Jebb Q. Stewart

688 total citations
16 papers, 400 citations indexed

About

Jebb Q. Stewart is a scholar working on Atmospheric Science, Global and Planetary Change and Artificial Intelligence. According to data from OpenAlex, Jebb Q. Stewart has authored 16 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Atmospheric Science, 7 papers in Global and Planetary Change and 2 papers in Artificial Intelligence. Recurrent topics in Jebb Q. Stewart's work include Meteorological Phenomena and Simulations (11 papers), Climate variability and models (6 papers) and Atmospheric and Environmental Gas Dynamics (2 papers). Jebb Q. Stewart is often cited by papers focused on Meteorological Phenomena and Simulations (11 papers), Climate variability and models (6 papers) and Atmospheric and Environmental Gas Dynamics (2 papers). Jebb Q. Stewart collaborates with scholars based in United States and United Kingdom. Jebb Q. Stewart's co-authors include Vladimir M. Krasnopolsky, Sid‐Ahmed Boukabara, Ross N. Hoffman, Imme Ebert‐Uphoff, C. David Whiteman, W. James Steenburgh, Xindi Bian, Ryan Lagerquist, S. I. Gutman and Tracy Lorraine Smith and has published in prestigious journals such as Monthly Weather Review, Bulletin of the American Meteorological Society and Journal of Atmospheric and Oceanic Technology.

In The Last Decade

Jebb Q. Stewart

15 papers receiving 389 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jebb Q. Stewart United States	8	248	198	103	94	84	16	400
Jan Saynisch‐Wagner Germany	11	140 0.6×	122 0.6×	191 1.9×	62 0.7×	108 1.3×	29	414
Di Xian China	9	244 1.0×	142 0.7×	63 0.6×	57 0.6×	67 0.8×	19	336
P. Ciotti Italy	13	373 1.5×	188 0.9×	101 1.0×	215 2.3×	147 1.8×	62	518
Brett Candy United Kingdom	14	483 1.9×	359 1.8×	110 1.1×	80 0.9×	81 1.0×	25	573
Mitch Goldberg United States	12	450 1.8×	376 1.9×	35 0.3×	175 1.9×	48 0.6×	43	565
Christopher Irrgang Germany	11	127 0.5×	126 0.6×	196 1.9×	23 0.2×	54 0.6×	31	406
Hwan‐Jin Song South Korea	16	579 2.3×	529 2.7×	108 1.0×	33 0.4×	45 0.5×	43	713
Evan Manning United States	10	362 1.5×	339 1.7×	46 0.4×	66 0.7×	48 0.6×	48	448
V. Mattioli Italy	12	360 1.5×	209 1.1×	81 0.8×	194 2.1×	85 1.0×	59	472
Laurent Barthès France	15	494 2.0×	329 1.7×	68 0.7×	172 1.8×	172 2.0×	38	696

Countries citing papers authored by Jebb Q. Stewart

Since Specialization

Citations

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

Fields of papers citing papers by Jebb Q. Stewart

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jebb Q. Stewart. 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 Jebb Q. Stewart. The network helps show where Jebb Q. Stewart may publish in the future.

Co-authorship network of co-authors of Jebb Q. Stewart

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

All Works

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16 of 16 papers shown

Ebert‐Uphoff, Imme, et al.. (2025). A Comparison of AI Weather Prediction and Numerical Weather Prediction Models for 1–7-Day Precipitation Forecasts. Weather and Forecasting. 40(4). 561–575. 2 indexed citations

Ebert‐Uphoff, Imme, John S. Schreck, María J. Molina, et al.. (2025). Measuring Sharpness of AI-Generated Meteorological Imagery. 1 indexed citations

Ebert‐Uphoff, Imme, et al.. (2024). Accelerating Community-Wide Evaluation of AI Models for Global Weather Prediction by Facilitating Access to Model Output. Bulletin of the American Meteorological Society. 106(1). E68–E76.

Frolov, Sergey, et al.. (2024). Integration of Emerging Data-Driven Models into the NOAA Research-to-Operations Pipeline for Numerical Weather Prediction. Bulletin of the American Meteorological Society. 106(2). E430–E437. 1 indexed citations

Lagerquist, Ryan, David D. Turner, Imme Ebert‐Uphoff, & Jebb Q. Stewart. (2023). Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity. Journal of Atmospheric and Oceanic Technology. 40(11). 1407–1432. 5 indexed citations

McGovern, Amy, Ann Bostrom, Phillip Davis, et al.. (2022). NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Bulletin of the American Meteorological Society. 103(7). E1658–E1668. 11 indexed citations

Lagerquist, Ryan, et al.. (2021). Using deep learning to emulate and accelerate a radiative-transfer model. Journal of Atmospheric and Oceanic Technology. 33 indexed citations

Lagerquist, Ryan, et al.. (2021). Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review. 149(12). 3897–3921. 31 indexed citations

Boukabara, Sid‐Ahmed, et al.. (2020). Realizing the Benefits of AI across the Numerical Weather Prediction Value Chain. Bulletin of the American Meteorological Society. 101(1). 29–33. 4 indexed citations

10.

Boukabara, Sid‐Ahmed, Vladimir M. Krasnopolsky, Stephen G. Penny, et al.. (2020). Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences. Bulletin of the American Meteorological Society. 102(5). E1016–E1032. 50 indexed citations

11.

Stewart, Jebb Q., et al.. (2019). The Need for HPC for Deep Learning with Real-Time Satellite Observations. 1 indexed citations

12.

Boukabara, Sid‐Ahmed, et al.. (2019). Artificial Intelligence May Be Key to Better Weather Forecasts. Eos. 100. 9 indexed citations

13.

Boukabara, Sid‐Ahmed, et al.. (2019). Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges. Bulletin of the American Meteorological Society. 100(12). ES473–ES491. 73 indexed citations

14.

Stewart, Jebb Q., et al.. (2018). Machine Learning: Defining Worldwide Cyclone Labels for Training. 753–760. 7 indexed citations

15.

Gutman, S. I., et al.. (2004). Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts. Journal of the Meteorological Society of Japan Ser II. 82(1B). 351–360. 104 indexed citations

16.

Stewart, Jebb Q., C. David Whiteman, W. James Steenburgh, & Xindi Bian. (2002). A Climatological Study of Thermally Driven Wind Systems of the U.S. Intermountain West. Bulletin of the American Meteorological Society. 83(5). 699–708. 68 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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