Experiments On Buoyant Plume Dispersion In A Laboratory Convection Tank

Plume dispersion in the convective boundary layer (CBL) is investigated experimentally in a laboratory convection tank. The focusis on highly-buoyant plumes that loft near or become trapped in the CBL capping inversion and resistdownward mixing. Such plumes are defined by dimensionless buoyancy fluxes F_* 0.1, where F_* = F_b/(U w_* ² z_i), F_b is the stack buoyancy flux,U is the mean wind speed, w_* is the convective velocity scale, and z_i is the CBL depth. The aim is to obtain statistically-reliable mean (C) and root-mean-square (rms, _c) concentration fields as a function of F_* and the dimensionless distance X = w_*x/(U z_i), where x is the distance downstream of the source.The experiments reveal the following mainresults: (1) For 3 X 4and F_* 0.1, the crosswind-integrated concentration (CWIC) fields exhibit distinctly uniform profiles below z_i with a CWIC maximum aloft, in contrast to the nonuniform profiles obtained earlier by Willis and Deardorff. (2) The lateral dispersion (_y) variation with X is consistent with Taylor's theory for _* 0.1 and a buoyancy-enhanced dispersion, _y/z_i F_* ^1/3X^2/3, forF_* = 0.2 and 0.4. (3) The entrapment, the plume fraction above z_i, has a mean (E) that follows a systematic variationwith X and F_*, and a variability (_e/E) that is broad ( 0.3 to 2) near the source but subsides to 0.25 far downstream. (4) Vertical profiles of the concentration fluctuation intensity (c/C) are uniform for z < z_i and X > 1.5, but exhibit significant increases: (a) at the surface and close to the source (X 1.5), and(b) in the entrainment zone. (5) The cumulative distribution functions (CDFs) of the scaled concentration fluctuations (c/_c) separate into mixed-layer and entrainment-layer CDFs for X 2, with the mixed-layer group collapsing to a single distribution independent of z.These are the first experiments to obtain all components of the lateral and vertical dispersion parameters (rms meander, relative dispersion, total dispersion) for continuous buoyant releases in a convection tank. They also are the first tank experiments to demonstrate agreement with field observations of: (1) the scaled ground-level concentration along the plume centreline, and (2) the dimensionless lateral dispersion _y/z_i of buoyant plumes.     

Numerical simulation of particle trajectories in inhomogeneous turbulence,II: Systems with variable turbulent velocity scale

It is shown that for the purpose of trajectory simulation, the vertical velocityw _L (t) of a fluid element, which is moving in a system (such as a forest canopy, or the unstably stratified atmospheric surface layer) whose turbulent velocity scale _w is height-dependent, must be chosen from a frequency-distribution which is asymmetric aboutw _L = 0. If the gradient _w /z varies only slowly with height, correct trajectories may be obtained by adding a bias (where _L is the length scale) to a fluctuating velocity chosen from a symmetric distribution with variance _w ²(z).     

Boundary-layer parameterizations for applied dispersion modeling over urban areas

Many applied dispersion models require the knowledge of boundary-layer parameters such as sensible heat flux,Q _H , friction velocity,u _*, and turbulent energy components, _w and _v . Formulas are suggested for calculating these parameters over a wide variety of types of ground surfaces, based on simple observations of wind speed near the ground and fractional cloud cover, and specification of constants such as roughness length, albedo, and soil moisture availability. Observations ofu _*,Q _H , _w , and _v during field experiments in St. Louis and Indianapolis are used to test the formulas for urban sites. Relative errors of about ±20% in the predictions are seen to occur whenu _*,Q _H , _w , and _v are large. However, when these quantities are small (e.g.,u _* < 0.2 m/s), the errors in the predictions are as large as the mean value of the quantity itself.In addition, it is concluded from studies of available field data and theories that the magnitude of _w is not well-known at elevations above about 100m during the late afternoon and night. Some simple parameterizations for _w . are suggested that are consistent with the observed steady decrease in ground-level concentration in the afternoon and the sudden increase in concentration that can occur a few hours after sunset due to wind shears associated with a low-level jet, for continuous plumes emitted from moderate to tall stacks.     

