Atmospheric rivers are narrow bands of strong vertically integrated water vapour transport (IVT), conjuring the image of a river in the sky. This phenomenon has been studied extensively in relation to extreme rainfall in the United States, where they closely relate to the warm conveyor belts of extratropical storms (Dacre and Clark, 2025).
Atmospheric rivers are now receiving growing attention in the tropics, with for example recent analyses attributing extreme rainfall events and flooding in India moisture imported by atmospheric rivers (e.g. Mahto et al. 2023). So far, the dynamical drivers of tropical atmospheric rivers have received little attention, despite their association to high impact rainfall events such as those driving devastating floods in Chennai in 2015 (Lakshmi & Satyanarayana, 2019), Kerala in 2018 (Lyngwa & Nayak, 2021), and Pakistan in 2022 (Nanditha et al., 2023) alongside broader evidence that atmospheric rivers making landfall over India are linked to flooding (Mahto et al., 2023). Atmospheric rivers are poorly forecast on the subseasonal scale, and regions identified as having particularly poor skill include the Indian monsoon region, Madagascar, and Indonesia (DeFlorio et al., 2019).
There is therefore a pressing need to better understand the dynamics of tropical atmospheric rivers, their links to flooding, and their contribution to precipitation forecast skill and error. In this PhD project, we will analyse the dynamical processes driving tropical atmospheric rivers, their interactions with low-pressure systems such as monsoon lows and tropical cyclones, and the skill of the Met Office seasonal-to-subseasonal forecasting ensemble in modelling and forecasting these processes. As datasets develop, there may also be opportunities to assess simulation skill of AI forecasts.
Figure 1: A house is cut off by floodwater, Barpeta, India.
This project is a CENTA Flagship Project.
This project is not suitable for CASE funding
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We will make use of the global atmospheric database, with data available based on ERA-5 (Guan and Waliser, 2024) and MERRA-2 (Guan et al., 2023). This will be analysed in conjunction with the relevant reanalysis datasets. For tropical cyclones, we will use the international best track archive for climate stewardship (IBTrACS; Knapp et al. 2010). To differentiate processes driving different atmospheric river signals, we will apply methods including Self-Organising Maps and kinematic analysis (Dacre and Clark, 2025).
For linkages between atmospheric rivers and flooding, we will initially work the Global Runoff Data Centre (GRDC), GRUN, and Dartmouth Flood Observatory datasets. Recently developed datasets, such as the CEH-led ROBIN dataset, and HydroSOS, as well as modelled flows (HMF) will be incorporated where appropriate.
For seasonal-to-subseasonal forecast data, we will primarily analyse the Met Office Global Seasonal Forecasting System version 6 (GloSea6).
DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.
The student will build specialist knowledge in tropical climate dynamics, and experience in analysing and interpreting climate data. The student will also gain computing skills, in particular coding and visualising data with Python, with the opportunity to build further transferrable skills in version control (Git). The student will be supported to learn data science/machine learning methods used in Dr Geen’s group, e.g. SOMs, UMAP. In the longer term, this PhD should provide a strong foundation for an academic career in climate dynamics research, modelling and forecasting, or a career in the private sector e.g. the growing Climate Intelligence industry.
Hazel Thornton manages the UK Met Office Monthly to Decadal Prediction and Impacts team. Her research explores the seasonal forecast skill of current coupled modelling systems, with focus areas including applications for flood risk and prediction.
Mark Rhodes-Smith is a hydrological modeller in UKCEH’s Water Quality and Catchment Processes group, with experience in modelling of natural hazards and tropical hydrology, including the West Africa and South & Southeast Asia regions.
These collaborations provide an excellent opportunity to engage with Met Office and UKCEH scientists, and support translation of results from the PhD project to applications.
Year 1: Assess atmospheric rivers across the global tropics and their regional relationships to tropical cyclones, monsoon circulations, and interannual and intraseasonal variability (e.g. ENSO and the MJO).
Year 2: Conduct a detailed study of tropical atmospheric river meteorology, identifying the dynamical processes underlying the relationships identified in year 1. Investigate how atmospheric rivers relating to different processes drive regional flooding in focus areas for which suitable hydrological datasets are available.
Year 3: Interrogate the representation of atmospheric rivers in seasonal to subseasonal forecasting models, using insights from year 2 to identify the processes that must be simulated for a skilful forecast
Dacre, H. F., & Clark, P. A. (2025). A kinematic analysis of extratropical cyclones, warm conveyor belts and atmospheric rivers. npj Climate and Atmospheric Science, 8(1), 97.
DeFlorio, M. J., Waliser, D. E., Guan, B., Ralph, F. M., & Vitart, F. (2019). Global evaluation of atmospheric river subseasonal prediction skill. Climate Dynamics, 52(5), 3039-3060.
Guan, B., & Waliser, D. E. (2024). A regionally refined quarter-degree global atmospheric rivers database based on ERA5. Scientific Data, 11(1), 440.
Guan, B., Waliser, D. E., & Ralph, F. M. (2023). Global application of the atmospheric river scale. Journal of Geophysical Research: Atmospheres, 128(3), e2022JD037180.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J. (2010). The international best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data. Bulletin of the American Meteorological Society, 91(3), 363-376.
Lakshmi, D. D., & Satyanarayana, A. N. V. (2019). Influence of atmospheric rivers in the occurrence of devastating flood associated with extreme precipitation events over Chennai using different reanalysis data sets. Atmospheric Research, 215, 12-36.
Lyngwa, R. V., & Nayak, M. A. (2021). Atmospheric river linked to extreme rainfall events over Kerala in August 2018. Atmospheric Research, 253, 105488.
Mahto, S. S., Nayak, M. A., Lettenmaier, D. P., & Mishra, V. (2023). Atmospheric rivers that make landfall in India are associated with flooding. Communications Earth & Environment, 4(1), 120.
Nanditha, J. S., Kushwaha, A. P., Singh, R., Malik, I., Solanki, H., Chuphal, D. S., … & Mishra, V. (2023). The Pakistan flood of August 2022: causes and implications. Earth’s Future, 11(3), e2022EF003230.
Please contact Dr. Ruth Geen, University of Birmingham, [email protected] for more information about the project and application process.
To apply to this project:
Applications must be submitted by 23:59 GMT on Wednesday 7th January 2026.