316:
Scientific Discovery in the Heliosphere Through Data Analytics and Assimilation II


Session ID#: 43100

Session Description:
Data Analytics and assimilation represent the future direction of understanding our models and measurements. Individual efforts across geospace and heliophysics are already employing sophisticated analysis and informatics methods in order to enhance return on missions and scientific efforts. There are informal networks within each sub-discipline that regularly share knowledge through existing partnerships. This session will be a forum for discussion of cutting-edge data analytics, including data fusion and assimilation, applied to Heliophysics.

In this first oral session, we focus on innovative techniques rather than the unique scientific objectives regarding space weather and large dataset analysis. Commonalities in technique tie together all components of the Heliophysics system, benefiting from the fields of statistical inference, information theory, data fusion and machine learning to generate new scientific understanding. This cross-disciplinary session has a goal to promote interaction among researchers across the Heliophysics domain and lead to wider application of cutting-edge techniques.
Primary Convener:  Michael S Kirk, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Conveners:  Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Jack Ireland, ADNET Systems Inc. Greenbelt, Greenbelt, MD, United States
Chairs:  Michael S Kirk, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Ryan Michael McGranaghan, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Jack Ireland, ADNET Systems Inc. Greenbelt, Greenbelt, MD, United States

Abstracts Submitted to this Session:

Learning from terrestrial weather: improved verification measures for space weather forecasting (Invited) (334149)
Sophie A. Murray, Trinity College Dublin, School of Physics, Dublin, Ireland
Magnetic Cloud Prediction Algorithm using Bayesian Inference for Real-Time Forecasts of Geomagnetic Storms (335383)
Hazel M Bain, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Douglas Alan Biesecker, Space Weather Prediction Center, Boulder, CO, United States, Alysha Reinard, NOAA Boulder, SWPC, Boulder, CO, United States, Michele D Cash, NOAA-Space Weather Prediction Center, Boulder, CO, United States and James Chen, Naval Research Lab, Washington, DC, United States
Using Data Assimilation Methods for Physics-Based Capabilities to Predict Solar Activity Cycles (335996)
Irina Kitiashvili, NASA Ames Research Center, Moffett Field, CA, United States
Tracking algorithms and machine learning for the characterization of active regions over the solar cycle 24 (335866)
Raphael AttiƩ1, Barbara J Thompson2, Michael S Kirk1 and Aimee Ann Norton3, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA/GSFC, Greenbelt, MD, United States, (3)Stanford University, Stanford, CA, United States
Searching 13 Million Light Curves for Coronal Dimming (333931)
James Paul Mason1, Charles Nickolos Arge1 and Barbara J Thompson2, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA/GSFC, Greenbelt, MD, United States
Deep Learning Applications for Space Science (334658)
Chun Ming Mark Cheung, Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, United States
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