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Advances in Meteorology
Volume 2015 (2015), Article ID 412658, 15 pages
Research Article

Quality of the Governing Temperature Variables in WRF in relation to Simulation of Primary Biological Aerosols

1National Pollen and Aerobiology Research Unit, Institute of Science and the Environment, University of Worcester, Henwick Grove, Worcester WR2 6AJ, UK
2Department of Climatology and Atmosphere Protection, University of Wrocław, Ulica Kosiby 8, 51-621 Wrocław, Poland

Received 9 April 2015; Revised 23 June 2015; Accepted 16 August 2015

Academic Editor: Enrico Ferrero

Copyright © 2015 C. A. Skjøth et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We have evaluated three prognostic variables in Weather Research and Forecasting (WRF) model, mean daily temperature, daily maximum temperature, and daily minimum temperature using 9 months of model simulations at 36 and 12 km resolution, and compared the results with 1182 observational sites in north and central Europe. The quality of the results is then determined in the context of the governing variables used in crop science, forestry, and aerobiological models. We use the results to simulate the peak of the birch pollen season (aerobiology), growth of barley (crop science), and development of the invasive plant pathogen Hymenoscyphus pseudoalbidus (the cause of ash-dieback). The results show that the crop and aerobiological models are particularly sensitive to grid resolution and much higher quality is obtained from the 12 km simulations compared to 36 km. The results also show that the summer months have a bias, in particular for maximum and minimum temperatures, and that the low/high bias is clustered in two areas: continental and coastal influenced areas. It is suggested that the use of results from meteorological models as an input into biological models needs particular attention in the quality of the modelled surface data as well as the applied land surface modules.