The model developed by Shamekh learns on its own how to measure the clustering of clouds, a metric of organisation, and then uses this metric to improve the prediction of precipitation.
Scientists at Columbia University have developed an algorithm that improves the accuracy of predicting extreme weather events. The algorithm addresses the issue of cloud organization, which has been lacking in traditional climate models. Cloud organization plays a crucial role in predicting precipitation intensity and variability.
A new study has used global storm-resolving simulations and machine learning to create an algorithm that can deal separately with two different scales of cloud organization: those resolved by a climate model, and those that cannot be resolved as they are too small. This new approach addresses the missing piece of information in traditional climate model parameterizations and provides a way to predict precipitation intensity and variability more precisely.
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.
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