Dispersion Coefficients for Gaussian Puff Models

The Gaussian distribution is a good approximation for transient (instantaneously released) puff concentration distributions within a short period of time after release. Artificial neural network (ANN) models for puff dispersion coefficients were developed, based on observations from field experiments covering a wide range of meteorological conditions (in March, May, August and November). Their average predictions were in very good agreement with measurements, having high correlation coefficients (r > 0.99). A non-linear multi-variable regression model for dispersion coefficients was also developed, under the assumption that puff dispersion coefficients increase with time, and follow power laws. Both ANN-based and multi-regression non-linear models were able to use easily measured atmospheric parameters directly, without the necessity of predefining the Pasquill stability category. Predictions of ANN-based and multi-regression-based Gaussian puff models were compared with those of Gaussian puff models using Slade’s dispersion coefficients and COMBIC, a sophisticated model based on Gaussian distributions. Predictions from our two new models showed better agreement with concentration measurements than the other Gaussian puff models, by having a much higher fraction within a factor of two of measured values, and lower normalized mean square errors.     

Extension of a fluctuating plume model of tracer dispersion to a sheared boundary layer and to a large array of obstacles

Fluctuating plume models provide a useful conceptual paradigm in the understanding of plume dispersion in a turbulent flow. In particular, these models have enabled analytical predictions of higher-order concentration moments, and the form of the one-point concentration probability density function (PDF). In this paper, we extend the traditional formalism of these models, grounded in the theory of homogeneous and isotropic turbulent flow, to two cases: namely, a simple sheared boundary layer and a large array of regular obstacles. Some very high-resolution measurements of plume dispersion in a water channel, obtained using laser-induced fluorescence (LIF) line-scan techniques are utilised. These data enable us to extract time series of plume centroid position (plume meander) and dispersion in the relative frame of reference in unprecedented detail. Consequently, experimentally extracted PDFs are able to be directly compared with various theoretical forms proposed in the literature. This includes the PDF of plume centroid motion, the PDF of concentration in the relative frame, and a variety of concentration moments in the absolute and relative frames of reference. The analysis confirms the accuracy of some previously proposed functional forms of model components used in fluctuating plume models, as well as suggesting some new forms necessary to deal with the complex boundary conditions in the spatial domain.     

Modelling Near-Surface Low Winds over Land under Stable Conditions: Sensitivity Tests,Flux-Gradient Relationships,and Stability Parameters

Low or weak wind-speed conditions, roughly defined as the periods when the mean wind speed at 10 m above the ground is 2 ms⁻¹ or less, are of considerable practical interest. However, they are not readily amenable to treatment within prognostic meteorological models and, consequently, difficult to predict, especially when the ambient stability is strong. In this paper, we apply an E − ε prognostic meteorological model to simulate near-surface meteorology and, focusing on low wind speeds, compare the predictions with measurements from two independent datasets. A sensitivity analysis is performed to investigate the possible reasons for the relatively inferior model performance for low winds when the atmosphere is stably stratified. A comprehensive data analysis is carried out to study low wind stable conditions, concentrating on the validity of various forms of flux–gradient relationships for momentum and heat within the framework of the Monin-Obukhov similarity theory, which models employ for calculating surface fluxes. The observed behaviour of various stability parameters, such as the Richardson number, is investigated. The results point to inadequacies of the current flux–gradient relationships, especially regarding momentum, under strongly stable conditions as being a dominant reason for the poor low wind predictions. The modelling issues identified are not just restricted to the present model, but are general in nature. The use of an alternative stability function for momentum under strongly stable conditions is explored. It results in improved model performance for low winds; however, further research is needed to better understand strongly stable flows in the lower atmosphere and to develop methods that can translate that understanding to operational meteorological modelling.     

