Extreme precipitation Italy 2002
In the second part of November 2002, the synoptic situation over the Mediterranean was characterized by a sequence of perturbations entering the area from the North Atlantic. In particular, the persistence of an upper-level North-Atlantic trough between the 23th and 27th of the month, set the conditions for very heavy precipitation over North Italy with high ground impact over some areas and floods (i.e. Milan, Pordenone). The impact at ground was possibly amplified by the ground wet saturation for precipitations that occurred in the previous period. The evolution of the perturbation entering the Mediterranean basin on Nov. the 24th was very slow due to a steady North Africa high pressure blocking the central-east basin side.
The prefrontal humid and warm flow associated with the trough, invested North of Italy and the interaction with the orography induced precipitation across the whole region. The persistence of the precipitation for a long time, caused very high rain accumulation, especially in mountainous areas (Fig. 1) with peaks of day precipitation of 170 mm in Liguria on Nov. 24 over Northern Apennines region (NAP on Fig. 1), 130 mm in Northern Alps (NAL) on Nov. 25th and of about 320 mm over Eastern Alps (EAL) on Nov. 25. On November 26th a cut-off low pressure isolated over Sardinia (West Mediterranean), evolving south-eastward, and continued to feed Northern regions with wet-unstable flow which made the precipitation still persisting there. On the next days the low pressure became shallower and moved farther to South-East, while a second trough entered the western Europe on Nov. 28th, thus inducing wet, warm and unstable air-flow over the western basin and North Italy, again with associated precipitation. The total accumulation for the period (10 days of persisting rain) reached very high peaks with about 400 total mm in NAL, 470 mm in NAP, and more than 700 mm in EAL (Fig. 1).
Figure1: Accumulated precipitation during the period 22 November to 02 December 2002 from high resolution observations (GRIPHO, 3km, Fantini, 2019); color bar unit is x10mm ; the black boxes show areas of maximum precipitation over the North Alps (NAL), North Apennines (NAP) and Eastern Alps (EAL).
Heavy precipitation is an episode of abnormally high rain or snow (95th percentile). The definition of “extreme” is a statistical concept that varies depending on location, season, and length of the historical record. The mechanisms (perturbation, air mass, water content, instability, interaction with local forcings, persistence, etc.) that generate a heavy/extreme event can be very different among different regions. Same amount of heavy/extreme precipitation over different areas can lead to different responses at ground (in terms of floods). In the case presented here the key factor for the event to be extreme is for sure persistence of a very common perturbation for the area during the fall period.
XAIDA aims at developing an Artificial Intelligence based method to detect such kinds of events and in combination with high resolution convection permitting modeling dataset at emulating the costly dynamical models in representing them. By means of the AI methodology XAIDA will try to better understand the physical mechanisms/key factors which drive the most extreme events among the whole population of precipitating events over Europe.
XAIDA also aims to understand if such events observed in the present day period can be projected in a future climate scenario with the AI methodology and if the method can be extended to directly connect the climate variables to the observed hazard (flood) both in present and future climate conditions.
XAIDA also aims to develop an AI based method to attribute such events to climate change; the identifications of their driving mechanisms/ingredients would allow to eventually perform such attribution trough key-variables in addition to precipitation.
Fantini A (2019) Ph. D. Thesis: climate change impact on flood hazard over Italy. http://hdl.handle.net/11368/2940009
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003469.