Sukanta Basu

3.9k total citations
155 papers, 2.7k citations indexed

About

Sukanta Basu is a scholar working on Atmospheric Science, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Sukanta Basu has authored 155 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Atmospheric Science, 59 papers in Environmental Engineering and 53 papers in Global and Planetary Change. Recurrent topics in Sukanta Basu's work include Meteorological Phenomena and Simulations (64 papers), Wind and Air Flow Studies (50 papers) and Climate variability and models (29 papers). Sukanta Basu is often cited by papers focused on Meteorological Phenomena and Simulations (64 papers), Wind and Air Flow Studies (50 papers) and Climate variability and models (29 papers). Sukanta Basu collaborates with scholars based in United States, Netherlands and Germany. Sukanta Basu's co-authors include Fernando Porté‐Agel, A.A.M. Holtslag, B.J.H. van de Wiel, Efi Foufoula‐Georgiou, Alfred Fettweis, Lance Manuel, Gert‐Jan Steeneveld, Peter Baas, N.K. Bose and A.F. Moene and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, IEEE Transactions on Automatic Control and Proceedings of the IEEE.

In The Last Decade

Sukanta Basu

143 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sukanta Basu United States 27 1.4k 1.1k 1.1k 623 491 155 2.7k
Adrian Sandu United States 35 2.4k 1.7× 1.6k 1.5× 781 0.7× 840 1.3× 133 0.3× 228 4.8k
Philip Jonathan United Kingdom 29 612 0.4× 732 0.7× 512 0.5× 148 0.2× 97 0.2× 148 2.8k
John Michalakes United States 25 2.2k 1.6× 1.7k 1.6× 1.3k 1.2× 747 1.2× 1.5k 3.1× 50 4.3k
Adam H. Monahan Canada 30 1.7k 1.2× 1.8k 1.6× 522 0.5× 113 0.2× 289 0.6× 131 2.9k
Roddam Narasimha India 29 456 0.3× 658 0.6× 891 0.8× 2.6k 4.1× 1.3k 2.7× 186 3.8k
Themistoklis P. Sapsis United States 32 451 0.3× 260 0.2× 312 0.3× 967 1.6× 169 0.3× 128 3.0k
Antonio Iodice Italy 33 769 0.5× 450 0.4× 1.1k 1.0× 169 0.3× 2.0k 4.0× 247 3.5k
Roland Potthast Germany 32 962 0.7× 845 0.8× 221 0.2× 113 0.2× 60 0.1× 131 3.3k
D. H. Staelin United States 33 2.6k 1.8× 1.7k 1.6× 512 0.5× 110 0.2× 408 0.8× 159 4.5k
C. E. Grosch United States 29 551 0.4× 272 0.3× 229 0.2× 1.7k 2.7× 505 1.0× 93 2.7k

Countries citing papers authored by Sukanta Basu

Since Specialization
Citations

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

Fields of papers citing papers by Sukanta Basu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sukanta Basu

This figure shows the co-authorship network connecting the top 25 collaborators of Sukanta Basu. A scholar is included among the top collaborators of Sukanta Basu 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 Sukanta Basu. Sukanta Basu 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.
Basu, Sukanta, et al.. (2024). Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling. Journal of Renewable and Sustainable Energy. 16(6). 3 indexed citations
2.
3.
Watson, Simon, et al.. (2023). Quantifying the impacts of synoptic weather patterns on North Sea wind power production and ramp events under a changing climate. Research Repository (Delft University of Technology). 4. 100113–100113. 3 indexed citations
4.
Basu, Sukanta, et al.. (2023). A multi-physics ensemble modeling framework for reliable C2n estimation. Research Repository (Delft University of Technology). 12777. 24–24. 2 indexed citations
5.
Basu, Sukanta, et al.. (2023). Π-ML: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer. Optics Letters. 48(17). 4484–4484. 3 indexed citations
6.
Basu, Sukanta, et al.. (2023). A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset. Wind energy science. 8(10). 1533–1551. 3 indexed citations
7.
Basu, Sukanta, et al.. (2020). Automated classification of simulated wind field patterns from multiphysics ensemble forecasts. Wind Energy. 23(4). 898–914. 8 indexed citations
8.
Watson, Simon, et al.. (2020). A simple methodology to detect and quantify wind power ramps. Wind energy science. 5(4). 1731–1741. 5 indexed citations
9.
Basu, Sukanta, et al.. (2018). Investigating the impact of atmospheric stability on thunderstorm outflow winds and turbulence. Wind energy science. 3(1). 203–219. 9 indexed citations
10.
Basu, Sukanta, et al.. (2017). Estimating higher-order structure functions from geophysical turbulence time series: Confronting the curse of the limited sample size. Physical review. E. 95(5). 52114–52114. 2 indexed citations
11.
Horváth, Ákos, et al.. (2015). High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data. Geoscientific model development. 8(8). 2645–2653. 17 indexed citations
12.
Basu, Sukanta, et al.. (2015). Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range. Physical Review E. 92(3). 33005–33005. 3 indexed citations
13.
Basu, Sukanta, et al.. (2015). Direct numerical simulation of intermittent turbulence under stably stratified conditions. Nonlinear processes in geophysics. 22(4). 447–471. 16 indexed citations
14.
Basu, Sukanta, et al.. (2013). On the Periodicity of Atmospheric von K\'{a}rm\'{a}n Vortex Streets. Bulletin of the American Physical Society. 1 indexed citations
15.
Holtslag, A.A.M., et al.. (2012). Overview of the GEWEX Atmospheric Boundary Layer Study (GABLS). Socio-Environmental Systems Modeling. 11–23. 4 indexed citations
16.
Basu, Sukanta, et al.. (2010). Local scaling characteristics of Antarctic surface layer turbulence. ˜The œcryosphere. 4(3). 325–331. 3 indexed citations
17.
Basu, Sukanta. (2009). Stable boundary layers with low-level jets: what did we learn from the LES intercomparison within GABLS3?. AGUFM. 2009. 1 indexed citations
18.
Basu, Sukanta, Gert‐Jan Steeneveld, A.A.M. Holtslag, & F. C. Bosveld. (2008). Large-eddy simulation intercomparison case setup for GABLS3. Socio-Environmental Systems Modeling. 1 indexed citations
19.
Vinuesa, J.‐F., Sukanta Basu, & Stefano Galmarini. (2007). The diurnal evolution of 222 Rn and its progeny in the atmospheric boundary layer during the Wangara experiment. Atmospheric chemistry and physics. 7(18). 5003–5019. 22 indexed citations
20.
Basu, Sukanta, Efi Foufoula‐Georgiou, & Fernando Porté‐Agel. (2004). Synthetic turbulence, fractal interpolation, and large-eddy simulation. Physical Review E. 70(2). 26310–26310. 50 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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