How machine learning could help to improve climate forecasts

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According to Nature 

Greg Kendall-Ball

Many of the latest climate models seek to increase the detail in simulations of cloud structure.

As Earth-observing satellites become more plentiful and climate models more powerful, researchers who study global warming are facing a deluge of data. Some are now turning to the latest trend in artificial intelligence (AI) to help trawl through all the information, in the hope of discovering new climate patterns and improving forecasts.

“Climate is now a data problem,” says Claire Monteleoni, a computer scientist at George Washington University in Washington DC who has helped to pioneer the marriage of machine-learning techniques with climate science. In machine learning, AI systems improve in performance as the amount of data that they analyse grows. This approach is a natural fit for climate science: a single run of a high-resolution climate model can produce a petabyte of data, and the archive of climate data maintained by the UK Met Office, the national weather service, now holds about 45 petabytes of information — and adds 0.085 petabytes a day.

Researchers hoping to wrangle all these data will meet next month in Boulder, Colorado, to assess the state of science in the field known as climate informatics. Work in this area has grown rapidly. In the past several years, researchers have used AI systems to help them to rank climate models, spot cyclones and other extreme weather events — in both real and modelled climate data — and identify new climate patterns. “The pace seems to be picking up,” says Monteleoni.


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This article and images were originally posted on [Nature – Issue – science feeds] August 23, 2017 at 01:17PM

Credit to Author and Nature – Issue – science feeds