Properties of spatial temperature derivatives in the atmospheric surface layer

A pair of parallel cold wires separated in either the vertical or lateral direction was used to obtain the three components _x, _y, _z of the temperature derivative in the streamwise, lateral and vertical directions, respectively. The average absolute skewness values of _x and _z are nonzero and approximately equal, while the skewness of _y is approximately zero. These results appear to be consistent with the presence of a large, three-dimensional organised structure in the surface layer. There is an apparent low-frequency contamination in the spectral density of _y and _z due mainly to small errors in estimating the sensitivity of the cold wires. The temperature derivatives were high-pass filtered, the filter being set to remove possible contributions from the large structure and to minimise low-frequency sensitivity contamination. The filtered rms ratios \~_x/\~_y and \~_x/\~_z were in the range 0.7 to 0.9, a result in qualitative agreement with that obtained in the laboratory boundary layer by Sreenivasan et al. (1977). The skewness of filtered _x or _z is negligible, consistent with local isotropy of small-scale temperature fluctuations and in support of the high wavenumber spectral isotropy discussed in Antonia and Chambers (1978).     

Variability of surface roughness and turbulence intensities at a coastal site in India

Surface-layer features with different prevailing wind directions for two distinct seasons (Southwest Monsoon and Northeast Monsoon) on the west coast of India are studied using data obtained from tower-based sensors at a site located about 500 m from the coast. Only daytime runs have been used for the present analysis. The surface boundary-layer fluxes have been estimated using the eddy correlation method. The surface roughnessz ₀ obtained using the stability-corrected wind profiles (Paulson, 1970) has been found to be low for the Southwest monsson season. For the other season,z ₀ is relatively high. The drag coefficientC _D varies with height in the NE monsoon season but not in the season with lowz ₀. This aspect is reflected in the wind profiles for the two seasons and is discussed in detail. The scaling behaviour of friction velocityu _* and the turbulence intensity of longitudinal, lateral and vertical winds _u, _v and _w, respectively) are further examined to study their dependence on fetch. Our study shows that for the non-dimensional case, _u/u_* and _v/u_* do not show any surface roughness dependence in either season. On the other hand, for _w/u_* for the season with lowz ₀, the values are seen to agree well with that of Panofskyet al. (1977) for homogeneous terrain whereas for the other season with highz ₀, the results seem to conform more to the values observed by Smedman and Högström (1983) for coastal terrain. The results are discussed in the light of observations by other investigators.     

The atmospheric tide as a continuous spectrum: Lunar semidiurnal tide in surface pressure

