C. N. Arge

5.4k total citations · 1 hit paper
103 papers, 3.6k citations indexed

About

C. N. Arge is a scholar working on Astronomy and Astrophysics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, C. N. Arge has authored 103 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Astronomy and Astrophysics, 42 papers in Molecular Biology and 11 papers in Artificial Intelligence. Recurrent topics in C. N. Arge's work include Solar and Space Plasma Dynamics (96 papers), Ionosphere and magnetosphere dynamics (55 papers) and Geomagnetism and Paleomagnetism Studies (42 papers). C. N. Arge is often cited by papers focused on Solar and Space Plasma Dynamics (96 papers), Ionosphere and magnetosphere dynamics (55 papers) and Geomagnetism and Paleomagnetism Studies (42 papers). C. N. Arge collaborates with scholars based in United States, United Kingdom and Austria. C. N. Arge's co-authors include V. J. Pizzo, D. Odstrčil, C. J. Henney, J. G. Luhmann, M. J. Owens, Pete Riley, Y. Li, C. J. Schrijver, S. L. McGregor and William Hughes and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, The Astrophysical Journal and Scientific Reports.

In The Last Decade

C. N. Arge

96 papers receiving 3.4k citations

Hit Papers

Improvement in the prediction of solar wind conditions us... 2000 2026 2008 2017 2000 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
C. N. Arge United States 31 3.5k 1.3k 411 306 194 103 3.6k
Alexei Pevtsov United States 37 4.5k 1.3× 1.5k 1.2× 592 1.4× 327 1.1× 132 0.7× 212 4.6k
Vasyl Yurchyshyn United States 32 3.2k 0.9× 881 0.7× 453 1.1× 109 0.4× 169 0.9× 141 3.3k
D. Odstrčil United States 38 4.5k 1.3× 1.3k 1.1× 255 0.6× 270 0.9× 228 1.2× 138 4.7k
R. S. Bogart United States 20 3.0k 0.9× 964 0.8× 511 1.2× 263 0.9× 71 0.4× 68 3.1k
R. Lionello United States 34 3.5k 1.0× 1.0k 0.8× 297 0.7× 158 0.5× 98 0.5× 102 3.6k
R. I. Bush United States 14 3.8k 1.1× 1.0k 0.8× 732 1.8× 212 0.7× 92 0.5× 27 3.9k
M. Rempel United States 28 2.4k 0.7× 812 0.6× 401 1.0× 230 0.8× 100 0.5× 106 2.6k
Yong‐Jae Moon South Korea 34 4.4k 1.3× 1.2k 0.9× 676 1.6× 230 0.8× 152 0.8× 243 4.6k
R. J. Forsyth United Kingdom 41 5.7k 1.6× 1.8k 1.4× 306 0.7× 191 0.6× 158 0.8× 192 5.8k

Countries citing papers authored by C. N. Arge

Since Specialization
Citations

This map shows the geographic impact of C. N. Arge's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by C. N. Arge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. N. Arge more than expected).

Fields of papers citing papers by C. N. Arge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. N. Arge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by C. N. Arge. The network helps show where C. N. Arge may publish in the future.

