Disaster response

Scientists led by Themistoklis Sapsis at MIT's Department of Mechanical Engineering have developed a strategy to "correct" the predictions of coarse global climate models, enhancing the accuracy of risk analysis for extreme weather events. Global climate models, used by policymakers to assess a community's risk of severe weather, can predict weather patterns decades or even…

Global climate models predict future weather conditions, but these models are limited in their ability to provide detailed forecasts for specific locations. Policymakers often need to supplement these coarse-scale models with high-resolution ones to predict local extreme weather events. However, the accuracy of these predictions heavily depends on the initial coarse model’s accuracy. Themistoklis Sapsis,…

To better predict the risks of extreme weather events due to climate change, scientists at MIT have developed a method that refines the predictions from large, coarse climate models. The key to this approach is leveraging machine learning and dynamical systems theory to make the climate models' large-scale simulations more realistic. By correcting the climate…

Scientists from MIT have developed a technique that helps to fine-tune predictive models for extreme weather events by combining machine learning and dynamical systems theory. Currently, climate models are run decades and even centuries in advance to assess a community's risk to extreme weather but these generally operate at a rough resolution. As a result,…

Researchers at the Massachusetts Institute of Technology (MIT) have developed a new method to improve the accuracy of large-scale climate models. These models, used by policymakers to understand the future risk of extreme weather like flooding, often lack precise data for smaller scales without considerable computational power. By combining machine learning with dynamical systems theory,…

A team of scientists from MIT's Department of Mechanical Engineering has developed a new method using machine learning to correct and enhance prediction accuracy in climate models. These advancements could provide significantly greater insights into the frequency of extreme weather events with more localized precision, improving the ability to plan and mitigate for future climatic…

Policymakers rely on global climate models to assess a community’s risk of extreme weather. These models, run decades and even centuries forward, gauge future climate conditions over large areas but have a coarse resolution and are not definitive at the city level. To remedy this overlap, they may combine predictions from a coarse model with…

MIT scientists have developed a method to "correct" the predictions made by climate change models, thus enabling more accurate risk analysis of extreme weather events. Specifically, they have combined machine learning with dynamical systems theory to fine-tune global climate model predictions for the long-term. This enables policymakers and planners to assess community-specific risks of extreme…

Climate change experts are turning to an innovative approach to better predict extreme weather events and the impacts of climate change on specific locations. This new methodology "corrects" global climate models, combining machine learning with dynamical systems theory to bring the models' simulations much closer to expected real-world patterns. This approach can help policymakers effectively…

Researchers from the Massachusetts Institute of Technology (MIT) have developed a new method that can make long-term predictions regarding the risk of extreme weather events more accurate. The new technique combines machine learning with dynamical systems theory to make better predictions about extreme weather events such as floods and tropical cyclones in specific areas. Currently, policymakers…