WHEN: Sept 20-21, 2018 (plus optional hackathon on Sept 19). WHERE: Mesa Lab, National Center for Atmospheric Research (NCAR), in Boulder, CO.
WEBSITE: https://www2.cisl.ucar.edu/EVENTS/WORKSHOPS/CLIMATE-INFORMATICS/2018/HOME The 8th International Workshop on CLIMATE INFORMATICS WHEN: Sept 20-21, 2018 (plus optional hackathon on Sept 19). WHERE: Mesa Lab, National Center for Atmospheric Research (NCAR), in Boulder, CO. WEBSITE: www2.cisl.ucar.edu/EVENTS/WORKSHOPS/CLIMATE-INFORMATICS/... This workshop is open to anyone with interest in using advanced data science methods (from statistics, machine learning, data mining, etc) for climate applications. No invitation required. Simply register to attend (no submission necessary) or, if you want to present your work, submit an abstract/short paper for poster presentation by June 30. Join about 100 researchers spanning both climate science and data science. WORKSHOP OVERVIEW: 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. Submission Deadline: June 30, 2018, see website for submission instructions 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
