Large-eddy simulation of turbulent flow and dispersion over a complex urban street canyon

Turbulent flow and dispersion characteristics over a complex urban street canyon are investigated by large-eddy simulation using a modified version of the Fire Dynamics Simulator. Two kinds of subgrid scale (SGS) models, the constant coefficient Smagorinsky model and the Vreman model, are assessed. Turbulent statistics, particularly turbulent stresses and wake patterns, are compared between the two SGS models for three different wind directions. We found that while the role of the SGS model is small on average, the local or instantaneous contribution to total stress near the surface or edge of the buildings is not negligible. By yielding a smaller eddy viscosity near solid surfaces, the Vreman model appears to be more appropriate for the simulation of a flow in a complex urban street canyon. Depending on wind direction, wind fields, turbulence statistics, and dispersion patterns show very different characteristics. Particularly, tall buildings near the street canyon predominantly generate turbulence, leading to homogenization of the mean flow inside the street canyon. Furthermore, the release position of pollutants sensitively determines subsequent dispersion characteristics.     

The flow structure in the wake of a fractal fence and the absence of an “inertial regime”

Recent theoretical work has highlighted the importance of multi-scale forcing of the flow for altering the nature of turbulence energy transfer and dissipation. In particular, fractal types of forcing have been studied. This is potentially of real significance in environmental fluid mechanics where multi-scale forcing is perhaps more common than the excitation of a specific mode. In this paper we report the first results studying the detail of the wake structure behind fences in a boundary layer where, for a constant porosity, we vary the average spacing of the struts and also introduce fractal fences. As expected, to first order, and in the far-wake region, in particular, the response of the fences is governed by their porosity. However, we show that there are some significant differences in the detail of the turbulent structure between the fractal and non-fractal fences and that these override differences in porosity. In the near wake, the structure of the fence dominates porosity effects and a modified wake interaction length seems to have potential for collapsing the data. With regards to the intermittency of the velocities, the fractal fences behave more similarly to homogeneous, isotropic turbulence. In addition, there is a high amount of dissipation for the fractal fences over scales that, based on the energy spectrum, should be dominated by inter-scale transfers. This latter result is consistent with numerical simulations of flow forced at multiple scales and shows that what appears to be an “inertial regime” cannot be as production and dissipation are both high.     

Canopy-wake dynamics and wind sheltering effects on Earth surface fluxes

The atmospheric boundary layer adjustment at the abrupt transition from a canopy (forest) to a flat surface (land or water) is investigated in a wind tunnel experiment. Detailed measurements examining the effect of canopy turbulence on flow separation, reduced surface shear stress and wake recovery are compared to data for the classical case of a solid backward-facing step. Results provide new insights into the interpretation for flux estimation by eddy-covariance and flux gradient methods and for the assessment of surface boundary conditions in turbulence models of the atmospheric boundary layer in complex landscapes and over water bodies affected by canopy wakes. The wind tunnel results indicate that the wake of a forest canopy strongly affects surface momentum flux within a distance of 35–100 times the step or canopy height, and mean turbulence quantities require distances of at least 100 times the canopy height to adjust to the new surface. The near-surface mixing length in the wake exhibits characteristic length scales of canopy flows at the canopy edge, of the flow separation in the near wake and adjusts to surface layer scaling in the far wake. Components of the momentum budget are examined individually to determine the impact of the canopy wake. The results demonstrate why a constant flux layer does not form until far downwind in the wake. An empirical model for surface shear stress distribution from a forest canopy to a clearing or lake is proposed.     

The application of generalized linear mixed models to multi-level sampling for insect population monitoring

Iwao's quadratic regression or Taylor's Power Law (TPL) are commonly used to model the variance as a function of the mean for sample counts of insect populations which exhibit spatial aggregation. The modeled variance and distribution of the mean are typically used in pest management programs to decide if the population is above the action threshold in any management unit (MU) (e.g., orchard, forest compartment). For nested or multi-level sampling the usual two-stage modeling procedure first obtains the sample variance for each MU and sampling level using ANOVA and then fits a regression of variance on the mean for each level using either Iwao or TPL variance models. Here this approach is compared to the single-stage procedure of fitting a generalized linear mixed model (GLMM) directly to the count data with both approaches demonstrated using 2-level sampling. GLMMs and additive GLMMs (AGLMMs) with conditional Poisson variance function as well as the extension to the negative binomial are described. Generalization to more than two sampling levels is outlined. Formulae for calculating optimal relative sample sizes (ORSS) and the operating characteristic curve for the control decision are given for each model. The ORSS are independent of the mean in the case of the AGLMMs. The application described is estimation of the variance of the mean number of leaves per shoot occupied by immature stages of a defoliator of eucalypts, the Tasmanian Eucalyptus leaf beetle, based on a sample of trees within plots from each forest compartment. Historical population monitoring data were fitted using the above approaches.     

