Planning the Future Space Weather Operations and Research Infrastructure Proceedings of the Phase II Workshop (2022) / Chapter Skim
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5 Modeling, Validation, and Data Science
5 Modeling, Validation, and Data Science
Pages 57-73
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From page 57... ...
• Space weather data science would benefit from further cross-agency effort to coordinate data-archival standards, promote data fusion and reuse, and support data revitalization for ML, ensemble modeling, and data assimilation.
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From page 58... ...
KEYNOTES: DATA ASSIMILATION AND MACHINE LEARNING The workshop's data science and analytics sessions were kicked off by two keynote speakers, Richard Todling of NASA's Goddard Space Flight Center and Enrico Camporeale of the University of Colorado Boulder and the National Oceanic and Atmospheric Administration's (NOAA's) Space Weather Prediction Center.
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From page 59... ...
In tandem, these two methods provide a large toolkit with which to tackle space weather forecasting challenges. SOURCE: Ricardo Todling, NASA Goddard Space Flight Center, presentation to workshop, April 13, 2022.
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Machine Learning in Space Weather Forecasting In the next presentation Camporeale began by claiming that ML is "reinventing space weather." To back up this contention he provided an extensive list of topics in space weather to which ML has been applied, including the forecasting of global or average indices, such as the disturbance storm time (Dst) index, solar wind classification, solar wind speed forecasting, and predicting the arrival time of coronal mass ejections (CMEs)
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MACHINE LEARNING AND VALIDATION The Data Science and Analytics: Machine Learning and Validation panel, moderated by committee member KD Leka, had presentations from Jacob Bortnik of the University of California, Los Angeles; Asti Bhatt of SRI; Shasha Zou of the University of Michigan; Morris Cohen of the Georgia Institute of Technology; 1 T Malik, 2022, "SpaceX Says a Geomagnetic Storm Just Doomed 40 Starlink Internet Satellites," Space.com, February 9, https://www.space.com/spacex-starlink-satellites-lost-geomagnetic-storm.
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From page 62... ...
In the future, Bortnik said, all space scientists will need a working knowledge of ML both because the rapidly growing data volumes cannot be analyzed in traditional ways and because ML supersedes physics models in many cases. Thus, space science education will need to include ML principles as well as provide experience in building ML models.
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From page 63... ...
To encourage advances in this area, Morris suggested incentivizing space-based data science course development at computing programs and departments, funding new space scientist faculty hires in computer science departments, and specifically tackling the challenge of "misbehaved data" in data science using the space weather data as an example. Fouhey, an ML and computer vision (CV)
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Fouhey continued that the handling of operational (lower-quality) data is already a data science research topic, which reinforces the point that space physics provides research topics that are of interest to ML and data scientists as well.
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He added that the Solar Dynamics Observatory, the key data source today, does not have redundancy or continuity, and he said that it is important to now invest in future infrastructure, such as ngGONG or space-borne vector magnetographs. Data buys could help solve the problem if they would lead to lower costs of the magnetograph data.
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Successful examples include deducing the heliospheric magnetic field using ground-based Faraday rotation measurements of galactic sources combined with models of ionospheric total electron content, or constructing an extreme ultraviolet irradiance emulator from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly instrument using training data (fusion)
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of NOAA, Dan Welling of the University of Texas at Arlington, Nick Pedatella of the University Corporation for Atmospheric Research, Kent Tobiska of Space Environment Technologies, and Edmund Henley of the UK Meteorological Office. The following questions were posed to this panel: • The terrestrial weather community does multiple-model ensemble modeling.
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Blue and green lines are different model runs, while the black line is the actual observation. SOURCE: Eric Adamson, NOAA Space Weather Prediction Center, presentation to workshop, April 14, 2022.
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In the case of applying DA or ensemble modeling to the magnetosphere, Welling said that distributed multipoint measurements can provide localized nudging without an overall large impact, but the success of the process will depend on the details of the assimilated information. In the Zoom chat participants brought up the issue of MMEs versus ensemble DA versus a broader definition of a "perturbed physics, empirical, statistical, and machine-learning model." The point was made that MMEs are preferred over a single "best-performing" model, both because the different models, despite their performance, can bring useful information and because a combination of multiple models can outperform even the best single model.
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From page 70... ...
The unmet data infrastructure needs of that community include an easy-access, user-friendly data infrastructure, file and data standardization, documentation of data for record keeping and end users, and the development of one or more data repositories. NSF supports solar and space physics data systems, such as the Madrigal Database for archival and real-time data from upper atmospheric science instruments and the Community Coordinated Modeling Center for space research models.
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To do so, NASA has set out four broad strategies: enabling open science, lowering current barriers to doing research, implementing new critical capabilities, and being responsive to changing community needs. The science infrastructure for these strategies has four components: the Heliophysics Data and Model Consortium, the Space Physics Data Facility, the Solar Data Analysis Center, and various collaborators, such as the Community Coordinated Modeling Center (CCMC)
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. SOURCE: Masha Kuznetsova, NASA Community Coordinated Modeling Center, presentation to workshop, April 13, 2022.
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From page 73... ...
With regard to data buys, concerns were expressed about quality control, longevity, reliability, and user access. NOAA's current radio occultation data buys are made available to the research community with a 24-hour delay, but the committee heard nothing about standards or practices regarding these data.
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