Sungduk Yu

415 total citations · 1 hit paper
19 papers, 272 citations indexed

About

Sungduk Yu is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, Sungduk Yu has authored 19 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Atmospheric Science, 11 papers in Global and Planetary Change and 3 papers in Oceanography. Recurrent topics in Sungduk Yu's work include Climate variability and models (11 papers), Meteorological Phenomena and Simulations (8 papers) and Oceanographic and Atmospheric Processes (3 papers). Sungduk Yu is often cited by papers focused on Climate variability and models (11 papers), Meteorological Phenomena and Simulations (8 papers) and Oceanographic and Atmospheric Processes (3 papers). Sungduk Yu collaborates with scholars based in United States, Switzerland and Japan. Sungduk Yu's co-authors include Michael S. Pritchard, Alexey V. Fedorov, Brian White, Christopher S. Bretherton, Kai Ziervogel, Carol Arnosti, Roberto Camassa, Paul A. O’Gorman, Jennifer C. Prairie and Janni Yuval and has published in prestigious journals such as Journal of Climate, Geophysical Research Letters and Science Advances.

In The Last Decade

Sungduk Yu

16 papers receiving 270 citations

Hit Papers

Climate-invariant machine learning 2024 2026 2025 2024 10 20 30 40

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sungduk Yu United States 9 175 151 104 22 21 19 272
Adam J. Christman United States 8 104 0.6× 119 0.8× 115 1.1× 36 1.6× 15 0.7× 12 266
David G. Ortiz‐Suslow United States 12 65 0.4× 176 1.2× 214 2.1× 14 0.6× 21 1.0× 32 306
João H. Bettencourt Ireland 7 55 0.3× 88 0.6× 167 1.6× 25 1.1× 9 0.4× 13 210
W. L. Chang China 7 252 1.4× 299 2.0× 135 1.3× 11 0.5× 6 0.3× 9 328
Algot K. Peterson Norway 9 98 0.6× 300 2.0× 241 2.3× 31 1.4× 6 0.3× 12 388
Zijun Gan China 9 72 0.4× 86 0.6× 266 2.6× 30 1.4× 22 1.0× 18 323
Keith MacHutchon United States 8 44 0.3× 240 1.6× 151 1.5× 14 0.6× 7 0.3× 20 303
Olivier Traullé France 9 201 1.1× 205 1.4× 32 0.3× 4 0.2× 10 0.5× 14 278
James I. Belanger United States 5 202 1.2× 348 2.3× 213 2.0× 5 0.2× 7 0.3× 7 378
K. Hanawa Japan 7 161 0.9× 88 0.6× 203 2.0× 17 0.8× 2 0.1× 11 252

Countries citing papers authored by Sungduk Yu

Since Specialization
Citations

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

Fields of papers citing papers by Sungduk Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungduk Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Sungduk Yu. A scholar is included among the top collaborators of Sungduk Yu 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 Sungduk Yu. Sungduk Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Subramaniam, Akshay, Zhiming Kuang, Sungduk Yu, et al.. (2025). Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations. Journal of Advances in Modeling Earth Systems. 17(7).
2.
Yu, Sungduk, et al.. (2025). Navigating the Noise: Bringing Clarity to ML Parameterization Design With O(100) Ensembles. Journal of Advances in Modeling Earth Systems. 17(4). 2 indexed citations
3.
Beucler, Tom, Fernando Iglesias‐Suarez, Sungduk Yu, et al.. (2025). Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations. Journal of Advances in Modeling Earth Systems. 17(4). 2 indexed citations
4.
Kundu, Souvik, et al.. (2025). LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression. 1554–1570. 2 indexed citations
5.
Olson, Matthew, et al.. (2024). Why do LLaVA Vision-Language Models Reply to Images in English?. 13402–13421. 1 indexed citations
6.
Beucler, Tom, Pierre Gentine, Janni Yuval, et al.. (2024). Climate-invariant machine learning. Science Advances. 10(6). eadj7250–eadj7250. 46 indexed citations breakdown →
8.
Yu, Sungduk, et al.. (2023). Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3(1).
9.
Yu, Sungduk, et al.. (2022). The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model “Wind Stress Shaving” Experiments. Journal of Climate. 35(22). 7519–7538. 13 indexed citations
10.
Yu, Sungduk & Alexey V. Fedorov. (2020). The Role of Westerly Wind Bursts During Different Seasons Versus Ocean Heat Recharge in the Development of Extreme El Niño in Climate Models. Geophysical Research Letters. 47(16). 24 indexed citations
11.
Yu, Sungduk & Michael S. Pritchard. (2019). A Strong Role for the AMOC in Partitioning Global Energy Transport and Shifting ITCZ Position in Response to Latitudinally Discrete Solar Forcing in CESM1.2. Journal of Climate. 32(8). 2207–2226. 34 indexed citations
12.
Kang, Sarah M., Matt Hawcroft, Baoqiang Xiang, et al.. (2019). Extratropical–Tropical Interaction Model Intercomparison Project (Etin-Mip): Protocol and Initial Results. Bulletin of the American Meteorological Society. 100(12). 2589–2606. 37 indexed citations
13.
Kang, Sarah M., Matt Hawcroft, Baoqiang Xiang, et al.. (2019). ETIN-MIP Extratropical-Tropical Interaction Model Intercomparison Project – protocol and initial results. University of Southern Queensland ePrints (University of Southern Queensland). 2 indexed citations
14.
Yu, Sungduk. (2019). Settling of porous spheres, as a proxy for marine snow, through density stratification. Carolina Digital Repository (University of North Carolina at Chapel Hill). 1 indexed citations
15.
Yu, Sungduk, et al.. (2017). Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1. Journal of Advances in Modeling Earth Systems. 10(1). 126–144. 25 indexed citations
16.
Yu, Sungduk, et al.. (2016). Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration. Journal of Advances in Modeling Earth Systems. 8(2). 634–649. 8 indexed citations
17.
Yu, Sungduk & Michael S. Pritchard. (2015). The effect of large‐scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM. Journal of Advances in Modeling Earth Systems. 7(4). 1977–1996. 9 indexed citations
18.
Ziervogel, Kai, et al.. (2013). Delayed settling of marine snow at sharp density transitions driven by fluid entrainment and diffusion-limited retention. Marine Ecology Progress Series. 487. 185–200. 44 indexed citations
19.
McLaughlin, Richard M., et al.. (2013). Retention and entrainment effects: Experiments and theory for porous spheres settling in sharply stratified fluids. Physics of Fluids. 25(8). 22 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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