NG005
Machine Learning in Space Weather I

Tuesday, 15 December 2020: 07:00-08:00
Virtual
Primary Convener:  Enrico Camporeale, University of Colorado at Boulder, Boulder, United States
Conveners:  Jacob Bortnik, University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, Tomoko Matsuo, University of Colorado Boulder, Boulder, CO, United States and Ryan Michael McGranaghan, Atmospheric and Space Technology Research Associates (ASTRA), Louisville, CO, United States
Primary Liaison:  Enrico Camporeale, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
Chairs:  Jacob Bortnik, University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States and Enrico Camporeale, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
OSPA Liaison:  Enrico Camporeale, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
07:00
Prediction of Soft Proton Contamination in XMM-Newton: a Machine Learning Approach (690999)
Elena A Kronberg1, Fabio Gastaldello2, Stein Haaland3, Artem Smirnov4, Max Berrendorf5, Simona Ghizzardi2, Kip D Kuntz6, Nithin Sivadas7, Robert Colby Allen8, Andrea Tiengo9, Raluca Ilie10, Yu Huang11 and Lynn M Kistler12, (1)Ludwig Maximilians University of Munich, Munich, Germany, (2)INAF-IASF, Milano, Italy, (3)MPS/BCSS, IFT, Bergen, Norway, (4)GFZ, Potsdam, Germany, (5)Ludwig Maximilians University of Munich, Database Systems and Data Mining, Munich, Germany, (6)Johns Hopkins University, Baltimore, MD, United States, (7)Boston University, Department of Electrical and Computer Engineering, Boston, MA, United States, (8)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (9)Scuola Universitaria Superiore IUSS, Pavia, Italy, (10)University of Illinois at Urbana Champaign, Urbana, IL, United States, (11)University of Illinois, Urbana-Champaign, United States, (12)Univ New Hampshire, Durham, NH, United States
07:04
Quantifying contributions of external drivers to Global Ionospheric Map variability (679242)
Xing Meng, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Olga P Verkhoglyadova, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
07:08
Plasmaspheric dynamics studied using a three-dimensional machine learning based plasma density model in the inner magnetosphere (710196)
Hannah Ace, University of Vermont, Burlington, VT, United States, Xiangning Chu, Laboratory for Atmospheric and Space Physics, Boulder, CO, United States, Jacob Bortnik, University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States and Richard Eugene Denton, Dartmouth College, Department of Physics and Astronomy, Hanover, NH, United States
07:12
AIDA: a project for using machine learning to extract space science information from big data generated by observations and simulations. (Invited) (665536)
Giovanni Lapenta, Katholieke Universiteit Leuven, Department of Mathematics, Leuven, Belgium and AIDA Consortium (Aida-space.eu)
07:16
Concurrent Empirical Magnetic Reconstruction of Storm and Substorm Spatial Scales: Overcoming the Disparate Data Density Curse (701050)
Grant Killian Stephens1, Mikhail I. Sitnov1, Sam Bingham2, Viacheslav G Merkin1 and Matina Gkioulidou1, (1)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (2)Johns Hopkins University Applied Physics Laboratory, (Deceased during the planning stages of the session), Laurel, MD, United States
07:20
Geomagnetically Induced Currents at Middle Latitudes: Quiet-time Climatology, Significance during Geomagnetic Disturbances, and Machine-Learning Modeling (747736)
Adam C Kellerman, University of California Los Angeles, Los Angeles, CA, United States, Ryan Michael McGranaghan, Atmospheric and Space Technology Research Associates (ASTRA), Louisville, CO, United States, Jacob Bortnik, University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, Robert F Arritt, Electric Power Research Institute Palo Alto, Palo Alto, TN, United States, Morris Cohen, Georgia Institute of Technology Main Campus, School of Electrical and Computer Engineering, Atlanta, GA, United States, Joe Hughes, Atmospheric and Space Technology Research Associates, LLC, Boulder, CO, United States, Karthik Venkataramani, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, Jackson McCormick, Georgia Institute of Technology Main Campus, Atlanta, GA, United States, Chigomezyo Ngwira, ASTRA LLC, Science, Louisville, CO, United States and Charles Perry, Electric Power Research Institute Palo Alto, Palo Alto, United States
07:24
Machine Learning Forecast of Ionosphere Total Electron Content (686255)
Jiaen Ren1, Hu Sun2, Zhijun Hua3, Shasha Zou1, Yang Chen3, Lei Liu1 and Zihan Wang1, (1)University of Michigan, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States, (2)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (3)University of Michigan, Department of Statistics, Ann Arbor, MI, United States
07:28
Spatiotemporal Deep Learning Network for High­-latitude Ionospheric Scintillation Forecasting (689151)
Yunxiang Liu1, Zhe Yang1, Jade Morton2 and Ruoyu Li3, (1)University of Colorado at Boulder, Smead Aerospace Engineering Sciences Department, Boulder, CO, United States, (2)University of Colorado at Boulder, Smead Aerospace Engineering Sciences, Boulder, CO, United States, (3)University of Texas at Arlington, Arlington, TX, United States
07:32
Questions and Answers
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