How sensitive are elasticities of long-run stochastic growth to how environmental variability is modelled?

A variable environment leaves a signature in a population's dynamics. Deriving statistical and mathematical models of how environmental variability affects population projections has - in the wake of reports of substantial climatic fluctuations - received much recent attention. If the model changes, then so too does the population projection. This is because a different model of environmental variability changes estimates of long-run stochastic growth, which is a function of demographic rates and their temporal sequence. Decomposing elasticities of long-run stochastic growth into constituent parts can assess the relative influence of different components. Here, we investigate the consequences of changing the environmental state definition, and therefore altering the shape of demographic rate distributions and their temporal sequence, by using age-structured matrix models to project vertebrate populations into the future under a range of environmental scenarios. The identity of the most influential demographic rate was consistent among all approaches that perturbed only the mean, but was not when only the variance was perturbed. Furthermore, the influence of each demographic rate fluctuated among projections by up to factors of six and two for changes to the variance and mean, respectively. These changes in influence depend in part upon how environmental variability - in particular, the color of environmental noise - is incorporated. In the light of predictions of increasing climatic variability in the future, these results suggest caution when drawing quantitative conclusions from stochastic population projections.     

Optimal foraging: Some simple stochastic models

Summary Some simple stochastic models of optimal foraging are considered. Firstly, mathematical renewal theory is used to make a general model of the combined processes of search, encounter, capture and handling. In the case where patches or prey items are encountered according to a Poisson process the limiting probability distribution of energy gain is found. This distribution is found to be normal and its mean and variance are specified. This result supports the use of Holling's disc equation to specify the rate of energy intake in foraging models. Secondly, a model based on minimization of the probability of death due to an energetic shortfall is presented. The model gives a graphical solution to the problem of optimal choices when mean and variance are related. Thirdly, a worked example using these results is presented. This example suggests that there may be natural relationships between mean and variance which make solutions to the problems of energy maximization and minimization of the probability of starvation similar. Finally, current trends in stochastic modeling of foraging behavior are critically discussed.     

Spatial–temporal structure of mixing interface turbulence at two large river confluences

Converging flows at stream confluences often produce highly turbulent conditions. The shear layer/mixing interface that develops within the confluence hydrodynamic zone (CHZ) is characterized by complex patterns of three-dimensional flow that vary both spatially and temporally. Previous research has examined in detail characteristics of mean flow and turbulence along mixing interfaces at small stream confluences and laboratory junctions; however few, if any, studies have examined these characteristics within mixing interfaces at large river confluences. This study investigates the structure of mean velocity profiles as well as spatial and temporal variations in velocity, backscatter intensity, and temperature within the mixing interfaces of two large river confluences. Velocity, temperature, and backscatter intensity data were obtained at stationary locations within the mixing interfaces and at several cross sections within the CHZ using acoustic Doppler current profilers. Results show that mean flow within the mixing interfaces accelerates over distance from the junction apex. Turbulent kinetic energy initially increases rapidly over distance, but the rate of increase diminishes downstream. Hilbert–Huang transform analysis of time series data at the stationary locations shows that multiple dominant modes of fluctuations exist within the original signals of velocity, backscatter intensity, and temperature. Frequencies of the largest dominant modes correspond well with predicted frequencies for shallow wake flows, suggesting that mixing-interface dynamics include wake vortex shedding—a finding consistent with spatial patterns of depth-averaged velocities at measured cross sections. Spatial patterns of temperature and backscatter intensity show that the converging flows at both confluences do not mix substantially, indicating that turbulent structures within the mixing interfaces are relatively ineffective at producing mixing of the flows in the CHZ.     

Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?

Quasi-Poisson and negative binomial regression models have equal numbers of parameters, and either could be used for overdispersed count data. While they often give similar results, there can be striking differences in estimating the effects of covariates. We explain when and why such differences occur. The variance of a quasi-Poisson model is a linear function of the mean while the variance of a negative binomial model is a quadratic function of the mean. These variance relationships affect the weights in the iteratively weighted least-squares algorithm of fitting models to data. Because the variance is a function of the mean, large and small counts get weighted differently in quasi-Poisson and negative binomial regression. We provide an example using harbor seal counts from aerial surveys. These counts are affected by date, time of day, and time relative to low tide. We present results on a data set that showed a dramatic difference on estimating abundance of harbor seals when using quasi-Poisson vs. negative binomial regression. This difference is described and explained in light of the different weighting used in each regression method. A general understanding of weighting can help ecologists choose between these two methods.     

