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.
The GOES-R Geostationary Lightning Mapper (GLM)
2013384 citationsSteven J. Goodman, Richard J. Blakeslee et al.Atmospheric Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Monte G. Bateman
Since
Specialization
Citations
This map shows the geographic impact of Monte G. Bateman'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 Monte G. Bateman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monte G. Bateman more than expected).
Fields of papers citing papers by Monte G. Bateman
This network shows the impact of papers produced by Monte G. Bateman. 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 Monte G. Bateman. The network helps show where Monte G. Bateman may publish in the future.
Co-authorship network of co-authors of Monte G. Bateman
This figure shows the co-authorship network connecting the top 25 collaborators of Monte G. Bateman.
A scholar is included among the top collaborators of Monte G. Bateman 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 Monte G. Bateman. Monte G. Bateman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bateman, Monte G., Douglas M. Mach, Richard J. Blakeslee, & William J. Koshak. (2018). Preliminary Assessment of Detection Efficiency for the Geostationary Lightning Mapper Using Intercomparisons with Ground-Based Systems. NASA Technical Reports Server (NASA).1 indexed citations
Lang, Timothy J., et al.. (2015). Investigation of the Electrification of Pyrocumulus Clouds. NASA Technical Reports Server (NASA).1 indexed citations
8.
Bateman, Monte G.. (2013). A High-fidelity Proxy Dataset for the Geostationary Lightning Mapper (GLM).2 indexed citations
9.
Goodman, Steven J., Richard J. Blakeslee, William J. Koshak, et al.. (2013). The GOES-R Geostationary Lightning Mapper (GLM). Atmospheric Research. 125-126. 34–49.384 indexed citations breakdown →
10.
Bateman, Monte G., Douglas M. Mach, S. J. Goodman, et al.. (2011). Intercomparisons of ground-based and satellite-based lightning measurements used in creating a proxy dataset for the Geostationary Lightning Mapper. AGU Fall Meeting Abstracts. 2011.1 indexed citations
Mach, Douglas M., Richard J. Blakeslee, & Monte G. Bateman. (2009). Global Electric Circuit Implications of Total Current Measurements Over Electrified Clouds. NASA Technical Reports Server (NASA). 2009.1 indexed citations
Boccippio, Dennis J., Francis J. Merceret, Paul T. Willis, et al.. (2003). Comparison of in-situ electric field and radar derived parameters for stratiform clouds in central Florida [presentation]. AGU Fall Meeting Abstracts. 2003.1 indexed citations
17.
Goodman, S. J., Richard J. Blakeslee, H. J. Christian, et al.. (2002). The North Alabama Lightning Mapping Array: Recent Results and Future Prospects. NASA Technical Reports Server (NASA).
Bateman, Monte G.. (1992). The Charge and Size of Precipitation Particles Inside Thunderstorms and Mesoscale Convective Systems on the Great Plains.. PhDT.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.