Enabling data-driven modeling and real-time prediction of the dynamic solar atmosphere Article Swipe
Lucas A. Tarr
,
M. Rempel
,
Xudong Sun
,
K. D. Leka
,
Mark C. M. Cheung
,
V. Martı́nez Pillet
,
N. D. Kee
,
Maria D. Kazachenko
,
J. E. Leake
,
A. N. Afanasyev
,
Yuhong Fan
,
B. T. Welsch
,
Benoît Tremblay
,
Anna Malanushenko
,
Tibor Török
,
Georgios Chintzoglou
,
Mausumi Dikpati
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3847/25c2cfeb.4d8afac8
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3847/25c2cfeb.4d8afac8
Cutting edge research of the region between the solar photosphere and Alfvén surface, where the solar wind disconnects from the solar surface, increasingly relies on largescale computational modeling.This is true not just for numerical experiments that only involve simulations, but also for interpreting the ever-more-intricate observations coming out of new facilities.A paradigm shift is currently underway in the field, away from ad hoc and ab initio models of the atmosphere and towards simulations that are directly driven by observations.While this topic is currently at the level of basic research into the techniques, the intent is for it to serve both basic research and operational space-weather needs.We must advance data driven simulations and their supporting infrastructure to the point where community members can use time dependent dynamic models on a regular basis as a tool to interpret observations and test physical theories.Because of the complexity of the task, the immense computational resources required, and the required longevity of such a project, we argue that this effort should be strategically funded.Such an effort is required to make the most out of current and upcoming multi-mission, multi-instrument, heterogeneous observational data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3847/25c2cfeb.4d8afac8
- https://baas.aas.org/pub/2023n3i385/download/pdf
- OA Status
- gold
- References
- 64
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4386350217Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3847/25c2cfeb.4d8afac8Digital Object Identifier
- Title
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Enabling data-driven modeling and real-time prediction of the dynamic solar atmosphereWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-31Full publication date if available
- Authors
-
Lucas A. Tarr, M. Rempel, Xudong Sun, K. D. Leka, Mark C. M. Cheung, V. Martı́nez Pillet, N. D. Kee, Maria D. Kazachenko, J. E. Leake, A. N. Afanasyev, Yuhong Fan, B. T. Welsch, Benoît Tremblay, Anna Malanushenko, Tibor Török, Georgios Chintzoglou, Mausumi DikpatiList of authors in order
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https://doi.org/10.3847/25c2cfeb.4d8afac8Publisher landing page
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https://baas.aas.org/pub/2023n3i385/download/pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://baas.aas.org/pub/2023n3i385/download/pdfDirect OA link when available
- Concepts
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Atmosphere (unit), Computer science, Environmental science, Real-time computing, Meteorology, GeographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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64Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.physical | 139 |
| abstract_inverted_index.project, | 159 |
| abstract_inverted_index.required | 154, 172 |
| abstract_inverted_index.research | 2, 88, 101 |
| abstract_inverted_index.surface, | 12, 21 |
| abstract_inverted_index.underway | 55 |
| abstract_inverted_index.upcoming | 181 |
| abstract_inverted_index.community | 119 |
| abstract_inverted_index.currently | 54, 82 |
| abstract_inverted_index.dependent | 124 |
| abstract_inverted_index.interpret | 135 |
| abstract_inverted_index.longevity | 155 |
| abstract_inverted_index.numerical | 33 |
| abstract_inverted_index.required, | 151 |
| abstract_inverted_index.resources | 150 |
| abstract_inverted_index.atmosphere | 69 |
| abstract_inverted_index.complexity | 143 |
| abstract_inverted_index.largescale | 25 |
| abstract_inverted_index.supporting | 113 |
| abstract_inverted_index.disconnects | 17 |
| abstract_inverted_index.experiments | 34 |
| abstract_inverted_index.funded.Such | 168 |
| abstract_inverted_index.operational | 103 |
| abstract_inverted_index.photosphere | 9 |
| abstract_inverted_index.simulations | 72, 110 |
| abstract_inverted_index.techniques, | 91 |
| abstract_inverted_index.facilities.A | 50 |
| abstract_inverted_index.increasingly | 22 |
| abstract_inverted_index.interpreting | 42 |
| abstract_inverted_index.observations | 45, 136 |
| abstract_inverted_index.simulations, | 38 |
| abstract_inverted_index.computational | 26, 149 |
| abstract_inverted_index.heterogeneous | 184 |
| abstract_inverted_index.modeling.This | 27 |
| abstract_inverted_index.observational | 185 |
| abstract_inverted_index.space-weather | 104 |
| abstract_inverted_index.strategically | 167 |
| abstract_inverted_index.infrastructure | 114 |
| abstract_inverted_index.multi-mission, | 182 |
| abstract_inverted_index.theories.Because | 140 |
| abstract_inverted_index.multi-instrument, | 183 |
| abstract_inverted_index.observations.While | 78 |
| abstract_inverted_index.ever-more-intricate | 44 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5051354155 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 17 |
| corresponding_institution_ids | https://openalex.org/I4210088762 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.12485097 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |