“Numerical Weather Prediction models and climate data interpretation using data mining techniques.”

Abstract

Numerical models along with some data mining techniques can represent a major goal of atmospheric modelling to provide a better understanding of processes determining the dynamical and chemical state of the atmosphere. The forecasting of air pollution, should provide a good simulation for the vertical fluxes diffusion of pollutants in the whole column of the air within the boundary layer. In this work an experiment was accomplished to study the using of the numerical model RegCM4.7 to predict the PM10 mass concentration levels originated by two cases of sand storm. As the pollutant dispersion occurred within the planetary boundary layer PBL, it was focused on three PBL schemes for running the model (Holtslag, UW and GFS). The results showed that the model gave a good representation for the case study. Concerning the PM10 concentration, the results of Holslag scheme came to be more accurate than UW and GFS schemes. The consistency of the model was also examined and the results of the relation among the wind speed, PBLH and PM10 concentration proved during the sand storm. Hence we can conclude that strong wind speed and thick PBL produce a good dispersion of pollutants which consequently led to decrease the concentration of the PM10, and give healthier conditions and produce higher ventilation index. The results of the numerical model was interpreted by using data mining techniques to provide the conditions and the suitable criteria of the occurrence of different dust phenomena according to numerical model’s output data. The thesis consists of five chapters, In chapter one we introduced all the necessary definitions, mathematical background and tools on which the study is based on.