A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

• Published in: International Journal of Biometeorology
• Main Authors: Smith, Matt, Emberlin, Jean
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Springer Berlin Heidelberg 2006
• Subjects: GE Environmental Sciences; QR180 Immunology; North Atlantic; North Atlantic oscillation
• Online Access: http://eprints.worc.ac.uk/102/; http://link.springer.com/article/10.1007%2Fs00484-005-0010-y; https://doi.org/10.1007/s00484-005-0010-y

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.