Researchers have created an algorithm to deal with cloud organisation, which provides a way to predict precipitation intensity and variability more precisely, thus rendering event predictions more accurate.
Cloud organisation is said to be a missing piece of information in traditional climate model parameterisations.
The researchers, led by Pierre Gentine, director of the Learning the Earth with Artificial Intelligence and Physics (LEAP) Center, Columbia University, US, used global storm-resolving simulations and machine learning to create an algorithm that can deal separately with two different scales of cloud organisation: those resolved by a climate model, and those that cannot be resolved as they are too small.
Their findings have been published in the journal Proceedings of the National Academy of Sciences (PNAS).
Accurate predictions are becoming more critical for all of us with the rise of extreme weather events, which are becoming more frequent in our warming climate.
While in nature, the researchers said, precipitation can be very varied, with many extremes of precipitation, climate models predict a smaller variance in precipitation with a bias toward light rain and have failed to accurately predict precipitation intensity, particularly extremes.
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“Our findings are especially exciting because, for many years, the scientific community has debated whether to include cloud organisation in climate models,” said Gentine, professor of geophysics in the departments of earth and environmental engineering and earth environmental sciences at the university.
“Our work provides an answer to the debate and a novel solution for including organisation, showing that including this information can significantly improve our prediction of precipitation intensity and variability,” said Gentine.
Sarah Shamekh, a PhD student working with Gentine, developed a neural network algorithm that learns the relevant information about the role of fine-scale cloud organisation (unresolved scales) on precipitation.
A neural network algorithm teaches computers to process data in a way that is inspired by the human brain.
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. Shamekh trained the algorithm on a high-resolution moisture field, encoding the degree of small-scale organisation.
“We discovered that our organisation metric explains precipitation variability almost entirely and could replace a stochastic parameterisation in climate models,” said Shamekh, lead author of the study.
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“Including this information significantly improved precipitation prediction at the scale relevant to climate models, accurately predicting precipitation extremes and spatial variability,” said Shamekh.
The researchers said that this study opened up new avenues for investigation, such as exploring the possibility of precipitation creating memory, where the atmosphere retains information about recent weather conditions. This, in turn, influences atmospheric conditions later on, in the climate system.
This approach, the researchers said, could have wide-ranging applications, including better modelling of ice sheet and ocean surface.