Research on atmospheric turbulence by Wilfried Brutsaert and collaborators

Dias, Nelson Luı́s

doi:10.1002/wrcr.20461

Cited by 8 publications

(8 citation statements)

References 147 publications

(261 reference statements)

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“…Then, we compare the results with those obtained by Brutsaert [1972]. Some of the central results of Dias and Brutsaert [1998] used in this letter are summarized in Dias [2013].…”

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confidence: 91%

Reconciling radiation dissipation in the spatial and spectral domains under stable conditions

Dias

2013

Water Resour. Res.

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[1] Two formulations for the radiative dissipation of temperature fluctuations, in the spatial and the spectral domains, are reconciled. Both can be written in terms of a first-order decay of temperature variance. The decay constant was obtained originally by Brutsaert (1972) for stable conditions from considerations on the temperature autocovariance function (in the spatial domain). Here it is obtained directly from a spectral dissipation function, using the appropriate Monin-Obukhov dimensionless functions for a stable atmosphere. The general behavior of the constant with stability is the same for the two formulations, with Brutsaert's original formulation producing stronger dissipation than its spectral counterpart. [2] Although radiative cooling of the mean temperature field is widely recognized as the main physical cause of the nighttime stable layer close to the surface, longwave radiation also plays a role in the dampening of the temperature fluctuations. This has importance in the maintenance of turbulence itself as shown in the pioneering study of Brutsaert [1972], and on the stable temperature spectrum [Coantic and Simonin, 1984;Dias and Brutsaert, 1998].[3] Dias and Brutsaert's [1998] conclusion was that, close to the surface, these radiative effects were relatively unimportant. Most of the time, more recent works on stable conditions have not given much attention to radiation effects on the dissipation of temperature fluctuations [e.g., Vickers and Mahrt, 2004;Edwards, 2009].[4] However, more and better data under stable conditions continue to become available, along with important developments in understanding the collapse of turbulence in stable conditions [Acevedo et al., 2012;van de Wiel et al., 2012avan de Wiel et al., , 2012b, and a better understanding of radiative dissipation of temperature fluctuations remains in order. In this work, we reconcile two approaches to model this process.[5] The maintenance of temperature fluctuations in a stable surface layer depends in various ways on their production, transfer, and dissipation. In addition to molecular dissipation of temperature variance 0 0 , an additional dissipation term exists which is due to the emission of longwave radiation. Radiative dissipation can be modeled both using the spectral budget of temperature fluctuations,and the budget of temperature variance[6] We use the standard symbols x, y, z for the coordinate axes and u, v, w for the corresponding velocity components. In (1), is the mean air temperature ; E w and E are spherical shell averages (with wave number radius k) of the corresponding 3-D turbulence spectra and cospectra ; T is the inertial transfer term, v is the molecular diffusivity of heat, and N(k) is the spectral dissipation function. A detailed description of N(k) and its calculation can be found in Coantic and Simonin [1984] and Dias and Brutsaert [1998]. In (2), stationarity and horizontal homogeneity are assumed ; in (1), 3-D homogeneity is assumed.[7] Brutsaert [1972] modeled the radiative dissipation term E R i...

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“…Then, we compare the results with those obtained by Brutsaert [1972]. Some of the central results of Dias and Brutsaert [1998] used in this letter are summarized in Dias [2013].…”

mentioning

confidence: 91%

Reconciling radiation dissipation in the spatial and spectral domains under stable conditions

Dias

2013

Water Resour. Res.

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“…The correlation coefficients among three scalars are statistically different from unity while the relative transport efficiencies are not. This highlights the difference between the correlation coefficients and the relative transport efficiencies, as they represent scalar similarity and scalar flux similarity, respectively (Bink and Meesters 1997;Katul and Hsieh 1997;Cancelli et al 2012;Dias 2013), and the correlation coefficients are bounded by unity while the relative transport efficiencies are not. The random errors also complicate the relationship between correlation coefficients and the relative transport efficiencies, which might explain why different relationships between log 10 (R wT /R wq )/log 10 (R qT ) and the Bowen ratio have been reported in different studies (Lamaud and Irvine 2006;Guo et al 2009).…”

Section: Conclusion and Discussionmentioning

confidence: 96%

“…The relationship between R st and R ws /R wt has been discussed in many previous studies (e.g., Katul et al 1995;Bink and Meesters 1997;Katul and Hsieh 1997;Lamaud and Irvine 2006;Guo et al 2009;Dias 2013); however, there is no consensus on the exact relationship The relationship between R st and R ws /R wt when the random errors are considered. a R st is on the x-axis and only R ws /R wt < 1 are shown on the y-axis.…”

