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Andrea Libertino   Dr.  Graduate Student or Post Graduate 
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Andrea Libertino published an article in May 2018.
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(2015 - 2018)
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3
 
Publications
Article 0 Reads 1 Citation Technical note: Space–time analysis of rainfall extremes in Italy: clues from a reconciled dataset Andrea Libertino, Daniele Ganora, Pierluigi Claps Published: 07 May 2018
Hydrology and Earth System Sciences, doi: 10.5194/hess-22-2705-2018
DOI See at publisher website ABS Show/hide abstract
Like other Mediterranean areas, Italy is prone to the development of events with significant rainfall intensity, lasting for several hours. The main triggering mechanisms of these events are quite well known, but the aim of developing rainstorm hazard maps compatible with their actual probability of occurrence is still far from being reached. A systematic frequency analysis of these occasional highly intense events would require a complete countrywide dataset of sub-daily rainfall records, but this kind of information was still lacking for the Italian territory. In this work several sources of data are gathered, for assembling the first comprehensive and updated dataset of extreme rainfall of short duration in Italy. The resulting dataset, referred to as the Italian Rainfall Extreme Dataset (I-RED), includes the annual maximum rainfalls recorded in 1 to 24 consecutive hours from more than 4500 stations across the country, spanning the period between 1916 and 2014. A detailed description of the spatial and temporal coverage of the I-RED is presented, together with an exploratory statistical analysis aimed at providing preliminary information on the climatology of extreme rainfall at the national scale. Due to some legal restrictions, the database can be provided only under certain conditions. Taking into account the potentialities emerging from the analysis, a description of the ongoing and planned future work activities on the database is provided.
Article 0 Reads 5 Citations A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design Andrea Libertino, Ashish Sharma, Venkat Lakshmi, Pierluigi C... Published: 25 April 2016
Environmental Research Letters, doi: 10.1088/1748-9326/11/5/054003
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The tropical rainfall measuring mission (TRMM) has revolutionized the measurement of precipitation worldwide. However, TRMM significantly underestimates rainfall in deep convection systems, being therefore of little help for the analysis of extreme precipitation depths. This work evaluates the ability of both TRMM and the recently launched global precipitation measurement (GPM) mission to help in the identification of the timing of severe rainfall events. We compare the date of occurrence of the most severe daily rainfall recorded each year by a global rain gauge network with the ones estimated by TRMM. The match rate between the two is found to approach 50%, indicating significant consistency between the two data sources. This figure rises to 60% for GPM, indicating the potential for this new mission to improve the accuracy associated with TRMM. Further efforts are needed in improving the GPM conversion algorithms in order to reduce the bias affecting the estimation of intense depths. The results however show that the timing estimated from GPM can provide a solid basis for an extensive characterization of the spatio-temporal distribution of extreme rainfall in poorly gauged regions of the world.
Article 1 Read 4 Citations Radar Estimation of Intense Rainfall Rates through Adaptive Calibration of the Z-R Relation Andrea Libertino, Paola Allamano, Pierluigi Claps, Roberto C... Published: 22 October 2015
Atmosphere, doi: 10.3390/atmos6101559
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Rainfall intensity estimation from weather radar is still significantly uncertain, due to local anomalies, radar beam attenuation, inappropriate calibration of the radar reflectivity factor (Z) to rainfall rate (R) relationship, and sampling errors. The aim of this work is to revise the use of the power-law equation commonly adopted to relate radar reflectivity and rainfall rate to increase the estimation quality in the presence of intense rainfall rates. We introduce a quasi real-time procedure for an adaptive in space and time estimation of the Z-R relation. The procedure is applied in a comprehensive case study, which includes 16 severe rainfall events in the north-west of Italy. The study demonstrates that the technique outperforms the classical estimation methods for most of the analysed events. The determination coefficient improves by up to 30% and the bias values for stratiform events decreases by up to 80% of the values obtained with the classical, non-adaptive, Z-R relations. The proposed procedure therefore shows significant potential for operational uses.
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