2017 Workshop: Next generation systems science

Central points:

    - Given the expanse of the geospace system, we must rely on distributed, diverse observations to provide observational support for new understanding
    - Innovation is required to gain the most utility from our diverse observational system
    - Data-driven efforts deliver on understanding through innovation. Three categories were highlighted in our session:
        1. Data fusion
        2. Machine learning
        3. Improved feature identification (through both statistical and computer-aided methods)

We heard from an excellent set of speakers that covered a wide range of analysis techniques aimed to investigate and understand the complex and highly coupled geospace system. Ryan McGranaghan opened the session with a scene-setting presentation. McGranaghan Defining the Next Generation of Geospace Research (pdf)

First, Seebany Datta-Barua (sdattaba@iit.edu) spoke about coherence in ionosphere-thermosphere flows and introduced Lagrangian Coherent Structures as a promising method to characterize the coherence. She also discussed broader themes pertaining to innovation in geospace research, specifically highlighting the importance of uncertainty quantification and placing emphasis on producing actionable forecasts. Datta-Barua Innovative Methods for Ionosphere-Thermosphere Discovery (pdf)

Chunming Wang (cwang@dornsife.usc.edu) next presented results from the Space Weather Forecasting Testbed (SWFT), a machine learning tool created through the ‘Medium Range Ionosphere-Thermosphere Forecasts’ Living With a Star (LWS) project. Chunming stressed that effective forecasting systems must be a combination of model and data-driven approaches, and demonstrated the potential of machine learning techniques to improve space weather forecasting with existing models and data. Wang Machine-Learning Approach for Medium Range Forecast of Ionosphere Anomalies (pdf)

Josh Semeter (jls@bu.edu) gave the final invited talk of the session. He discussed that improving technologies (processing power of an iPhone, for instance) has made heliophysics a data-intensive field. The Mahali Space Weather Monitoring Project was highlighted as a prototype to a revolutionary new architecture that will harness mobile devices to form a global space weather monitoring network. He concluded by echoing a theme that developed from Chunming’s presentation, and continued throughout the session, that scalable machine assistance provides great potential to help humans in the scientific discovery process. Semeter Computer-Aided Discovery in Geospace (pdf)

Roger Varney (roger.varney@sri.com) spoke about the future of geospace observational facilities, raising two important questions: 1) How do we change the way we use our existing facilities to better meet community needs? and 2) How should we plan future facilities? He detailed the requirements for an operational system and highlighted the success of the Poker Flat Incoherent Scatter Radar in meeting these requirements. He concluded by outlining the near, medium, and long range directions for geospace observational systems to bring about innovation in CEDAR science. Varney Incoherent Scatter Radars for System Science and Operational Applications (pdf)

Sierra Flynn (sierra.flynn@colorado.edu) presented results from an Empirical Orthogonal Function (EOF) analysis of nitric oxide flux, and extrapolated the broader implications of such advanced statistical approaches. These broader implications included better understanding complex data sets in compact and comprehensible ways and gaining new scientific insight through data mining and advanced analytics. Flynn Empirical Orthogonal Function (EOF) Analysis and Thermospheric Nitric Oxide Flux (pdf)

Romina Nikoukar (romina.nikoukar@jhuapl.edu) presented the results of a novel data assimilation method for the plasmasphere, which was also given as a science highlight on the final day of the workshop. Romina demonstrated that assimilation, statistically fusing observations with models, is capable of drastically improving geospace specification, extending this paradigm from the ionosphere-thermosphere (where assimilation is generally used in our field) into the inner magnetosphere. Nikoukar A Novel Data Assimilation Model for the Plasmasphere (pdf)

Finally, Asti Bhatt (asti.bhatt@sri.com) spoke about how next generation system science can be approached through networks of homogeneous instruments providing consistent data products using NSF CEDAR program funded Mid-latitude All-sky-imager Network for Geospace Observations (MANGO) as an example. She also provided a software-centric perspective for innovation in geospace by detailing the Integrated Geoscience Observatory (InGeO). InGeO, funded by the National Science Foundation EarthCube program, is a new community resource based on JupyterHub that allows users to discover and collect data from different ionospheric data sources. Networks of homogeneous instruments and Integrated Geoscience Observatory (pdf)