Tanja Amerstorfer

963 total citations
25 papers, 437 citations indexed

About

Tanja Amerstorfer is a scholar working on Astronomy and Astrophysics, Molecular Biology and Oceanography. According to data from OpenAlex, Tanja Amerstorfer has authored 25 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Astronomy and Astrophysics, 13 papers in Molecular Biology and 4 papers in Oceanography. Recurrent topics in Tanja Amerstorfer's work include Solar and Space Plasma Dynamics (25 papers), Ionosphere and magnetosphere dynamics (17 papers) and Geomagnetism and Paleomagnetism Studies (13 papers). Tanja Amerstorfer is often cited by papers focused on Solar and Space Plasma Dynamics (25 papers), Ionosphere and magnetosphere dynamics (17 papers) and Geomagnetism and Paleomagnetism Studies (13 papers). Tanja Amerstorfer collaborates with scholars based in Austria, United Kingdom and United States. Tanja Amerstorfer's co-authors include Christian Möstl, Jürgen Hinterreiter, Rachel Bailey, Manuela Temmer, M. L. Mays, Martin Reiß, Ute Amerstorfer, Andreas Weiß, Mateja Dumbović and Peter Boakes and has published in prestigious journals such as The Astrophysical Journal, The Astrophysical Journal Supplement Series and Astronomy and Astrophysics.

In The Last Decade

Tanja Amerstorfer

24 papers receiving 407 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tanja Amerstorfer Austria 13 416 133 48 37 21 25 437
R. C. Colaninno United States 15 687 1.7× 186 1.4× 55 1.1× 24 0.6× 19 0.9× 25 698
S. L. McGregor United States 10 409 1.0× 142 1.1× 46 1.0× 28 0.8× 30 1.4× 17 427
Jürgen Hinterreiter Austria 12 280 0.7× 84 0.6× 34 0.7× 29 0.8× 17 0.8× 17 300
C. Verbeke Belgium 10 388 0.9× 136 1.0× 44 0.9× 51 1.4× 24 1.1× 17 416
Yufen Zhou China 12 523 1.3× 153 1.2× 42 0.9× 52 1.4× 24 1.1× 23 578
Camilla Scolini United States 14 478 1.1× 166 1.2× 37 0.8× 39 1.1× 16 0.8× 45 500
Shuhong Yang China 16 788 1.9× 131 1.0× 61 1.3× 17 0.5× 19 0.9× 54 805
I. Kontogiannis Greece 12 475 1.1× 118 0.9× 120 2.5× 16 0.4× 19 0.9× 33 502
J. S. Newmark United States 5 948 2.3× 178 1.3× 49 1.0× 20 0.5× 27 1.3× 16 956

Countries citing papers authored by Tanja Amerstorfer

Since Specialization
Citations

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

Fields of papers citing papers by Tanja Amerstorfer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tanja Amerstorfer. 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 Tanja Amerstorfer. The network helps show where Tanja Amerstorfer may publish in the future.

