Chun‐Hsu Su

5.3k total citations · 1 hit paper
102 papers, 3.9k citations indexed

About

Chun‐Hsu Su is a scholar working on Atmospheric Science, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Chun‐Hsu Su has authored 102 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Atmospheric Science, 35 papers in Environmental Engineering and 25 papers in Global and Planetary Change. Recurrent topics in Chun‐Hsu Su's work include Precipitation Measurement and Analysis (29 papers), Soil Moisture and Remote Sensing (27 papers) and Meteorological Phenomena and Simulations (22 papers). Chun‐Hsu Su is often cited by papers focused on Precipitation Measurement and Analysis (29 papers), Soil Moisture and Remote Sensing (27 papers) and Meteorological Phenomena and Simulations (22 papers). Chun‐Hsu Su collaborates with scholars based in Australia, United States and Austria. Chun‐Hsu Su's co-authors include Rida M. Mirie, Dongryeol Ryu, Wade T. Crow, George Em Karniadakis, S. H. Lam, Andrew D. Greentree, Wolfgang Wagner, Andrew W. Western, Didier Lucor and Alexander Gruber and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Journal of Geophysical Research Atmospheres.

In The Last Decade

Chun‐Hsu Su

94 papers receiving 3.7k citations

Hit Papers

Diamond-based single-photon emitters 2011 2026 2016 2021 2011 100 200 300 400
What are hit papers?

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.

  1. Diamond-based single-photon emitters
    2011 432 citations Chun‐Hsu Su, Andrew D. Greentree et al. profile →

Peers

Chun‐Hsu Su
Karl K. Sabelfeld Russia
M.M.R. Williams United Kingdom
Marc Bocquet France
Robert D. Richtmyer United States
Elaine S. Oran United States
Leung Tsang United States
S. Corrsin United States
Valerio Lucarini United Kingdom
Gregory Falkovich Israel
T. Brooke Benjamin United Kingdom
Karl K. Sabelfeld Russia
Chun‐Hsu Su
Citations per year, relative to Chun‐Hsu Su Chun‐Hsu Su (= 1×) peers Karl K. Sabelfeld

Countries citing papers authored by Chun‐Hsu Su

Since Specialization
Citations

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

Fields of papers citing papers by Chun‐Hsu Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chun‐Hsu Su. 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 Chun‐Hsu Su. The network helps show where Chun‐Hsu Su may publish in the future.

Co-authorship network of co-authors of Chun‐Hsu Su

This figure shows the co-authorship network connecting the top 25 collaborators of Chun‐Hsu Su. A scholar is included among the top collaborators of Chun‐Hsu Su 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 Chun‐Hsu Su. Chun‐Hsu Su 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.
Wang, Quan J., et al.. (2025). Calibration of precipitation forecasts from NWP models for ungauged locations. Journal of Hydrology. 661. 133733–133733.
2.
Bell, Samuel S., Andrew Dowdy, Savin S. Chand, & Chun‐Hsu Su. (2024). Occurrence and trends of historical tropical cyclone rainfall on near-coastal regions of Australia. Journal of Southern Hemisphere Earth System Science. 74(2).
3.
Howard, Emma, Chun‐Hsu Su, Acacia Pepler, et al.. (2024). Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1. Geoscientific model development. 17(2). 731–757. 6 indexed citations
4.
Wang, Quan J., et al.. (2023). Estimating daily precipitation climatology by postprocessing high‐resolution reanalysis data. International Journal of Climatology. 43(9). 4151–4165. 4 indexed citations
5.
Bell, Samuel S., et al.. (2022). Using historical tropical cyclone climate datasets to examine wind speed recurrence for coastal Australia. Scientific Reports. 12(1). 11612–11612. 4 indexed citations
6.
Su, Chun‐Hsu, Dörte Jakob, Paul Fox‐Hughes, et al.. (2021). BARRA v1.0: kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains. Geoscientific model development. 14(7). 4357–4378. 30 indexed citations
7.
Acharya, Suwash Chandra, et al.. (2020). Ability of an Australian reanalysis dataset to characterise sub-daily precipitation. Hydrology and earth system sciences. 24(6). 2951–2962. 10 indexed citations
8.
Acharya, Suwash Chandra, et al.. (2019). An evaluation of daily precipitation from atmospheric reanalyses overAustralia. 2 indexed citations
9.
Su, Chun‐Hsu, Peter Steinle, Dörte Jakob, et al.. (2019). BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia. Geoscientific model development. 12(5). 2049–2068. 110 indexed citations
10.
Acharya, Suwash Chandra, et al.. (2019). An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia. Hydrology and earth system sciences. 23(8). 3387–3403. 38 indexed citations
11.
Brocca, Luca, Thierry Pellarin, Wade T. Crow, et al.. (2016). Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia. Journal of Geophysical Research Atmospheres. 121(20). 66 indexed citations
12.
Ryu, Dongryeol, et al.. (2015). Towards reliable hydrological model calibrations with river level measurements. 1 indexed citations
13.
Álvarez-Garretón, Camila, Dongryeol Ryu, Andrew W. Western, et al.. (2015). Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrology and earth system sciences. 19(4). 1659–1676. 94 indexed citations
14.
Choi, Minha, et al.. (2015). Intercomparison of AMSR2 and AMSR-E Soil Moisture Retrievals with MERRA-L data set over Australia. 2015 AGU Fall Meeting. 2015. 1 indexed citations
15.
Su, Chun‐Hsu & Dongryeol Ryu. (2015). Multi-scale analysis of bias correction of soil moisture. Hydrology and earth system sciences. 19(1). 17–31. 43 indexed citations
16.
Pipunic, Robert C., et al.. (2013). Evaluation of real-time satellite rainfall products in semi-arid/arid Australia. Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation. 5 indexed citations
17.
Ryu, Dongryeol, Chun‐Hsu Su, Andrew W. Western, & Wolfgang Wagner. (2012). Inter-comparison of microwave satellite soil moisture retrievals over Australia. AGUFM. 2012. 2 indexed citations
18.
Quach, James Q., Chun‐Hsu Su, A. Martin, Andrew D. Greentree, & Lloyd C. L. Hollenberg. (2010). A new class of dynamic quantum metamaterials. arXiv (Cornell University).
19.
20.
Su, Chun‐Hsu & Rida M. Mirie. (1979). On head-on collision between two solitary-waves. NASA STI/Recon Technical Report N. 79. 21311. 7 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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