J. M. Fontenla

4.7k total citations · 1 hit paper
78 papers, 3.1k citations indexed

About

J. M. Fontenla is a scholar working on Astronomy and Astrophysics, Artificial Intelligence and Atmospheric Science. According to data from OpenAlex, J. M. Fontenla has authored 78 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Astronomy and Astrophysics, 23 papers in Artificial Intelligence and 23 papers in Atmospheric Science. Recurrent topics in J. M. Fontenla's work include Solar and Space Plasma Dynamics (60 papers), Stellar, planetary, and galactic studies (24 papers) and Solar Radiation and Photovoltaics (23 papers). J. M. Fontenla is often cited by papers focused on Solar and Space Plasma Dynamics (60 papers), Stellar, planetary, and galactic studies (24 papers) and Solar Radiation and Photovoltaics (23 papers). J. M. Fontenla collaborates with scholars based in United States, France and United Kingdom. J. M. Fontenla's co-authors include E. H. Avrett, R. Loeser, J. W. Harder, T. N. Woods, Martin Snow, G. J. Rottman, Erik Richard, Oran R. White, Kevin France and Jeffrey L. Linsky and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, The Astrophysical Journal and Geophysical Research Letters.

In The Last Decade

J. M. Fontenla

72 papers receiving 3.0k citations

Hit Papers

Energy balance in the solar transition region. III - Heli... 1993 2026 2004 2015 1993 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
J. M. Fontenla United States 28 2.7k 1.0k 610 343 275 78 3.1k
P. Foukal United States 32 2.3k 0.8× 647 0.6× 609 1.0× 423 1.2× 176 0.6× 128 2.9k
O. R. White United States 25 1.8k 0.7× 763 0.7× 290 0.5× 200 0.6× 215 0.8× 75 2.2k
F. G. Eparvier United States 39 4.8k 1.8× 1.2k 1.2× 377 0.6× 179 0.5× 565 2.1× 136 5.1k
Phillip C. Chamberlin United States 30 5.4k 2.0× 768 0.7× 699 1.1× 108 0.3× 348 1.3× 93 5.7k
G. E. Brueckner United States 36 6.2k 2.3× 833 0.8× 585 1.0× 253 0.7× 271 1.0× 110 6.7k
S. M. Bailey United States 37 3.9k 1.4× 2.9k 2.8× 167 0.3× 870 2.5× 208 0.8× 145 4.4k
R. A. Viereck United States 24 2.3k 0.8× 828 0.8× 271 0.4× 161 0.5× 357 1.3× 70 2.5k
P. Lamy France 34 4.7k 1.7× 351 0.3× 161 0.3× 55 0.2× 268 1.0× 234 5.1k
L. Floyd United States 15 854 0.3× 600 0.6× 189 0.3× 179 0.5× 255 0.9× 40 1.1k
H. Uitenbroek United States 29 2.6k 1.0× 276 0.3× 420 0.7× 75 0.2× 31 0.1× 101 2.7k

Countries citing papers authored by J. M. Fontenla

Since Specialization
Citations

This map shows the geographic impact of J. M. Fontenla'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 J. M. Fontenla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. M. Fontenla more than expected).

Fields of papers citing papers by J. M. Fontenla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. M. Fontenla. 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 J. M. Fontenla. The network helps show where J. M. Fontenla may publish in the future.

