Estimating the daily Poaceae pollen concentration in Hungary by linear regression conditioning on weather types

Abstract

Objectively defined clusters of meteorological elements and weather types described by weather fronts and precipitation occurrences are produced in order to classify Poaceae pollen levels. The Poaceae pollen concentration was then estimated one day ahead for each of the above categories of days at the villages of Szeged and Győr in Hungary. The database describes an 11-year period from 1997 to 2007. For weather-front recognition purposes, the ECMWF ERA-INTERIM database was used. In order to estimate the actual daily Poaceae pollen concentrations, previous-day values of five meteorological variables and previous-day Poaceae pollen concentrations were applied. We find that both for Szeged and Győr, as well as both for the subjective and objective classifications, high daily mean Poaceae pollen levels are favoured by anticyclone ridge weather situations, as we might expect. When estimating the Poaceae pollen level, the previous-day pollen concentration, previous-day mean temperature, and previous-day mean global solar flux for Győr were statistically significant, but for Szeged it was only the previous-day pollen concentration. Taking into account the clusters, the objective classification based on original data proved the most effective. For the subjective classification, the best estimates were obtained for days with a warm front and precipitation.