The 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 non-linear relations between these parameters. In this study, the meteorological, air quality, and atmospheric visibility data are analyzed together to demonstrate the capabilities of the multidimensional logistic regression model for the 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 km, 10-20 km, 20-30 km, 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) distribution with empirical (measured) with Kolmogorov-Smirnov test yielded p-values always above 0.27, and in half cases above 0.52.

中文翻译：

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根据气象和空气质量数据，预测华沙（波兰）特定范围内能见度的天数

大气能见度是环境的重要参数，取决于气象和空气质量状况。由于众多参数以及这些参数之间的非线性关系，可见度的预测是一项复杂的任务。在这项研究中，对气象，空气质量和大气能见度数据进行了分析，以证明多维Logistic回归模型对能见度预测的功能。这种方法可以确定四个范围（即0-10 km，10-20 km，20-30 km和≥30 km）中的自变量及其对大气能见度值的重要性。我们证明了Iman-Conover（IC）方法可用于模拟气象和空气质量参数的时间序列。华沙（波兰）的能见度主要取决于空气的温度和湿度，降水以及PM10的环境浓度。三种可见度的逻辑模型使我们能够精确确定一个特定范围内可见度的一个月中的天数。模型的敏感性在75.53％至90.21％之间，特异性在78.51％至96.65％之间。使用Kolmogorov-Smirnov检验将理论（模型）分布与经验（测量）分布进行比较，得出p值始终高于0.27，一半情况下高于0.52。