I have recently returned from a three-month research visit (October to December 2019) to the UK Centre for Ecology and Hydrology (UK-CEH) where I spent time as part of the Water Resources Team. This collaboration originated from interest in our previous journal paper (Barnes et al., 2019) and previous work between Dr Thomas Kjeldsen and senior hydrologist Dr Cecilia Svensson. The goal of my visit was to collaborate with Dr Svensson in the use of advanced machine learning techniques, to identify the atmospheric conditions which lead to extreme rainfall events in Great Britain. This is an important question for understanding how non-stationarity in our climate can affect both rainfall and hence flood distributions.
After being welcomed to the team and shown the local meteorology site we set to work. The results we gained surpassed our original plans and we have been able to identify three types of atmospheric conditions, in both summer and winter, which relate to extreme rainfall. Following this we were able to draw causal relationships between these types and the related atmospheric indices such as the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO). This work is currently in preparation for submission to the journal of hydrology and we are now looking forwards to future collaborations.
I was even able to experience flooding first hand in Wallingford. I would like to personally thank everyone at UK-CEH for making this visit a fantastic experience, especially Dr Svensson who I cannot thank enough for her time and expertise.
Barnes, A., Santos, M., Carijo, C., Mediero, L., Prosdocimi, I., McCullen, N., Kjeldsen, T.R. (2019). Identifying Origins of Extreme Rainfall using Trajectory Classification. Journal of Hydroinformatics.