Summary The standard equations for the theory of atmospheric tides are solved here by an integral representation on the continuous spectrum of free oscillations. The model profile of back-ground temperature is that of the U.S. Standard Atmosphere in the lower and middle atmosphere, and in the lower thermosphere, above which an isothermal top extends to arbitrarily great heights. The top is warm enough to bring both the Lamb and the Pekeris modes into the continuous spectrum.Computations are made for semidiurnal lunar tidal pressure at sea level at the equator, and the contributions are partitioned according to vertical as well as horizontal structure. Almost all the response is taken up by the Lamb and Pekeris modes of the slowest westward-propagating gravity wave. At sea level, the Lamb-mode response is direct and is relatively insensitive to details of the temperature profile. The Pekeris mode at sea level has an indirect response-in competition with the Lamb mode-and, as has been known since the time of its discovery, it is quite sensitive to the temperature profile, in particular to stratopause temperature. In the standard atmosphere the Lamb mode contributes about +0.078 mb to tidal surface pressure at the equator and the Pekeris mode about –0.048 mb.The aim of this investigation is to illustrate some consequences of representing the tide in terms of the structures of free oscillations. To simplify that task as much as possible, all modifying influences were omitted, such as background wind and ocean or earth tide. Perhaps the main defect of this paper's implementation of the free-oscillation spectrum is that, in contrast to the conventional expansion in the structures of forced oscillations, it does not include dissipation, either implicity or explicity, and thus does not satisfy causality. Dissipation could be added implicity by means of an impedance condition, for example, which would cause up-going energy flux to exceed downgoing flux at the base of the isothermal top layer. To achieve complete causality, however, the dissipation must be modeled explicity. Nevertheless, since the Lamb and Pekeris modes are strongly trapped in the lower and middle atmosphere, where dissipation is rather weak (except possibly in the surface boundary layer), more realistic modeling is not likely to change the broad features of the present results.Symbols a earth's mean radius; expansion coefficient in (5.3) - b recursion variable in (7.4); proximity to resonance in (9.2) - c sound speed in (2.2); specific heatc _p in (2.2) - f Coriolis parameter 2sin in (2.2) - g standard surface gravity - h equivalent depth - i ; discretization index in (7.3) - j index for horizontal structure - k index for horizontal structure; upward unit vectork in (2.2) - m wave number in longitude - n spherical-harmonic degree; number of grid layers in a model layer - p tidal pressure perturbation; background pressurep ₀ - q heating function (energy per mass per time) - r tidal state vector in (2.1) - s tidal entropy perturbation; background entropys ₀ - t time - u tidal horizontal velocityu - w tidal vertical component of velocity - x excitation vector defined in (2.3); vertical coordinate lnp _*/p ₀ [except in (3.8), where it is lnp /p ₀] - y vertical-structure function in (7.1) - z geopotential height - A constant defined in (6.2) - C spherical-harmonic expansion coefficient in (3.6) - D vertical cross section defined in (5.6) and (5.9) - E eigenstate vector - F vertical-structure function for eigenstate pressure in (3.2) [re-defined with WKB scaling in (7.2)] - G vertical-structure function for eigenstate vertical velocity in (3.2) [re-defined with WKB scaling in (7.2)] - H pressure-scale height - I mode intensity defined in (8.1) - K quadratic form defined in (4.4) - L quadratic form defined in (4.4); horizontal-structure magnification factor defined in (5.11) - M vertical-structure magnification factor defined in (4.6) - P eigenstate pressure in (3.2); tidal pressure in (6.2) - R tidal state vector in (5.1) - S eigenstate entropy in (3.2); spherical surface area, in differential dS - T background molecular-scale (NOAA, 1976) absolute temperatureT ₀ - U eigenstate horizontal velocityU in (3.2); coefficient in (7.3) - V horizontal-structure functionV for eigenstate horizontal velocity in (3.2); recursion variable in (7.3) - W eigenstate vertical velocity in (3.2) - X excitation vector in (5.1) - Y surface spherical harmonic in (3.7) - Z Hough function defined in (3.6) - +dH/dz - (1––)/2 - Kronecker delta; Dirac delta; correction operator in (7.6) - equilibrium tide elevation - (square-root of Hough-function eigenvalue) - ratio of specific gas constant to specific heat for air=2/7 - longitude - - - background density ₀ - eigenstate frequency in (3.1) - proxy for heating functionq =c _P/t - latitude - tide frequency - operator for the limitz - horizontal-structure function for eigenstate pressure in (3.2) - Hough function defined in (6.2) - earth's rotation speed - horizontal gradient operator - ()₀ background variable - ()_* surface value of background variable - () value at base of isothermal top layer - Õ state vector with zerow-component - , energy product defined in (2.4) - | | energy norm - ()^* complex conjugate With 10 Figures     

Incorporation of internal fluctuations in a meandering plume model of concentration fluctuations

