Extreme precipitation events over northern Italy. Part I: A systematic classification with machine‐learning techniques

Federico Grazzini, George C. Craig, Christian Keil, Gabriele Antolini, Valentina Pavan

Quarterly Journal of the Royal Meteorological Society · 08 August 2019

In one sentence. Classifies 887 northern Italian extreme precipitation events into three dynamical classes using K-means and random forests, with integrated water vapour transport as key predictor.

Abstract

Abstract Extreme precipitation events (EPEs) are meteorological phenomena of major concern for society. They can have different characteristics depending on the physical mechanisms responsible for their generation, which in turn depend on the large and mesoscale conditions. This work provides a systematic classification of EPEs over northern–central Italy, one of the regions in Europe with the highest frequency of these events. The EPE statistics have been deduced using the new high‐resolution precipitation dataset ArCIS (Climatological Archive for Central–Northern Italy), that gathers together a very high number of daily, quality‐controlled and homogenized observations from different networks of 11 Italian regions. Gridded precipitation is aggregated over Italian operational warning‐area units (WA). EPEs are defined as events in which daily average precipitation in at least one of the 94 WAs exceeds the 99th percentile with respect to the climate reference 1979–2015. A list of 887 events is compiled, significantly enlarging the database compared to any previous study of EPEs. EPEs are separated into three different dynamical classes: Cat1, events mainly attributable to frontal/orographic uplift; Cat2, events due to frontal uplift with (equilibrium) deep convection embedded; Cat3, events mainly generated by non‐equilibrium deep convection. A preliminary version of this classification is based on fixed thresholds of environmental parameters, but the final version is obtained using a more robust machine‐learning unsupervised K‐means clustering and random forest algorithm. All events are characterized by anomalously high integrated water vapour transport (IVT). This confirms IVT as an important large‐scale predictor, especially for Cat2 events, which is shown to be the most important category in terms of impacts and EPE area extension. Large IVT values are caused by upper‐level waves associated with remotely triggered Rossby wave packets, as shown for two example Cat2 events.

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