New early warning and forecasting systems for unprecedented rainstorms causing flash floods

Hayley Fowler (NU), Paul Davies (Met Office), Abdullah Kahraman (NU)


Apply for this PhD here using application studentship code FLOOD243. Please contact Caspar Hewett ( if you have any questions about the application process. 




In summer 2021 there was a change in society’s relationship with extreme weather, with a record four $20 billion-plus weather-driven disasters. Understanding the local and large-scale ‘ingredients’ of intense rainstorms is important to improve forecasts and predictions of future change.

Changes to rainfall intensities depend on processes that range from the microscale to synoptic and planetary scales. Storms intensify with increases in latent heat release, with increases in updraft velocities and moisture-convergence producing larger rainstorms (Fowler et al., 2021a,b). However, these increases are dampened by enhanced atmospheric stratification in a warming climate, which increases static stability. Paul Davies (UK Met Office) has identified atmospheric configurations that produce life-threatening rainfall extremes, e.g., New York, Sep 2021 80mm/h; Liguria, Italy, Oct 2021, European record of 181mm/h; Zhengzhou, China, July 2020, near world-record 201.9mm/h. However, links to synoptic and meso-scale features are not well defined.  

New early warning and forecasting systems are needed for unprecedented rainstorms. The World Meteorological Organization calls for “extreme weather warning systems for all” within five years. The Joint Committee on National Security Strategy found overwhelming evidence that climate change is already impacting UK infrastructure; improving the resilience of critical national infrastructure includes developing better early warning systems.



We will take advantage of the new quality-controlled Global Sub Daily Rainfall Dataset (Lewis et al. 2019, 2021) and high-resolution reanalyses such as ERA5 to identify the synoptic and mesoscale features that increase the likelihood of short-duration extreme rainfall, identifying the types of atmospheric regimes/setups that can produce severe, often unprecedented, rainstorms. These will be used to develop a causal network which identifies dynamical situations with raised potential for severe rainstorms, with concept mapping methods used to pair these to identified local-scale triggering ‘ingredients’.  

To improve the accuracy of the causal network, this project will leverage machine learning techniques such as Random Forest and Back Propagation Neural Network algorithms to identify non-linear interactions and hidden dependencies that may elude traditional statistical methods. The PhD student will run a sandpit trial during year 3 of the project to test the new forecasting system on real weather situations and against current forecast methods collaborating with Prof Paul Davies, Chief Meteorologist at the UK Met Office (UKMO). 


Newcastle University
Background Reading: 

Fowler, HJ., Ali, H., Allan, R.P, Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Cabi, N.S., Chan, S., Dale, M., Dunn, R.J.H., Ekström, M., Evans, J.P., Fosser, G., Golding, B., Guerreiro, S.B., Hegerl, G.C., Kahraman, A., Kendon, E.J., Lenderink, G., Lewis, E., Li, X.-F., O’Gorman, P.A., Orr, H.G., Peat, K.L., Prein, A.F., Pritchard, D., Schär, C., Sharma, A., Stott, P.A., Villalobos-Herrera, R., Villarini, G., Wasko, C., Wehner, M.F., Westra, S., Whitford, A. 2021. Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes.  Phil. Trans. Roy. Soc. A., 379, 20190542, DOI: 10.1098/rsta.2019.0542.

Fowler, H.J., Lenderink, G., Prein, P., Westra, S., Allan, R.P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H.X., Guerreiro, S., Haerter, J.O., Kendon, E., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., Zhang, X. 2021. Anthropogenic intensification of short-duration rainfall extremes. Nature Reviews Earth and Environment, 2, 107–122, DOI: 10.1038/s43017-020-00128-6.

Lewis, E., Fowler, H.J., Alexander, L., Dunn, R., McClean, F., Barbero, R., Guerreiro, S., Li, X-.F., Blenkinsop, S. 2019. GSDR: A global sub-daily rainfall dataset. Journal of Climate, 32(15), 4715-4729, DOI: 10.1175/JCLI-D-18-0143.1.

Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, S., McClean, F., Guerreiro, S., Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Fowler, H.J. 2021. Quality control of a global hourly rainfall dataset. Environmental modelling and software, 144, 105169, DOI: 10.1016/j.envsoft.2021.105169.


Contact Email: