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<p>Pollen allergies affect a large proportion of the UK population, resulting in significant socio-economic costs to the country. The existing Met Office pollen forecast, produced manually, provides a single daily level for 16 UK administrative regions. A new pollen modelling system using the Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) dispersion model is presented. Initial developments are for the three taxa which are the most allergenic across the UK population: birch, oak and grass. Pollen grain emission maps have been estimated using species distribution modelling methods. The timing of the pollen season is controlled within NAME by an accumulated temperature sum parametrisation, while pollen release is estimated with short-term meteorological dependencies based on precipitation, wind speed, vapour pressure deficit and a diurnal cycle. When examined as hindcasts, the performance of NAME (verified against pollen observations independent of those used in model development) is comparable with the Copernicus Atmosphere Monitoring Service ensemble median prediction for birch and grass. NAME Daily Pollen Index predictions show an improved correlation coefficient (0.58, 0.61) compared to the existing manual forecast (0.53, 0.59) for the years 2022 and 2023, respectively. The NAME model provides taxa-specific outputs at high temporal (hourly) and spatial (0.05°) resolutions, which will eventually transform the level of detail in a future forecast system and therefore be of significantly greater use to the public and health professionals for managing pollen risks.</p>

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Development and verification of a taxa-specific gridded pollen modelling system for the UK

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月26日

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  • Lucy Sarah Neal,
  • Katherine Brown,
  • Paul Agnew,
  • Jonathan Bennie,
  • Yolanda Clewlow,
  • Regan Early,
  • Deborah Hemming

摘要

Pollen allergies affect a large proportion of the UK population, resulting in significant socio-economic costs to the country. The existing Met Office pollen forecast, produced manually, provides a single daily level for 16 UK administrative regions. A new pollen modelling system using the Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) dispersion model is presented. Initial developments are for the three taxa which are the most allergenic across the UK population: birch, oak and grass. Pollen grain emission maps have been estimated using species distribution modelling methods. The timing of the pollen season is controlled within NAME by an accumulated temperature sum parametrisation, while pollen release is estimated with short-term meteorological dependencies based on precipitation, wind speed, vapour pressure deficit and a diurnal cycle. When examined as hindcasts, the performance of NAME (verified against pollen observations independent of those used in model development) is comparable with the Copernicus Atmosphere Monitoring Service ensemble median prediction for birch and grass. NAME Daily Pollen Index predictions show an improved correlation coefficient (0.58, 0.61) compared to the existing manual forecast (0.53, 0.59) for the years 2022 and 2023, respectively. The NAME model provides taxa-specific outputs at high temporal (hourly) and spatial (0.05°) resolutions, which will eventually transform the level of detail in a future forecast system and therefore be of significantly greater use to the public and health professionals for managing pollen risks.