2027-B12 AI-Enabled Prediction of UK Pollen Season Magnitude for Next-Generation Forecasting
PROJECT HIGHLIGHTS
- Work directly with the UK Met Office on next-generation operational pollen forecasting
- Develop novel AI and machine-learning methods to predict pollen season magnitude months in advance
- Join an interdisciplinary team spanning environmental science, mathematics and artificial intelligence
Overview
Understanding and forecasting airborne pollen is essential for improving operational environmental prediction across the UK. While daily pollen forecasts have improved substantially in recent years, one of the greatest remaining challenges is predicting the overall magnitude of each pollen season. For example, total birch pollen concentrations can vary by a factor of eight between consecutive years, driven by complex interactions between previous-season pollen production, meteorological conditions during the preceding summer, and weather during the flowering season itself. These processes remain poorly understood and are not currently represented within operational forecasting systems.
This project will harness a unique UK-wide pollen reanalysis (2000–2025), developed by the UK Met Office under the stewardship of Co-I Lucy Neal, together with long-term pollen observations and meteorological datasets, to develop next-generation machine learning approaches for predicting seasonal pollen magnitude. The resulting models will improve understanding of the environmental drivers of interannual variability while providing practical tools to enhance the operational Met Office NAME pollen forecast.
With only 8 active regulatory-grade pollen monitoring sites across the UK, equivalent to approximately one site per eight million people, long-term pollen observations are extremely sparse. The pollen reanalysis provides an unprecedented spatially and temporally complete dataset for developing and validating artificial intelligence methods. The student will investigate how biological memory, antecedent weather conditions and environmental variables combine to determine pollen production, developing interpretable machine learning models capable of predicting seasonal severity months in advance.
This PhD will be embedded in a vibrant interdisciplinary research environment at the University of Birmingham, combining expertise in environmental science, artificial intelligence and applied mathematics. Joint supervision from Environmental Sciences, the School of Mathematics, and the UK Met Office will provide outstanding training in machine learning, environmental modelling and operational forecasting. The research will directly address a major limitation in current pollen forecasting systems, delivering new scientific understanding alongside AI tools with clear operational impact for the UK Met Office and wider environmental forecasting community.
Figure 1: Example output from the Met Office NAME model showing the daily pollen index for the UK on the 14th June 2023. The circles represent the official UK pollen monitoring sites with the local observations overplotted on the NAME model data.
This project is a CENTA Flagship Project.
Case Projects
This project offers a CASE studentship
Host
- University of Birmingham
Theme
- Climate and Environmental Sustainability
Supervisors
Project investigator
- Professor Francis Pope, f.pope@bham.ac.uk
Co-investigators
- Lucy Neal, UK Met Office, lucy.neal@metoffice.gov.uk; Christian Offen, University of Birmingham (School of Mathematics), c.offen@bham.ac.uk
How to apply
Each host has a slightly different application process.
Find out how to apply for this studentship.
All applications must include the CENTA application form.
Methodology
This project will combine long-term pollen observations, the UK Met Office pollen reanalysis, and meteorological datasets to investigate the drivers of interannual variability in pollen season magnitude. The student will develop and evaluate machine learning methods, including modern deep learning and hybrid statistical approaches, to predict seasonal pollen magnitude in advance of the flowering season. Particular emphasis will be placed on incorporating previous-season biological memory, antecedent meteorological conditions, and other environmental drivers into predictive models. Model explainability techniques will be used to identify the key environmental controls on pollen production and quantify forecast uncertainty. The developed models will be validated against historical observations and assessed for their potential integration into the operational Met Office NAME pollen forecasting system. The project will deliver both improved scientific understanding of interannual pollen variability and practical AI tools to enhance operational pollen forecasting.
Further details and How to Apply
For any enquiries related to this project please contact Prof. Francis Pope f.pope@bham.ac.uk.
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2027
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://admissions.bham.ac.uk/course-finder-landing-page/?code=LES068 Please select the PhD in Geography (CENTA) 2027 entry year Apply Now button. The CENTA Studentship Application Form 2027 and CV can both be uploaded to the Personal Statement section of the online form. In the funding section of the online form please select Research Council Funding and then choose Natural Environment Research Council (NERC). Please quote CENTA 2027-B12 when completing the application form.
Applications must be submitted by 23:59 GMT on Wednesday 6th January 2027.