A meandering plume model that explicitly incorporates the effects of small-scale structure in the instantaneous plume has been formulated. The model requires the specification of two physically based input parameters; namely, the meander ratio,M, which is dependent on the ratio of the meandering plume dispersion to the instantaneous relative plume dispersion and, a relative in-plume fluctuation measure,k, that is related inversely to the fluctuation intensity in relative coordinates. Simple analytical expressions for crosswind profiles of the higher moments (including the important shape parameters such as fluctuation intensity, skewness, and kurtosis) and for the concentration pdf have been derived from the model. The model has been tested against some field data sets, indicating that it can reproduce many key aspects of the observed behavior of concentration fluctuations, particularly with respect to modeling the change in shape of the concentration pdf in the crosswind direction.List of Symbols C Mean concentration in absolute coordinates - C _r Mean concentration in relative coordinates - C₀ Centerline mean concentration in absolute coordinates - C _r,0 Centerline mean concentration in relative coordinates - f Probability density function of concentration in absolute coordinates - f _c Probability density function of plume centroid position - f _r Probability density function of concentration in relative coordinates - i Absolute concentration fluctuation intensity (standard deviation to mean ratio) - i _r Relative concentration fluctuation intensity (standard deviation to mean ratio) - k Relative in-plume fluctuation measure:k=1/i _r ² - K Concentration fluctuation kurtosis - M Meander ratio of meandering plume variance to relative plume variance - S Concentration fluctuation skewness - x Downwind distance from source - y Crosswind distance from mean-plume centerline - z Vertical distance above ground - Instantaneous (random) concentration - Crosswind dispersion ofnth concentration moment about zero - _ny Mean-plume crosswind (absolute) dispersion - _y Plume centroid (meandering) dispersion in crosswind direction - _y,c Instantaneous plume crosswind (relative) dispersion - Normalized mean concentration in absolute coordinates:C/C ₀ - Particular value taken on by instantaneous concentration,      

Final results of the CONDORS convective diffusion experiment

TheConvectiveDiffusionObserved byRemoteSensors (CONDORS) field experiment conducted at the Boulder Atmospheric Observatory used innovative techniques to obtain three-dimensional mappings of plume concentration fields, /Q, of oil fog detected by lidar and chaff detected by Doppler radar. It included extensive meteorological measurements and, in 1983, tracer gases measured at a single sampling arc. Final results from ten hours of elevated and surface release data are summarized here. Many intercomparisons were made. Oil fog /Q measured 40m above the arc are mostly in good agreement withSF ₆ values, except in a few instances with large spacial inhomogeneities over short distances. After a correction scheme was applied to compensate for the effect of its settling speed, chaff dy/Q agreed well with those of oil except in two cases of oil fog hot spots. Mass or frequency distribution vs. azimuth or elevation angle comparisons were made for chaff, oil, and wind, with mostly good agreements. Spacial standard deviations, _y and _z, of chaff and oil agree overall and are consistent at short range with velocity standard deviations _vand _w 0.6w^* (the convective scale velocity), as measured atz>100m. Surface release _y is enhanced up to 60% at smallx, consistent with the Prairie Grass measurements and with larger _v and reduced wind speed measured near the surface. Decreased _y at small dimensionless average times is also noted. Finally, convectively scaled dy, C _y, were plotted versus dimensionlessx andz for oil, chaff, and corrected chaff for each 30–60 min period. Aggregated CONDORSC _y fields compare well with laboratory tank and LES numerical simulations; surface-released oil fog compares expecially well with the tank experiments. However, large deviations from the norm occurred in individual averaging periods; these deviations correlated strongly with anomalies in measured distributions.On assignment to the US Environmental Protection Agency, Atmospheric Research and Exposure Assessment Laboratory, RTP, NC.     

Laboratory Simulation Of Vertical Plume Dispersion Within A Convective Boundary Layer

Mesoscale measurements of the vertical dispersion coefficient ₂ by using a composite turbulence water tank were validated through a comparison with CONDORS (Convective Diffusion Observed with Remote Sensors) field data, and were analysed with respect to the intensity of the thermal flux, mechanical turbulence, and plume release height.It seems possible to correct the plume _z values for different release heights below 0.5z_i (z_i is the mixing height) by applying an equation expressing the height dependency of turbulence intensity. The downwind distance where the plume's mass centre height approaches its final level was also analysed with respect to the above three parameters, and an empirical equation to estimate the downwinddistance derived.     

