Duk-Jin Won

1.5k total citations
9 papers, 340 citations indexed

About

Duk-Jin Won is a scholar working on Atmospheric Science, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Duk-Jin Won has authored 9 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Atmospheric Science, 6 papers in Global and Planetary Change and 3 papers in Environmental Engineering. Recurrent topics in Duk-Jin Won's work include Meteorological Phenomena and Simulations (8 papers), Climate variability and models (6 papers) and Tropical and Extratropical Cyclones Research (3 papers). Duk-Jin Won is often cited by papers focused on Meteorological Phenomena and Simulations (8 papers), Climate variability and models (6 papers) and Tropical and Extratropical Cyclones Research (3 papers). Duk-Jin Won collaborates with scholars based in South Korea and United States. Duk-Jin Won's co-authors include Dale Barker, Qingnong Xiao, Ying‐Hwa Kuo, Xin Zhang, Jimy Dudhia, Xiaoyan Zhang, Hui-Chuan Lin, Wei Huang, Zaizhong Ma and John Michalakes and has published in prestigious journals such as Monthly Weather Review, Bulletin of the American Meteorological Society and Journal of the Meteorological Society of Japan Ser II.

In The Last Decade

Duk-Jin Won

6 papers receiving 325 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Duk-Jin Won South Korea	4	317	260	54	42	19	9	340
S. Rugg United States	4	456 1.4×	406 1.6×	32 0.6×	57 1.4×	15 0.8×	8	482
Jason E. Nachamkin United States	11	334 1.1×	289 1.1×	34 0.6×	62 1.5×	17 0.9×	26	358
Chong Wu China	9	393 1.2×	294 1.1×	38 0.7×	58 1.4×	18 0.9×	33	447
M. Das Gupta India	11	323 1.0×	291 1.1×	85 1.6×	32 0.8×	6 0.3×	32	379
Teresa Valkonen Norway	10	251 0.8×	184 0.7×	28 0.5×	18 0.4×	10 0.5×	15	266
Munn Vinayak Shukla India	9	183 0.6×	166 0.6×	22 0.4×	28 0.7×	16 0.8×	28	223
Hyo‐Jong Song South Korea	11	296 0.9×	295 1.1×	55 1.0×	52 1.2×	4 0.2×	39	341
Saji Mohandas India	13	322 1.0×	309 1.2×	51 0.9×	67 1.6×	5 0.3×	37	374
Alexis Doerenbecher France	9	165 0.5×	151 0.6×	37 0.7×	14 0.3×	14 0.7×	16	192

Countries citing papers authored by Duk-Jin Won

Since Specialization

Citations

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

Fields of papers citing papers by Duk-Jin Won

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Duk-Jin Won. 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 Duk-Jin Won. The network helps show where Duk-Jin Won may publish in the future.

Co-authorship network of co-authors of Duk-Jin Won

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

All Works

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9 of 9 papers shown

Kim, Man‐Hae, et al.. (2024). Light-absorption Properties of Fresh Biomass Burning Aerosols Observed at the Anmyeondo Global Atmosphere Watch (GAW) Regional Station: A Case Study in April 2023. Journal of Korean Society for Atmospheric Environment. 40(6). 614–627.

Won, Duk-Jin, et al.. (2017). Performance Assessment of Weekly Ensemble Prediction Data at Seasonal Forecast System with High Resolution. 27(3). 261–276. 1 indexed citations

Son, Seok‐Woo, et al.. (2016). Assessment of Stratospheric Prediction Skill of the GloSea5 Hindcast Experiment. Atmosphere. 26(1). 203–214. 2 indexed citations

Seo, Eunkyo, Myong‐In Lee, Jee‐Hoon Jeong, Hyun‐Suk Kang, & Duk-Jin Won. (2016). Improvement of Soil Moisture Initialization for a Global Seasonal Forecast System. Atmosphere. 26(1). 35–45. 1 indexed citations

Kim, Sunyong, Hyeong‐Seog Kim, Seung‐Ki Min, et al.. (2015). Intra-winter atmospheric circulation changes over East Asia and North Pacific associated with ENSO in a seasonal prediction model. Asia-Pacific Journal of Atmospheric Sciences. 51(1). 49–60. 10 indexed citations

Kim, Sung-Min, et al.. (2011). Development of Tools for calculating Forecast Sensitivities to the Initial Condition in the Korea Meteorological Administration (KMA) Unified Model (UM). 21(2). 163–172. 3 indexed citations

Xiao, Qingnong, Eunha Lim, Duk-Jin Won, et al.. (2008). Doppler Radar Data Assimilation in KMA's Operational Forecasting. Bulletin of the American Meteorological Society. 89(1). 39–44. 35 indexed citations

Huang, Xiang‐Yu, Qingnong Xiao, Dale Barker, et al.. (2008). Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results. Monthly Weather Review. 137(1). 299–314. 256 indexed citations

Xiao, Qingnong, Ying‐Hwa Kuo, Ying Zhang, Dale Barker, & Duk-Jin Won. (2006). A Tropical Cyclone Bogus Data Assimilation Scheme in the MM5 3D-Var System and Numerical Experiments with Typhoon Rusa (2002) Near Landfall. Journal of the Meteorological Society of Japan Ser II. 84(4). 671–689. 32 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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