing climate models to make the models' predictions closer to real-world observations. This…

Assessing a community’s risk of extreme weather events – such as catastrophic storms and catastrophic flooding – is a key task for policymakers who are trying to prepare for the potential impacts of global climate change. Despite major advancements in technology and modeling techniques, however, these forecasts leave a lot to be desired. Now, a…