Storm surges—sudden, abnormal rises in sea levels during storms—pose a major threat to low-lying coastal communities. Some of the most extreme sea levels occur during major tropical cyclones, when harsh winds and low atmospheric pressure drive ocean waters higher.  

Currently, there is a strong scientific consensus on the long-term changes of mean sea-level rise that is supported by tide gauge observations and data-informed modeling.  However, estimates of extreme storm surge trends in these events have been inconsistent and largely constrained to studies analyzing tide gauge data from a limited number of locations.  These limitations hinder scientists’ ability to evaluate how extreme storm surge events may respond to climate variability and change, thereby complicating the development of cost-effective strategies for coastal flood adaptation.  In a recent study(Link is external) conducted by the University of Central Florida, Princeton, Rutgers and four other research centers, researchers analyzed tide gauge data from across the U.S. to better identify trends in extreme storm surges.  

How should we prioritize patients waiting for kidney transplants? A new study could help policymakers analyze methods of matching donated kidneys and their recipients. 

MIT researchers developed a machine-learning-guided technique to solve complex, long-horizon planning problems more efficiently than some traditional approaches, while arriving at an optimal solution that better meets a user’s goals. 

A new class of semiconductors that can store information in electric fields could enable computers that run on less power, sensors with quantum precision, and the conversion of signals between electrical, optical and acoustic forms—but how they maintained two opposite electric polarizations in the same material was a mystery.