Predicting the Number of Days With Visibility in a Specific Range in Warsaw (Poland) Based on Meteorological and Air Quality Data

Abstract

Atmospheric visibility is an important parameter of the environment which is dependent on meteorological and air quality conditions. Forecasting of visibility is a complex task due to the multitude of parameters and nonlinear relations between these parameters. In this study, meteorological, air quality, and atmospheric visibility data were analyzed together to demonstrate the capabilities of the multidimensional logistic regression model for visibility prediction. This approach allowed determining independent variables and their significance to the value of the atmospheric visibility in four ranges (i.e., 0–10, 10–20, 20–30, and ≥ 30 km). We proved that the Iman–Conover (IC) method can be used to simulate a time series of meteorological and air quality parameters. The visibility in Warsaw (Poland) is dependent mainly on air temperature and humidity, precipitation, and ambient concentration of PM10. Three logistic models of visibility allowed us to determine precisely the number of days in a month with visibility in a specific range. The sensitivity of the models was between 75.53 and 90.21%, and the specificity 78.51 and 96.65%. The comparison of the theoretical (modeled) with empirical (measured) distribution with the Kolmogorov–Smirnov test yielded p-values always above 0.27 and, in half of the cases, above 0.52.