Co-authorship network of co-authors of J. M. Fontenla

This figure shows the co-authorship network connecting the top 25 collaborators of J. M. Fontenla. A scholar is included among the top collaborators of J. M. Fontenla 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 J. M. Fontenla. J. M. Fontenla 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.
Lee, Jae N., Dong L. Wu, A. Ruzmaikin, & J. M. Fontenla. (2018). Solar cycle variations in mesospheric carbon monoxide. Journal of Atmospheric and Solar-Terrestrial Physics. 170. 21–34. 12 indexed citations
2.
Xu, Shaosui, M. W. Liemohn, W. K. Peterson, J. M. Fontenla, & Phillip C. Chamberlin. (2015). Comparison of different solar irradiance models for the superthermal electron transport model for Mars. Planetary and Space Science. 119. 62–68. 25 indexed citations
3.
Sakai, Shotaro, A. Rahmati, D. L. Mitchell, et al.. (2015). Model insights into energetic photoelectrons measured at Mars by MAVEN. Geophysical Research Letters. 42(21). 8894–8900. 32 indexed citations
4.
Harder, J. W., J. M. Fontenla, Mark Rast, Peter Pilewskie, & T. N. Woods. (2010). Measured and modeled trends in solar spectral irradiance variability in the visible and infrared. cosp. 38. 6. 1 indexed citations
5.
Platnick, Steven & J. M. Fontenla. (2008). Model Calculations of Solar Spectral Irradiance in the 3.7-μm Band for Earth Remote Sensing Applications. Journal of Applied Meteorology and Climatology. 47(1). 124–134. 12 indexed citations
6.
Fontenla, J. M. & Georg Harder. (2005). Physical modeling of spectral irradiance variations. MmSAI. 76. 826. 11 indexed citations
7.
Pankratz, C. K., Barry G. Knapp, J. M. Fontenla, et al.. (2005). SORCE Solar Irradiance Data Products. AGU Spring Meeting Abstracts. 2005. 1 indexed citations
8.
Fontenla, J. M., J. W. Harder, G. J. Rottman, et al.. (2004). The Signature of Solar Activity in the Infrared Spectral Irradiance. The Astrophysical Journal. 605(1). L85–L88. 42 indexed citations
9.
Fontenla, J. M., Oran R. White, Peter Fox, E. H. Avrett, & Robert L. Kurucz. (1999). Calculation of Solar Irradiances. I. Synthesis of the Solar Spectrum. The Astrophysical Journal. 518(1). 480–499. 202 indexed citations
10.
Avrett, E. H., J. M. Fontenla, & R. Loeser. (1994). Formation of the Solar 10830 Angstrom Line. 154. 35. 2 indexed citations
11.
Rovira, M. G., J. M. Fontenla, J. C. Vial, & P. Gouttebroze. (1994). Effects of Ambipolar Diffusion on Prominence Thread Models. International Astronomical Union Colloquium. 144. 315–321. 1 indexed citations
12.
Fontenla, J. M., D. M. Rabin, David H. Hathaway, & R. L. Moore. (1993). Measurement of p-mode energy propagation in the quiet solar photosphere. The Astrophysical Journal. 405. 787–787. 8 indexed citations
13.
Davis, John M. & J. M. Fontenla. (1991). Considerations for Flare Related Magnetic Field Measurements. Bulletin of the American Astronomical Society. 23(2). 1055–4.
14.
MacNeice, P. J., J. M. Fontenla, & N. N. Ljepojević. (1991). Non-Maxwellian electron distributions in models of the solar atmosphere. The Astrophysical Journal. 369. 544–544. 10 indexed citations
15.
Fineschi, Silvano, J. M. Fontenla, P. J. MacNeice, & N. N. Ljepojević. (1990). Impact Line Polarization in Hot Solar Plasmas with Non-Maxwellian Electron Distributions. Bulletin of the American Astronomical Society. 22. 826. 1 indexed citations
16.
Fontenla, J. M., E. H. Avrett, & R. Loeser. (1990). Energy balance in the solar transition region. I - Hydrostatic thermal models with ambipolar diffusion. The Astrophysical Journal. 355. 700–700. 117 indexed citations
17.
Fontenla, J. M. & E. J. Reichmann. (1987). Lyman alpha SMM/UVSP absolute calibration and geocoronal correction. NASA STI/Recon Technical Report N. 88. 12446.
18.
Fontenla, J. M., E. H. Avrett, & R. Loeser. (1987). The Energy Balance at the Base of the Solar Transition Region. Bulletin of the American Astronomical Society. 19. 931. 1 indexed citations
19.
Sahade, Jorge, et al.. (1984). IUE low dispersion observations of symbiotic objects.. Astronomy & Astrophysics Supplement Series. 56. 17–41.
20.
Fontenla, J. M., et al.. (1981). Transition region models for B (sube) stars. 6. 209–214. 1 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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