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How Machine Learning Could Help to Improve Climate Forecasts


A radar map showing Hurriane Harvey hitting Texas on Friday, Aug. 25.

Mixing artificial intelligence with climate science helps researchers to identify previously unknown atmospheric processes and rank climate models.

Credit: National Weather Service

Researchers are combining artificial intelligence (AI) and climate science to create deep-learning analyses of weather patterns, and a September conference in Colorado will evaluate the state of climate informatics.

Last year, a team at the Lawrence Berkeley National Laboratory (LBNL) reported on the first use of a deep-learning system to identify tropical cyclones, atmospheric rivers, and weather fronts, demonstrating the algorithm could replicate human expertise.

The researchers plan to apply similar methods to analyze a broader range of extreme weather events, including those as yet uncategorized.

The team's objective is to better rank and predict shifts in these phenomena related to climate change.

Similar work by George Washington University's Claire Monteleoni has led to machine-learning algorithms that produce weighted averages of about 30 climate models used by the Intergovernmental Panel on Climate Change.

LBNL's William Drew Collins envisions AI algorithms being used to test next-generation climate models, and some scientists are using them for weather forecasts.

From Nature
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