Ricerc@Sapienza

This study provides a unique procedure for validating and reconstructing temperature and precipitation data. Although developed from data in Middle Italy, the validation method is intended to be universal, subject to appropriate calibration according to the climate zones analysed. This research is an attempt to create shared applicative procedures that are most of the time only theorized or included in some software without a clear definition of the methods.

Extreme precipitation trends and events are fundamental for the definition of the region's climate and allow the subsequent analysis of the risk for the territory and the possible countermeasures. This study takes into account the Marche Region (Central Italy) with 128 rain gauges from 1921 to 2017. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921-2017 was evaluated. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921-2017 was evaluated.

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band.

Medicanes, hurricane-like cyclonic systems in the Mediterranean Sea, are becoming an increasingly severe problem for many Mediterranean countries because climate projections suggest a higher risk under anthropogenic forcing even under an intermediate scenario. Due to the small size of these weather systems, high-resolution data are required to better resolve their structure and evolution. Here we investigate medicanes from the perspective of precipitation using the high-resolution (0.25°) ERA-5 reanalysis data released by European Centre for Medium-Range Weather Forecasts.