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Sukanta Basu

Publications and source records attributed to Sukanta Basu.

18 recordsLinked to original sources
arXiv2026

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

Accurate high-resolution vertical profiles of optical turbulence ($C_n^2$), which reflect local meteorology and topography, are crucial for ground-based optical astronomy and free-space optical communication. However, measuring these profiles or generating them with numerical weather models requires substantial operational or computational effort. In this work, we present OTProf, a deep-learning method that estimates high-resolution $C_n^2$ profiles from widely available coarse-resolution ERA5 reanalysis data. We evaluate the approach in the Netherlands and compare it with the commonly used Hufnagel-Valley model. Overall, OTProf reproduces the vertical structure of $C_n^2$ more accurately than Hufnagel-Valley and yields more accurate estimates of the Fried parameter $r_0$ and the scintillation index $\sigma_I^2$. As typical in machine learning, the $C_n^2$ predictions are slightly smoothed compared to reference data, especially in cases of rare strong turbulence. This smoothing affects the integrated parameters, sometimes leading to overly optimistic $r_0$ and $\sigma_I^2$ values. Despite this limitation, OTProf offers a more accurate, efficient, and physically consistent alternative to traditional analytical models and computationally expensive mesoscale models.

physics.ao-ph↗
arXiv2024

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

This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence strength ($C_n^2$) using gradient boosting machines. OTCliM addresses the challenge of efficiently obtaining reliable site-specific $C_n^2$ climatologies near the surface, crucial for ground-based astronomy and free-space optical communication. Using gradient boosting machines and global reanalysis data, OTCliM extrapolates one year of measured $C_n^2$ into a multi-year time series. We assess OTCliM's performance using $C_n^2$ data from 17 diverse stations in New York State, evaluating temporal extrapolation capabilities and geographical generalization. Our results demonstrate accurate predictions of four held-out years of $C_n^2$ across various sites, including complex urban environments, outperforming traditional analytical models. Non-urban models also show good geographical generalization compared to urban models, which capture non-general site-specific dependencies. A feature importance analysis confirms the physical consistency of the trained models. It also indicates the potential to uncover new insights into the physical processes governing $C_n^2$ from data. OTCliM's ability to derive reliable $C_n^2$ climatologies from just one year of observations can potentially reduce resources required for future site surveys or enable studies for additional sites with the same resources.

physics.ao-ph↗
arXiv2023

{\Pi}-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ($C_n^2$) is highly relevant for the successful development and deployment of future free-space optical communication links. In this letter, we propose a physics-informed machine learning (ML) methodology, $\Pi$-ML, based on dimensional analysis and gradient boosting to estimate $C_n^2$. Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting $C_n^2$. For statistical robustness, we train an ensemble of models which yields high performance on the out-of-sample data of $R^2=0.958\pm0.001$.

physics.ao-ph↗
arXiv2021

Revisiting and revising Tatarskii's formulation for the temperature structure parameter ($C_T^2$) in atmospheric flows

In this paper, we revisit a well-known formulation of temperature structure parameter ($C_T^2$), originally proposed by V.~I.~Tatarskii. We point out its limitations and propose a revised formulation based on turbulence variance and flux budget equations. Our formulation includes a novel physically-based outer length scale which can be estimated from routine meteorological data.

physics.ao-ph↗
arXiv2021

Turbulent Prandtl number and characteristic length scales in stably stratified flows: steady-state analytical solutions

In this study, the stability dependence of turbulent Prandtl number ($Pr_t$) is quantified via a novel and simple analytical approach. Based on the variance and flux budget equations, a hybrid length scale formulation is first proposed and its functional relationships to well-known length scales are established. Next, the ratios of these length scales are utilized to derive an explicit relationship between $Pr_t$ and gradient Richardson number. In addition, theoretical predictions are made for several key turbulence variables (e.g., dissipation rates, normalized fluxes). The results from our proposed approach are compared against other competing formulations as well as published datasets. Overall, the agreement between the different approaches is rather good despite their different theoretical foundations and assumptions.

physics.ao-ph↗
arXiv2020

Parameterizing the Energy Dissipation Rate in Stably Stratified Flows

We use a database of direct numerical simulations to evaluate parametrizations for energy dissipation rate in stably stratified flows. We show that shear-based formulations are more appropriate for stable boundary layers than commonly used buoyancy-based formulations. As part of the derivations, we explore several length scales of turbulence and investigate their dependence on local stability.

physics.ao-ph↗
arXiv2020

On the Dissipation Rate of Temperature Fluctuations in Stably Stratified Flows

In this study, we explore several integral and outer length scales of turbulence which can be formulated by using the dissipation of temperature fluctuations ($χ$) and other relevant variables. Our analyses directly lead to simple yet non-trivial parameterizations for both $χ$ and the structure parameter of temperature ($C_T^2$). For our purposes, we make use of high-fidelity data from direct numerical simulations of stratified channel flows.

