Shih‐Yu Wang

7.2k total citations · 2 hit papers
224 papers, 4.8k citations indexed

About

Shih‐Yu Wang is a scholar working on Global and Planetary Change, Atmospheric Science and Oceanography. According to data from OpenAlex, Shih‐Yu Wang has authored 224 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 152 papers in Global and Planetary Change, 126 papers in Atmospheric Science and 28 papers in Oceanography. Recurrent topics in Shih‐Yu Wang's work include Climate variability and models (129 papers), Meteorological Phenomena and Simulations (76 papers) and Tropical and Extratropical Cyclones Research (50 papers). Shih‐Yu Wang is often cited by papers focused on Climate variability and models (129 papers), Meteorological Phenomena and Simulations (76 papers) and Tropical and Extratropical Cyclones Research (50 papers). Shih‐Yu Wang collaborates with scholars based in United States, Taiwan and China. Shih‐Yu Wang's co-authors include Robert R. Gillies, Jin‐Ho Yoon, Tsing-Chang Chen, Dim Coumou, Giorgia Di Capua, S. J. Vavrus, Wan‐Ru Huang, Lawrence E. Hipps, Hyungjun Kim and Nae‐Lih Wu and has published in prestigious journals such as Science, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Shih‐Yu Wang

211 papers receiving 4.7k citations

Hit Papers

The influence of Arctic amplification on mid-latitude sum... 2018 2026 2020 2023 2018 2020 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shih‐Yu Wang United States 38 3.2k 2.7k 576 359 246 224 4.8k
Guus J. M. Velders Netherlands 31 3.3k 1.1× 3.1k 1.1× 498 0.9× 281 0.8× 435 1.8× 75 6.4k
Hongmei Li China 44 2.3k 0.7× 1.5k 0.6× 1.7k 3.0× 301 0.8× 1.0k 4.1× 228 7.3k
Wenxia Zhang China 43 2.4k 0.8× 1.9k 0.7× 432 0.8× 325 0.9× 236 1.0× 147 4.8k
Xuan Wang China 36 1.4k 0.4× 2.3k 0.9× 155 0.3× 294 0.8× 186 0.8× 164 4.5k
Hailong Liu China 37 2.5k 0.8× 1.8k 0.7× 2.0k 3.6× 151 0.4× 226 0.9× 290 4.6k
Qin Zhang China 27 3.5k 1.1× 3.0k 1.1× 1.3k 2.3× 317 0.9× 300 1.2× 105 5.3k
Xuejun Zhang China 30 1.7k 0.5× 879 0.3× 239 0.4× 790 2.2× 136 0.6× 131 3.5k
Yulan Zhang China 43 1.2k 0.4× 2.7k 1.0× 238 0.4× 352 1.0× 606 2.5× 215 8.0k
S. R. Arnold United Kingdom 40 3.0k 1.0× 3.2k 1.2× 276 0.5× 129 0.4× 365 1.5× 139 5.4k
Liping Zhang China 33 2.4k 0.8× 1.9k 0.7× 1.6k 2.9× 196 0.5× 416 1.7× 132 4.2k