Co-authorship network of co-authors of C. N. Arge

This figure shows the co-authorship network connecting the top 25 collaborators of C. N. Arge. A scholar is included among the top collaborators of C. N. Arge based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with C. N. Arge. C. N. Arge is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kirk, Michael S., et al.. (2025). Magnetic Field-Constrained Ensemble Image Segmentation of Coronal Holes in Chromospheric Observations. Solar Physics. 300(1). 2 indexed citations
2.
Wang, Tongjiang, C. N. Arge, & Shaela I. Jones. (2025). Improved Tomographic Reconstruction of 3D Global Coronal Density from STEREO/COR1 Observations. Solar Physics. 300(4). 1 indexed citations
3.
Heinemann, Stephan G., Jens Pomoell, Ronald M. Caplan, et al.. (2025). Quantifying Uncertainties in Solar Wind Forecasting due to Incomplete Solar Magnetic Field Information. The Astrophysical Journal. 986(2). 166–166. 1 indexed citations
5.
Uritsky, V. M., et al.. (2025). The Quasi-radial Field-line Tracing (QRaFT): An Adaptive Segmentation of the Open-flux Solar Corona. The Astrophysical Journal. 995(1). 75–75.
6.
Provornikova, Elena, V. G. Merkin, A. Vourlidas, et al.. (2024). MHD Modeling of a Geoeffective Interplanetary Coronal Mass Ejection with the Magnetic Topology Informed by In Situ Observations. The Astrophysical Journal. 977(1). 106–106. 5 indexed citations
7.
Jones, Shaela I., Ronald M. Caplan, C. N. Arge, et al.. (2024). Quantitative Comparisons between WSA Implementations. The Astrophysical Journal. 970(1). 35–35. 2 indexed citations
8.
Wallace, Samantha, et al.. (2023). Ensemble Forecasts of Solar Wind Connectivity to 1 Rs Using ADAPT‐WSA. Space Weather. 21(10). 4 indexed citations
9.
Barnes, G., Marc L. DeRosa, Shaela I. Jones, et al.. (2023). Implications of Different Solar Photospheric Flux-transport Models for Global Coronal and Heliospheric Modeling. The Astrophysical Journal. 946(2). 105–105. 10 indexed citations
10.
Badman, Samuel T., David H. Brooks, Nicolas Poirier, et al.. (2022). Constraining Global Coronal Models with Multiple Independent Observables. The Astrophysical Journal. 932(2). 135–135. 22 indexed citations
11.
Arge, C. N., N. J. Chanover, Christopher W. Churchill, et al.. (2022). Solar Wind Model Supported by Parker Solar Probe Observations During Faint Venusian Auroral Emission. The Astrophysical Journal. 929(1). 45–45. 2 indexed citations
12.
Jones, Shaela I., et al.. (2022). Quantitative Evaluation of Coronal Magnetic Field Models Using Tomographic Reconstructions of Electron Density. The Astrophysical Journal. 928(2). 131–131. 4 indexed citations
13.
Reiß, Martin, K. Muglach, Christian Möstl, et al.. (2021). The observational uncertainty of coronal hole boundaries in automated detection schemes. University of Oulu Repository (University of Oulu). 17 indexed citations
14.
Posner, A., C. N. Arge, O. C. St. Cyr, et al.. (2021). A Multi‐Purpose Heliophysics L4 Mission. Space Weather. 19(9). 21 indexed citations
15.
Meadors, G. D., et al.. (2020). Data Assimilative Optimization of WSA Source Surface and Interface Radii using Particle Filtering. Space Weather. 18(5). 12 indexed citations
16.
Odstrčil, D., M. L. Mays, Phillip Hess, et al.. (2020). Operational Modeling of Heliospheric Space Weather for the Parker Solar Probe. The Astrophysical Journal Supplement Series. 246(2). 73–73. 17 indexed citations
17.
MacNeice, P. J., L. K. Jian, S. K. Antiochos, et al.. (2018). Assessing the Quality of Models of the Ambient Solar Wind. Space Weather. 16(11). 1644–1667. 44 indexed citations
18.
Luhmann, J. G., Imke de Pater, D. Odstrčil, et al.. (2008). Manifestations of solar differential rotation in the solar wind: An update. AGUSM. 2008. 1 indexed citations
19.
Odstrčil, D., V. J. Pizzo, Pete Riley, & C. N. Arge. (2004). Propagation of the 12 May 1997 ICME in Evolving Solar Wind Structures. AGUFM. 2004. 1 indexed citations
20.
Luhmann, J. G., T. Mulligan, J. T. Hoeksema, et al.. (2001). Earthward directed CMEs seen in large‐scale coronal magnetic field changes, SOHO LASCO coronagraph and solar wind. Journal of Geophysical Research Atmospheres. 106(A11). 25103–25120. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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