Identifying the pathways of extreme rainfall in South Africa using storm trajectory analysis and unsupervised machine learning techniques - Rhys Phillips; Katelyn Ann Johnson; Andrew Paul Barnes; Thomas Rodding Kjeldsen (Journal of Hydroinformatics, 2024)
This study has utilised National Oceanic and Atmospheric Administration (NOAA) NCEP/NCAR Reanalysis 1 project meteorological data and the HYSPLIT model to extract the air parcel trajectories for selected historical extreme rainfall events in South Africa. The k-means unsupervised machine learning algorithm has been used to cluster the resulting trajectories, and from this, the spatial origin of moisture for each of the rainfall events has been determined. It has been demonstrated that rainfall events on the east coast with moisture originating from the Indian Ocean have distinctly larger average maximum daily rainfall magnitudes (279 mm) compared to those that occur on the west coast with Atlantic Ocean influences (149 mm) and those events occurring in the central plateau (150 mm) where moisture has been continentally recirculated. Further, this study has suggested new metrics by which the HYSPLIT trajectories may be assessed and demonstrated the applicability of trajectory clustering in a region not previously studied. This insight may in future facilitate improved early warning systems based on monitoring of atmospheric systems, and an understanding of rainfall magnitudes and origins can be used to improve the prediction of design floods for infrastructure design. HIGHLIGHTS Clustering extreme rainfall events based on origins and storm track.; Observed differences in magnitude between event clusters.; Depth of rainfall events (mm/day) from weather systems originating in the Indian Ocean is larger than other events.;