physics.ao-ph↗
arXiv2020

Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers

In this study, we have identified certain fundamental limitations of a mixing length parameterization used in a popular turbulent kinetic energy-based subgrid-scale model. Replacing this parameterization with a more physically realistic one significantly improves the overall quality of the large-eddy simulations (LESs) of stable boundary layers. For the range of grid sizes considered here (specifically, 1 m -- 12.5 m), the revision dramatically reduces the grid-size sensitivity of the simulations. Most importantly, the revised scheme allows us to reliably estimate the first- and second-order statistics of a well-known LES intercomparison case, even with a coarse grid-size of O(10 m).

physics.ao-ph↗
arXiv2018

A Hybrid Profile-Gradient Approach for the Estimation of Surface Fluxes

The Monin--Obukhov similarity theory-based wind speed and potential temperature profiles are inherently coupled to each other. We have developed hybrid approaches to disentangle them, and as a direct consequence, the estimation of Obukhov length (and associated turbulent fluxes) from either wind-speed or temperature measurements becomes an effortless task. Additionally, our approaches give rise to two easily measurable indices of atmospheric stability. We compare these approaches with the traditional gradient and profile methods that require both wind-speed and temperature profile data. Using Monte-Carlo-type numerical experiments we demonstrate that, if the input profiles are free of any random errors, the performance of the proposed hybrid approaches is almost equivalent to the profile method and better than the gradient method. However, the proposed hybrid approaches are less competitive in comparison to their traditional counterparts in the presence of random errors.

physics.ao-ph↗
arXiv2018

A Simple Recipe for Estimating Atmospheric Stability Solely Based on Surface-Layer Wind Speed Profile

The wind energy community is gradually recognizing the significance of atmospheric stability in both power production and structural loading. However, estimating stability requires temperature gradient data which are not commonly measured by the wind farm developers or operators. To circumvent this problem, we propose a simple approach \emph{á la} Swinbank, to estimate stability from only three levels of wind speed measurements. As such, this approach is ideally suited for sodar and lidar--based wind measurements owing to their high vertical resolution in the surface layer.

physics.ao-ph↗
arXiv2013

Coupled Mesoscale-Large-Eddy Modeling of Realistic Stable Boundary Layer Turbulence

Site-specific flow and turbulence information are needed for various practical applications, ranging from aerodynamic/aeroelastic modeling for wind turbine design to optical diffraction calculations. Even though highly desirable, collecting on-site meteorological measurements can be an expensive, time-consuming, and sometimes a challenging task. In this work, we propose a coupled mesoscale-large-eddy modeling framework to synthetically generate site-specific flow and turbulence data. The workhorses behind our framework are a state-of-the-art, open-source atmospheric model called the Weather Research and Forecasting (WRF) model and a tuning-free large-eddy simulation (LES) model. Using this coupled framework, we simulate a nighttime stable boundary layer (SBL) case from the well-known CASES-99 field campaign. One of the unique aspects of this work is the usage of a diverse range of observations for characterization and validation. The coupled models reproduce certain characteristics of observed low-level jets. They also capture various scaling regimes of energy spectra, including the so-called spectral gap. However, the coupled models are unable to capture the intermittent nature of the observed surface fluxes. Lastly, we document and discuss: (i) the tremendous spatio-temporal variabilities of observed and modeled SBL flow fields, and (ii) the significant disagreements among different observational platforms. Based on these results, we strongly recommend that future SBL modeling studies consider rigorous validation exercises based on multi-sensor/multi-platform datasets. In summary, we believe that the numerical generation of realistic SBL is not an impossible task. Without any doubt, there remain several computational and fundamental challenges. The present work should be viewed as a first step to confront some of these challenges.

physics.ao-ph↗
arXiv2007

Estimating Intermittency Exponent in Neutrally Stratified Atmospheric Surface Layer Flows: A Robust Framework based on Magnitude Cumulant and Surrogate Analyses

This study proposes a novel framework based on magnitude cumulant and surrogate analyses to reliably detect and estimate the intermittency coefficient from short-length coarse-resolution turbulent time series. Intermittency coefficients estimated from a large number of neutrally stratified atmospheric surface layer turbulent series from various field campaigns are shown to remarkably concur with well-known laboratory experimental results. In addition, surrogate-based hypothesis testing significantly reduces the likelihood of detecting a spurious non-zero intermittency coefficient from non-intermittent series. The discriminatory power of the proposed framework is promising for addressing the unresolved question of how atmospheric stability affects the intermittency properties of boundary layer turbulence.

