Sukanta Basu is a Professor of Empire Innovation at the University at Albany. He is affiliated with the Atmospheric Sciences Research Center (ASRC) and the Environmental and Sustainable Engineering (ESE) Department. Dr. Basu received a Bachelor of Technology (Honors) degree (1998) in Civil Engineering from the Indian Institute of Technology, Kharagpur, India; a Master of Applied Science degree (2000) in Environmental Engineering from the University of Windsor, Canada; and a Ph.D. degree (2004) in Civil Engineering from the University of Minnesota. His previous appointments were at Texas Tech University, North Carolina State University, and Delft University of Technology (the Netherlands).
Dr. Basu’s current research interests include atmospheric boundary layer processes, atmospheric optics, machine learning, numerical weather prediction, renewable energy, and turbulence modeling. Over the past twenty years, his research has been funded by the US National Science Foundation (including an NSF-CAREER award), the US Department of Defense, the US Department of Energy, the Texas Advanced Research Program, the Carbon Trust (UK), TKI Wind op Zee (the Netherlands), Horizon Europe, and other organizations. Dr. Basu’s research has been disseminated through over 65 peer-reviewed journal publications.
Dr. Basu was an elected member of the American Meteorological Society (AMS) Boundary Layers and Turbulence Committee during 2009-11. He coordinated the met-ocean program line of the Dutch GROW consortium during 2018-20. He is also one of the co-authors of the Netherlands’ Long-Term Offshore Wind R&D Agenda, published in 2019. Dr. Basu is an associate editor of the Journal of Geophysical Research – Atmosphere and Wind Energy Science. In addition, he serves on the editorial boards of Boundary-Layer Meteorology, Wind Energy and Environmental Fluid Mechanics journals.
Dr. Basu enjoys participating in various machine learning competitions. He is a contributor to the Kaggle platform. In 2021, he ranked first in the global SHELL.ai hackathon on solar energy forecasting; he came in third in the NASA Airathon competition (involving NOx prediction) in 2022.
Ph.D. in Civil Engineering, University of Minnesota, USA [2000-2004]
M.A.Sc. in Environmental Engineering, University of Windsor, Canada [1999-2000]
B. Tech. (Honors) in Civil Engineering, Indian Institute of Technology (IIT) – Kharagpur, India [1994-1998]
Atmospheric boundary layer processes; atmospheric optics; machine learning; numerical weather prediction; renewable energy; and turbulence modeling.
- Dr. Basu's Research
- Atmospheric Turbulence Research at SUNY Albany
- ResearchGate Profile - Publickations and Citiations
- Google Scholar Citations
Kartal, S., Basu, S., and Watson, S. J. (2023). A decision tree-based measure-correlate-predict approach for peak wind gust estimation from a global reanalysis dataset. Wind Energy Science Discussions, https://wes.copernicus.org/preprints/wes-2023-30/
Basu, S., and Holtslag, A. A. M. (2022a). A novel approach for deriving the stable boundary layer height and eddy viscosity profiles from the Ekman equations. Boundary-Layer Meteorology, https://doi.org/10. 1007/s10546-022-00757-y.
Basu, S., and Holtslag, A. A. M. (2022b). Revisiting and revising Tatarskii’s formulation for the temperature structure parameter (CT2 ) in atmospheric flows. Environmental Fluid Mechanics, 22, 1107–1119.
Li, B., Basu, S., & Watson, S. J. (2022). Automated identification of “Dunkelflaute” events: A convolutional neural network-based autoencoder approach. Artificial Intelligence for the Earth Systems, https://journals.ametsoc.org/view/journals/aies/1/4/AIES-D-22-0015.1.xml.
Veers, P., Dykes, K., Basu, S., Bianchini, A., Clifton, A., Green, P., Holttinen, H., Kitzing, L., Kosovic, B., Lundquist, J. K., Meyers, J., O’Malley, M., Shaw, W. J., and Straw, B. (2022). Grand challenges: Wind energy research needs for a global energy transition. Wind Energy Science, 7, 2491–2496.
Basu, S., DeMarco, A. W., and He, P. (2021). On the dissipation rate of temperature fluctuations in stably stratified flows. Environmental Fluid Mechanics, 21, 63–82.
Basu, S., He, P., and DeMarco, A. W. (2021). Parameterizing the energy dissipation rate in stably stratified flows. Boundary-Layer Meteorology, 178, 167–184.
Basu, S., and Holtslag, A. A. M. (2021). Turbulent Prandtl number and characteristic length scales in stably stratified flows: Steady-state analytical solutions. Environmental Fluid Mechanics, 21, 1273–1302.
Cheneka, B. R., Watson, S. J., and Basu, S. (2021). Associating synoptic-scale weather patterns with aggregated offshore wind power production and ramps. Energies, 14, 3903.
Li, B., Basu, S., Watson, S. J., and Russchenberg, H. W. J. (2021a). A brief climatology of Dunkelflaute events over and surrounding the North and Baltic sea areas. Energies, 14, 6508.
Li, B., Basu, S., Watson, S. J., and Russchenberg, H. W. J. (2021b). Mesoscale modeling of a ‘Dunkelflaute’ event. Wind Energy, 24, 5–23.
Lu, N.-Y., Manuel, L., Hawbecker, P. H., and Basu, S. (2021). A simulation study on risks to wind turbine arrays from thunderstorm downbursts in different atmospheric stability conditions. Energies, 14, 5407.