Countries citing papers authored by Shih‐Yu Wang

Since Specialization
Citations

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

Fields of papers citing papers by Shih‐Yu Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shih‐Yu Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Shih‐Yu Wang. A scholar is included among the top collaborators of Shih‐Yu Wang 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 Shih‐Yu Wang. Shih‐Yu Wang 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.
Bhandari, Biplov, et al.. (2025). Advancing wildfire prediction in Nepal using machine learning algorithms. Environmental Research Communications. 7(5). 55003–55003. 2 indexed citations
2.
Deng, Liping, et al.. (2024). Ocean Temperatures Do Not Account for a Record-Setting Winter in the U.S. West. Atmosphere. 15(3). 284–284. 1 indexed citations
3.
Chang, Hsin-I, et al.. (2024). Enhancing Extreme Precipitation Predictions With Dynamical Downscaling: A Convection‐Permitting Modeling Study in Texas and Oklahoma. Journal of Geophysical Research Atmospheres. 129(8). 4 indexed citations
4.
Deng, Liping, et al.. (2024). Explainable AI in lengthening ENSO prediction from western north pacific precursor. Ocean Modelling. 192. 102431–102431.
5.
Wang, Shih‐Yu, et al.. (2024). Stepwise construction of a metallocatenane based on non-labile bis(terpyridine)-CdII complexes. Chemical Communications. 60(61). 7914–7917. 3 indexed citations
6.
Wang, Shih‐Yu, et al.. (2024). East Pacific ENSO Offers Early Predictive Signals for Harvest Yields. Journal of Applied Meteorology and Climatology. 63(8). 909–919.
7.
Wang, Shih‐Yu, et al.. (2022). North American fire weather catalyzed by the extratropical transition of tropical cyclones. Climate Dynamics. 61(1-2). 65–78. 3 indexed citations
8.
Ma, Po‐Lun, Hailong Wang, Shih‐Yu Wang, et al.. (2022). Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States. Journal of Advances in Modeling Earth Systems. 14(10). 7 indexed citations
9.
Wang, Shih‐Yu, et al.. (2022). On the Changing Cool Season Affecting Rice Growth and Yield in Taiwan. Agronomy. 12(11). 2625–2625. 4 indexed citations
10.
Song, Fengfei, Zhe Feng, L. Ruby Leung, et al.. (2021). Crucial Roles of Eastward Propagating Environments in the Summer MCS Initiation Over the U.S. Great Plains. Journal of Geophysical Research Atmospheres. 126(16). 22 indexed citations
11.
Kim, Hyungjun, Shih‐Yu Wang, Jee‐Hoon Jeong, et al.. (2021). Changes in fire weather climatology under 1.5 °C and 2.0 °C warming. Environmental Research Letters. 16(3). 34058–34058. 20 indexed citations
12.
Zhang, Wei, et al.. (2021). Fewer Troughs, Not More Ridges, Have Led to a Drying Trend in the Western United States. Geophysical Research Letters. 49(1). 19 indexed citations
13.
Wang, Shih‐Yu, et al.. (2021). Recurrent pattern of extreme fire weather in California. Environmental Research Letters. 16(9). 94031–94031. 15 indexed citations
14.
Wang, Shih‐Yu, et al.. (2021). Atmospheric Rivers Impacting Northern California Exhibit a Quasi‐Decadal Frequency. Journal of Geophysical Research Atmospheres. 126(15). 9 indexed citations
15.
Chikamoto, Yoshimitsu, et al.. (2020). El Niño–Southern Oscillation Evolution Modulated by Atlantic Forcing. Journal of Geophysical Research Oceans. 125(8). 44 indexed citations
16.
Chikamoto, Yoshimitsu, et al.. (2020). Pacific decadal oscillation remotely forced by the equatorial Pacific and the Atlantic Oceans. Climate Dynamics. 55(3-4). 789–811. 49 indexed citations
17.
Wang, Shih‐Yu, et al.. (2020). Three Western Pacific Typhoons Strengthened Fire Weather in the Recent Northwest U.S. Conflagration. Geophysical Research Letters. 48(3). 12 indexed citations
18.
Kim, Hyungjun, Gavin D. Madakumbura, Shih‐Yu Wang, et al.. (2019). Flood and heatwave in Japan 2018 and future increase of consecutive compound risk in a warmer world. AGU Fall Meeting Abstracts. 2019. 3 indexed citations
19.
Wang, Shih‐Yu & Robert R. Gillies. (2012). Modern Climatology - Full Text. Digital Commons - USU (Utah State University). 7(9-10). CLXIX–XXIX(626. 1 indexed citations
20.
Wang, Shih‐Yu, et al.. (2010). Optimal wet-furnace machine allocation for daily fab production. 1–4. 2 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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