A 3-dimensional nonhydrostatic dispersion modeling system for modeling of atmospheric transport and diffusion over coastal complex terrain in the Hongkong-Shenzhen area

Summary A dispersion modeling system consisting of a three-dimensional PBL model NHECM (non-hydrostaticE- closure model) and SLPTDM (seven-level puff transport and diffusion model) is developed to simulate the transport and dispersion of pollutant over coastal complex terrain. As an application of the system, the transport and dispersion of SO₂ released from an elevated point source are simulated during typical sea-land breeze circulation in the Hongkong-Shenzhen area of China. The NHECM provides time-varying, three-dimensional distributions of wind and turbulence fields to the SLPTDM. The NHECM predictions compare well with observational data. Reflection of both the ground and the mixing layer top and penetration of the mixing layer top are improved in the SLPTDM. Results obtained from the system indicate that temporal variation and nonuniformity of airflow and turbulence obviously affect the concentration distributions, especially during the sea-land breeze transition period. A diurnal cycle of the GLC (ground-level concentration) is discussed. The results are compared with those estimated using a Gaussian model. The study's results illustrate the complexity of the dispersion patterns due to diurnal effects and mesoscale circulations, and demonstrate the potential of the mesoscale atmospheric dispersion modeling system for studies of air quality in complex terrain.With 8 Figures     

基于遗传优化BP神经网络的水稻气象产量预报模型

利用1951—2010年江苏省水稻产量及同期14个气象站点的逐日平均气温、降水资料,采用因子膨化及相关分析,研究了水稻气象产量的影响因子及影响时段。在此基础上建立了逐步回归、PCA-BP神经网络以及PCA-GA-BP神经网络3种产量预报模型。结果表明:(1)7—9月份是水稻产量形成的关键时期,对气温、降水的变化最为敏感,气温对气象产量的影响大于降水;(2)两种神经网络模型预报效果好于回归模型;(3)遗传优化的神经网络模型比未优化模型的训练速度提高了70%左右,预报精度也提高了4.3%。     

Modeling of pollutant dispersion in sea breeze conditions using a Lagrangian model

Summary This work presents a numerical study of non-reactive pollutant dispersion in sea breeze conditions. Sea breeze circulation is investigated using a 3-D mesoscale meteorological model. Simulation was conducted for the area of Tarragona (Spain) which has an important petrochemical industry in the coastal region and complex terrain. Results from the meteorological model were used as input to a Lagrangian particle model in order to analyze the pollutant dispersion of an elevated plume emitting near the shoreline. The simulation was performed for 24 h and an analysis of the meteorological and concentration fields was untertaken for this time period. The results are compared with measured surface data. Good correlation exists between observed and simulated conditions indicating that the coupling of the meteorological and particle models provides a good tool for analyzing air pollution in complex situations.With 13 Figures     

A study of vertical velocity distributions in the planetary boundary layer

Until recently, pollution dispersion models have made predictions on the basis that the pollutant concentration is Gaussian. Such is not the case for convective conditions where the observed vertical velocity distribution is skewed towards the updraught portion of the distribution. One recent dispersion model assumes that the observed distribution can be synthesized by superimposing two Gaussians of appropriate means, variances and amplitudes.In the current paper, two techniques for deriving the constituent distributions are investigated. The first technique is based on conditionally sampling the vertical velocity time series and partitioning the vertical velocity samples into two sets — one set recorded when the sensor was experiencing an updraught and the other when the sensor was experiencing a downdraught. The second method consists of fitting two Gaussian distributions to the observed data and adjusting these using an iterative procedure until a specified tolerance is achieved.Both techniques give similar results which compare favourably with results obtained by other researchers. Assumptions, as well as advantages and disadvantages of each technique are also discussed.     

层状云播云线源非均匀、非定常输送扩散数值模拟

大多数层状云播云线源输送扩散模式常采用一些不合理的假设，使得输送扩散过程中起重要作用的一些物理过程被忽略。烟团轨迹模式能够处理源参数和气象要素的时空变化，适用于各种尺度和各类源的输送扩散模拟。因此，本文建立了一个模拟层状云播云线源输送扩散的三维时变烟团轨迹模式，并考虑了地形，垂直风切变作用，播云参数的时空变化和线源的湿清除效应。增雨作业实例我的数值模拟研究结果表明：模拟的播云线源输送扩散特征与输送扩散的基本理论和一般规律相吻合，并且再现了非垂直（与风向上）多条播云线源非均匀非定常的输送扩散特点。浓度等值线水平，垂直分布均不规则，比均匀定常点源更为复杂，整体不满足高斯浓度分布，表明在这种情况下均匀定常模式已不适用。另外，模式能够精确地模拟出播云线间的精细结构。     

Short-term droughts forecast using Markov chain model in Victoria,Australia

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

     

Evolving Turbulence Realizations of Atmospheric Flow

A significant difference exists between estimates of contaminant atmospheric transport and dispersion calculated by an ensemble-averaged model and the turbulent details of any particular atmospheric transport and dispersion realization. In some cases, however, it is important to be able to make inferences of these realizations using ensemble-averaged models. It is possible to make such inferences if there are sensors in the field to report contaminant concentration observations. Any information determined about the atmospheric transport and dispersion realization can then be assimilated into a forecast model. This approach can enhance the accuracy of the atmospheric transport and dispersion forecast of a particular event. This work adopts that approach and reports on a genetic algorithm used to optimize the variational problem. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments. The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.     