AN EVALUATION OF DISPERSION COEFFICIENTS FOR USE IN AIR QUALITY MODELS

Standard deviations of concentration in horizontal andvertical directions i.e. y andz have been estimated by using fivedifferent schemes based on empirical(due to Pasquill and Briggs)schemes and sophisticated methods(due to Irwin, Draxler, Taylor, Hanna et al.). The fiveschemes are discussed atlength. The purpose of this study is to make use ofmeteorological observations whichare routinely available, to test all the above methods andintercompare the resultswith one another and observations so that the sensitivityof each schemeunder various atmospheric stability conditions could beassessed. It has beenfound that the existing schemes are good enough to providereasonable estimates ofdispersion coefficient (y) during highly unstableconditions (Pasquill stability classes A and B). However, thesame is not true for the case when the stability increasesfrom C to F and turbulencedecreases, specifically during stable atmospheric conditions,when the observedvalues were found to be much higher than all the existingschemes. This suggests thatwhile we continue to use the current methods of estimatingthe dispersion parameters,a rigorous search is required for methods which give predictionswhich are close-to-realityduring such conditions which are represented by lowlevels (in terms of magnitude)of atmospheric turbulence leading to higher levelsof pollution.As one of the sophisticated methods requiresthe use of v and w (standard deviationsof wind velocity fluctuation in y and z directions),these have been estimated andvalidated with observed data (field experiments conductedby EPRI at Kincaid).Statistical evaluation of v and wbased on performance measures indicate a goodperformance of the parameterisations adopted in thisstudy. In the case of w duringunstable conditions a comparison of three differentschemes with observations is made.     

Statistics of Surface-Layer Turbulence Over Terrain with Metre-Scale Heterogeneity

Refuge has patchy vegetation in sandy soil. During midday and at night, the surface sources and sinks for heat and moisture may thus be different. Although the Sevilleta is broad and level, its metre-scale heterogeneity could therefore violate an assumption on which Monin-Obukhov similarity theory (MOST) relies. To test the applicability of MOST in such a setting, we measured the standard deviations of vertical (_w) and longitudinal velocity (_u), temperature (_t), and humidity (_q), the temperature-humidity covariance (¯tq), and the temperature skewness (S_t). Dividing the former five quantities by the appropriate flux scales (u_*, _*, and q_*) yielded the nondimensional statistics _w/u_*, _u/u_*, _t/|t_*|, _q/|q_*|, and ¯tq/t_*q_*. _w/u_*, _t/|t_*|, and S_t have magnitudes and variations with stability similar to those reported in the literature and, thus, seem to obey MOST. Though _u/u_* is often presumed not to obey MOST, our _u/u_* data also agree with MOST scaling arguments. While _q/|q_*| has the same dependence on stability as _t/|t_*|, its magnitude is 28% larger. When we ignore ¯tq/t_*q_* values measured during sunrise and sunset transitions – when MOST is not expected to apply – this statistic has essentially the same magnitude and stability dependence as (_t/t_*)². In a flow that truly obeys MOST, (_t/t_*)², (_q/q_*)², and ¯tq/t_*q_* should all have the same functional form. That (_q/q_*)² differs from the other two suggests that the Sevilleta has an interesting surface not compatible with MOST. The sources of humidity reflect the patchiness while, despite the patchiness, the sources of heat seem uniformly distributed.     

