Formulation of turbulence diffusion relationships under stable atmospheric conditions and its effect on pollution disper
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ORIGINAL PAPER
Formulation of turbulence diffusion relationships under stable atmospheric conditions and its effect on pollution dispersion P. T. Rakesh1 · R. Venkatesan1 · P. Roubin2 · C. V. Srinivas1 · R. Baskaran1 · B. Venkatraman1 Received: 30 June 2019 / Accepted: 27 January 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract In this article, we formulate Monin–Obukhov similarity theory (MOST)-based relationships, for normalized standard deviations of wind velocity components under the local scaling framework, and investigate their applicability under stable and highly stable atmospheric conditions. We used the fast response data collected using an ultrasonic anemometer over a flat terrain of Kalpakkam in India and a complex hilly terrain at Cadarache, France, for arriving at these formulations. The study shows that after filtering of the submesoscale motions from the sonic anemometer data, the turbulence diffusion relationships follow local scaling, under stable conditions. The study further indicates that these relationships follow similar behavior for the sites taken for this study. At neutral conditions, the values of the scaled standard deviations are found to be 1.9 ± 0.07, 1.8 ± 0.06 and 1.3 ± 0.02, for longitudinal, crosswind and vertical component, respectively, for the complex terrain and 1.8 ± 0.03, 1.9 ± 0.06 and 1.1 ± 0.04, respectively, for the flat terrain. The research also investigates the effect of the new diffusion relationships in simulating atmospheric dispersion, using the Lagrangian particle dispersion model FLEXPARTWRF. Simulations using these new diffusion relationships show a higher dose estimate relative to the model default Hanna’s method, in the case of radioactivity dispersion. Detailed comparisons of the simulated dose rate estimates against measurements using Environmental Radiation Monitors (ERM) indicate that the new relationships give better correlation (r2 = 0.62) under stable conditions over model default relationships (r2 = 0.50). Keywords Monin–obukhov similarity theory · Local scaling · Multi-resolution decomposition · WRF · FLEXPART · Dispersion modeling
1 Introduction The Monin–Obukhov similarity theory (MOST) (Monin and Obukhov 1954; Businger et al. 1971) is a widely used framework, to obtain turbulent fluxes in the atmospheric surface layer, as a function of the standard scaling variable such as friction velocity u *, surface roughness z0, boundary layer height zi, and Monin–Obukhov length L. Following the MOST, the turbulent fluxes of wind, temperature, Responsible Editor: S. Trini Castelli. * P. T. Rakesh [email protected] 1
Radiological Safety Division, Indira Gandhi Centre for Atomic Research, HBNI, Kalpakkam, Tamil Nadu 603 102, India
DTN/SMTM/LMTE,, bat727 p103, BP 1 C.E.Cadarache, 13108 St Paul lès Durance, France
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and moisture in the surface layer are related to the vertical gradients of their mean quantities by an eddy diffusivity coefficient, Kz (Garrat 1994). Many boundary layer models use the MOST, for obtaining th
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