William E. Chapman

2.2k total citations
48 papers, 710 citations indexed

About

William E. Chapman is a scholar working on Atmospheric Science, Global and Planetary Change and Pulmonary and Respiratory Medicine. According to data from OpenAlex, William E. Chapman has authored 48 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Atmospheric Science, 13 papers in Global and Planetary Change and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in William E. Chapman's work include Meteorological Phenomena and Simulations (14 papers), Climate variability and models (13 papers) and Hydrological Forecasting Using AI (3 papers). William E. Chapman is often cited by papers focused on Meteorological Phenomena and Simulations (14 papers), Climate variability and models (13 papers) and Hydrological Forecasting Using AI (3 papers). William E. Chapman collaborates with scholars based in United States, Germany and Canada. William E. Chapman's co-authors include Peter A. Ward, Luca Delle Monache, Aneesh C. Subramanian, F. Martin Ralph, Lorraine K. Miller, Barnett Zumoff, Ruby T. Senie, T S Croxson, Shang‐Ping Xie and Peter B. Gibson and has published in prestigious journals such as Science, The Journal of Immunology and The Journal of Clinical Endocrinology & Metabolism.

In The Last Decade

William E. Chapman

43 papers receiving 671 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William E. Chapman United States 14 235 235 111 63 52 48 710
Partha S. Bhattacharjee United States 25 284 1.2× 198 0.8× 26 0.2× 53 0.8× 31 0.6× 73 1.7k
Dong‐Soon Kim South Korea 24 79 0.3× 41 0.2× 33 0.3× 34 0.5× 18 0.3× 130 1.7k
Wenli Zhao China 18 102 0.4× 319 1.4× 171 1.5× 50 0.8× 7 0.1× 61 957
Wenjian Zhang China 13 195 0.8× 158 0.7× 27 0.2× 223 3.5× 16 0.3× 36 889
Nand Lal Sharma India 19 156 0.7× 150 0.6× 15 0.1× 107 1.7× 5 0.1× 51 859
Rongman Cai United States 18 298 1.3× 144 0.6× 28 0.3× 49 0.8× 32 0.6× 29 1.5k
Long United States 13 35 0.1× 108 0.5× 11 0.1× 37 0.6× 12 0.2× 122 788
Eviatar Bach United States 11 186 0.8× 173 0.7× 144 1.3× 15 0.2× 9 0.2× 21 674
Tatsuya Mikami Japan 18 135 0.6× 139 0.6× 26 0.2× 37 0.6× 33 0.6× 143 1.4k
Scott M. Brown United States 15 62 0.3× 34 0.1× 20 0.2× 20 0.3× 65 1.3× 52 578

Countries citing papers authored by William E. Chapman

Since Specialization
Citations

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

Fields of papers citing papers by William E. Chapman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William E. Chapman. 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 William E. Chapman. The network helps show where William E. Chapman may publish in the future.

Co-authorship network of co-authors of William E. Chapman

This figure shows the co-authorship network connecting the top 25 collaborators of William E. Chapman. A scholar is included among the top collaborators of William E. Chapman 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 William E. Chapman. William E. Chapman 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.
Chapman, William E. & Judith Berner. (2025). Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections. Geophysical Research Letters. 52(6). 1 indexed citations
2.
Schreck, John S., David John Gagne, Charles Becker, et al.. (2024). Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3(4). 8 indexed citations
3.
Wirz, Christopher D., Julie L. Demuth, Kirsten J. Mayer, et al.. (2024). Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods. Earth and Space Science. 11(7). 2 indexed citations
4.
Mayer, Kirsten J., et al.. (2024). Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability. Geophysical Research Letters. 51(10). 3 indexed citations
5.
Keenlyside, Noel, François Counillon, Alberto Carrassi, et al.. (2023). Supermodeling: Improving Predictions with an Ensemble of Interacting Models. Bulletin of the American Meteorological Society. 104(9). E1670–E1686. 3 indexed citations
6.
Subramanian, Aneesh C., et al.. (2023). Increase in MJO predictability under global warming. Nature Climate Change. 14(1). 68–74. 9 indexed citations
7.
Gibson, Peter B., William E. Chapman, Alphan Altınok, et al.. (2021). Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts. Communications Earth & Environment. 2(1). 93 indexed citations
8.
Chapman, William E., Aneesh C. Subramanian, Luca Delle Monache, Shang‐Ping Xie, & F. Martin Ralph. (2019). Improving Atmospheric River Forecasts With Machine Learning. Geophysical Research Letters. 46(17-18). 10627–10635. 58 indexed citations
9.
Chapman, William E., et al.. (2005). Differentiating between Squamous Cell Carcinoma and Pigmented Squamous Cell Carcinoma. Ear Nose & Throat Journal. 84(12). 766–767. 2 indexed citations
10.
Giede, Christopher, Ants Toi, William E. Chapman, & Barry P. Rosen. (2004). The use of transrectal ultrasound to biopsy pelvic masses in women. Gynecologic Oncology. 95(3). 552–556. 9 indexed citations
11.
Patlas, Michael N., Barry P. Rosen, William E. Chapman, & Stephanie R. Wilson. (2004). Sonographic Diagnosis of Primary Malignant Tumors of the Fallopian Tube. Ultrasound Quarterly. 20(2). 59–64. 14 indexed citations
12.
Schuster, David M., et al.. (2001). Jejunal Diverticular Hemorrhage Localized by Red Blood Cell Scintigraphy. Clinical Nuclear Medicine. 26(11). 936–937. 6 indexed citations
13.
Russell, Kenneth, Celestia S. Higano, Michel A. Boileau, et al.. (1989). Combined 5-fluorouracil and irradiation for the treatment of invasive bladder cancer. International Journal of Radiation Oncology*Biology*Physics. 17. 165–166. 3 indexed citations
14.
Croxson, T S, et al.. (1989). Changes in the Hypothalamic-Pituitary-Gonadal Axis in Human Immunodeficiency Virus-Infected Homosexual Men*. The Journal of Clinical Endocrinology & Metabolism. 68(2). 317–321. 131 indexed citations
15.
Johnson, K J, William E. Chapman, & Peter A. Ward. (1979). Immunopathology of the lung: a review.. PubMed Central. 95(3). 795–844. 30 indexed citations
16.
Chapman, William E.. (1973). Rechargeable Cardiac Pacemaker. Postgraduate Medicine. 54(4). 235–236. 1 indexed citations
17.
Chapman, William E., Richard L. Rapport, F. W. Lancaster, & J. Kiffin Penry. (1972). Critical Evaluation of a Computer-Based Medical Literature Search and Retrieval System. Postgraduate Medicine. 51(5). 47–50. 1 indexed citations
18.
Chapman, William E. & Langdon Gilkey. (1971). Religion and the Scientific Future. Review of Religious Research. 12(3). 195–195. 9 indexed citations
19.
Chapman, William E.. (1971). New Applications of Ultrasound. Postgraduate Medicine. 49(1). 41–42. 18 indexed citations
20.
Chapman, William E., et al.. (1966). Smoking and pregnancy. A statistical study of 5,659 patients.. PubMed. 104(3). 187–187. 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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