Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Space Weather Modeling Framework: A new tool for the space science community
2005570 citationsA. A. Chan et al.Journal of Geophysical Research Atmospheresprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of A. A. Chan'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 A. A. Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. A. Chan more than expected).
This network shows the impact of papers produced by A. A. Chan. 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 A. A. Chan. The network helps show where A. A. Chan may publish in the future.
Co-authorship network of co-authors of A. A. Chan
This figure shows the co-authorship network connecting the top 25 collaborators of A. A. Chan.
A scholar is included among the top collaborators of A. A. Chan 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 A. A. Chan. A. A. Chan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Elkington, S. R., A. A. Chan, A. N. Jaynes, D. Malaspina, & J. M. Albert. (2019). K2: Towards a Comprehensive Simulation Framework of the Van Allen Radiation Belts. AGU Fall Meeting Abstracts. 2019.4 indexed citations
Elkington, S. R., et al.. (2018). Generalizing Global Simulations of the Radiation Belts: Addressing Advective and Diffusive Processes in a Common Simulation Framework. AGUFM. 2018.1 indexed citations
9.
Elkington, S. R., et al.. (2013). A Comprehensive Simulation Method for Examining Radiation Belt Dynamics. AGU Fall Meeting Abstracts. 2013.1 indexed citations
10.
Chan, A. A., et al.. (2012). Development of a 3D Radiation Belt Model in Adiabatic Invariant Coordinates Using Stochastic Differential Equations. AGUFM. 2012.1 indexed citations
11.
Chan, A. A., S. R. Elkington, & J. M. Albert. (2010). Development of MHD-SDE Methods for Radiation Belt Simulations. cosp. 38. 4.1 indexed citations
Loto'aniu, P. T. M., I. R. Mann, L. G. Ozeke, et al.. (2005). Radial diffusion of relativistic electrons into the radiation belt slot region during the 2003 Halloween geomagnetic storms. AGUFM. 2005.5 indexed citations
14.
Reeves, G. D., R. H. Friedel, M. G. G. T. Taylor, et al.. (2003). Phase Space Distribution of Relativistic Electrons at Geosynchronous Orbit. AGUFM. 2003.1 indexed citations
15.
Chan, A. A., et al.. (2003). Radial diffusion simulation of relativistic electron transport by ULF waves in the September 1998 storm. AGUFM. 2003.7 indexed citations
16.
Elkington, S. R., M. K. Hudson, & A. A. Chan. (2001). Enhanced Radial Diffusion of Outer Zone Electrons in an Asymmetric Geomagnetic Field. AGU Spring Meeting Abstracts. 2001.1 indexed citations
17.
Díaz, Franklin R. Chang, Jared Squire, Andrew Ilin, et al.. (2000). An Overview of Current Research on the VASIMR Engine. APS Division of Plasma Physics Meeting Abstracts. 42.4 indexed citations
Chan, A. A.. (1991). Interaction of Energetic Ring Current Protons with Magnetospheric Hydromagnetic Waves. PhDT.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.