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.
PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies
20141.1k citationsHamed Ashouri, Kuolin Hsu et al.Bulletin of the American Meteorological Societyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Brian R. Nelson
Since
Specialization
Citations
This map shows the geographic impact of Brian R. Nelson'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 Brian R. Nelson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian R. Nelson more than expected).
This network shows the impact of papers produced by Brian R. Nelson. 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 Brian R. Nelson. The network helps show where Brian R. Nelson may publish in the future.
Co-authorship network of co-authors of Brian R. Nelson
This figure shows the co-authorship network connecting the top 25 collaborators of Brian R. Nelson.
A scholar is included among the top collaborators of Brian R. Nelson 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 Brian R. Nelson. Brian R. Nelson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Prat, O. P., et al.. (2019). Developing an Interactive Global Drought Information Dashboard Using Remotely Sensed Near-Real Time Monitoring. AGU Fall Meeting Abstracts. 2019.1 indexed citations
Prat, O. P., Ronald D. Leeper, Jesse E. Bell, et al.. (2018). Toward Earlier Drought Detection Using Remotely Sensed Precipitation Data from the Reference Environmental Data Record (REDR) CMORPH. EGUGA. 11468.2 indexed citations
Prat, O. P., et al.. (2015). Merging Radar Quantitative Precipitation Estimates (QPEs) from the High-resolution NEXRAD Reanalysis over CONUS with Rain-gauge Observations. AGU Fall Meeting Abstracts. 2015.1 indexed citations
Prat, O. P., et al.. (2014). Long-Term Large-Scale Bias-Adjusted Precipitation Estimates at High Spatial and Temporal Resolution Derived from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Precipitation Reanalysis over CONUS. AGU Fall Meeting Abstracts. 2014.1 indexed citations
10.
Ashouri, Hamed, Kuolin Hsu, Soroosh Sorooshian, et al.. (2014). PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bulletin of the American Meteorological Society. 96(1). 69–83.1058 indexed citations breakdown →
Qi, Yujin, Kenneth W. Howard, Brian Kaney, et al.. (2013). Retrospective Analysis of High-Resolution Multi-Radar Multi-Sensor QPEs for the Unites States. AGUFM. 2013.
Prat, O. P., Brian R. Nelson, & Thomas M. Rickenbach. (2010). A Multi-Sensor Approach to Access Precipitation Patterns and Hydro-Climatic Extremes in the Southeastern United States. AGU Fall Meeting Abstracts. 2010.1 indexed citations
Nelson, Brian R.. (1981). Western Political Thought: From Socrates to the Age of Ideology.8 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.