Space-time estimation of grid-cell hourly ozone levels for assessment of a deterministic model

We present an approach to estimate hourly grid-cell surface ozone concentrations based on observations from point monitoring sites in space, for comparison with grid-based results from the SARMAP photochemical air-quality model for a region of northern California. Statistical estimation is carried out on a transformed (square root) scale, followed by back-transforming to the original scale of ozone in parts per billion, adjusting for bias and variance. We estimate a spatially-varying diurnal mean structure and a non-separable space-time correlation structure on the transformed scale. Temporal pre-whitening is followed by modelling of a spatially non-stationary, diurnally-varying spatial correlation structure using a spatial deformation approach. Comparisons of SARMAP model results with the estimated grid-cell ozone levels are presented.     

Shallow wake behind exposed wood-induced bar in a gravel-bed river

Recent theoretical research indicates that dynamics of shallow flows can be strongly affected by waves developing on the free surface. In this study a shallow wake with an oblique pressure wave behind a model of a tree-centered emergent bar is investigated in a gravel-bed river. A bar was constructed in a shallow river reach with nearly uniform flow. The structure of flow was assessed with an array of velocimeters. Flow visualization with a solute of fluorescent dye complemented the measurements and provided qualitative information on the wake behavior. This study indicates that quantitative criteria for shallow wakes classification developed in laboratory setups are not straightforward in explaining the field results. According to the wake stability criteria, the expected dynamics for examined wake flow is a vortex street (VS) type. Contrary to this expectation, measurements and visualizations in this study show that mean momentum differential and turbulent vortices in the wake decrease stronger than expected in VS type and therefore the wake should be classified as unsteady bubble type with a weak downstream instability. Analysis of velocity differential dynamics in the examined shallow wake suggests that the bed friction alone is insufficient to explain the inconsistency of VS criterion whereas accounting for advective fluxes driven by inhomogeneous pressure field leads to a correct prediction of the wake behavior.     

A discrete model for estimating the development time from egg to infecting larva of Ostertagia ostertagi parametrized using a fuzzy rule-based system

Ostertagia ostertagi is a nematode, predominantly affecting cattle in the Pampean region of Argentina. A mathematical model parametrized using fuzzy rule-based systems of the Takagi-Sugeno-Kant type (FTSK) for estimating the development time from egg to infecting larval stage L3 of the gastrointestinal parasite O. ostertagi is here proposed. The estimation of development time of O. ostertagi is essential for the generation of appropriate control mechanisms, since this provides information about the time when parasites are ready to migrate to pastures. For the purpose of reflecting the natural environmental conditions, the mean daily temperature is taken as the main and only regulator of the development time. Humidity conditions are considered to be sufficient for the normal development of the larvae. Hence the individual's daily growth is a function of its length and the mean temperature recorded on the previous day. It is expressed in terms of a difference equation with fuzzy parameters, which are defined using laboratory data. Model outputs are tested against results of field experiments. Simulation results are very satisfactory, yielding a mean estimation error (MEE) of 0.64 weeks, with variance 0.34, and a determination coefficient R² = 0.74. The model clearly exhibits an inverse relationship between development time and temperature both in controlled and in field conditions. It also exhibits a very sensitive response both to the order in which the temperature sequence occurs, - reproducing the differences observed between spring and autumn - and to the amplitude of the temperature range.     

Coefficient of variation of sea surface temperature (SST) as an indicator of coral bleaching

