A new convection‐permitting regional reanalysis, SPHERA (High Resolution REAnalysis over Italy), has been developed over Italy and the surrounding seas using the COSMO model at 2.2 km horizontal resolution. The reanalysis system is nested in the global reanalysis ERA5; upper‐air and surface observations are assimilated at the convection‐permitting scale by the COSMO nudging scheme. Before the complete production of the hourly three‐dimensional fields and surface/soil parameters over the period 1995–2020, general issues regarding the reanalysis set‐up needed to be addressed over a shorter test period. These include the identification of the best approach to downscale the lateral boundary conditions from the global driver, and the definition of the bottom boundary condition related to deep soil temperature. With respect to the downscaling methodology, the results show a clear benefit in using lateral boundary conditions directly from the global ERA5, despite the large resolution difference between the two modes (1:15), instead of providing them from an intermediate resolution COSMO‐based reanalysis. Moreover, the soil bottom boundary condition for temperature is reconstructed from the shallower ERA5 soil, using a site‐dependent method based on a delayed running mean of the ERA5 temperature at the deepest soil level. Finally, an evaluation of SPHERA has been performed with respect to the skill in simulating daily precipitation over 2 years. Compared with ERA5, SPHERA shows a higher ability in simulating moderate and intense events, markedly during summer, in terms of skill scores, frequency of occurrence and bias.
The major flood that affected the Piedmont region in Italy in November 1994 is re-forecast after 25 years in ensemble mode at the convection-permitting resolution of 2.2 km using the regional model COSMO. The performance of the probabilistic forecast of precipitation is assessed against rain-gauge observations, also in comparison with the driver system, i.e., the probabilistic re-forecast produced by ECMWF based on the operational IFS (Cycle 46r1) at grid spacings of 18 km. The convection-allowing system dynamically downscales the ECMWF ensemble and includes an explicit treatment of deep convection. Results indicate that both systems can predict up to 4 days in advance the timing and the spatial patterns of the precipitation, although with higher confidence for the convection-resolving system. The benefit of high resolution is shown mainly in the prediction of intense precipitation and in terms of correct amounts and locations, and confidence of occurrence (at day 3, the estimated probability of exceedance of 200 mm was higher than 90% over areas actually hit by such rainfall amounts). Additionally, convection-permitting resolution improves the representation of orographic precipitation, reducing the upwind precipitation displacement typical of coarser models and including the possible development of strong convection episodes embedded in the large-scale-forced orographic rise. For the high-resolution ensemble, the spread indicates large uncertainty at the local scale, mainly in defining the flow tendency to flank or flow over each mountain.
The regions facing the northern Adriatic Sea are particularly vulnerable to sea-level rise. Several trade ports are located there, and the area is important from social and economical viewpoints. Since tourism and cultural heritage are a significant source of income, an increase in sea-level could hinder the development of these regions. One of the longest sea-level time series in the northern Adriatic, which goes back to the late 1880s, has been recorded at Marina di Ravenna, in Emilia-Romagna region. The record is anomalous, showing a rate of increase that largely exceeds that observed in nearby stations. During the last few decades, geodetic campaigns based on geometric high precision leveling, SAR interferometry, and GPS have monitored the Ravenna area. In this work, tide gauge observations are merged with yet unpublished geodetic data, aiming at a coherent interpretation of vertical land movements. We confirm that land subsidence is the major cause of relative sea-level change at Marina di Ravenna, at least during the period allowing for a quantitative analysis (1990-2011). The rate of absolute sea-level change (2.2±1.3 mm yr−1 during the same time period), given by the difference between the rate of relative sea-level change and the rate of subsidence, is consistent with the rate of absolute sea-level change observed by altimetry in the northern Adriatic Sea.
Regional reanalyses allow us to better describe weather patterns related to rapidly evolving high‐impact events thanks to substantially finer detailing than global datasets. However, most regional datasets still do not permit the explicit representation of deep convection. SPHERA (High rEsolution ReAnalysis over Italy) is a new high‐resolution convection‐permitting reanalysis centred over Italy. It covers 26 years (1995–2020), is based on the non‐hydrostatic limited‐area model COSMO, and is produced by dynamically downscaling the global reanalysis ERA5. A nudging data assimilation scheme steers the model toward observations. The fine horizontal grid spacing of 2.2 km allows us to switch off deep‐convection parametrization. This study reports the added value of SPHERA over ERA5 in representing rainfall over Italy, particularly for severe precipitation, using rain‐gauge observations during 2003–2017 as reference. Concerning the 95th percentile of spatial rainfall distributions, ERA5 presents dry estimates with biases reaching −12 mm·day−1 over mountainous regions. At the same time, the enhanced locally driven effects of SPHERA produce seasonal biases ranging from wet in JJA (up to +12 mm·day−1) to dry in DJF (down to −9 mm·day−1). For daily maximum rates, the regional reanalysis shows better skill in detecting occurred events (with hit rates higher than ERA5 by roughly 0.4 points in the range of 15–80 mm·day−1) and frequency biases closer to 0 at all intensities when coming to daily averages. Similarly, for hourly maximum accumulations, improved adherence to observations is detected for SPHERA at all intensities, conversely to the underprediction of the global driver (with frequency biases <1 starting from 1.5 mm·hr−1). Additionally, the analyses of two specific events reveal the enhancements of SPHERA in simulating extreme precipitation, with a maximum intensity underestimation on the order of 24% versus the 73% detected for ERA5. Further improvements include the spatial detailing, timing, and temporal evolution of the events.
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