Scavenging Efficiency of ‘Aerosol Carbon’ and Sulfate in Supercooled Clouds at Mt. Sonnblick (3106 m a.s.l., Austria)

Cloud water and interstitial aerosol samples collected at Mt. Sonnblick (SBO) were analyzed for sulfate and aerosol carbon to calculate in-cloud scavenging efficiencies. Scavenging efficiencies for sulfate (_SO) ranged from 0.52 to 0.99 with an average of 0.80. Aerosol carbon was scavenged less efficiently with an average value (_AC) of 0.45 and minimum and maximum values of 0.14 and 0.81, respectively. Both _SO and _AC showed a marked, but slightly different, dependence on the liquid water content (LWC) of the cloud. At low LWC, _SO increased with rising LWC until it reached a relatively constant value of 0.83 above an LWC of 0.3 g/m³. In the case of aerosol carbon, we obtained a more gradual increase of _AC up to an LWC of 0.5 g/m³. At higher LWCs, _{_} remained relatively constant at 0.60. As the differences between _SO and _A varied across the LWC range observed at SBO, we assume that part of the aerosol carbon was incorporated into the cloud droplets independently from sulfate. This hypothesis is supported by size classified aerosol measurements. The differences in the size distributions of sulfate and total carbon point to a partially external mixture. Thus, the different chemical nature and the differences in the size and mixing state of the aerosol particles are the most likely candidates for the differences in the scavenging behavior.     

Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations

Summary A radiative transfer model has been used to determine the large scale effective 6.6 GHz and 37 GHz optical depths of the vegetation cover. Knowledge of the vegetation optical depth is important for satellite-based large scale soil moisture monitoring using microwave radiometry. The study is based on actual observed large scale surface soil moisture data and observed dual polarization 6.6 and 37 GHz Nimbus/SMMR brightness temperatures over a 3-year period. The derived optical depths have been compared with microwave polarization differences and polarization ratios in both frequencies and with Normalized Difference Vegetation Index (NDVI) values from NOAA/AVHRR. A synergistic approach to derive surface soil emissivity from satellite observed brightness temperatures by inverse modelling is described. This approach improves the relationship between satellite derived surface emissivity and large scale top soil moisture fromR ²=0.45 (no correction for vegetation) toR ²=0.72 (after correction for vegetation). This study also confirms the relationship between the microwave-based MPDI and NDVI earlier described and explained in the literature.List of Symbols f frequency [Hz] - f _i(p) fractional absorption at polarizationp - h surface roughness - h h cos² - H horizontal polarization - n _i complex index of refraction - p polarization (H orV) - R _s microwave surface reflectivity - T _B(p) brightness temperature at polarizationp - T ^* normalized brightness temperature - T polarization difference (T _v-T _H) - T _s temperature of soil surface - T _c temperature of canopy - T _max daily maximum air temperature - T _min daily minimum air temperature - V vertical polarization - soil moisture distribution factor; also used for the constant to partition the influence of bound and free water components to the dielectric constant of the mixture - empirical complex constant related to soil texture - microwave transmissivity of vegetation (=e ^–) - ^* effective transmissivity of vegetation (assuming =0) - microwave emissivity - _s emissivity of smooth soil surface - _rs emissivity of rough soil surface - _vs emissivity of vegetated surface - soil moisture content (% vol.) - K dielectric constant [F·m^–1] - K _fw dielectric constant of free water [F·m^–1] - K _ss dielectric constant of soil solids [F·m^–1] - K _m dielectric constant of mixture [F·m^–1] - K _o permittivity of free space [8.854·10^–12 F·m^–1] - high frequency limit ofK _wf [F·m^–1] - wavelength [m] - incidence angle [degrees from nadir] - polarization ratio (T _H/T _V) - b soil bulk density [gr·cm^–3] - s soil particle density [gr·cm^–3] - R surface reflectivity in red portion of spectrum - NIR surface reflectivity in near infrared portion of spectrum - _eff effective conductivity of soil extract [mS·cm^–1] - vegetation optical depth - _6.6 vegetation optical depth at 6.6 GHz - ₃₇ vegetation optical depth at 37 GHz - ^* effective vegetation optical depth (assuming =0) - single scattering albedo of vegetation With 12 Figures     

On Modelling the One-Dimensional Atmospheric Boundary Layer

Several commonly used turbulence closure schemes forthe atmospheric boundarylayer (ABL) are applied to simulate neutral, nocturnal and diurnal cyclesituations in a one-dimensional ABL. Results obtained with the differentschemes, E-, E- and its modified versions, and twoversions ofthe q ² Level 2.5, are compared and discussed.     

