PhD Studentship: CENTA 2027-B12 AI-Enabled Prediction of UK Pollen Season Magnitude for Next-Generation Forecasting

Doctorat / PhD Département : School of Geography, Earth and Environmental Sciences Type de contrat : Full Time,Fixed-Term/Contract Date limite de candidature : 2027-01-06T00:00:00+00:00

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. For further information on this project and details of how to apply to it please click the 'Apply' button above. Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/apply/ Funding notes: This project is offered through the CENTA3 DLA, funded by the Natural Environment Research…