Climate Informatics 2017 - 7th International Workshop on Climate Informatics

Climate informatics broadly refers to any research combining climate science with approaches from statistics, machine learning and data mining. The Climate Informatics workshop series, now in its seventh year, seeks to bring together researchers from all of these areas. We aim to stimulate the discussion of new ideas, foster new collaborations, grow the climate informatics community, and thus accelerate discovery across disciplinary boundaries. The format of the workshop seeks to overcome cross disciplinary language barriers and to emphasize communication between participants by featuring a hackathon, invited talks, panel discussions, posters and breakout sessions.
Short Papers:
Submission Deadline: July 22nd, 2017
We encourage conference paper submissions up to four pages on topics anywhere at the interface of climate science and machine learning, statistics, data mining, or related fields. Reviews, position papers, and works in progress, are also encouraged. The papers will be peer reviewed and published in an NCAR OpenSky repository.
Topics include but are not limited to:
Machine learning, statistics, or data mining, applied to climate science
Management and processing of large climate datasets
Long and short term climate prediction
Ensemble characterization of climate model projections
Paleoclimate reconstruction
Uncertainty quantification
Spatiotemporal methods applied to climate data
Time series methods applied to climate data
Methods for modeling, detecting and predicting climate extremes
Climate change attribution
Dependence and causality among climate variables
Detection and characterization of climate teleconnections
Data assimilation
Climate model parameterizations
Hybrid methods
Other data science approaches at the nexus of climate and earth system sciences
Keynote Speakers:
Alexis Hannart - Franco-Argentine Institute on Climate Studies and their Impacts (IFAECI) and the French National Center for Scientific Research (CNRS)
Dr. Hannart is a researcher at the Franco-Argentine Institute for Climate Studies and Impacts (IFAECI), an international laboratory of the CNRS based in Buenos Aires. Its main research topic concerns the detection and attribution of climate change, the purpose of which is to highlight possible causal links between the observed climatic responses (long-term trends or punctual events) and external (natural forcings or anthropogenic) for which it develops statistical methods.
Robert Lund - Clemson University
Dr. Lund received his Ph.D. degree in Statistics from The University of North Carolina in 1993. He is currently a Professor in the Department of Mathematical Sciences at Clemson University. He is a Fellow of the American Statistical Association and was the 2005-2007 Editor of the Journal of the American Statistical Association, Reviews Section. He has published over 70 refereed papers and has graduated 15 doctoral students. His interests are in time series, applied probability, statistical climatology, and veterinary disease mapping.
Elisabeth Moyer - University of Chicago
Dr. Moyer’s research interests fall in two main threads. The first includes the use of the isotopic composition of atmospheric water vapor as a tracer of convective processes, cirrus formation, and stratosphere-troposphere exchange; and the design of spectroscopic techniques for in-situ trace gas measurements. The second includes climate (and human) response to greenhouse-gas forcing; development of tools for impacts assessment; statistical emulation of climate model output; and climate and energy policy evaluation.
Prabhat - National Energy Research Computing Center, Lawrence Berkeley National Laboratory
Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization. He is also interested in applied statistics, machine learning, computer graphics and computer vision. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.
Sai Ravela - Massachusetts Institute of Technology (MIT)
Within the broader arena of estimation, control and information theories, and topics in statistical pattern recognition and statistical inference and learning, Dr. Ravela's focus is on the design of numerical methods for succinctly representing stochastic signals and systems. Research in his group develops new algorithms to overcome the "curses" of nonlinearity, dimensionality and uncertainty inference problems, such as estimation, planning and control and key characteristics of data-driven applications and cyber-physical systems with applications including tracking, autonomous sampling and mapping, data assimilation and uncertainty quantification.
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Organizing Committee:
Workshop Co-Chairs:
Andy Rhines, University of Washington
Slava Lyubchich, University of Maryland Center for Environmental Science
Program Committee Co-Chairs:
Nikunj C. Oza, NASA
Eniko Szekely, New York University
Publicity and Publications Chair:
Erich Seamon, University of Idaho
Travel and Budget Chair:
Mohammad Gorji, Syntelli Solutions Inc.
Steering Committee:
Imme Ebert-Uphoff, Colorado State University

Claire Monteleoni, George Washington University

Doug Nychka, National Center for Atmospheric Research
Local Administrative Support:
Michelle Patton, NCAR
Cecilia Banner, NCAR