An application of the E-ε turbulence model for studying coupled air-sea boundary-layer structure

An E- turbulence model is used to study air-sea interaction characteristics and turbulence structure using a coupled model for air-sea boundary layers. The E- turbulence model consists of equations for the turbulent kinetic energy, the energy-dissipation, and for the turbulent exchange coefficient expressed in terms of turbulent kinetic energy and energy-dissipation. The energy-dissipation equations for the air-sea interface are solved analytically to obtain boundary conditions for energy-dissipation at the interface. The air-sea interaction and turbulence characteristics of the E- model are compared with those of the mixing-length model and with available observations.The simulations demonstrate that the air-sea interaction parameters obtained by the E- model agree well with observations. The numerical studies also show that the E- turbulence model with appropriate constants can give good results in modeling coupled air-sea boundary-layer flows.     

The impact of turbulence closure schemes on predictions of the Mixed Spectral Finite-Difference model for flow over topography

Impacts of different closure schemes in the Mixed Spectral Finite-Difference model (Beljaarset al., 1987) for neutrally stratified atmospheric surface-layer flow over complex terrain are studied. Six different closure schemes, (Z+z ₀), Mixing Length,E–(Z+z ₀),E–,E–– andq ² l are compared. Model results for flow over an infinite series of sinusoidal ridges are examined in the context of the inner and outer layers defined by Jackson and Hunt (1975). Results are compared with rapid distortion estimates of the changes in normal stresses. The effects of streamline curvature are also examined in a qualitative sense.     

Turbulence mechanisms at an agricultural site

An extensive set of turbulence data from the 3- and 12-m heights taken over an agricultural site (Marsta, Sweden) are analyzed and compared with data from ideal sites.In unstable air, Monin-Obukhov similarity is found to be valid for the non-dimensional gradients of wind, _m , temperature, _h , and humidity, _e , for (only a few data), for _T /|T _*|,/ _E /|E _*| and for the non-dimensionalized inertial subrange spectra of temperature and humidity. Where comparison is possible, the unstable data also agree with those found in the Kansas study, with one remarkable exception, the inertial subrange constant of the temperature spectrum, ₁, being only 0.39, compared to the value 0.80 found at the Kansas site.On the stable side, most similarity predictions break down, with most of the data differing systematically from the corresponding Kansas results, the only exception being . The inertial subrange constants for temperature, ₁, and for humidity, ₁ are found to have the same values, 0.39 and 0.30, respectively, as they do on the unstable side. Remarkable similarity is found for the shape of the stable u- and - and e-spectra. In addition, this shape is found to be identical with that found in Kansas. The peak wavelength of the stable u-, and -spectra is found to be about four times larger than it is for the corresponding Kansas spectra. This is interpreted to be a result of the increased macro-roughness at the Marsta site as compared with that at the Kansas site. A possible explanation for the low ₁-value is discussed, suggesting that ₁ is not a universal constant, but instead dependent on the turbulent structure.     

Local Similarity Relationships In The Urban Boundary Layer

To investigate turbulent structures in an urban boundary layer (UBL) with many tallbuildings, a number of non-dimensional variable groups based on turbulent observationsfrom a 325-m meteorological tower in the urban area of Beijing, China, are analyzedin the framework of local similarity. The extension of surface-layer similarity to localsimilarity in the stable and unstable boundary layer is also discussed. According to localsimilarity, dimensionless quantities of variables: e.g., velocity and temperature standarddeviations _i/u_*l (i=u,v,w) and_T/T_*l,correlation coefficients of uw and wT covariance, gradients of wind and temperature_m and _h, and dissipation rates of turbulent kinetic energy (TKE) andtemperature variance and _N can be represented as a functiononly of a local stability parameter z/, where is the local Obukhovlength and z is the height above ground. The average dissipation rates of TKE andtemperature variance are computed by using the u spectrum, and the uw and wTcospectra in the inertial subrange. The functions above were found to be in a goodagreement with observational behaviour of turbulence under unstable conditions, butthere were obvious differences in the stable air.     

