Height of the Mixing Layer under Conditions of Temperature Inversions: Experimental Data and Model Estimates

Odintsov, S. L.; Gladkikh, V. A.; Kamardin, A. P.; Nevzorova, I. V.

doi:10.1134/s1024856022060173

Cited by 3 publications

(6 citation statements)

References 27 publications

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“…This property allows us to determine the height m H , where 0 2 → σ T . In [17], we have presented, in sufficient detail, the results of comparison of model S h values calculated according to [18][19] with the experimental data on m H obtained by the sodar under conditions of temperature inversions in the lower atmospheric layer. The main conclusion was that S m h H > at temperature inversions.…”

Section: Introductionmentioning

confidence: 99%

“…( 1) and ( 2), except for U N , correspond to the surface atmospheric layer. The procedure of calculating S h by Equation (1), including the technique of estimating U N , is considered in detail in [17]. The value of U N was estimated for the height range above 600 m.…”

Section: Introductionmentioning

confidence: 99%

“…For more details about the measurement site and instrumentation characteristics, see, for example, [17]. According to the estimates reported in [20], at the BEO site under favorable conditions (low external noise level, alight wind, no precipitation, etc.…”

Section: Introductionmentioning

confidence: 99%

“…It was assumed in [17] that one of the possible causes of the cases S m h H >> at temperature inversion is associated with horizontal turbulent heat fluxes, which may be significant even at the weak vertical turbulent heat exchange. The results of testing this assumption are presented below.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Variance of air temperature in the atmospheric surface layer with temperature inversion

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et al. 2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variance of air temperature in the atmospheric surface layer with temperature inversion

Odintsov,

Gladkikh,

Kamardin

et al. 2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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“…temperature inversion is considered in [20]. It should be noted that the literature gives equations directly for the temperature variance, rather than for its time derivative as in Equation (1).…”

mentioning

confidence: 99%

Turbulent flows of temperature variance in the atmospheric surface layer

Gladkikh,

Nevzorova,

Odintsov

2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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The turbulence characteristics that determine changes in the air temperature variance in the atmospheric surface layer are considered. The main experimental material for the analysis was obtained with a sonic weather station set in the atmospheric surface layer (10 m) at a territory with natural landscape. Some episodes observed in the summer and winter seasons are analyzed. It is found that the flows of temperature variance decrease markedly in winter as compared to summer. A pronounced diurnal profile of the studied characteristic was observed in summer and absent in winter.

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Estimation of Astronomical Seeing with Neural Networks at the Maidanak Observatory

Shikhovtsev,

Kiselev,

Kovadlo

et al. 2023

Atmosphere

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In the present article, we study the possibilities of machine learning for the estimation of seeing at the Maidanak Astronomical Observatory (38∘40′24″ N, 66∘53′47″ E) using only Era-5 reanalysis data. Seeing is usually associated with the integral of the turbulence strength Cn2(z) over the height z. Based on the seeing measurements accumulated over 13 years, we created ensemble models of multi-layer neural networks under the machine learning framework, including training and validation. For the first time in the world, we have simulated optical turbulence (seeing variations) during night-time with deep neural networks trained on a 13-year database of astronomical seeing. A set of neural networks for simulations of night-time seeing variations was obtained. For these neural networks, the linear correlation coefficient ranges from 0.48 to 0.68. We show that modeled seeing with neural networks is well-described through meteorological parameters, which include wind-speed components, air temperature, humidity, and turbulent surface stresses. One of the fundamental new results is that the structure of small-scale (optical) turbulence over the Maidanak Astronomical Observatory does not depend or depends negligibly on the large-scale vortex component of atmospheric flows.

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Height of the Mixing Layer under Conditions of Temperature Inversions: Experimental Data and Model Estimates

Cited by 3 publications

References 27 publications

Variance of air temperature in the atmospheric surface layer with temperature inversion

Variance of air temperature in the atmospheric surface layer with temperature inversion

Turbulent flows of temperature variance in the atmospheric surface layer

Estimation of Astronomical Seeing with Neural Networks at the Maidanak Observatory

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