Meteorological normalization uses data-driven regression models, most often Random Forests and gradient boosted models as they have shown superior performance over generalized linear and additive models , to predict daily or hourly pollutant concentrations given seasonality and meteorological variables. Once the predictive model becomes trained and maps the relationships to the predictive meteorological (X) and 35 non-meteorological, often seasonal (T ) features, one permutes or shuffles the meteorological variables while the seasonal ones remain fixed. This results in a "corrected model" which is excluding the meteorological contribution.