The Kolmogorov constant for carbon dioxide in the atmospheric surface layer over a paddy field

From measurements in the atmospheric surface layer over a paddy field, the Kolmogorov constants for CO₂ and longitudinal wind velocity were obtained. In this study, the nondimensional dissipation rate _nc = (1–16 _v )^-1/2 for CO₂ variance and = (1–16 _v )^-1/4 – _v for turbulent energy were used, assuming the equality of the local production term and the local dissipation term, and neglecting the divergence flux term in the budget equation. The value of the constant for CO₂ was consistent with recent determinations for temperature and humidity. The constant for longitudinal wind velocity showed good agreement with other recent observations.     

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.     

Turbulent Dissipation Rate In The Boundary Layer Via Uhf Wind Profiler Doppler Spectral Width Measurements

The study is focused on the retrieval and validation of the turbulent kinetic energy dissipation rate from spectral width measurements made by a UHF wind profiler in the convective atmospheric boundary layer. The possibility to deduce , which is one of the most important parameters for boundary-layer study and monitoring, from Doppler radar spectral width measurements has a firm theoretical basis established by numerous earlier works. However, the major drawback of this approach lies in various meteorological and instrumental sources of non-turbulent spectral width broadening which have to be recognised and accounted for. In the first part of the study, the theoretical background of the retrieval is detailed and all possible causes of spectral broadening are listed and evaluated. In the second part, the method is applied to four days of UHF diurnal boundary-layer observations, collected during the TRAC-98 experiment, for which in situ aircraft measurements were available. Comparison between radar-retrieved and in situ aircraft measurements yields a fairly good agreement with a linear correlation coefficient of about 0.9 and a residual bias less than 2 × 10^-4 m² s^-3. The analysis of derived from vertical and off-zenith observation leads to the recommendation, that in the boundary layer, where the wind is usually moderate, data collected by off-zenith beams should be used. Indeed, the measurement of the vertical spectral width, less affected by the large-scale broadening factor, can still be altered by the ground clutter removal.     

AN E – ε – ℓ TURBULENCE CLOSURE SCHEME FOR PLANETARY BOUNDARY-LAYER MODELS: THE NEUTRALLY STRATIFIED CASE

The standard E – model generates aplanetary boundary layerthat appears to be much too deep. The cause of theproblem is traced to the equation for the dissipationrate () of turbulent kinetic energy (E), specifically theparameterization of dissipation production anddestruction. In the context of atmosphericboundary-layer modelling, we argue that a part of thedissipation production should be modelled as the inputto the spectral cascade from the energy-containingpart of the spectrum, with a characteristic length , while the equilibrium imbalancebetween local production and destruction ofdissipation is modelled as proportional toE2/E, as in the standard model. Wepropose an E – – turbulence closurescheme, in which both the mixing length, m, and are prescribed. The importance ofthe equation is diminished, though itstill determines the dissipation rate in the Eequation.     

Markov-chain simulation of particle dispersion in inhomogeneous flows: The mean drift velocity induced by a gradient in Eulerian velocity variance

The Langevin equation is used to derive the Markov equation for the vertical velocity of a fluid particle moving in turbulent flow. It is shown that if the Eulerian velocity variance _wE is not constant with height, there is an associated vertical pressure gradient which appears as a force-like term in the Markov equation. The correct form of the Markov equation is: w(t + t) = aw(t) + b _wE + (1 – a)T _L ( _wE ²)/z, where w(t) is the vertical velocity at time t, a random number from a Gaussian distribution with zero mean and unit variance, T _L the Lagrangian integral time scale for vertical velocity, a = exp(–t/T _L), and b = (1 – a ²)^1/2. This equation can be used for inhomogeneous turbulence in which the mean wind speed, _wE and T _L vary with height. A two-dimensional numerical simulation shows that when this equation is used, an initially uniform distribution of tracer remains uniform.     

