IN13C:
Convergence in Space Physics and Earth Science: Discovery Through Machine Learning Posters


Submit an Abstract to this Session


Session ID#: 57379

Session Description:
Machine learning is increasingly used in a wide variety of scientific disciplines to advance knowledge in the age of big data and novel data analytics. However, application of machine learning algorithms to Earth and Space Science datasets is still in its infancy, and the question of how to incorporate uncertainties and physics knowledge remains unanswered. The common paradigm of applying machine learning to create new knowledge in both Space Physics and Earth Science provides a foundation to discuss their convergence. Besides being the pinnacle of evolutionary integration across disciplines, convergent research is now a central investment focus at both NASA and NSF. We invite contributions that exemplify the application of machine learning in Space Physics and Earth Sciences. The session will spark discussion of how machine learning methodology transfer may serve to bridge disciplines, leading to the opening of new research vistas and collaboration among researchers seeking future funding opportunities.
Primary Convener:  Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Boulder, United States
Conveners:  Justin Jay Hnilo, US Department of Energy, Washington, DC, United States, Kerstin Lehnert, Columbia University, Lamont-Doherty Earth Observatory, Palisades, United States and Renu Joseph, US Department of Energy, Germantown, MD, United States
Primary Liaison:  Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Boulder, United States
Chairs:  Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Boulder, United States and Justin Jay Hnilo, US Department of Energy, Washington, DC, United States
OSPA Liaison:  Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Boulder, United States
Co-Organized with:
Earth and Space Science Informatics, and SPA-Solar and Heliospheric Physics

Cross-Listed:
  • A - Atmospheric Sciences
  • NG - Nonlinear Geophysics
  • SA - SPA-Aeronomy
  • SH - SPA-Solar and Heliospheric Physics

Proposed Co-Organized Session with:
  • A - Atmospheric Sciences
  • NG - Nonlinear Geophysics
  • SA - SPA-Aeronomy
  • SH - SPA-Solar and Heliospheric Physics
Index Terms:

1906 Computational models, algorithms [INFORMATICS]
1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]
1978 Software re-use [INFORMATICS]

Abstracts Submitted to this Session:

Using artificial neural networks to generate "continuous" vertical plasma drifts from C/NOFS data (407741)
John Bosco Habarulema and Makhosonke Dubazane, South African National Space Agency, Hermanus, South Africa
Extracting Solar Physics from 151 Million Images (410335)
Michael S Kirk1, Raphael Attie2, Barbara J Thompson3, Nicholeen M Viall3 and Peter R Young4, (1)Atmospheric and Space Technology Research Associates, LLC, Boulder, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)NASA GSFC, Greenbelt, United States, (4)George Mason University Fairfax, Fairfax, United States
The development of a real time forecasting tool for the Dst Index: A system identification approach (412095)
Richard Boynton, University of Sheffield, Sheffield, United Kingdom, Hua-Liang Wei, University of Sheffield, Dept Automatic Control & Systems Engineering, School of Electrical and Electronic Engineering, Sheffield, S10, United Kingdom and Michael A Balikhin, Univ Sheffield, Sheffield, United Kingdom
Data-driven Modeling, Prediction and Predictability: The Complex Systems Framework (420333)
A Surjalal Sharma, Univ Maryland, College Park, United States, Erin Michelle Lynch, University of Maryland, College Park, MD, United States, Venkat Krishnamurthy, George Mason University Fairfax, Fairfax, VA, United States and Eugenia Kalnay, University of Maryland College Park, Department of Atmospheric and Oceanic Science, College Park, MD, United States
A machine learning informed algorithm for the identification of tornadic severe storms (452847)
Leigh Munchak, Athenium Analytics, Washington, DC, United States
Projection Learning with Local and Global Consistency Constraints for High Spatial Resolution Remote Sensing Images Scene Classification (351047)
Pan-pan Zhu, Liqiang Zhang, Yuebin Wang and Jie Mei, Beijing Normal University, Beijing, China
A Discriminative Distance Metric Learning with Adaptive Label Consistency for Semi-supervised Scene Parsing from Point Clouds (351135)
Jie Mei, Liqiang Zhang, Yuebin Wang, Pan-pan Zhu, Yang Li and Xingang Li, Beijing Normal University, Beijing, China
GEONEX: Progressive Conditional Generative Adversarial Training Using Transfer learning (397903)
Edward Durham Collier1, Sangram Ganguly2, Kate Duffy3, Geri Elise Madanguit4, Subodh Kalia4, Ramakrishna R Nemani5, Thomas Vandal6, Shuang Li5, Andrew Michaelis5 and Supratik Mukhopadhyay1, (1)Louisiana State University, Computer Science, Baton Rouge, LA, United States, (2)NASA Ames Research Center / BAERI, Moffet Field, CA, United States, (3)Zeus AI, Cambridge, United States, (4)BAER Institute, Mountain View, CA, United States, (5)NASA Ames Research Center, Moffett Field, CA, United States, (6)Northeastern University, Boston, MA, United States
A Machine Learning Approach to Estimate Multi-Aerosol Mixing State Metrics at a Global Scale in Earth System Models (412361)
Zhonghua Zheng1, Sajal Dash2, Drew Schmidt3, Junqi Yin4, Nicole Riemer5, Matthew West6 and Valentine G Anantharaj3, (1)The University of Manchester, Department of Earth and Environmental Sciences, Manchester, United Kingdom, (2)Virginia Polytechnic Institute and State University, Department of Computer Science, Blacksburg, VA, United States, (3)Oak Ridge National Laboratory, National Center for Computational Sciences, Oak Ridge, TN, United States, (4)Oak Ridge National Laboratory, National Center for Computational Sciences, Oak Ridge, United States, (5)University of Illinois at Urbana-Champaign, Department of Atmospheric Sciences, Urbana, IL, United States, (6)University of Illinois at Urbana Champaign, Mechanical Science and Engineering, Urbana, United States
Using Machine Learning to Emulate Critical Cloud Microphysical Processes (466907)
Richard Loft, National Center for Atmospheric Research, Boulder, CO, United States, Negin Sobhani, NCAR | University of Iowa, Iowa City, IA, United States, Andrew Gettelman, Pacific Northwest National Laboratory, Richland, WA, United States, Chih-Chieh Chen, NCAR, Boulder, United States and David John Gagne II, NCAR/MMM, Boulder, United States
Machine Learning techniques to evaluate reproducibility of climate model simulations on hybrid architectures. (467466)
Salil Mahajan, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Katherine J Evans, Oak Ridge National Laboratory, Oak Ridge, United States and Joseph H Kennedy, University of Alaska Fairbanks, Alaska Satellite Facility, Fairbanks, United States

See more of: Earth and Space Science Informatics