physics.flu-dyn↗
arXiv2006

Comparison of Two Scale-Dependent Dynamic Subgrid-Scale Models for Simulations of Neutrally Buoyant Shear-Driven Atmospheric Boundary Layer Flows

A new scale-dependent dynamic subgrid-scale (SGS) model based on Kolmogorov's scaling hypothesis is presented. This SGS model is utilized in large-eddy simulation of a well-known case study on shear-driven neutral atmospheric boundary layer flows. The results are compared comprehensively with an alternate scale-dependent dynamic SGS model based on the popular Smagorinsky closure. Our results show that, in the context of this particular problem, the scale-dependent dynamic modeling approach is extremely useful, and reproduces several establised results (e.g., the surface layer similarity theory) with fidelity. Results from both the SGS base models are generally in close agreement, although we find a consistent superiority of the Smagorinsky-based SGS model for predicting the inertial range scaling of spectra.

physics.ao-ph↗
arXiv2005

Subgrid-scale modeling of reacting scalar fluxes in large-eddy simulations of atmospheric boundary layers

In large-eddy simulations of atmospheric boundary layer turbulence, the lumped coefficient in the eddy-diffusion subgrid-scale (SGS) model is known to depend on scale for the case of inert scalars. This scale dependence is predominant near the surface. In this paper, a scale-dependent dynamic SGS model for the turbulent transport of reacting scalars is implemented in large-eddy simulations of a neutral boundary layer. Since the model coefficient is computed dynamically from the dynamics of the resolved scales, the simulations are free from any parameter tuning. A set of chemical cases representative of various turbulent reacting flow regimes is examined. The reactants are involved in a first-order reaction and are injected in the atmospheric boundary layer with a constant and uniform surface flux. Emphasis is placed on studying the combined effects of resolution and chemical regime on the performance of the SGS model. Simulations with the scale-dependent dynamic model yield the expected trends of the coefficients as function of resolution, position in the flow and chemical regime, leading to resolution-independent turbulent reactant fluxes.

physics.ao-ph↗
arXiv2005

Revisiting the Local Scaling Hypothesis in Stably Stratified Atmospheric Boundary Layer Turbulence: an Integration of Field and Laboratory Measurements with Large-eddy Simulations

The `local scaling' hypothesis, first introduced by Nieuwstadt two decades ago, describes the turbulence structure of stable boundary layers in a very succinct way and is an integral part of numerous local closure-based numerical weather prediction models. However, the validity of this hypothesis under very stable conditions is a subject of on-going debate. In this work, we attempt to address this controversial issue by performing extensive analyses of turbulence data from several field campaigns, wind-tunnel experiments and large-eddy simulations. Wide range of stabilities, diverse field conditions and a comprehensive set of turbulence statistics make this study distinct.

physics.ao-ph↗
arXiv2005

Large-Eddy Simulation of Stably Stratified Atmospheric Boundary Layer Turbulence: A Scale-Dependent Dynamic Modeling Approach

A new tuning-free subgrid-scale model, termed `locally-averaged scale-dependent dynamic' (LASDD) model, is developed and implemented in large-eddy simulations (LESs) of stable boundary layers. The new model dynamically computes the Smagorinsky coefficient and the subgrid-scale Prandtl number based on the local dynamics of the resolved velocity and temperature fields. Overall, the agreement between the statistics of the LES-generated turbulence and some well-established empirical formulations and theoretical predictions (e.g., Nieuwstadt's local scaling hypothesis) is remarkable. The results show clear improvements over most of the traditional subgrid-scale models in the surface layer. Moreover, in contrast to previous large-eddy simulations of stable boundary layers that have strong dependence on grid resolution, the simulated statistics obtained with the LASDD model show relatively little resolution dependence for the range of grid sizes considered here. In essence, we show that the new LASDD model is a robust subgrid-scale parameterization for reliable, tuning-free simulations of stable boundary layers, even with relatively coarse resolutions.

physics.ao-ph↗
arXiv2004

Synthetic Turbulence, Fractal Interpolation and Large-Eddy Simulation

Fractal Interpolation has been proposed in the literature as an efficient way to construct closure models for the numerical solution of coarse-grained Navier-Stokes equations. It is based on synthetically generating a scale-invariant subgrid-scale field and analytically evaluating its effects on large resolved scales. In this paper, we propose an extension of previous work by developing a multiaffine fractal interpolation scheme and demonstrate that it preserves not only the fractal dimension but also the higher-order structure functions and the non-Gaussian probability density function of the velocity increments. Extensive a-priori analyses of atmospheric boundary layer measurements further reveal that this Multiaffine closure model has the potential for satisfactory performance in large-eddy simulations. The pertinence of this newly proposed methodology in the case of passive scalars is also discussed.

physics.flu-dyn↗