An important aspect in the design of wind farms and consequently in wind power production, is the sensitivity of wind turbines to wind gusts. This study aims at estimating the probability of occurrence of daily wind gusts for different atmospheric circulation regimes. The analysis is performed for a 30-year period (1979-2009) for the Hellinikon station in Athens, Greece. The proposed methodology estimates the probability of exceeding a wind gust speed value from the mean daily wind speed observations for a given atmospheric circulation pattern. The large-scale atmospheric circulation classification is based on a two-stage clustering approach, using Self-Organizing Maps as a clustering methodology. The classification is based on the Sea Level Pressure, the geopotential at 500hPa, the zonal and meridional wind components at 10m and at 850hPa, the specific humidity at 700hPa and the air and dew-point temperature at 2m. Following the gust factor method, different statistical models are trained for each of the eight identified atmospheric modes. The results demonstrate the suitability of the lognormal distribution for associating wind gusts and mean wind speeds. The adopted gust factor method provides accurate estimates of daily wind gust speeds and that the atmospheric circulation enhances the precision of the statistical models.
The importance of meteorological parameters and topography in determining the air pollution levels in a specific area is well established. The aim of this study is to model the association between air pollution levels and meteorological parameters for a single site in Athens, Greece. The statistical analysis is based on the Multiple Linear Regression (MLR) models for simulating the relationship amongst primary and secondary pollutants (CO, NO, NO2, O3 and SO2) and air temperature, wind speed, relative humidity and atmospheric boundary layer (ABL) depth. The meteorological variables are used as explanatory variables for training different statistical models for each pollutant. The analysis is performed for a twenty-year period (1990-2009) at 00Z and 12Z. Special emphasis is given to the most accurate representation of the ABL depth by using three different methods (i.e. Holzworth, virtual Richardson number and potential temperature gradient). The modeling results indicate the superior performance in the case where the ABL depth was calculated by the virtual Richardson number method. The results indicate the importance of meteorology in air quality along with the significance of other factors that increase air pollution variability in urban environments.
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