AI Research for Climate Change and Environmental Sustainability
Despite the scientific consensus on climate change, drastic uncertainties remain. Crucial questions about regional climate trends, changes in extreme events, such as heat waves and mega-storms, and understanding how climate varied in the distant past, must be answered in order to improve predictions, assess impacts and vulnerability, and inform mitigation and sustainable adaptation strategies. Machine learning can help answer such questions and shed light on climate change. An overview of our climate informatics research, focusing on challenges in learning from spatiotemporal data, along with semi- and unsupervised deep learning approaches to studying rare and extreme events, and precipitation and temperature downscaling.
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Claire Monteleoni is a professor of computer science at the University of Colorado Boulder and the founding editor-in-chief of Environmental Data Science, an “open-access transdisciplinary journal dedicated to advances in data-driven methods to understand and predict environmental processes and impacts, and their patterns in space and time.”