Co-authorship network of co-authors of Tanja Amerstorfer

This figure shows the co-authorship network connecting the top 25 collaborators of Tanja Amerstorfer. A scholar is included among the top collaborators of Tanja Amerstorfer 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 Tanja Amerstorfer. Tanja Amerstorfer 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.
Amerstorfer, Tanja, et al.. (2025). Solar Transient Recognition Using Deep Learning (STRUDL) for Heliospheric Imager Data. Space Weather. 23(9).
2.
Möstl, Christian, Emma E. Davies, Astrid Veronig, et al.. (2025). First Observations of a Geomagnetic Superstorm With a Sub‐L1 Monitor. Space Weather. 23(3). 2 indexed citations
3.
Amerstorfer, Tanja, et al.. (2025). Beacon2Science: Enhancing STEREO/HI Beacon Data With Machine Learning for Efficient CME Tracking. Space Weather. 23(7). 1 indexed citations
4.
Laker, R., T. S. Horbury, H. O’Brien, et al.. (2024). Using Solar Orbiter as an Upstream Solar Wind Monitor for Real Time Space Weather Predictions. Space Weather. 22(2). 8 indexed citations
5.
Windisch, Andreas, et al.. (2022). Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data. Space Weather. 20(10). 4 indexed citations
6.
Nieves‐Chinchilla, Teresa, et al.. (2022). Writhed Analytical Magnetic Flux Rope Model. Journal of Geophysical Research Space Physics. 127(12). e2022JA030898–e2022JA030898. 7 indexed citations
7.
Barnard, Luke, M. J. Owens, Christopher J. Scott, et al.. (2021). Quantifying the Uncertainty in CME Kinematics Derived From Geometric Modeling of Heliospheric Imager Data. Space Weather. 20(1). 8 indexed citations
8.
Amerstorfer, Tanja, Jürgen Hinterreiter, Andreas Weiß, et al.. (2021). Predicting CMEs Using ELEvoHI With STEREO‐HI Beacon Data. Space Weather. 19(12). 2 indexed citations
9.
Hinterreiter, Jürgen, Tanja Amerstorfer, Manuela Temmer, et al.. (2021). Drag‐Based CME Modeling With Heliospheric Images Incorporating Frontal Deformation: ELEvoHI 2.0. Space Weather. 19(10). 11 indexed citations
10.
Green, Lucie M., Emma E. Davies, Christian Möstl, et al.. (2021). Solar origins of a strong stealth CME detected by Solar Orbiter. Astronomy and Astrophysics. 656. L6–L6. 15 indexed citations
11.
Möstl, Christian, Rachel Bailey, Ute Amerstorfer, et al.. (2021). Machine Learning for Predicting the Bz Magnetic Field Component From Upstream in Situ Observations of Solar Coronal Mass Ejections. Space Weather. 19(12). 13 indexed citations
12.
Bailey, Rachel, C. N. Arge, Christian Möstl, et al.. (2021). Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions. Space Weather. 19(5). 19 indexed citations
13.
Amerstorfer, Tanja, Jürgen Hinterreiter, Martin Reiß, et al.. (2020). Evaluation of CME Arrival Prediction Using Ensemble Modeling Based on Heliospheric Imaging Observations. Space Weather. 19(1). e2020SW002553–e2020SW002553. 18 indexed citations
14.
Möstl, Christian, Andreas Weiß, Rachel Bailey, et al.. (2020). Prediction of the In Situ Coronal Mass Ejection Rate for Solar Cycle 25: Implications for Parker Solar Probe In Situ Observations. The Astrophysical Journal. 903(2). 92–92. 33 indexed citations
15.
Vršnak, B., Tanja Amerstorfer, Mateja Dumbović, et al.. (2019). Heliospheric Evolution of Magnetic Clouds. The Astrophysical Journal. 877(2). 77–77. 26 indexed citations
16.
Möstl, Christian, Martin Reiß, Tanja Amerstorfer, et al.. (2018). Statistics and parameters of solar coronal mass ejections in the inner heliosphere: what to expect for Parker Solar Probe?. EGUGA. 3293. 1 indexed citations
17.
Riley, Pete, M. L. Mays, Jesse Andries, et al.. (2018). Forecasting the Arrival Time of Coronal Mass Ejections: Analysis of the CCMC CME Scoreboard. Space Weather. 16(9). 1245–1260. 99 indexed citations
18.
Möstl, Christian, Tanja Amerstorfer, Erika Palmerio, et al.. (2018). Forward Modeling of Coronal Mass Ejection Flux Ropes in the Inner Heliosphere with 3DCORE. Space Weather. 16(3). 216–229. 42 indexed citations
19.
Amerstorfer, Tanja, Christian Möstl, Phillip Hess, et al.. (2018). Ensemble Prediction of a Halo Coronal Mass Ejection Using Heliospheric Imagers. Space Weather. 16(7). 784–801. 30 indexed citations
20.
Möstl, Christian, et al.. (2016). PREDICTION OF GEOMAGNETIC STORM STRENGTH FROM INNER HELIOSPHERIC IN SITU OBSERVATIONS. The Astrophysical Journal. 833(2). 255–255. 24 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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