ARM/ASR workshop explores the role of machine learning in atmospheric science
Machine learning (ML), an algorithm-driven application of artificial intelligence (AI), is used to augment science and discovery, and it is beginning to supplant traditional statistical approaches. ML has the potential to revolutionize science, which is increasingly overwhelmed by big data sets that require analysis.
ML helps computers learn by automating some of the most complex parts of analysis. It sifts through data in search of correlations and predictors that would otherwise remain hidden or require intensive human labor to uncover.
Once ML is in motion and its algorithms are “trained” on data, it requires no explicit programming. In time, as more data are available, these algorithms learn to produce increasingly accurate solutions.
All this could drastically boost the productivity of researchers by allowing them to process larger and more comprehensive data sets than previously feasible.
In atmospheric science, emerging ML tools are important because weather and earth system modelers grapple with intersecting and complex variables.
Over two days in the fall of 2020, a star-power list of researchers affiliated with the U.S. Department of Energy (DOE) gathered for a virtual workshop on ML, statistical constraints, and other emerging methods for streamlining investigations of earth systems and weather.
The online meeting took the place of a breakout session that would have occurred in person at the June 2020 Joint Atmospheric Radiation Measurement (ARM) User Facility/Atmospheric System Research (ASR) Principal Investigators Meeting. That event was abbreviated by the need to meet virtually.
The October 19–20 workshop was also a natural follow-up to previous ARM/ASR joint meeting breakout sessions on ML.
# # #This work was supported by the U.S. Department of Energy’s Office of Science, through the Biological and Environmental Research program as part of the Atmospheric System Research program.