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

Smith, Matthew and Emberlin, Jean (2006) A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom. International Journal of Biometeorology, 50 (4). pp. 233-242. ISSN 0020-7128 (Print) 1432-1254 (Online)

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Abstract

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.

Item Type: Article
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The original publication is available at www.springerlink.com

Uncontrolled Keywords: aerobiology, grass pollen counts, London, forecast models, North Atlantic Oscillation
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QR Microbiology > QR180 Immunology
Divisions: Research Centres > National Pollen and Aerobiology Research Unit
Related URLs:
Depositing User: Matthew Smith
Date Deposited: 12 Jul 2007 15:08
Last Modified: 21 May 2013 16:12
URI: https://eprints.worc.ac.uk/id/eprint/102

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