Section: Random Errors In R St and R Ws /R Wtmentioning

confidence: 97%

“…Following MOST, it can be derived theoretically that temperature and water vapour are transported similarly (Hill 1989;Dias 2013), and it is not surprising to see that this assumption is often extended to other scalars such as trace gases. However, several assumptions central to MOST break down under certain conditions, which may then induce dissimilarity among different scalars (Dias and Brutsaert 1996;McNaughton and Laubach 1998;Sempreviva and Hojstrup 1998;De Bruin et al 1999;Lee et al 2004;Asanuma et al 2007;Assouline et al 2008;Detto et al 2008;Katul et al 2008;Moene and Schuttemeyer 2008;Li et al 2012).…”

Section: Introductionmentioning

confidence: 97%

“…As such, when non-stationarity (McNaughton and Laubach 1998), advection (Lee et al 2004), surface heterogeneity (Williams et al 2007; Moene and Schuttemeyer 2008), and outer-layer influences on the ASL (Asanuma et al 2007;Katul et al 2008;Li et al 2012) are prominent, scalar dissimilarity is often observed, which can be manifest in various similarity functions such as flux-profile relationships and flux-variance relationships (also see Li et al 2012 for scalar dissimilarity in flux-structure parameter relationships). In this study, we focus on flux-variance relationships due to their widespread use and relation to correlation coefficients between the vertical velocity and the scalar concentrations (R ws , where s denotes the scalar concentration), which are usually used to indicate turbulent transport efficiencies (Dias 2013).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying the Influence of Random Errors in Turbulence Measurements on Scalar Similarity in the Atmospheric Surface Layer

Sun

Tao

et al. 2015

Boundary-Layer Meteorol

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The influence of random errors in turbulence measurements on scalar similarity for temperature, water vapour, CO 2 , and NH 3 is investigated using two eddy-covariance datasets collected over a lake and a cattle feedlot. Three measures of scalar similarity, namely, the similarity constant in the flux-variance relationship, the correlation coefficient between two scalars and the relative transport efficiency, are examined. The uncertainty in the similarity constant C s in the flux-variance relationship resulting from random errors in turbulence measurements is quantified based on error propagation analyses and a Monte-Carlo sampling method, which yields a distribution instead of a single value for C s . For different scalars, the distributions of C s are found to significantly overlap, implying that scalars are transported similarly under strongly unstable conditions. The random errors in the correlation coefficients between scalars and the relative transport efficiencies are also quantified through error propagation analyses, and they increase as the atmosphere departs from neutral conditions. Furthermore, the correlation coefficients between three scalars (water vapour, CO 2 , and NH 3 ) are statistically different from unity while the relative transport efficiencies are not, which highlights the difference between these two measures of scalar similarity. The results suggest that uncertainties in these measures of scalar similarity need to be quantified when using them to diagnose the existence of dissimilarity among different scalars.

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Contribution of impervious surfaces to urban evaporation

Ramamurthy

Bou‐Zeid

2014

Water Resources Research

105

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Observational data and the Princeton urban canopy model, with its detailed representation of urban heterogeneity and hydrological processes, are combined to study evaporation and turbulent water vapor transport over urban areas. The analyses focus on periods before and after precipitation events, at two sites in the Northeastern United States. Our results indicate that while evaporation from concrete pavements, building rooftops, and asphalt surfaces is discontinuous and intermittent, overall these surfaces accounted for nearly 18% of total latent heat fluxes (LE) during a relatively wet 10 day period. More importantly, these evaporative fluxes have a significant impact on the urban surface energy balance, particularly during the 48 h following a rain event when impervious evaporation is the highest. Thus, their accurate representation in urban models is critical. Impervious evaporation after rainfall is also shown to correlate the sources of heat and water at the earth surface, resulting in a conditional scalar transport similarity over urban terrain following rain events.

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Research on atmospheric turbulence by Wilfried Brutsaert and collaborators

Cited by 8 publications

References 147 publications

Reconciling radiation dissipation in the spatial and spectral domains under stable conditions

Reconciling radiation dissipation in the spatial and spectral domains under stable conditions

Quantifying the Influence of Random Errors in Turbulence Measurements on Scalar Similarity in the Atmospheric Surface Layer

Contribution of impervious surfaces to urban evaporation

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