Coral bleaching has become a major problem on reefs around the world in recent decades. It is believed that mean temperature alone is the primary force driving this ecological phenomenon. We propose that variance in temperature in the short term is just as important as the mean. Thirty years of daily sea surface temperature (SST) data have been collected by the University of Puerto Rico at Mayaguez Marine Laboratory in La Parguera, PR. These data were collated and analyzed initially (by Amos Winter) for their relationship to coral bleaching in this area. We found that the data fell into three categories: high mean temperatures associated with severe bleaching, cooler mean temperatures associated with no bleaching, and years of high SSTs but with no coral bleaching. Here, we examined the relationship between mean temperature during those months in which bleaching occurred, temperature variance (as measured by standard deviation), and coefficient of variation (CV; i.e., SD standardized by the mean). We also derived a critical threshold temperature and level of resolution in time for calculating these statistics to clearly describe the circumstances of bleaching versus non-bleaching events, particularly at marginal bleaching temperatures. These characteristics were compared for the four warmest months of the year (July–October) for four warm bleaching years (1969, 1987, 1990, and 1995), four cool non-bleaching years (1984, 1985, 1986, and 1988), and two warm non-bleaching years (1994 and 2000). No relationship was found between the mean SST and SD in terms of predicting bleaching. The two primary statistics which, in concert, did indicate bleaching, however, were the short-term, biweekly mean temperature and its the associated CV. Bleaching occurs in association with both high temperatures and a high CV. The CV becomes a critical determinant of bleaching only when temperatures are ∼29.1–29.8°C. The warm, non-bleaching years were generally characterized by a CV of < 1.9 and a temperature range between 28.5 and 29.9°C. We conclude that increased mean SSTs alone are not sufficient to induce coral bleaching; a high variance in SST at marginal, lower bleaching temperatures can induce bleaching, and likewise, a low variance of such will not induce bleaching. This variance is most clearly described by the CV.     

Numerical simulation and optimization study of the wind flow through a porous fence

Three turbulence closure models (RNG k-ε, SST k-ω and RSM) were used to investigate the flow characteristics around a two-dimensional isolated porous fence. The comparison between the numerical results and the experimental measurements indicated that RSM model shows a better performance than the other two models. The aim of this paper is to accurately and efficiently determine the optimum porosity that attain the best shelter effect of the wind fence in the near wake region (0–4h_b) and in the far wake region (4h_b–10h_b) respectively, where h_b is the height of the fence. The gradient algorithm was adopted as the optimization algorithm and the RSM model was used to model turbulent features of the flow. The shelter effect was parameterized by the peak velocity ratio involving velocity and turbulence. The objective was to reduce the peak velocity ratio in the near or far wake region by changing the design variable porosity (?) of the fence, which ranged between 2 and 60%. The results revealed that a porosity of 10.2% was found as the optimum value giving rise to the best shelter effect in the near wake region, and ? = 22.1% was determined in the case of the far wake region. In addition, based on the proposed optimization method, it is found that the recirculating bubble behind the fence can only be detected when ? < 29.9%.     

Flow structure and turbulence characteristics downstream of a spanwise suspended linear array

Laboratory experiments are conducted to quantify the mean flow structure and turbulence properties downstream of a spanwise suspended linear array in a uniform ambient water flow using Particle Tracking Velocimetry. Eighteen experimental scenarios, with four depth ratios (array depth to water column depth) of 0.35, 0.52, 0.78, and 0.95 and bulk Reynolds number (length scale is the array depth) from 11,600 to 68,170, are investigated. Three sub-layers form downstream of the array: (1) an internal wake zone, where the time-averaged velocity decreases with increasing distance downstream, (2) a shear layer which increases in vertical extent with increasing distance downstream of the array, and the rate of the increase is independent of the bulk Reynolds number or the depth ratio, and (3) an external wake layer with enhanced velocity under the array. The location of the shear layer is dependent on the depth ratio. The spatially averaged and normalized TKE of the wake has a short production region, followed by a decay region which is comparable to grid turbulence decay and is dependent on the depth ratio. The results suggest that the shear layer increases the transfer of horizontal momentum into the internal wake zone from the fluid outside of the array and that the turbulence in the internal wake zone can be modeled similarly to that of grid turbulence.     

Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model

Mesoscale transport and dispersion of air pollutants from a few major point sources in the Mississippi Gulf coastal region is calculated using a coupled modeling system consisting of the atmospheric dynamical model WRF and the lagrangian particle model HYSPLIT. The sensitivity of the dispersion model results to the meteorological fields is studied by conducting an ensemble of simulations using the WRF model for the same dispersion case. Several parameterization schemes for the physical processes of boundary layer turbulence and land surface temperature/moisture prediction in WRF are used in various combinations to produce different meteorological members which are then used for dispersion simulation. The uncertainty in the simulated concentration probabilities to the meteorological model configurations and the ensemble mean are presented. The parameters used for determining the uncertainties include the wind fields, temperature, area of concentration and the levels of concentration. The results indicate that dispersion model results are influenced by the choices made in respect of the planetary boundary layer and land surface schemes in the mesoscale model to produce the meteorological forecast thereby leading to certain amount of uncertainty in the resultant concentrations. Results show that the specific choices made about the atmospheric model configuration can significantly after the simulated concentrations.     

