IN24A:
Climate Informatics: Methods and Applications I


Submit an Abstract to this Session


Session ID#: 61392

Session Description:
Over the last decades, the magnitude and complexity of climate and biogeochemical data from satellite sensors, stations, and climate models has substantially increased. This is starting to overwhelm the relatively simple tools and methods currently used to analyze the data. On the other hand, recent developments in machine learning have led to powerful new methods which promise to yield novel insights - if they properly cope with the particular challenges of such data. The new field of Climate Informatics could contribute to substantially enhance our understanding of the Earth system and confidence in future climate projections. This session invites contributions of new statistical and machine learning methodology including (but not limited to) causal discovery, deep learning, probabilistic and Bayesian inference, computer vision, and advanced model-data integration applied to Earth system models and observations.
Primary Convener:  Jakob Runge, German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany
Conveners:  Gustau Camps-Valls, Image Processing Laboratory, Universitat de València, Paterna, Spain, Markus Reichstein, Max Planck Institute for Biogeochemistry, Jena, Germany and Veronika Eyring, German Aerospace Center DLR Oberpfaffenhofen, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany
Primary Liaison:  Jakob Runge, German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany
Chairs:  Jakob Runge, German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany and Veronika Eyring, German Aerospace Center DLR Oberpfaffenhofen, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany
OSPA Liaison:  Jakob Runge, German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany
Co-Organized with:
Earth and Space Science Informatics, and Biogeosciences

Proposed Co-Organized Session with:
  • A - Atmospheric Sciences
  • B - Biogeosciences
Index Terms:

0414 Biogeochemical cycles, processes, and modeling [BIOGEOSCIENCES]
1942 Machine learning [INFORMATICS]
1986 Statistical methods: Inferential [INFORMATICS]
3305 Climate change and variability [ATMOSPHERIC PROCESSES]

Abstracts Submitted to this Session:

Algorithms for Climate Informatics: Learning from Spatiotemporal Data with Both Spatial and Temporal Non-stationarity (Invited) (434571)
Claire Monteleoni, University of Colorado Boulder, Computer Science, Boulder, United States
A Bayesian framework for causality estimation (Invited) (410543)
Peter Jan van Leeuwen, University of Reading, Meteorology, Reading, United Kingdom; Colorado State University, Atmospheric Science, Fort Collins, United States
Project DisCo: Physics-Based Discovery of Coherent Structures in Spatiotemporal Systems (422265)
Adam Rupe1, Karthik Kashinath2, Nalini Kumar3, James P Crutchfield1 and Mr Prabhat4, (1)University of California Davis, Physics, Davis, CA, United States, (2)NVIDIA Corporation, Santa Clara, CA, United States, (3)Intel, Santa Clara, United States, (4)Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Large-scale causal network discovery in CMIP5 models: robustness and intercomparison (411521)
Peer Johannes Nowack, Imperial College London, Faculty of Natural Sciences, Department of Physics, Grantham Institute and the Data Science Institute, London, United Kingdom and Jakob Runge, German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany
Spatio-Temporal Climate Data Causality Analytics – An Analysis of ENSO’s Global Impacts (417259)
Jianwu Wang, University of Maryland Baltimore County, Department of Information Systems, Baltimore, United States, Hua Song, University of Maryland Baltimore County, Baltimore, MD, United States, Zhibo Zhang, University of Maryland Baltimore County, Department of Physics, Baltimore, MD, United States and Pei Guo, University of Maryland Baltimore County, Information Systems, Baltimore, MD, United States
Towards Topological Pattern Detection Methods in Climate Data: Application to Atmospheric Blocking Events (428534)
Grzegorz Muszynski, University of Liverpool, Computer Science, Liverpool, L69, United Kingdom; British Antarctic Survey, Cambridge, United Kingdom, Karthik Kashinath, NVIDIA Corporation, Santa Clara, CA, United States, Vitaliy Kurlin, University of Liverpool, Computer Science, Liverpool, United Kingdom, Michael F Wehner, Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, United States and Mr Prabhat, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Exploring the Spatio-temporal Connectivity of Blizzard Conditions and Mid-latitude Cyclones: A Template for a Process-based Workflow (459372)
Michael Bauer1,2, Kwo-Sen Kuo3,4, Amidu Oloso5,6 and Michael Lee Rilee6,7, (1)NASA Goddard Institute for Space Studies, New York, NY, United States, (2)Columbia University of New York, Applied Physics and Applied Math, New York, NY, United States, (3)Bayesics, LLC, Bowie, MD, United States, (4)NASA Goddard Space Flight Center, Greenbelt, United States, (5)Science Systems and Applications, Inc., Lanham, MD, United States, (6)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (7)Rilee Systems Technologies, Derwood, MD, United States

See more of: Earth and Space Science Informatics