Standard deviation of vertical two-point longitudinal velocity differences in the atmospheric boundary layer

The standard deviation of vertical two-point longitudinal velocity fluctuation differences is analyzed experimentally with eleven sets of turbulence measurements obtained at the NASA 150-m ground-winds tower site at Cape Kennedy, Florida. It is concluded that /u _*0 is proportional to (fz/u _*0)^0.22, where the coefficient of proportionality is a function of fz/u _*0 and u _*0/fL ₀. The quantities f and L₀ denote the Coriolis parameter and the surface Monin-Obukhov stability length, respectively; u _*0 is the surface friction velocity; z is the vertical distance between the two points over which the velocity difference is calculated; and zz is the mean height of the mid-point of the interval z above natural grade. The results of the analysis are valid for 20<-u _*0/fL ₀<2000.     

Markov chain simulations of vertical dispersion in the neutral surface layer for surface and elevated releases

Vertical dispersion in the neutral surface layer is investigated using a Markov Chain simulation procedure. The conceptual basis of the procedure is discussed and computation procedures outlined. Wind and turbulence parameterizations appropriate to the neutral surface layer are considered with emphasis on the Lagrangian time scale. Computations for a surface release are compared with field data. Good agreement is found for the variation of surface concentration and cloud height to distances 500 m downwind of the source. The functional form of the vertical concentration profile is examined and an exponential with exponent 1.6 is found to give the best fit with simulations.For elevated releases, it is demonstrated that an initial dip of the mass mean height from the simulation can be normalized for various release heights using a non-dimensionalized downwind coordinate incorporating advective wind speed and wind shear. The vertical distribution standard deviation ( _z ), as employed in Gaussian models, shows a fair degree of independence with source height but close examination reveals an optimum source height for maximum _z at a given downwind distance,x. This source height increases with downwind distance. Also the simulations indicate that vertical wind shear is more important than vertical variation of Lagrangian time scale close to the source, with a reverse effect farther downwind.     

On the temperature coefficient of PCO 2 in seawater

The commonly reported temperature coefficient of P. the equilibrium partial pressure of CO₂, is (P/T) _A,C ,which is about 15 ppm/°C, or 5% of the atmospheric partial pressure of CO₂. This coefficient, however, applies only to deep water, not to surface water which can exchange CO₂ with the atmosphere. The coefficient (P/T) _A,C ,, where designates constancy of the sum of atmospheric and surface-ocean CO₂, is the appropriate value for air-sea exchange. Numerical values are mass-dependent because the depth of the exchanging ocean layer must be specified. For a 100-m surface layer, the value is ca. 1.5 ppm/°C, or 0.5% of ambient CO₂. Editor's Note:In view of the interdisciplinary importance of the carbon dioxide-climate problem, this note on seawater chemistry should be of interest to specialists beyond the discipline of ocean chemistry.     

On Trajectory Curvature as a Selection Criterion for Valid Lagrangian Stochastic Dispersion Models

Recently Wilson and Flesch (Boundary-Layer Meteorology, 84, 411-426, 1997) suggested that the average increment d z to the orientation = arctan(w/u) of the Lagrangian velocity-fluctuation vector can be used to distinguish the better Lagrangian stochastic models within the well-mixed class. Here it is demonstrated that the specification of d z constitutes neither a sufficient or universally applicable criterion to distinguish the better Lagrangian stochastic models within the well-mixed class. The hypothesis made by Wilson and Flesch that Lagrangian stochastic models with /P_E irrotational are zero-spin models, having d z=0, is proven     

Disagreements between gradient-diffusion and Lagrangian stochastic dispersion models,even for sources near the ground

It is well known that if turbulent mass convection is modelled as diffusion, errors result unless trajectories from the source (ath) to the point of observation (z _p ) comprise many statistically-independent segments (Taylor, 1921). We show that this is not guaranteed merely by the Lagrangian timescale () at the source being small (e.g., source at ground), but that a better criterion istmax[(h), (z _p )], wheret is a typical travel time toz _p .     

