SM23A:
Frontier Solar-Terrestrial Science Enabled by the Combination of Data-Driven Techniques and Physics-Based Understanding II Posters

Submit an Abstract to this Session



Session ID#: 22864

Session Description:
The Sun-Earth system is complex and highly coupled. Understanding and modeling observed Sun-Earth phenomena requires the combination of systems-level (global) approaches with extensive utilization of our growing and increasingly accessible observational system. Analysis techniques from the fields of statistical inference, information theory, and machine learning coupled with emerging state-of-the-art computational techniques are enabling a new frontier in Sun-Earth system specification. This session will be a forum for novel understanding of Solar-Terrestrial relations obtained from the use of data-driven approaches and technologies. Specific emphasis will be placed on improved understanding of complexities, including multi-scale coupling, causality, nonlinearity, and the implications for space weather prediction.
Primary Convener:  Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Pasadena, United States
Conveners:  Jacob Bortnik, University of California Los Angeles, Los Angeles, CA, United States, Enrico Camporeale, Centrum Wiskunde & Informatica, Amsterdam, Netherlands and Tomoko Matsuo, University of Colorado Boulder, Boulder, United States
Chairs:  Ryan Michael McGranaghan, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States and Enrico Camporeale, Centrum Wiskunde & Informatica, Amsterdam, Netherlands
OSPA Liaison:  Simon Wing, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
Co-Organized with:
SPA-Magnetospheric Physics, SPA-Aeronomy, and SPA-Solar and Heliospheric Physics

Cross-Listed:
  • SH - SPA-Solar and Heliospheric Physics
Index Terms:

1942 Machine learning [INFORMATICS]
1986 Statistical methods: Inferential [INFORMATICS]
2799 General or miscellaneous [MAGNETOSPHERIC PHYSICS]
7924 Forecasting [SPACE WEATHER]

Abstracts Submitted to this Session:

Hurdles to Overcome to Model Carrington Class Events (290959)
Miles Engel1, Michael G Henderson2, Vania K Jordanova2 and Steven Morley3, (1)Los Alamos National Laboratory, Los Alamos, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States, (3)LANL, Los Alamos, United States
Bayesian inference of radiation belt loss timescales. (239345)
Mandar Chandorkar, Centrum Wiskunde & Informatica, Multiscale Dynamics, Amsterdam, Netherlands and Enrico Camporeale, Center for Mathematics and Computer Science (CWI), Multiscale Dynamics, Amsterdam, Netherlands
Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks (254775)
Irina S S Zhelavskaya, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany, Yuri Shprits, Deutsches GeoForschungsZentrum GFZ, 2.8 Magnetospheric Physics, Potsdam, Germany and Maria Spasojevic, Stanford University, Stanford, CA, United States
The contribution of inductive electric fields to particle energization in the inner magnetosphere (286047)
Raluca Ilie, University of Illinois at Urbana Champaign, Electrical and Computer Engineering, Urbana, United States, Gabor Toth, University of Michigan, Climate and Space Sciences and Engineering, Ann Arbor, United States, Michael Warren Liemohn, University of Michigan Ann Arbor, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States and Anthony Arthur Chan, Rice Univ-Physics & Astronomy, Houston, TX, United States
3D tomographic reconstruction of the terrestrial exosphere and its time-dependent coupling to the magnetospheric ring current (296986)
Lara Waldrop1, Gonzalo Cucho-Padin2 and Raluca Ilie2, (1)University of Illinois at Urbana-Champaign, Electrical and Computer Engineering, Urbana, IL, United States, (2)University of Illinois at Urbana Champaign, Electrical and Computer Engineering, Urbana, United States
Examining Magnetotail Stretching and Substorm Onsets using GOES Satellite Data and Information Theory (225127)
Nathan Verrill1, Darrick Horton1, Jay Johnson2 and Simon Wing3, (1)Andrews University, Berrien Springs, MI, United States, (2)Andrews University, Berrien Springs, United States, (3)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
Neural Network Substorm Identification: Enabling TREx Sensor Web Modes (256513)
Darren Chaddock, Emma Spanswick, Kirsten Michelle Arnason, Eric Donovan, Jun Liang, Saabiha Ahmad and Brian J Jackel, University of Calgary, Calgary, AB, Canada
Modeling Geomagnetic Variations using a Machine Learning Framework (281227)
Chun Ming Mark Cheung1, Casey Handmer2, Burcu Kosar3, George Gerules2, Bala Poduval4, Graham Mackintosh2, Andres Munoz Jaramillo5, Monica Bobra6, Troy Hernandez7 and Ryan Michael McGranaghan8, (1)Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, United States, (2)NASA Frontier Development Lab, Mountain View, CA, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)Space Science Institute Boulder, Boulder, CO, United States, (5)Southwest Research Institute, Boulder, United States, (6)Stanford University, W.W Hansen Experimental Physics Laboratory, Stanford, CA, United States, (7)IBM, Chicago, IL, United States, (8)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Riometer based Neural Network Prediction of Kp (287407)
Kirsten Michelle Arnason1, Emma Spanswick1, Darren Chaddock1, Aysa Fakheri Tabrizi2 and Laleh Behjat2, (1)University of Calgary, Calgary, AB, Canada, (2)University of Calgary, Department of Electrical and Computer Engineering, Calgary, AB, Canada
Eigenanalysis and Graph Theory Combined to Determine the Seasonal and Solar-Cycle Variations of Polar Magnetic Fields (280635)
Robert Michael Shore1, Mervyn P Freeman1 and Jesper W Gjerloev2, (1)British Antarctic Survey, Cambridge, United Kingdom, (2)Applied Physics Laboratory Johns Hopkins, Laurel, MD, United States
Statistical Maps of Ground Magnetic Disturbance Derived from Global Geospace Models (262293)
Erin Joshua Rigler1, Michael James Wiltberger2 and Jeffrey J Love1, (1)USGS Geologic Hazards Science Center, Geomagnetism Program, Golden, CO, United States, (2)National Center for Atmospheric Research, Boulder, United States

See more of: SPA-Magnetospheric Physics