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Roland Stull

Abstract

Three of the atmospheric datasets that were originally used to verity statistical dispersion theory are reevaluated. These datasets are described as well by transilient turbulence theory as by statistical theory over the range of time periods of interest for practical dispersion problems. The limitations of transilient turbulence theory, particularly its inability to duplicate the linear growth of plume-width standard deviation at infinitesimally short times, are also investigated.

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Roland Stull

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An equation is presented for wet-bulb temperature as a function of air temperature and relative humidity at standard sea level pressure. It was found as an empirical fit using gene-expression programming. This equation is valid for relative humidities between 5% and 99% and for air temperatures between −20° and 50°C, except for situations having both low humidity and cold temperature. Over the valid range, errors in wet-bulb temperature range from −1° to +0.65°C, with mean absolute error of less than 0.3°C.

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Roland Stull
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Roland B. Stull

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Abstract not available.

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Roland B. Stull

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No abstract available.

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Edi Santoso and Roland Stull

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In the middle of the convective atmospheric boundary layer is often a deep layer of vertically uniform wind speed (M UL), wind direction, and potential temperature (θ UL). A radix layer is identified as the whole region below this uniform layer, which includes the classic surface layer as a shallower subdomain. An empirical wind speed (M) equation with an apparently universal shape exponent (A) is shown to cause observations from the 1973 Minnesota field experiment to collapse into a single similarity profile, with a correlation coefficient of roughly 0.99. This relationship is M/M UL = F(z/z R), where F is the profile function, z is height above ground, and z R is depth of the radix layer. The profile function is F = (z/z R)A exp[A(1 − z/z R)] in the radix layer (z/z R ⩽ 1), and F = 1 in the uniform layer (z R < z < 0.7z i). The radix-layer equations might be of value for calculation of wind power generation, wind loading on buildings and bridges, and air pollutant transport.

The same similarity function F with a different radix-layer depth and shape exponent is shown to describe the potential temperature (θ) profile: (θθ UL)/(θ 0θ UL) = 1 − F(z/z R), where θ 0 is the potential temperature of the air near the surface. These profile equations are applicable from 1 m above ground level to the midmixed layer and include the little-studied region above the surface layer but below the uniform layer. It is recommended that similarity profiles be formulated as mean wind or potential temperature versus height, rather than as shears or gradients versus height because shear expressions disguise errors that are revealed when the shear is integrated to get the speed profile.

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Edi Santoso and Roland Stull

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Lawrence Greischar and Roland Stull

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Turbulent flux measurements from five flights of the National Center for Atmospheric Research Electra aircraft during the Tropical Oceans and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) are used to test convective transport theory (CTT) for a marine boundary layer. Flights during light to moderate winds and under the clearest sky conditions available were chosen. Fluxes of heat, moisture, and momentum were observed by the eddy-correlation method. Mean kinematic values for the observed sensible and latent heat fluxes and momentum flux were 0.0061 K m s−1, 0.0313 g kg−1 m s−1, and 0.0195 m2 s−2, respectively.

For the range of mixed-layer wind speeds (0.8–8.4 m s−1) studied here, the version of CTT that includes the mixed effects of buoyant and shear-driven transport give a better fit to the observations than either the COARE bulk algorithm or the pure free-convection version of CTT. This is to be expected because both of those latter parameterizations were designed for light winds (<5 m s−1 approximately).

The CTT empirical coefficients listed in exhibited slight sensitivity to the COARE light flux conditions, compared to their previous estimates during larger fluxes over land. For example, COARE heat fluxes were roughly 10 times smaller than previous land-based flux measurements used to calculate CTT coefficients, but the corresponding empirical mixed-layer transport coefficients were only 3% smaller. COARE momentum fluxes were also roughly 10 times smaller, but the CTT coefficients were about four times smaller. The greater variation in momentum coefficient may be due, in part, to insufficient flight-leg length used to compute momentum fluxes, to uncertainties in the effects of the ocean surface current and waves, or perhaps to roughness differences.

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Qing Zhang and Roland Stull

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Two alternative parameterizations for nonlocal turbulence mixing are tested in a 1D boundary-layer model against a dataset from the 1983 Boundary-Layer Experiment (BLX83) in Oklahoma. One method, proposed previously by Stull and Driedonks, is based on a nonlocal approximation to the turbulence kinetic energy (TKE) equation. An alternate method, based on a nonlocal approximation to the Richardson number, is simplified here from earlier parameterizations for transilient turbulence theory. Convective mixed-layer simulations of the vertical profiles of mean variables and fluxes using both methods are compared to the BLX83 observations and to simulations using a traditional slab model.

The TKE method develops a surface layer that is too thick compared to BLX83 data, particularly in the early morning. It also lacks the subadiabatic lapse rate that is observed in the top of the mixed layer. The Richardson number approach produces more accurate mixed-layer profiles, but lacks the general physical interpretation of the TKE method. Nonlocal spectral decompositions of the flux and intensity of mixing confirm that large-size eddies dominate within the middle of the mixed layer. Based on this limited validation, the Richardson number method is recommended for convective boundary layers, but the TKE approach should be used for modeling more general boundary layers that can include clouds and stable and/or windy conditions.

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Roland B. Stull

Abstract

Cover hours are defined as the cloud-cover fraction times the number of hours those clouds are observed. Case study statistics of cover hours during 1990 for nonprecipitating low clouds at Madison, Wisconsin, indicate the potential for climatic impact by boundary-layer clouds. A total of 1476.6 cover hours by all low clouds are observed, of which the subset of scattered boundary-layer clouds contributes 33%. The subset of low clouds that are turbulently coupled to the ground contributes 1199.1 cover hours, which is 81% of the total observed and 13.7% of the total possible 8760 hours per year.

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