All Outputs (3)

North Atlantic air pressure and temperature conditions associated with heavy rainfall in Great Britain (2021)
Journal Article
Barnes, A. P., Svensson, C., & Kjeldsen, T. R. (2022). North Atlantic air pressure and temperature conditions associated with heavy rainfall in Great Britain. International Journal of Climatology, 42(5), 3190-3207. https://doi.org/10.1002/joc.7414

Severe flooding in the United Kingdom is often linked to the occurrence of heavy rainfall events, which can be characterized by the synoptic scale meteorological conditions over the North Atlantic region. Seasonal heavy rainfall events (summer and wi... Read More about North Atlantic air pressure and temperature conditions associated with heavy rainfall in Great Britain.

Identifying and interpreting extreme rainfall events using image classification (2021)
Journal Article
Barnes, A. P., McCullen, N., & Kjeldsen, T. R. (2021). Identifying and interpreting extreme rainfall events using image classification. Journal of Hydroinformatics, 23(6), 1214-1223. https://doi.org/10.2166/hydro.2021.030

This study presents the first attempt to identify extreme rainfall events based on surrounding sea-level pressure anomalies, using neural network-based classification. Sensitivity analysis was also performed to identify the spatial importance of sea-... Read More about Identifying and interpreting extreme rainfall events using image classification.

Improving regional rainfall forecasts using convolutional-neural networks (2021)
Presentation / Conference
Barnes, A., McCullen, N., & Kjeldsen, T. R. (2021, April). Improving regional rainfall forecasts using convolutional-neural networks. Paper presented at EGU General Assembly 2021, Online

Traditional weather forecasting approaches utilize numerous numerical simulations and empirical models to produce a gridded estimate of rainfall, the cells of which often span multiple regions and struggle to capture extreme events. The approach pres... Read More about Improving regional rainfall forecasts using convolutional-neural networks.