A Quantitative Analysis of Energy Dissipation among Three Typical Air Entrainment Phenomena

This study investigates energy dissipation due to air bubble entrainment for three typical phenomena; a hydraulic jump, a 2-D vertical plunging jet and a vertical circular plunging jet into water. A simple model is presented here which enables to estimate the energy transformation and dissipation achieved by air bubbles quantitatively for three above phenomena. The average rate of energy dissipation by air bubbles obtained from the experimental data are 25%, 1.4%, and 2.15% with respect to total energy loss for the hydraulic jump, 2-D vertical plunging jet and vertical circular plunging jet, respectively.     

Statistical inference using stratified judgment post-stratified samples from finite populations

This paper develops statistical inference for population mean and total using stratified judgment post-stratified (SJPS) samples. The SJPS design selects a judgment post-stratified sample from each stratum. Hence, in addition to stratum structure, it induces additional ranking structure within stratum samples. SJPS is constructed from a finite population using either a with or without replacement sampling design. Inference is constructed under both randomization theory and a super population model. In both approaches, the paper shows that the estimators of population mean and total are unbiased. The paper also constructs unbiased estimators for the variance (mean square prediction error) of the sample mean (predictor of population mean), and develops confidence and prediction intervals for the population mean. The empirical evidence shows that the proposed estimators perform better than their competitors in the literature.     

Sensitivity of benthic community respiration and primary production to changes in temperature and light

In-situ measurements of benthic community respiration and primary production on an intertidal sandflat in Nova Scotia, Canada were used to test the hypothesis that community responses to light and temperature were similar within and between seasons. Multiple regression indicated that mean incubation temperature explained 57% of the variance in total sediment oxygen demand (TOD). The logarithmic relationship between TOD and temperature (Q₁₀=6.5) was not significantly different between fall and spring, suggesting no acclimation within season. Chemical oxygen demand measured in formalin-poisoned cores averaged 30% of TOD. Microalgal gross primary production (GROSS) was measured as oxygen production in light cores. Mean incubation temperature and sediment chlorophylla explained 56% of the variance. The linear relationship between GROSS and temperature had Q₁₀=2.0. When production was normalized to chlorophylla [GROSS(SP)], seasonal production-temperature curves were significantly different. The spring curve had Q₁₀=3.3; in the fall, production and temperature were not related. GROSS(SP)-light curves derived from Plexiglas shade experiments in the field were linear and not significantly different between fall and spring. Temperature alone was a better predictor of GROSS(SP) than light, even when P/I curves were adjusted for temperature. One can therefore model community respiration and photosynthesis at this site using daily averages in temperature, which are then summed over longer time scales to estimate monthly or seasonal rates.     

Characteristics of surface layer turbulence in coastal area of Qatar

A characterization of the atmospheric surface layer in coastal region of Qatar is presented in the present paper. The micrometeorological data is collected in the New Doha International Airport (25.25N, 51.62E), at a pier 500 m into the ocean by deploying tower based instruments during the month of June-2011. The normalized variance of the three components of velocity and temperature are studied within the framework of Monin–Obukhov similarity theory. The normalized variances of stream wise and transverse velocity at near-neutral condition are found to be consistent with the reported values from measurement sites around the world, but the normalized variance of vertical velocity at near-neutral condition is found to be consistently lower in the present case. The dependency of the normalized velocity variances with buoyancy parameter indicates a higher growth rate of variances, compared to other investigations. The non-dimensional variance of temperature matches well with the past investigations, but the correlation between vertical velocity and temperature indicates higher magnitude for the present location.     

近50年沈阳气温变化与城市化发展的关系

利用沈阳地区5个气象站近50年的年平均气温数据,采用线性倾向估计、累积距平、M-K检验和Yamamoto法分析沈阳地区气温的年代际变化及跃变;采用趋势拟合与相关分析,研究年平均气温与城市人口、GDP、全社会固定资产投资总额、能源消耗量、建成区面积和竣工房屋建筑面积6项城市发展指标的关系。结果表明：（1）近50年沈阳地区的年平均气温缓慢上升,城区气温高于郊区,城区站增温率为0.210℃/10 a,郊区站为0.321℃/10 a,总的增温率为0.284℃/10 a。（2）近50年沈阳气温变化分为2个阶段,1960—1987年是偏冷阶段,大多数年份为负距平;1988—2009年是增暖阶段,其中21年为正距平值。（3）M—K法和Yamamoto法都检测出1987年为沈阳气温的突变年份。（4）沈阳人口、GDP、全社会固定资产投资总额、竣工房屋建筑面积和年平均气温有着显著的相关性,表明它们与沈阳年平均气温变化关系密切,建成区面积和能源消耗与气温变化不显著。