Machine learning | Sukanta Basu

2026

OTProf: estimating high-resolution profiles of optical turbulence (\(C_n^2\)) from reanalysis using deep learning

M Pierzyna, S Basu, and R Saathof

Artificial Intelligence for the Earth Systems, 2026

Under review

@article{pierzyna2026AIES_ml,
  title = {{OTProf}: estimating high-resolution profiles of optical turbulence (\(C_n^2\)) from reanalysis using deep learning},
  author = {Pierzyna, M and Basu, S and Saathof, R},
  journal = {Artificial Intelligence for the Earth Systems},
  year = {2026},
  note = {Under review}
}

S2G-DI: a deep learning framework for generating high-resolution wind gust fields from sparse Mesonet observations

H Baki, M Pierzyna, and S Basu

Artificial Intelligence for the Earth Systems, 2026

Early Online Release

DOI Bib Code

@article{baki2026AIES_ml,
  title = {{S2G-DI}: a deep learning framework for generating high-resolution wind gust fields from sparse Mesonet observations},
  author = {Baki, H and Pierzyna, M and Basu, S},
  journal = {Artificial Intelligence for the Earth Systems},
  year = {2026},
  doi = {10.1175/AIES-D-25-0111.1},
  note = {Early Online Release}
}

Leveraging deep learning-based foundation models for optical turbulence (\(C_n^2\)) estimation under data scarcity

S Basu

Applied Optics, 2026

Editor’s Pick, Applied Optics DOI Bib Code

true

@article{basu2026AO_ml,
  title = {Leveraging deep learning-based foundation models for optical turbulence (\(C_n^2\)) estimation under data scarcity},
  author = {Basu, S},
  journal = {Applied Optics},
  year = {2026},
  doi = {10.1364/ao.585045},
  volume = {65},
  pages = {2497--2504},
}

2025

OTCliM: generating a near-surface climatology of optical turbulence strength (\(C_n^2\)) using gradient boosting

M Pierzyna, S Basu, and R Saathof

Artificial Intelligence for the Earth Systems, 2025

DOI Bib

@article{pierzyna2025AIES_ml,
  title = {{OTCliM}: generating a near-surface climatology of optical turbulence strength (\(C_n^2\)) using gradient boosting},
  author = {Pierzyna, M and Basu, S and Saathof, R},
  journal = {Artificial Intelligence for the Earth Systems},
  year = {2025},
  doi = {10.1175/aies-d-24-0076.1},
  volume = {4},
  pages = {e240076},
}

Analysis of atmospheric turbulence dynamics during the total solar eclipse with AI-based sensing

M Vorontsov, E Polnau, S Hammel, and 2 more authors

Journal of the Optical Society of America A, 2025

DOI Bib

@article{vorontsov2025JOSA_ml,
  title = {Analysis of atmospheric turbulence dynamics during the total solar eclipse with {AI}-based sensing},
  author = {Vorontsov, M and Polnau, E and Hammel, S and Basu, S and Hettiarachchi, D L N},
  journal = {Journal of the Optical Society of America A},
  year = {2025},
  doi = {10.1364/josaa.563583},
  volume = {42},
  pages = {1112--1119},
}

2024

Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet

H Baki and S Basu

Environmental Data Science, 2024

DOI Bib Code

@article{baki2024EDS_ml,
  title = {Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using {TabNet}},
  author = {Baki, H and Basu, S},
  journal = {Environmental Data Science},
  year = {2024},
  doi = {10.1017/eds.2024.41},
  volume = {3: e32},
  pages = {1--10},
}

2023

\(\Pi\)-ML: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer

M Pierzyna, R Saathof, and S Basu

Optics Letters, 2023

Editor’s Pick, Optics Letters DOI Bib Code

true

@article{pierzyna2023OL_ml,
  title = {\(\Pi\)-{ML}: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer},
  author = {Pierzyna, M and Saathof, R and Basu, S},
  journal = {Optics Letters},
  year = {2023},
  doi = {10.1364/ol.492652},
  volume = {48},
  pages = {4484--4487},
}

A decision tree-based measure-correlate-predict approach for peak wind gust estimation from a global reanalysis dataset

S Kartal, S Basu, and S J Watson

Wind Energy Science, 2023

DOI Bib

@article{kartal2023WES_ml,
  title = {A decision tree-based measure-correlate-predict approach for peak wind gust estimation from a global reanalysis dataset},
  author = {Kartal, S and Basu, S and Watson, S J},
  journal = {Wind Energy Science},
  year = {2023},
  doi = {10.5194/wes-8-1533-2023},
  volume = {8},
  pages = {1533--1551},
}

2022

Automated identification of “Dunkelflaute" events: a convolutional neural network-based autoencoder approach

B Li, S Basu, and S J Watson

Artificial Intelligence for the Earth Systems, 2022

DOI Bib

@article{li2022_ml,
  title = {Automated identification of {``Dunkelflaute"} events: a convolutional neural network-based autoencoder approach},
  author = {Li, B and Basu, S and Watson, S J},
  journal = {Artificial Intelligence for the Earth Systems},
  year = {2022},
  doi = {10.1175/aies-d-22-0015.1},
  volume = {1: e220015},
  pages = {1--17},
}

2020

Image shift due to atmospheric refraction: prediction by numerical weather modeling and machine learning

W Al-Younis, C Nevarez, M Abdullah-Al-Mamun, and 2 more authors

Optical Engineering, 2020

DOI Bib

@article{al2020_ml,
  title = {Image shift due to atmospheric refraction: prediction by numerical weather modeling and machine learning},
  author = {Al-Younis, W and Nevarez, C and Abdullah-Al-Mamun, M and Basu, S and Voelz, D},
  journal = {Optical Engineering},
  year = {2020},
  doi = {10.1117/1.oe.59.8.081803},
  volume = {59},
  pages = {081803},
}

Automated classification of simulated wind field patterns from multi-physics ensemble forecasts

P Durán, S Basu, C Meißner, and 1 more author

Wind Energy, 2020

DOI Bib

@article{duran2020_ml,
  title = {Automated classification of simulated wind field patterns from multi-physics ensemble forecasts},
  author = {Dur\'{a}n, P and Basu, S and Mei{\ss}ner, C and Adaramola, M S},
  journal = {Wind Energy},
  year = {2020},
  doi = {10.1002/we.2462},
  volume = {23},
  pages = {898--914},
}

2016

Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii

Y Wang and S Basu

Optics Letters, 2016

DOI Bib

@article{wang2016b_ml,
  title = {Using an artificial neural network approach to estimate surface-layer optical turbulence at {Mauna Loa, Hawaii}},
  author = {Wang, Y and Basu, S},
  journal = {Optics Letters},
  year = {2016},
  doi = {10.1364/ol.41.002334},
  volume = {41},
  pages = {2334--2337},
}

2002

Prediction of gas-phase adsorption isotherms using neural nets

S Basu, P. F. Henshaw, N. Biswas, and 1 more author

The Canadian Journal of Chemical Engineering, 2002

Bib

@article{basu2002c_ml,
  title = {Prediction of gas-phase adsorption isotherms using neural nets},
  author = {Basu, S and Henshaw, P. F. and Biswas, N. and Kwan, H. K.},
  journal = {The Canadian Journal of Chemical Engineering},
  year = {2002},
  volume = {80},
  pages = {506--512},
}