An observational aircraft-based study of sea-breeze frontogenesis

In the summer of 1988/89 flights were carried out in the Coorong coastal area of South Australia to investigate sea-breeze fronts. The flights yielded data sets of the structure of the fronts in the cross-frontal direction with a spatial resolution of approximately 3 m. The study is focused on the budgets of sensible and latent heat in the vicinity of the front and on frontogenesis/frontolysis processes which are closely related to budget considerations.The frontogenesis relationships and the budgets were established on a 2 km length scale by low-pass filtering of the space series. As the wind components were measured with high accuracy, all processes which determine frontogenesis could be evaluated and are displayed in x,z-cross-sections: these are the confluence, shear and diabatic effects, all of which play a role in q/x-, q/z-, /x- as well as /z-frontogenesis. A detailed analysis is given for two different states of frontal development. The presented results shed much light on the governing physical processes in the frontal region with strong emphasis on the effects of confluence-generated updrafts, on shear instabilities causing bulges and clefts in the frontal surface as well as producing the elevated frontal head, and on processes related to differential heating and moistening.     

The use of σ T as a temperature scale in the atmospheric surface layer

The standard deviation of temperature _T is proposed as a temperature scale and as a velocity scale to describe the behaviour of turbulent flows in the Atmospheric Surface Layer (ASL), instead of _* andu _* of the Monin—Obukhov similarity theory, and ofT _f andU _f used for free convection stability conditions. On the basis of experimental evidence reported in the literature, it is shown that _T T _f andv _* U _f in the free convection region, and _{T *} andv _* U _* in nearneutral and stable conditions. This implies that the proposed scales can be applied for all stabilities. Furthermore, a new length scale is proposed and its relation with Obukhov length is given. Also, a simple semi-empirical expression is presented with which _T andv _* can be evaluated in a rather simple way. Some examples of practical applications are given, e.g., a stability classification for unstable conditions.     

The Effect of Atmospheric Stability on the Surface-Layer Characteristics in a Low-Wind Area of Tropical West Africa

This study details the observed effects ofatmospheric stability on characteristics of thesurface layer in a low wind speed (U = 1.5 m s-1)regime of tropical West Africa. Theaerodynamic roughness length, z0, anddisplacement height, d, obtained from profilewind-speed data at our bush land site (height 2 m)have values of 0.24 ± 0.10 m and 1.54 ± 0.04 mrespectively. In the unstable range (-2.5 < Ri < -0.1; Riis gradient Richardson number), thestandard deviation in wind speed fluctuations, u, increased from 0.57 ± 0.19 m s-1 toa maximum of 0.7 ± 0.2 m s-1 in near-neutralconditions, and in the stable range, the parameterdecreased rapidly to 0.41 ± 0.15 m s-1 at Ri 0.2.In the same stability range, the horizontal winddispersion, , decreased withincreasing stability from 19 ± 8 deg. to 13 ± 5 deg.The surface-layer integral quantity, u/u*, when plottedas a function of stability, is in agreement with theempirical results. The ratio ofsensible heat flux (estimated) to the net radiationranged between 0.1 and 0.2 at nighttime,increasing to about 0.5 during the daytime, and showeda strong dependency on season.     

Wind stress over water waves: Field experiments on lake of Geneva

Summary A fixed platform (Fig.3), installed 100 m from the shoreline in 3 m water depth, was instrumented with velocity, temperature and wave-height sensors. 132 data (10 minutes averages) were analysed to calculate the wind stress; from these, 99 data were used to investigate the vertical distribution of the wind stress; all data are presented with Table 1.It was postulated that the total stress, _t being constant with height, is made up additively of two components, the wave-supporting stress, _w , and the turbulent stress, _c ; see Eq. 1. The vertical distribution of these two components is shown schematically in Fig. 1.The total stress, _t , evaluated outside the zone of wave influence, is given in the classical way with Fig. 4. The wave-supporting stress, _w (z), was evaluated from the data according to a relation proposed by Kitaigorodskii et al. (1984); it is given with Fig. 5. A height-dependency is clearly evident. The turbulent stress _c (z), was evaluated with data of the velocity gradient; it is given with Fig. 6. A height-dependency is not evident.The field data from the lake of Geneva give evidence that the additive relation of Eq. 1 seems to be justified.With 6 Figures