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A new high‐resolution Meteorological Reanalysis Italian Dataset: MERIDA
Author(s) -
Bonanno Riccardo,
Lacavalla Matteo,
Sperati Simone
Publication year - 2019
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3530
Subject(s) - weather research and forecasting model , environmental science , downscaling , precipitation , meteorology , climatology , geography , geology
During the last 15 years, weather extremes caused several disruptions to the Italian electric system. Their increasingly occurrence is mainly due to the exchanges along the meridians of air masses with very different thermal, density and moisture content properties. The Italian transmission system operator and the distribution companies have repeatedly stressed the need to have a reliable and updatable weather dataset with a history of at least 15 years to improve the resilience of the electric system. The aim of this work is to develop a new MEteorological Reanalysis Italian DAtaset (MERIDA) able to respond to the energy stakeholders, who need reliable meteorological data to implement effective adaptation strategies to operate the electric system safely. MERIDA consists of a dynamical downscaling of the new European Centre for Medium‐range Weather Forecasts (ECMWF) global reanalysis ERA5 using the Weather Research and Forecasting (WRF) model, which is configured to describe the typical weather conditions of Italy. Furthermore, the optimal interpolation (OI) technique is applied to the modelled 2 m temperature and precipitation data through the use of meteorological observations of the Regional Agencies for Environmental Protection. MERIDA is verified against COSMO REA6 of the Deutscher Wetterdienst (DWD) and ERA5 itself for the period 2010–2015, showing comparable or better results in the reconstruction of 2 m temperature and precipitation. The best results are obtained with MERIDA post‐processed by the OI. Some severe weather events that determined important electric disruptions are also analysed, showing that MERIDA is able to identify the meteorological conditions leading to significant events of wet snow, heatwaves and floods through their correct spatial and temporal location and through a quantitative assessment of each atmospheric phenomenon.