Three-dimensional modeling of synthetic cold fronts interacting with northern Alpine foehn

Summary The effect of the Alpine orography on prototype cold fronts approaching from the west is investigated by three-dimensional numerical model simulations. The numerical experiments cover a range of parameter constellations which govern the prefrontal environment of the front. Especially, the appearance and intensity of prefrontal northern Alpine foehn varies from case to case.The behaviour of a cold front north of the Alps depends much on the prefrontal condition it encounters. It is found that prefrontal foehn can either accelerate or retard the approaching front.An important feature is the pressure depression along the northern Alpine rim that results from the southerly foehn flow. In cases where this depression compensates the eastward directed pressure gradient associated with the largescale flow, the front tends to accelerate and the foehn breaks down as soon as the front passes. In contrast, the foehn prevents the front from a rapid eastward propagation if it is connected with a strong southerly wind component.No-foehn experiments are performed for comparison, where either the mountains are removed, or the static stability is set to neutral. Also shown are effects of different crossfrontal temperature contrasts.List of Symbols c _F propagation speed of a front - x, y horizontal grid spacing (cartesian system) - , horizontal grid spacing (geographic system) - t time step - z vertical grid spacing (cartesian system) - cross-frontal potential temperature difference - _i potential temperature step at an inversion - E turbulent kinetic energy - f Coriolis parameter - FGP frontogenesis parameter (see section 2.2) - g gravity acceleration (g=9.81 m s^–2) - vertical gradient of potential temperature - h terrain elevation (above MSL) - h _i height of an inversion (h _i =1000 m MSL) - H height of model lid (H=9000 m MSL) - K _M exchange coefficient of momentum - K _H exchange coefficient of heat and moisture - longitude - N Brunt-Väisäla-frequency - p pressure - Exner function (=T/) - latitude - q _v specific humidity - R _d gas constant of dry air (R _d =287.06 J kg^–1 K^–1) - density of dry air - t time - T temperature - potential temperature - TFP thermal front parameter (see section 2.2) - u, v, w cartesian wind components - u _g ,v _g geostrophic wind components - horizontal wind vector - x, y, z cartesian coordinates Abbreviations GND (above) ground level - MSL (above) mean sea level - UTC universal time coordinated With 20 Figures     

A comparison between two stochastic diffusion models in a complex three-dimensional flow

A Random Displacement Model (RDM) and a Langevin Equation Model (LEM) are used to simulate point releases in a complex flow around a building. The flow field is generated by a three-dimensional finite element model that uses the standardk- model to parameterize the turbulence. The RDM- and LEM-calculated concentration fields are compared, with particular emphasis on the structure in regions with high turbulence and/or recirculation. RDM and LEM results are similar qualitatively, but RDM tends to predict lower concentration levels. In part this is due to the higher early-time diffusion. However, the expected convergence at later times is prevented by the interaction of the diffusion with the strongly inhomogeneous mean flow.Notation a _i coefficient in the Langevin equation - b _ij coefficient in the Langevin equation - C ₀ the universal constant associated with the Lagrangian structure function - H building height (22.5 m) - K eddy viscosity - K _k eddy viscosity used in the definition of the off-diagonal Reynolds stresses - k turbulent kinetic energy - LEM Langevin Equation Model - p ₁ local unit vector in thexy-plane, orthogonal tos - p ₂ local unit vector, orthogonal to boths andp ₁ - RDM Random Displacement Model - s local unit vector in the streamline direction - T local decorrelation time (Lagrangian time scale) - U magnitude of the local Eulerian mean wind velocity - u _s total velocity in the streamline direction - u ₁ velocity component in thexy-plane, orthogonal to the streamline direction - u ₂ velocity component orthogonal to bothu _s andu ₁ - _i mean Eulerian wind velocity - W _i stochastic vector-valued Wiener process - x unit vector inx-direction - y unit vector iny-direction - z unit vector inz-direction - angle between thexy-plane and the mean wind streamline - angle between the projection in thexy-plane of the streamline and thex-axis - _ij the Kronecker delta function - rate of turbulence dissipation - _i/g_a the part ofa _i that contains mean wind and turbulence gradients - _ij inverse of a Reynolds stress tensor component - _ij shorthand for a quantity that defines a part of _i/g_a - _i shorthand for a quantity that defines a part of _i/g_a - _ij Reynolds stress tensor component     

Effects of heat and water vapor transport on eddy covariance measurement of CO2 fluxes

