OZONE AND GROSSWETTERLAGEN ANALYSIS FOR THE MUNICH METROPOLITAN-AREA

Meteorological conditions have a decisive impact on surface ozone conc entrations. In this study, an empirical model is used to explain the i nterdependence of ozone and grosswetterlagen. Different meteorological parameters such as air temperature, global solar radiation, relative humidify, wind direction and wind speed are used. Addit ional nitric o xide (NO) was taken as a representative for the emission situation and ozone maximum of the preceding day in order to evaluate the developme nt of the photochemical situation. The dataset includes data collected over a period of three years (1992-1994) from three stations outside of Munich and one in the center of Munich. All values become variables by calculating means, sums or maxima of the basic dataset consisting of half-hour means. Seasonal periodicity of data is detected with Four ier analysis and eliminated by a division method after computing a sea sonal index. The dataset is divided into three different grosswetterla gen groups, depending on main wind direction. One mostly cyclonic (wes terly winds), one mixed (alternating winds) and one only anticyclonic (easterly winds). The last is completed with one summertime group incl uding values from April to August. Factor analysis is performed for ea ch group to obtain independent linear variable combinations. Overall, relative humidity is the dominant parameter, a typical value indicatin g meteorological conditions during a grosswetterlage. Linear multiple regression analysis is performed using the factors obtained to reveal how the ozone concentrations are explained in terms of meteorological parameters and NO. The results improve from cyclonic to anticyclonic g rosswetterlagen in conformance with the increasing significance of pho tochemistry, indicated by the high solar radiation and high temperatur e, and the low relative humidity and low wind speed. The explained var iance r(2) reaches its maximum with more than 50% of the time in Munic h center. This empirical model is applicable to the forecasting of loc al ozone maximum concentrations with a total standard error deviation of 8.5 to 12.8% and, if ozone concentrations exceed 80 ppb, with a sta ndard error deviation of 5.4 to 9.5%.