IN31C:
Climate Informatics: Methods and Applications Posters


Submit an Abstract to this Session


Session ID#: 50877

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:  Veronika Eyring, German Aerospace Center DLR Oberpfaffenhofen, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany, Markus Reichstein, Max Planck Institute for Biogeochemistry, Jena, Germany and Gustau Camps-Valls, Image Processing Laboratory, Universitat de València, Paterna, Spain
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, Atmospheric Sciences, 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:

Domain Knowledge Integration for Causality Analysis of Carbon-Cycle Variables (354059)
Violeta Teodora Trifunov1,2, Maha Shadaydeh3, Joachim Denzler3,4, Jakob Runge5, Veronika Eyring6,7 and Markus Reichstein4,8, (1)Friedrich Schiller University of Jena, Mathematics and Informatics, Jena, Germany, (2)German Aerospace Center (DLR), Jena, Germany, (3)Friedrich Schiller University of Jena, Jena, Germany, (4)Michael-Stifel-Center Jena for Data-driven and Simulation Science, Jena, Germany, (5)German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany, (6)German Aerospace Center DLR Oberpfaffenhofen, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany, (7)University of Bremen, Institute of Environmental Physics, Bremen, Germany, (8)Max Planck Institute for Biogeochemistry, Jena, Germany
Causal discovery in Earth system science: State-of-the-art and a new Causality Challenge platform (407621)
Jakob Runge, German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany
SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data (403663)
Xavier-Andoni Tibau Alberdi1, Christian Requena-MEsa Sr.2, Christian Reimers Sr.2, Joachim Denzler3, Veronika Eyring4, Markus Reichstein5 and Jakob Runge6, (1)Jena, Germany, (2)German Aerospace Center, Jena, Germany, (3)Friedrich Schiller University of Jena, Jena, Germany, (4)German Aerospace Center DLR Oberpfaffenhofen, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany, (5)Max Planck Institute for Biogeochemistry, Jena, Germany, (6)German Aerospace Center DLR Jena, Institute of Data Science, Jena, Germany
Application of Graphical Models for Causal Detection in Hydrometeorological Systems (415015)
Mohammed Ombadi1, Phu Nguyen2, Soroosh Sorooshian3 and Kuo-lin Hsu3, (1)Lawrence Berkeley National Laboratory, Berkeley, United States, (2)University of California Irvine, Department of Civil and Environmental Engineering, Irvine, CA, United States, (3)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States
Crop yield prediction with nonlinear distribution regression (383769)
Gustau Camps-Valls1, Jose Adsuara2, Adrian Perez-Suay3, Jordi Munoz-Mari3, Anna Mateo-Sanchis2 and Maria Piles2, (1)Image Processing Laboratory, Universitat de València, Paterna, Spain, (2)Universitat de València, Image Processing Laboratory (IPL), València, Spain, (3)University of Valencia, Valencia, Spain
Eliminating Barriers to Climate Data with Columnar Storage Formats and Cloud Computing (462464)
Marty J Sullivan1, Toby Ault1 and Art T Degaetano2, (1)Cornell University, Department of Earth and Atmospheric Science, Ithaca, NY, United States, (2)Northeast Regional Climate Center, Ithaca, United States
Acquiring Carbonate Phase Barium Isotopic Compositions of Loess and Sediments and Its Significance in Palaeoenviromental Study (371549)
Lili Zhang1, Yingzeng Gong2, Liu Yi2 and Fang Huang3, (1)University of Science and Technology of China, Hefei, China, (2)USTC University of Science and Technology of China, Hefei, China, (3)University of Science and Technology of China, School of Earth and Space Sciences, Hefei, China

See more of: Earth and Space Science Informatics