Columbia Engineers develop machine-learning algorithm that will help researchers to better understand and mitigate the impact of extreme weather events, which are becoming more frequent in our warming climate.
Columbia Engineers develop machine-learning algorithm that will help researchers to better understand and mitigate the impact of extreme weather events, which are becoming more frequent in our warming climate.
With the rise of extreme weather events, which are becoming more frequent in our warming climate, accurate predictions are becoming more critical for all of us, from farmers to city-dwellers to businesses around the world. To date, climate models have failed to accurately predict precipitation intensity, particularly extremes. While in nature, precipitation can be very varied, with many extremes of precipitation, climate models predict a smaller variance in precipitation with a bias toward light rain.
Missing Piece in Current Algorithms: Cloud Organization
Researchers have been working to develop algorithms that will improve prediction accuracy but, as Columbia Engineering climate scientists report, there has been a missing piece of information in traditional climate model parameterizations--a way to describe cloud structure and organization that is so fine-scale it is not captured on the computational grid being used. These organization measurements affect predictions of both precipitation intensity and its stochasticity, the variability of random fluctuations in precipitation intensity. Up to now, there has not been an effective, accurate way to measure cloud structure and quantify its impact.
Read more at Columbia University School of Engineering and Applied Science
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