The performance of RAMS in representing the convective boundary layer structure in a very steep valley

Data from a comprehensive field study in the Riviera Valley of Southern Switzerland are used to investigate convective boundary layer structure in a steep valley and to evaluate wind and temperature fields, convective boundary layer height, and surface sensible heat fluxes as predicted by the mesoscale model RAMS. Current parameterizations of surface and boundary layer processes in RAMS, as well as in other mesoscale models, are based on scaling laws strictly valid only for flat topography and uniform land cover. Model evaluation is required to investigate whether this limits the applicability of RAMS in steep, inhomogeneous terrain. One clear-sky day with light synoptic winds is selected from the field study. Observed temperature structure across and along the valley is nearly homogeneous while wind structure is complex with a wind speed maximum on one side of the valley. Upvalley flows are not purely thermally driven and mechanical effects near the valley entrance also affect the wind structure. RAMS captured many of the observed boundary layer characteristics within the steep valley. The wind field, temperature structure, and convective boundary layer height in the valley are qualitatively simulated by RAMS, but the horizontal temperature structure across and along the valley is less homogeneous in the model than in the observations. The model reproduced the observed net radiation, except around sunset and sunrise when RAMS does not take into account the shadows cast by the surrounding topography. The observed sensible heat fluxes fall within the range of simulated values at grid points surrounding the measurement sites. Some of the scatter between observed and simulated turbulent sensible heat fluxes are due to sub-grid scale effects related to local topography.     

On a Strategy for Reducing Short-range Daytime Ground Level Concentrations Due to Emissions from Very Tall Stacks

Abstract

Airborne fine particle sulfur data from the summer intensive of Project MOHAVE (Measurement of Haze and Visual Effects) was analyzed by the Receptor Model Applied to Patterns in Space (RMAPS) model, a novel multivariate receptor-oriented model that applies to secondary and primary species. The sulfur data from 17 sites were found to be well predicted by three spatial patterns interpreted as sources along the valley of the Colorado River; transport from sources located to the southwest; and transport from sources located to the southeast. The model was tested by using parameters derived from the 17-site data set to apportion sulfur for six sites that were not part of the original data set. The sulfur apportionment for these six sites was in agreement with the original apportionment and the physical interpretation of the spatial patterns given above. The effects of systematic and random error on the sulfur apportionment were estimated. The amount of sulfur associated with the Colorado River valley sources was rather insensitive to both types of error. For the two sites in the Grand Canyon National Park, the fraction of total particulate sulfur from the Colorado River valley source is estimated to be in the range of 27-65% at Meadview and 11-28% at Hopi Point.     

Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants

We apply the entropy-based Bayesian optimizing approach of Le and Zidek to the spatial redesign of the extensive air pollution monitoring network operated by Metro Vancouver, in the Lower Fraser Valley, British Columbia. This method is chosen because of its statistical sophistication, relative to other possible approaches, and because of the very rich, two-decade long data record available from this network. The redesign analysis is applied to ozone, carbon monoxide and PM_2.5 pollutants.     

A framework for evaluating regional-scale numerical photochemical modeling systems

This paper discusses the need for critically evaluating regional-scale (~200–2,000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model’s credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.