Work Pakage 1

Activity 1.1: Building and exploiting a multi-source database 

We will collect and analyse long term rainfall and discharge data over Italy as well as meteorological fields from gridded reanalysis and satellite observations over the MED and surrounding areas. Data will be exploited to identify the most severe hydrometeorological events within an extended period (at least 2001-2020). The period will be commensurate to availability of information and datasets over common intervals and domains. Data will be also used for the detection of ARs and their connection to hydrometeorological extremes.

• Datasets to identify the most severe events

• Datasets to implement AR detection algorithms

• Datasets to explore the upper bound of predictability

Activity 1.2 Development and application of AR detection algorithms

Two approaches to AR detection will be pursued, one based on gridded reanalysis, one on satellite retrieval. The comparison among the two will assess their potentialities and possible limitations, and to attain a more robust identification of ARs in the MED. This activity will provide a clear picture of the most intense ARs in terms of frequency, intensity, seasonality, affected areas, duration and associated typical synoptic conditions.

Activity 1.3 Assessment of connections between ARs, extreme hydrometeorological events and water resources

Over the western US, the linkage among ARs, extreme rainfall and environmental impacts is rather direct due to the arid climate and low background moisture amounts. Over the complex MED basin, the link is surely more indirect and understanding the factors that can make ARs relevant, is rather imperative. 

• From top ARs to most extreme events

• From top extreme events to top ARs

• Attribution of precipitation to AR

• Signals of anomalies at monthly time scale