Flux densities of carbon dioxide were measured over an arid, vegetation-free surface by eddy covariance techniques and by a heat budget-profile method, in which CO₂ concentration gradients were specified in terms of mixing ratios. This method showed negligible fluxes of CO₂, consistent with the bareness of the experimental site, whereas the eddy covariance measurements indicated large downward fluxes of CO₂. These apparently conflicting observations are in quantitative agreement with the results of a recent theory which predicts that whenever there are vertical fluxes of sensible or latent heat, a mean vertical velocity is developed. This velocity causes a mean vertical convective mass flux (= _cw for CO₂, in standard notation). The eddy covariance technique neglects this mean convective flux and measures only the turbulent flux _c w. Thus, when the net flux of CO₂ is zero, the eddy covariance method indicates an apparent flux which is equal and opposite to the mean convective flux, i.e., _c w = – _c w. Corrections for the mean convective flux are particularly significant for CO₂ because _cw and _c w are often of similar magnitude. The correct measurement of the net CO₂ flux by eddy covariance techniques requires that the fluxes of sensible and latent heat be measured as well.     

An observational study of the structure of the nocturnal urban boundary layer

The formation mechanism of the nocturnal urban boundary layer (UBL), especially in the winter nighttime, was investigated based on the extensive field observations conducted during November 1984 in Sapporo, Japan. A strong, elevated inversion formed over the Sapporo urban area and the inversion base height was approximately twice the average building height. Velocity fluctuations _u, _w and Reynolds stress % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% WG1bWaaWbaaSqabeaacaaIXaaaaGGaaOGae8hiaaIaam4DamaaCaaa% leqabaGaaGymaaaaaaaaaa!3A9C!\[\overline {u^1 w^1 } \] had nearly uniform profiles within the nocturnal UBL and decreased with height above the UBL. On the other hand, temperature fluctuations _t , and heat fluxes % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% WG1bWaaWbaaSqabeaacaaIXaaaaGGaaOGae8hiaaIaeqiUde3aaWba% aSqabeaacaaIXaaaaaaaaaa!3B56!\[\overline {u^1 \theta ^1 } \] and % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% WG3bWaaWbaaSqabeaacaaIXaaaaGGaaOGae8hiaaIaeqiUde3aaWba% aSqabeaacaaIXaaaaaaaaaa!3B58!\[\overline {w^1 \theta ^1 } \] had peaks at the inversion base and small values within the nocturnal UBL. The turbulent kinetic energy budget showed that the turbulent transport term and shear generation from urban canopy elements are important in the nocturnal UBL development; the role of the buoyancy term is small. The turbulence data analysis and application of a simple advective model showed that the mechanism of UBL formation may be controlled by the downward transport of sensible heat from the elevated inversion caused by mechanically-generated turbulence.Nomenclature g accelaration due to gravity, m s^-2 - k turbulent kinetic energy, m² s^-1 - K _m eddy viscosity, m² s^-1 - L Monin-Obukhov lenght, m - p pressure, Kg m^-2 - U, V, W mean wind speed in the downwind, crosswind, and vertical directions, respectively, m s^-1 - u ¹, w ¹ wind speed fluctuation in the downwind and vertical direction, respectively, m s^-1 - u ₁ friction velocity, m s^-1 - % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% WG1bWaaWbaaSqabeaacaaIXaaaaGGaaOGae8hiaaIaam4DamaaCaaa% leqabaGaaGymaaaaaaaaaa!3A9C!\[\overline {u^1 w^1 } \] momentum flux, m²s^-2 - % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% WG1bWaaWbaaSqabeaacaaIXaaaaGGaaOGae8hiaaIaam4DamaaCaaa% leqabaGaaGymaaaaaaaaaa!3A9C!\[\overline {u^1 \theta^1 } \] sensible heat flux, m²s^-1°C - WD wind direction, deg - WS wind speed, m s^-1 - z altitude, m - Z _h inversion base height, m - Z _j wind maximum height, m - Z _t inversion top height, m - T _u-r heat island intensity, °C - temperature lapse rate at rural site, °C m^-1 - energy dissipation rate, m²s^-3 - ¹ Potential temperature fluctuation, °C - _* scaling temperature, (=-% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% WG1bWaaWbaaSqabeaacaaIXaaaaGGaaOGae8hiaaIaeqiUde3aaWba% aSqabeaacaaIXaaaaaaaaaa!3B56!\[\overline {u^1 \theta ^1 } \]/u_*) °C - mean potential temperature fluctuation, K - density of air, Kgm^-3 - K von Kármán constant (=0.4) - _u, _v, _w standard deviation of wind speed in the downwind, crosswind, and vertical directions, respectively, m s^-1 - standard diviation of temperature, °C     

