Minghuai Wang

7.9k total citations · 2 hit papers
149 papers, 4.9k citations indexed

About

Minghuai Wang is a scholar working on Atmospheric Science, Global and Planetary Change and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Minghuai Wang has authored 149 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 135 papers in Atmospheric Science, 131 papers in Global and Planetary Change and 12 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Minghuai Wang's work include Atmospheric aerosols and clouds (103 papers), Atmospheric chemistry and aerosols (95 papers) and Climate variability and models (58 papers). Minghuai Wang is often cited by papers focused on Atmospheric aerosols and clouds (103 papers), Atmospheric chemistry and aerosols (95 papers) and Climate variability and models (58 papers). Minghuai Wang collaborates with scholars based in China, United States and Israel. Minghuai Wang's co-authors include Xiaohong Liu, S. J. Ghan, Joyce E. Penner, Rune Grand Graversen, Yun Qian, Mikhail Ovchinnikov, Hailong Wang, Hugh Morrison, Kai Zhang and R. C. Easter and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Minghuai Wang

136 papers receiving 4.8k citations

Hit Papers

Aerosol-driven droplet concentrations dominate coverage a... 2019 2026 2021 2023 2019 2022 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Minghuai Wang China 40 4.3k 4.1k 579 388 238 149 4.9k
Guangyu Shi China 35 3.7k 0.9× 3.4k 0.8× 1.2k 2.1× 427 1.1× 267 1.1× 140 4.8k
Xin‐Zhong Liang United States 33 2.3k 0.5× 2.7k 0.7× 524 0.9× 655 1.7× 36 0.2× 108 3.9k
Norbert Kalthoff Germany 32 2.8k 0.7× 2.6k 0.6× 354 0.6× 865 2.2× 105 0.4× 140 3.3k
Johannes Quaas Germany 40 5.1k 1.2× 5.2k 1.3× 507 0.9× 244 0.6× 459 1.9× 154 5.8k
Zavisă Janjić United States 23 5.6k 1.3× 5.2k 1.3× 370 0.6× 1.2k 3.0× 232 1.0× 55 6.4k
Slobodan Ničković Serbia 31 3.5k 0.8× 3.2k 0.8× 679 1.2× 312 0.8× 842 3.5× 76 4.1k
Tian Zhou China 31 1.7k 0.4× 1.7k 0.4× 806 1.4× 503 1.3× 101 0.4× 101 3.0k
Philip Stier United Kingdom 47 7.2k 1.7× 6.8k 1.7× 1.3k 2.3× 358 0.9× 422 1.8× 163 7.7k
T.W. Horst United States 29 2.1k 0.5× 2.0k 0.5× 258 0.4× 1.2k 3.0× 188 0.8× 57 3.2k
Ellsworth J. Welton United States 43 6.1k 1.4× 6.2k 1.5× 767 1.3× 393 1.0× 264 1.1× 144 6.7k

Countries citing papers authored by Minghuai Wang

Since Specialization
Citations

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

Fields of papers citing papers by Minghuai Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minghuai Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Minghuai Wang. A scholar is included among the top collaborators of Minghuai 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 Minghuai Wang. Minghuai 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.
2.
Wang, Minghuai, Xinyi Dong, S. R. Arnold, et al.. (2024). Global modeling of aerosol nucleation with a semi-explicit chemical mechanism for highly oxygenated organic molecules (HOMs). Atmospheric chemistry and physics. 24(19). 11365–11389. 1 indexed citations
3.
Yu, Zechen, Kan Huang, Xuguang Chi, et al.. (2023). Impact of mineral dust photocatalytic heterogeneous chemistry on the formation of the sulfate and nitrate: A modelling study over East Asia. Atmospheric Environment. 316. 120166–120166. 5 indexed citations
4.
5.
Zhao, Delong, Minghuai Wang, Daniel Rosenfeld, et al.. (2023). Aircraft observation of fast initiation of mixed phase precipitation in convective cloud over the Tibetan Plateau. Atmospheric Research. 285. 106627–106627. 4 indexed citations
6.
Dong, Xinyi, Minghuai Wang, L. K. Emmons, et al.. (2023). Modeling the Air Pollution and Aerosol‐PBL Interactions Over China Using a Variable‐Resolution Global Model. Journal of Geophysical Research Atmospheres. 128(22). 8 indexed citations
7.
Zhang, Shipeng, Philip Stier, Guy Dagan, Chen Zhou, & Minghuai Wang. (2023). Publisher Correction: Sea surface warming patterns drive hydrological sensitivity uncertainties. Nature Climate Change. 13(9). 997–997. 2 indexed citations
8.
Dong, Xinyi, et al.. (2023). A Plant Species Dependent Wildfire Black Carbon Emission Inventory in Northern Eurasia. Geophysical Research Letters. 50(18).
10.
Zhu, Yannian, et al.. (2023). Robust Susceptibility of Cloud Cover and Radiative Effects to Biases in Retrieved Droplet Concentrations. Journal of Geophysical Research Atmospheres. 128(22). 4 indexed citations
11.
Wang, Minghuai, et al.. (2022). An Updated CLUBB PDF Closure Scheme to Improve Low Cloud Simulation in CAM6. Journal of Advances in Modeling Earth Systems. 14(12). 7 indexed citations
12.
Dong, Xinyi, Minghuai Wang, L. K. Emmons, et al.. (2021). Analysis of secondary organic aerosol simulation bias in the Community Earth System Model (CESM2.1). Atmospheric chemistry and physics. 21(10). 8003–8021. 15 indexed citations
13.
Wang, Minghuai, et al.. (2020). Strong Precipitation Suppression by Aerosols in Marine Low Clouds. Geophysical Research Letters. 47(7). 19 indexed citations
14.
Zhang, Rudong, Hailong Wang, Qiang Fu, et al.. (2018). Local Radiative Feedbacks Over the Arctic Based on Observed Short‐Term Climate Variations. Geophysical Research Letters. 45(11). 5761–5770. 32 indexed citations
16.
Takahashi, Hanii, Matthew Lebsock, Kentaroh Suzuki, Graeme L. Stephens, & Minghuai Wang. (2017). An investigation of microphysics and subgrid‐scale variability in warm‐rain clouds using the A‐Train observations and a multiscale modeling framework. Journal of Geophysical Research Atmospheres. 122(14). 7493–7504. 22 indexed citations
17.
Fu, Rong, Muhammad Shaikh, S. J. Ghan, et al.. (2017). Influence of Superparameterization and a Higher‐Order Turbulence Closure on Rainfall Bias Over Amazonia in Community Atmosphere Model Version 5. Journal of Geophysical Research Atmospheres. 122(18). 9879–9902. 12 indexed citations
18.
Storer, R. L., et al.. (2015). Parameterizing deep convection using the assumed probability density function method. Geoscientific model development. 8(1). 1–19. 36 indexed citations
19.
Wang, Hailong, R. C. Easter, Minghuai Wang, et al.. (2013). Sensitivity of remote aerosol distributions to representation of cloud–aerosol interactions in a global climate model. Geoscientific model development. 6(3). 765–782. 144 indexed citations
20.
Zhang, Kai, Xiaohong Liu, Minghuai Wang, et al.. (2013). Evaluating and constraining ice cloud parameterizations in CAM5 using aircraft measurements from the SPARTICUS campaign. Atmospheric chemistry and physics. 13(9). 4963–4982. 39 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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