Toward optimized temperature sum parameterizations for forecasting the start of the pollen season

The start date of flowering of allergenic species is of great interest in the context of allergy. Such forecasts can help to plan the therapy and design the medical treatment. For this purpose, temperature sum models are usually employed. Designing temperature sum models requires the selection of different parameters such as the base temperature and the start date for the temperature sum. However, the optimal parameterization is often unknown and varies depending on location and species. The purpose of this study was to systematically test parameterizations of temperature sum models based on 12 Swiss pollen stations. The examined taxa include Corylus, Alnus, Fraxinus, Betula, and Poaceae. We tested the simple thermal model type (forcing-only model hereafter) which relies solely on forcing temperatures and the sequential model type that includes also chilling temperatures. The mean absolute error was used to assess the performance of the models. Our study shows that the sequential model could not achieve a discernible reduction of the (statistical) error compared to the forcing-only model for all taxa. The mean absolute error lies roughly between 2 and 4 days with the lowest values for Betula. The optimized parameterizations in combination with temperature forecast and the climatological mean were used during the 2012 pollen season to provide daily updated forecasts of the start of flowering for all five taxa. Improvements of the models could possibly be achieved by testing additional temperature parameters as well as other meteorological factors such as precipitation or irradiation. Identification of a process-oriented model with high statistical performance for all stations would facilitate the implementation in numerical pollen dispersion models.