On the Data Assimilation for Operational Forecasting and Re-analysis of Allergenic Pollen Dispersion

Abstract

Operational forecasting of allergenic pollen faces several challenges, which are not common in the air chemical composition forecasting. One of them is practical inapplicability of the standard data assimilation (DA) methods, which originates from basic features of the problem. The pollen observations are manual and become available with a delay of about a week. The bulk of the information has daily resolution, whereas the diurnal variability of the concentrations exceeds one order of magnitude. Finally, the season duration at each particular place is rarely more than 1–2 weeks, which makes the 1–2 days old information outdated. Adaptation of the DA methods for pollen forecasting has to target the emission source parameters, which would have a longer lasting effect. Some of these parameters also have long correlation distance, which increases the value of sparse observational network.