Stuart Webster

2.9k total citations
60 papers, 1.5k citations indexed

About

Stuart Webster is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, Stuart Webster has authored 60 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Atmospheric Science, 48 papers in Global and Planetary Change and 6 papers in Oceanography. Recurrent topics in Stuart Webster's work include Climate variability and models (45 papers), Meteorological Phenomena and Simulations (45 papers) and Tropical and Extratropical Cyclones Research (19 papers). Stuart Webster is often cited by papers focused on Climate variability and models (45 papers), Meteorological Phenomena and Simulations (45 papers) and Tropical and Extratropical Cyclones Research (19 papers). Stuart Webster collaborates with scholars based in United Kingdom, Australia and United States. Stuart Webster's co-authors include A. R. Brown, C. P. Jones, Simon Vosper, Cathryn E. Birch, Douglas J. Parker, Andrew Orr, Andy Brown, Adrian Lock, Adrian J. Matthews and Simon Peatman and has published in prestigious journals such as Journal of Climate, Geophysical Research Letters and Journal of the Atmospheric Sciences.

In The Last Decade

Stuart Webster

59 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stuart Webster United Kingdom 21 1.3k 1.1k 194 146 76 60 1.5k
Martin B. Andrews United Kingdom 19 1.2k 0.9× 1.3k 1.2× 382 2.0× 64 0.4× 40 0.5× 39 1.5k
Jordan G. Powers United States 18 1.0k 0.8× 655 0.6× 132 0.7× 49 0.3× 22 0.3× 35 1.1k
Keith M. Hines United States 26 2.3k 1.7× 1.8k 1.6× 316 1.6× 21 0.1× 32 0.4× 42 2.4k
Daniel P. Grosvenor United Kingdom 26 1.8k 1.4× 1.7k 1.5× 111 0.6× 29 0.2× 71 0.9× 43 2.0k
Ándrás Horányi United Kingdom 13 668 0.5× 639 0.6× 187 1.0× 100 0.7× 81 1.1× 27 956
Lejiang Yu China 19 865 0.7× 790 0.7× 279 1.4× 9 0.1× 53 0.7× 90 1.1k
Ryo Oyama Japan 8 1.4k 1.0× 1.3k 1.2× 442 2.3× 53 0.4× 62 0.8× 14 1.6k
Forest Cannon United States 19 966 0.7× 878 0.8× 74 0.4× 13 0.1× 55 0.7× 43 1.2k
Ron McTaggart‐Cowan Canada 24 1.6k 1.2× 1.4k 1.2× 348 1.8× 18 0.1× 97 1.3× 51 1.7k
Neil Adams Australia 12 450 0.3× 267 0.2× 108 0.6× 23 0.2× 46 0.6× 21 633

Countries citing papers authored by Stuart Webster

Since Specialization
Citations

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

Fields of papers citing papers by Stuart Webster

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stuart Webster

This figure shows the co-authorship network connecting the top 25 collaborators of Stuart Webster. A scholar is included among the top collaborators of Stuart Webster 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 Stuart Webster. Stuart Webster 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.
Prasanna, Venkatraman, Anurag Dipankar, Jianyu Liu, et al.. (2025). Evaluating SINGV ‐ RCM for Long‐Term High‐Resolution Climate Simulations Over Southeast Asia. International Journal of Climatology. 45(11).
2.
Hansen, Nicolaj, Andrew Orr, Xun Zou, et al.. (2024). The importance of cloud properties when assessing surface melting in an offline-coupled firn model over Ross Ice shelf, West Antarctica. ˜The œcryosphere. 18(6). 2897–2916. 2 indexed citations
3.
Prasanna, Venkatraman, Anurag Dipankar, Jianyu Liu, et al.. (2024). SINGV-RCM: the convection-permitting regional climate model for Singapore. Climate Dynamics. 62(6). 5129–5141. 2 indexed citations
4.
Reeder, Michael J., et al.. (2024). Synoptic and Mesoscale Dynamics of Cold Surges Over the South China Sea and Their Control on Extreme Rainfall. Journal of Geophysical Research Atmospheres. 129(16). 2 indexed citations
5.
Klein, Stephen A., Hsi‐Yen Ma, Kwinten Van Weverberg, et al.. (2023). Summertime Near‐Surface Temperature Biases Over the Central United States in Convection‐Permitting Simulations. Journal of Geophysical Research Atmospheres. 128(22). 6 indexed citations
6.
Sánchez, Claudio, et al.. (2023). Impact of Domain Size on Tropical Precipitation Within Explicit Convection Simulations. Geophysical Research Letters. 50(17). 7 indexed citations
7.
Semeena, V. S., Cornelia Klein, Christopher M. Taylor, & Stuart Webster. (2023). Impact of land surface processes on convection over West Africa in convection‐permitting ensemble forecasts: A case study using the MOGREPS ensemble. Atmospheric Science Letters. 24(8). 2 indexed citations
8.
Matthews, Adrian J., et al.. (2023). Extreme precipitation at Padang, Sumatra triggered by convectively coupled Kelvin waves. Quarterly Journal of the Royal Meteorological Society. 149(755). 2281–2300. 11 indexed citations
9.
10.
Menon, Arathy, Andrew G. Turner, Ambrogio Volonté, et al.. (2021). The role of mid‐tropospheric moistening and land‐surface wetting in the progression of the 2016 Indian monsoon. Quarterly Journal of the Royal Meteorological Society. 148(747). 3033–3055. 3 indexed citations
11.
Mottram, Ruth, Nicolaj Hansen, Christoph Kittel, et al.. (2021). What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates. ˜The œcryosphere. 15(8). 3751–3784. 86 indexed citations
12.
Savage, Nick, et al.. (2021). Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution. Scientific Data. 8(1). 62–62. 10 indexed citations
13.
Jucker, Martin, Todd P. Lane, Claire Vincent, et al.. (2020). Locally forced convection in subkilometre‐scale simulations with the Unified Model and WRF. Quarterly Journal of the Royal Meteorological Society. 146(732). 3450–3465. 8 indexed citations
14.
Stratton, R. A., C. A. Senior, Simon Vosper, et al.. (2018). A Pan-African Convection-Permitting Regional Climate Simulation with the Met Office Unified Model: CP4-Africa. Journal of Climate. 31(9). 3485–3508. 114 indexed citations
15.
Jayakumar, A., E. N. Rajagopal, Ian Boutle, et al.. (2017). An operational fog prediction system for Delhi using the 330 m Unified Model. Atmospheric Science Letters. 19(1). 27 indexed citations
16.
Webster, Stuart. (2015). Progress in tropical regional modelling. 1 indexed citations
17.
Webster, Stuart. (2014). Assessing the sensitivity of Unified Model simulations of Super-typhoon Haiyan using two dynamical cores. 1 indexed citations
18.
Vosper, Simon, et al.. (2013). High resolution modelling of valley cold pools. Atmospheric Science Letters. 14(3). 193–199. 32 indexed citations
19.
Webster, Stuart, et al.. (2012). Assessing the sensitivity to horizontal resolution of Unified Model simulations of Hurricane Katrina. AGU Fall Meeting Abstracts. 2012. 1 indexed citations
20.
Webster, Stuart, et al.. (2008). A high‐resolution modelling case study of a severe weather event over New Zealand. Atmospheric Science Letters. 9(3). 119–128. 29 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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