Speaker: Claire Monteleoni, George Washington University
Title: Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science

Abstract: The threat of climate change is one of the greatest challenges currently facing society. Given the profound impact machine learning has made on the natural sciences to which it has been applied, such as the field of bioinformatics, machine learning is poised to accelerate discovery in climate science. Our recent progress on climate informatics reveals that collaborations with climate scientists also open interesting new problems for machine learning. I will give an overview of challenge problems in climate informatics, and present recent work from my research group in this nascent field.

A key problem in climate science is how to combine the predictions of the multi-model ensemble of global climate models that inform the Intergovernmental Panel on Climate Change (IPCC). I will present three approaches to this problem. Our Tracking Climate Models (TCM) work demonstrated the promise of an algorithm for online learning with expert advice, for this task. Given temperature predictions from 20 IPCC global climate models, and over 100 years of historical temperature data, TCM generated predictions that tracked the changing sequence of which model currently predicts best. On historical data, at both annual and monthly time-scales, and in future simulations, TCM consistently outperformed the average over climate models, the existing benchmark in climate science, at both global and continental scales. We then extended TCM to take into account climate model predictions at higher spatial resolutions, and to model geospatial neighborhood influence between regions. Our second algorithm enables neighborhood influence by modifying the transition dynamics of the Hidden Markov Model from which TCM is derived, allowing the performance of spatial neighbors to influence the temporal switching probabilities for the best climate model at a given location. We recently applied a third technique, sparse matrix completion, in which we create a sparse (incomplete) matrix from climate model predictions and observed temperature data, and apply a matrix completion algorithm to recover it, yielding predictions of the unobserved temperatures.

Time: Friday, November 22, 2013, 1:30-2:30 p.m.

Place: Exploratory Hall (formerly S & T II), Room 4106

Department of Mathematical Sciences
George Mason University
4400 University Drive, MS 3F2
Fairfax, VA 22030-4444
Tel. 703-993-1460, Fax. 703-993-1491