Comparison of Two Closely Located Meteorological Measurement Sites and Consequences for Their Areal Representativity

We compare meteorological data collected at the Met Office Research Unit, Cardington, UK with similar data from a temporary meteorological station located approximately 8.5 km away. Data were examined for a period of 10 months to ascertain differences in mean quantities, and in heat and radiation budgets, at different heights between the two locations, one of which is located in a wide shallow valley, the other on a plateau at the valley edge. Results reveal that screen-level variables at the two sites show the greatest differences in mean quantities, for most conditions, but that at 50 m the differences are negligible, indicating that temperatures had become aggregated and homogeneous at that height. For flux measurements between 10 and 50 m, however, significant differences were observed at certain times of the year, which appear to be related to local vegetation and soil conditions, and these are discussed. The study also presents data for stable conditions that show that temperature differences at screen level are again the most significant difference between the two sites. On average these were not large, but on occasions discrepancies at low levels (up to approximately 10 m) as large as 5°C were observed. It is thought that these greater differences during stable conditions may be caused by cold air pooling at the valley site, despite the very shallow orography there. Data from the two sites have been compared with forecasts from two Met Office mesoscale models. Results show that, during daytime hours, model-predicted values lie outside the range of values observed at the two sites, indicating a possible model bias. However, the opposite was true for most nighttime hours, during which model values fell within the range observed at the two sites, indicating that at night the model predictions are representative of the region. In this case, however, comparison of the model prediction with either one of the available observations could lead to the false conclusion that the model temperatures were either too high or too low, depending on which observational site was used for the comparison.     

Acceptance criteria for urban dispersion model evaluation

The authors suggested acceptance criteria for rural dispersion models’ performance measures in this journal in 2004. The current paper suggests modified values of acceptance criteria for urban applications and tests them with tracer data from four urban field experiments. For the arc-maximum concentrations, the fractional bias should have a magnitude <0.67 (i.e., the relative mean bias is less than a factor of 2); the normalized mean-square error should be <6 (i.e., the random scatter is less than about 2.4 times the mean); and the fraction of predictions that are within a factor of two of the observations (FAC2) should be >0.3. For all data paired in space, for which a threshold concentration must always be defined, the normalized absolute difference should be <0.50, when the threshold is three times the instrument’s limit of quantification (LOQ). An overall criterion is then applied that the total set of acceptance criteria should be satisfied in at least half of the field experiments. These acceptance criteria are applied to evaluations of the US Department of Defense’s Joint Effects Model (JEM) with tracer data from US urban field experiments in Salt Lake City (U2000), Oklahoma City (JU2003), and Manhattan (MSG05 and MID05). JEM includes the SCIPUFF dispersion model with the urban canopy option and the urban dispersion model (UDM) option. In each set of evaluations, three or four likely options are tested for meteorological inputs (e.g., a local building top wind speed, the closest National Weather Service airport observations, or outputs from numerical weather prediction models). It is found that, due to large natural variability in the urban data, there is not a large difference between the performance measures for the two model options and the three or four meteorological input options. The more detailed UDM and the state-of-the-art numerical weather models do provide a slight improvement over the other options. The proposed urban dispersion model acceptance criteria are satisfied at over half of the field experiments.     

Numerical simulations of pollutant dispersion in an idealized urban area,for different meteorological conditions

In order to estimate the impacts of buildings on air pollution dispersion, numerical simulations are performed over an idealized urban area, modelled as regular rows of large rectangular obstacles. The simulations are evaluated with the results of the Mock Urban Setting Test (MUST), which is a near full-scale experiment conducted in Utah’s West Desert area: it consists of releases of a neutral gas in a field of regularly spaced shipping containers. The numerical simulations are performed with the model Mercure_Saturne, which is a three-dimensional computational fluid dynamics code adapted to atmospheric flow and dispersion simulations. It resolves complex geometries and uses, in this study, a k–∈ closure for the turbulence model. Sensitivity studies focus on how to prescribe the inflow conditions for turbulent kinetic energy. Furthermore, different sets of coefficients available in the literature for the k–∈ closure model are tested. Twenty MUST trials with different meteorological conditions are simulated and detailed analyses are performed for both the dynamical variables and average concentration. Our results show overall good agreement according to statistical comparison parameters, with a fraction of predictions for average concentration within a factor of two of observations of 67.1%. The set of simulations offers several inflow wind directions and allows us to emphasize the impact of elongated buildings, which create a deflection of the plume centerline relative to the upstream wind direction.     

