Numerical Simulation Of Wind Field For Prediction Of Regional Air Pollution

In the process of analyzing and predicting the dispersion of pollutants in the atmosphere, it is necessary to consider the features of atmospheric movement. Mastery of the rules of atmospheric movement makes successful study of the dispersion of pollutants and the impacts of pollution sources on the ambient environment. An conventional model called Gaussian dispersion model, which is based on the assumptions of atmospheric movement, surface conditions and dispersion of pollutants, is adopted to study the dispersion of pollutants in the atmosphere. However, it has some limits during application. With the rapid development of computation, mathematic models and numerical simulations are applied to analyze the processes of air pollution, which has become one of the most effective methods in this field.The spatial and temporal variations of wind fields play a dominant role in the research of transportation and dispersion of air pollutants. Thus, it is necessary to improve the accuracy of wind fields and enhance the analytic ability. Up to now, diagnosis and prognosis are two main methods of simulating wind fields.Wind fields produced by prognostic methods objectively reflect the real atmosphere and depend less on observations. However, on the other hand, this kind of models is complex, has a long runtime and especially when terrain is complicated, high spatial resolutions are usually required. By contrast, although diagnostic methods deal with more observations and rely more on them, this kind of methods is simpler and more real and has a shorter runtime. This is why diagnostic methods are commonly used. In order to produce high quality wind fields, high quality initial conditions are needed. Hence, it is considerable to use low resolution wind fields produced by prognostic methods as initial conditions and enhance the quality of wind fields using diagnostic methods, which makes simulations of the dispersion of pollutants more effective and predictions of air quality more accurate.In this paper one of the diagnostic methods is studied to simulate wind fields, using the wind fields from a low resolution prognostic model called MM5 as inputs of CALMET, which is a high resolution diagnostic model. The results of this study indicate that utilizing the coarsest prognostic meteorological model output in a diagnostic model provides an attractive option for generating accurate meteorological inputs for air quality modeling.