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Optics Express|May 4, 2016
Extending a surface-layer Cn2 model for strongly stratified conditions utilizing a numerically generated turbulence datasetPing He, Sukanta BasuOptics Letters|September 15, 2015
A simple approach for estimating the refractive index structure parameter (Cn²) profile in the atmosphereSukanta BasuBoundary-Layer Meteorology|March 16, 2019
Hybrid Profile-Gradient Approaches for the Estimation of Surface FluxesSukanta BasuApplied Optics|March 17, 2026
Leveraging deep learning-based foundation models for optical turbulence (<i>C</i><i>n</i>2) estimation under data scarcitySukanta BasuOptics Letters|May 14, 2016
Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, HawaiiYao Wang, Sukanta BasuOptics Letters|September 9, 2016
Utilizing the Kantorovich metric for the validation of optical turbulence predictionsYao Wang, Sukanta BasuPhysical Review. E|June 17, 2017
Estimating higher-order structure functions from geophysical turbulence time series: Confronting the curse of the limited sample sizeAdam W DeMarco, Sukanta BasuOptics Letters|September 1, 2023
Π-ML: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layerMaximilian Pierzyna, Rudolf Saathof, Sukanta BasuPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|September 28, 2004
Synthetic turbulence, fractal interpolation, and large-eddy simulationSukanta Basu, Efi Foufoula-Georgiou, Fernando Porté-AgelApplied Optics|June 10, 2024
Intercomparison of flux-, gradient-, and variance-based optical turbulence (<i>C</i> <i>n</i>2) parameterizationsMaximilian Pierzyna, Oscar Hartogensis, Sukanta Basu, et al.Pageof 285