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Combining dispersion modelling with synoptic patterns to understand the wind-borne transport into the UK of the bluetongue disease vector

Burgin, Laura, Ekstrom, Marie and Dessai, Suraje 2017. Combining dispersion modelling with synoptic patterns to understand the wind-borne transport into the UK of the bluetongue disease vector. International Journal of Biometeorology 61 (7) , pp. 1233-1245. 10.1007/s00484-016-1301-1

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Abstract

Bluetongue, an economically important animal disease, can be spread over long distances by carriage of insect vectors (Culicoides biting midges) on the wind. The weather conditions which influence the midge’s flight are controlled by synoptic scale atmospheric circulations. A method is proposed that links wind-borne dispersion of the insects to synoptic circulation through the use of a dispersion model in combination with principal component analysis (PCA) and cluster analysis. We illustrate how to identify the main synoptic situations present during times of midge incursions into the UK from the European continent. A PCA was conducted on high-pass-filtered mean sea-level pressure data for a domain centred over north-west Europe from 2005 to 2007. A clustering algorithm applied to the PCA scores indicated the data should be divided into five classes for which averages were calculated, providing a classification of the main synoptic types present. Midge incursion events were found to mainly occur in two synoptic categories; 64.8% were associated with a pattern displaying a pressure gradient over the North Atlantic leading to moderate south-westerly flow over the UK and 17.9% of the events occurred when high pressure dominated the region leading to south-easterly or easterly winds. The winds indicated by the pressure maps generally compared well against observations from a surface station and analysis charts. This technique could be used to assess frequency and timings of incursions of virus into new areas on seasonal and decadal timescales, currently not possible with other dispersion or biological modelling methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Ocean Sciences
Publisher: Springer Verlag
ISSN: 0020-7128
Date of Acceptance: 17 December 2016
Last Modified: 16 Oct 2017 10:05
URI: http://orca.cf.ac.uk/id/eprint/105538

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