Shear stress results from a mixed spectral finite-difference model: Application to the Askervein Hill project data

Beljaars et al. (1987) developed a model for neutrally stratified boundary-layer flow over roughness changes and topography. It has been discovered that a constant parameter, , was missing in the algebraic-stress closure equations of their paper. This omission also occurred in the MSFD model code but only affects the Askervein Hill shear-stress results for the E-- turbulence closure in Beljaars et al. It also changes the stress results of Karpik (1988), but not his conclusions regarding the robustness of his improved numerical scheme. The present paper demonstrates the effect of the omission of the parameter, , and tests the sensitivity of the model to variations in its value. The new results are compared with the data and model results of Zeman and Jensen (1987).     

Statistical properties of 49 years of rainfall rate events

Summary The paper presents a detailed investigation of magnitudes and properties pertaining to the large population of rainfall events recorded during 49 years at the Fabra Observatory in Barcelona. The study includes a statistical analysis of event durationT and rainfall quantityQ together with the statistical rainfall rate parameters: , ²(R) and maximum Rm within an event. The decorrelation time is also analysed. It is found thatQ, T, and can be well modelled by a lognormal distribution, but , ² and Rm are only so for a limited range of precipitation ratesR. The regression analysis between pairs of logarithms of the magnitudes investigated is generally good and a regression coefficient is often better than 0.9. Comparison with published work is also carried out. An attempt is made to discriminate between heavy and non-heavy rainfall rates, and the 50 mm/h threshold is used for the study. The twelve-monthly running average indicates that the rainfall amount has a small increasing trend over the fifty year period. However, this trend is reversed when considering heavy rains. Finally, the return period in years to exceeding a thresholdR within an event is also investigated and the distribution of the population of annual extremes is found to be Gumbel II.With 6 Figures     

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     

Numerical modelling of the turbulent flow developing within and over a 3-d building array,part ii: a mathematical foundation for a distributed drag force approach

In this paper, we lay the foundations of a systematic mathematical formulation for the governing equations for flow through an urban canopy (e.g., coarse-scaled building array) where the effects of the unresolved obstacles on the flow are represented through a distributed mean-momentum sink. This, in turn, implies additional corresponding terms in the transport equations for the turbulence quantities. More specifically, a modified k-- model is derived for the simulation of the mean wind speed and turbulence for a neutrally stratified flow through and over a building array, where groups of buildings in the array are aggregated and treated as a porous medium. This model is based on time averaging the spatially averaged Navier--Stokes equations, in which the effects of the obstacle--atmosphere interaction are included through the introduction of a volumetric momentum sink (representing drag on the unresolved buildings in the array).The k-- turbulence closure model requires two additional prognostic equations, namely one for the time-averaged resolved-scale kinetic energy of turbulence,, and another for the dissipation rate, , of . The transport equation for is derived directly from the transport equation for the spatially averaged velocity, and explicitly includes additional sources and sinks that arise from time averaging the product of the spatially averaged velocity fluctuations and the distributed drag force fluctuations. We show how these additional source/sink terms in the transport equation for can be obtained in a self-consistent manner from a parameterization of the sink term in the spatially averaged momentum equation. Towards this objective, the time-averaged product of the spatially averaged velocity fluctuations and the distributed drag force fluctuations can be approximated systematically using a Taylor series expansion. A high-order approximation is derived to represent this source/sink term in the transport equation for . The dissipation rate () equation is simply obtained as a dimensionally consistent analogue of the equation. The relationship between the proposed mathematical formulation of the equations for turbulent flow within an urban canopy (where the latter is treated as a porous medium) and an earlier heuristic two-band spectral decomposition for parameterizing turbulence in a plant canopy is explored in detail.