Analysis and numerical simulation of the meteorological observations at a tropical coastal site Chennai in India

Meteorological measurements were carried out at North Chennai semi rural area during pre-monsoon period as a part of an air quality study program. Analysis of the data showed the effects of coastal terrain namely the land-sea breeze circulation, temperature cooling during the sea breeze, difference in onset times at these sites etc. Sea breeze onset was observed with a sharp turning of the wind from westerly to south easterly associated with rise in wind speed. Advection speed of the front was about 2.0 m s^− 1. A simple mesoscale meteorological model (MAM-I) developed at Kalpakkam for coastal atmospheric dispersion estimation was used to simulate the observed characteristics. All the major features observed could be simulated by the model while significant difference was noticed in sea breeze frontal movement. MAM results were also inter-compared with MM5. There were no significant differences in the estimate of mean parameters by both the models. It is concluded that the simple model, which takes less run time in a desktop PC, is adequate enough for practical application of providing wind field for plume dispersion models at coastal sites.     

基于统计降尺度方法的夏季降水精细化预报

利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好.     

Prediction Of Air-Pollution Episodes

Mathematical models that can properly predict dispersion and transport ofatmospheric pollutants are an essential element in the development of warningand control strategies. Proper forecasts of atmospheric boundary-layer heightand its vertical mean wind speed provide a basis for predictions of air-pollutionconcentrations under meteorological conditions that vary horizontally, vertically,and temporally.A prognostic model is developed to predict the ventilation factor using forecast valuesof meteorological elements normally provided from meteorological agencies, in addition to specifications of terrain. Therefore, the ventilation factor and concentrations of pollutants could be predicted. Subsequent to the dependence of the present method on easily obtainable data, it avoids the non-generality and uncertainty followed from the application of some empirical relations. Even more, the method seems to provide high accuracy in validity tests.     

季风转换对深圳地区呼吸系统疾病的影响及预测研究

深圳地处我国华南沿海季风敏感区,为探究季风等气象和污染要素对其呼吸系统疾病发病的影响和其预测相关就诊风险的可行性,本文利用当地2015-2016年呼吸系统疾病就诊人数资料及同期气象和污染物资料,并运用BP人工神经网络和LSTM网络构建呼吸系统疾病就诊人数预测模型。结果显示:每年九月份开始,冬季风的冷胁迫效应会使相关人群呼吸系统疾病发病人数波动式增加,直至次年冬季风向夏季风转换前的三月份发病人数达到峰值；而夏季风控制期间当地居民呼吸系统疾病发病人数呈波动式减少态势,比峰值期间减少35%；另外,该地不同呼吸系统疾病其主控因素也不相同；对比两种预测模型,总体上LSTM网络预报模型对深圳呼吸系统疾病风险预测准确率更高,可以满足健康气象预报服务业务需求。     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

Tracer Flux Balance at an Urban Canyon Intersection

Despite their importance for pollutant dispersion in urban areas, the special features of dispersion at street intersections are rarely taken into account by operational air quality models. Several previous studies have demonstrated the complex flow patterns that occur at street intersections, even with simple geometry. This study presents results from wind-tunnel experiments on a reduced scale model of a complex but realistic urban intersection, located in central London. Tracer concentration measurements were used to derive three-dimensional maps of the concentration field within the intersection. In combination with a previous study (Carpentieri et al., Boundary-Layer Meteorol 133:277–296, 2009) where the velocity field was measured in the same model, a methodology for the calculation of the mean tracer flux balance at the intersection was developed and applied. The calculation highlighted several limitations of current state-of-the-art canyon dispersion models, arising mainly from the complex geometry of the intersection. Despite its limitations, the proposed methodology could be further developed in order to derive, assess and implement street intersection dispersion models for complex urban areas.