High-Order Statistics of Temperature Fluctuations in an Unstable Atmospheric Surface Layer over Grassland

Lyu, Rui; Hu, Fei; Liu, Lei; Xu, Jinming; Cheng, Xueling

doi:10.1007/s00376-018-7248-x

Cited by 14 publications

(11 citation statements)

References 56 publications

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“…From figure 4( a ) and ( b ), it is clear that the skewness and kurtosis of the temperature fluctuations are strongly non-Gaussian ( and 5, respectively) in the local free convection limit (). The strong non-Gaussian nature of temperature fluctuations in highly unstable conditions is remarkably consistent with the previous studies in the ASL (Chu et al 1996; Garai & Kleissl 2013; Lyu et al 2018). Similar behaviour has also been observed in turbulent Rayleigh–Bénard convection experiments of Adrian, Ferreira & Boberg (1986).…”

Section: Resultssupporting

confidence: 90%

Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence

2020

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Section: Resultssupporting

confidence: 90%

Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence

2020

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“…This observation is in agreement with the previous studies in the convective surface layer. [60][61][62][63] Our results show that this non-Gaussianity in the T ′ signal is only realized at large sizes of persistent temperature patterns, approximately greater than four times the measurement height.…”

Section: Linkage Between the Persistence Pdfs And Asymmetric Distribumentioning

confidence: 64%

Persistence analysis of velocity and temperature fluctuations in convective surface layer turbulence

2020

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Persistence is defined as the probability that the local value of a fluctuating field remains at a particular state for a certain amount of time, before being switched to another state. The concept of persistence has been found to have many diverse practical applications, ranging from non-equilibrium statistical mechanics to financial dynamics to distribution of time scales in turbulent flows and many more. In this study, we carry out a detailed analysis of the statistical characteristics of the persistence probability density functions (PDFs) of velocity and temperature fluctuations in the surface layer of a convective boundary layer using a field-experimental dataset. Our results demonstrate that for the time scales smaller than the integral scales, the persistence PDFs of turbulent velocity and temperature fluctuations display a clear power-law behavior, associated with a self-similar eddy cascading mechanism. Moreover, we also show that the effects of non-Gaussian temperature fluctuations act only at those scales that are larger than the integral scales, where the persistence PDFs deviate from the power-law and drop exponentially. Furthermore, the mean time scales of the negative temperature fluctuation events persisting longer than the integral scales are found to be approximately equal to twice the integral scale in highly convective conditions. However, with stability, this mean time scale gradually decreases to almost being equal to the integral scale in the near-neutral conditions. Contrarily, for the long positive temperature fluctuation events, the mean time scales remain roughly equal to the integral scales, irrespective of stability.

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“…The data used in this paper are from the environmental monitoring station Olympic Sports Center Station; specific position is seen in Shi et al (2019),

\sim

2 km to the northeast of the Beijing 325‐m meteorological tower. The 325 m tower (39.96°N, 116.36°E, 49 m above the sea level) is located at Institute of Atmospheric Physics of the Chinese Academy of Sciences, and its underlying surface is a typical developed city surface (Al‐Jiboori & Hu, ; Lyu et al, ). There are 15 observation platforms (at 8, 15, 32, 47, 65, 80, 103, 120, 140, 160, 180, 200, 240, 280, and 320 m) equipped on the tower, and each platform is mounted with measurement instruments of wind speed (MetOne, USA), wind direction (MetOne, USA), temperature (HC2‐S3, Switzerland), and humidity (HC2‐S3, Switzerland).…”

Section: Data and Instrumentsmentioning

confidence: 99%

Ramp‐Like PM Accumulation Process and Z‐Less Similarity in the Stable Boundary Layer

Shi

2020

Geophysical Research Letters

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Similar ramp‐like structures exist in the time variations of surface PM 2.5 accumulation during the four different heavy haze pollution processes in Beijing, from 3 November 2017 to 15 January 2018. Based on the ultrasonic anemometer observations at seven different altitudes on a 325 m tower, it is shown that turbulence momentum flux exchanges between different altitudes were very weak during the ramp period of PM 2.5 concentration. Turbulence at the higher altitude associated with strong wind shear, occasionally mixed downward toward the surface, showing upside‐down transportation of turbulent kinetic energy (TKE), especially during the rapid removal stage of the pollution. This was different from the traditional upward TKE transportation above the surface. Vertical distribution of nondimensionalized standard deviation of the horizontal velocity, vertical velocity, and potential temperature in the stable boundary layer within the ramp‐like structure show obvious “z‐less” similarity, independent of normalz, and almost equal to constants ( σu/ u*l∼ 3.9, σw/ u*l∼ 1.52, and σθ/ T*∼ 3.96).

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High-Order Statistics of Temperature Fluctuations in an Unstable Atmospheric Surface Layer over Grassland

Cited by 14 publications

References 56 publications

Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence

Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence

Persistence analysis of velocity and temperature fluctuations in convective surface layer turbulence

Ramp‐Like PM Accumulation Process and Z‐Less Similarity in the Stable Boundary Layer

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