Experience of Studying the Turbulent Structure of the Atmospheric Boundary Layer Using an Unmanned Aerial Vehicle

Chechin, Dmitry; Artamonov, Arseniy; Bodunkov, Nikolay E.; Zhivoglotov, D. N.; Zaytseva, D. V.; Kalyagin, M. Yu.; Kouznetsov, D. D.; Kounashouk, A. A.; Shevchenko, A. M.; Шестакова, А. А.

doi:10.1134/s0001433821050042

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…One of the main trends in low-altitude sensing, i.e., to heights of about 500 m, is the development of methods for diagnostics of the turbulent atmosphere with UAVs. The results of diagnostics of the speed of air mass flows with UAVs are reported in [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In [36][37][38][39][40][41][42][43][44][45][46][47], the fundamental possibility of measuring the turbulence spectra with fixed-wing UAVs of various sizes and weights was shown.…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude Sensing of Urban Atmospheric Turbulence with UAV

Shelekhov

Afanasiev

Shelekhova

et al. 2022

Drones

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The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to study the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide, with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov–Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

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Section: Introductionmentioning

confidence: 99%

Low-Altitude Sensing of Urban Atmospheric Turbulence with UAV

Shelekhov

Afanasiev

Shelekhova

et al. 2022

Drones

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“…The latter would include large-eddy simulations serving to identify internal boundary layers and explicit representation of surface inhomogeneities. For the same reason, mobile eddycovariance platforms such as a fixed-wind UAV with fast-response sensors onboard would be especially useful, e.g., the UAV "Tsymlianin" [61].…”

Section: Surface Heat Budget and Surface Temperaturementioning

confidence: 99%

Quantifying Spatial Heterogeneities of Surface Heat Budget and Methane Emissions over West-Siberian Peatland: Highlights from the Mukhrino 2022 Campaign

Chechin,

Repina,

Artamonov

et al. 2024

Forests

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The study presents the first results from the multi-platform observational campaign carried out at the Mukhrino peatland in June 2022. The focus of the study is the quantification of spatial contrasts of the surface heat budget terms and methane emissions across the peatland, which arise due to the presence of microlandscape heterogeneities. It is found that surface temperature contrasts across the peatland exceeded 10 °C for clear-sky conditions both during day and night. Diurnal variation of surface temperature was strongest over ridges and drier hollows and was smallest over the waterlogged hollows and shallow lakes. This resulted in strong spatial variations of sensible heat flux (H) and Bowen ratio, while the latent heat varied much less. During the clear-sky days, H over ryam exceeded the one over the waterlogged hollow by more than a factor of two. The Bowen ratio amounted to about unity over ryam, which is similar to values over forests. Methane emissions estimated using the static-chamber method also strongly varied between various microlandscapes, being largest at a hollow within a ridge-hollow complex and smallest at a ridge. A strong nocturnal increase in methane mixing ratio was observed and was used in the framework of the atmospheric boundary layer budget method to estimate nocturnal methane emissions, which were found to be in the same order of magnitude as daytime emissions. Finally, the directions for further research are outlined, including the verification of flux-aggregation techniques, parameterizations of surface roughness and turbulent exchange, and land-surface model evaluation and development.

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Identification of the atmospheric boundary layer structure through vertical distribution of PM2.5 obtained by unmanned aerial vehicle measurements

Jiang

et al. 2022

Atmospheric Environment

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Experience of Studying the Turbulent Structure of the Atmospheric Boundary Layer Using an Unmanned Aerial Vehicle

Cited by 6 publications

References 20 publications

Low-Altitude Sensing of Urban Atmospheric Turbulence with UAV

Low-Altitude Sensing of Urban Atmospheric Turbulence with UAV

Quantifying Spatial Heterogeneities of Surface Heat Budget and Methane Emissions over West-Siberian Peatland: Highlights from the Mukhrino 2022 Campaign

Identification of the atmospheric boundary layer structure through vertical distribution of PM2.5 obtained by unmanned aerial vehicle measurements

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