SPHERA, a new convection‐permitting regional reanalysis over Italy: Improving the description of heavy rainfall

Abstract

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.

Enhanced Evaluation of Sub-daily and Daily Extreme Precipitation in Norway from Convection-Permitting Models at Regional and Local Scales

“…Additionally, the superior performance in capturing hourly and daily extreme precipitation including return-level, frequency and intensity from CPRCMs over Alpine in Europe, has also been highlighted by Adinolfi et al (2021), Dallan et al (2023) and Giordani et al (2023).…”

Using a convection-permitting climate model to predict wine grape productivity: two case studies in Italy

“…In addition to observations, the analysis uses a high-resolution convection-permitting reanalysis product, called SPHERA (High rEsolution ReAnalysis over Italy; Cerenzia et al, 2022;Giordani et al, 2023), produced by ARPAE-SIMC (Agency for Environmental Protection of the Emilia Romagna Region, Italy). Based on the non-hydrostatic 120 limited-area model COSMO (Schättler et al, 2018;Baldauf et al, 2011), SPHERA dynamically downscales the global reanalysis ERA5 (Hersbach et al, 2020) assimilating regional in situ observations to improve the quality of the simulation.…”

“…This new reanalysis product covers Italy at a horizontal resolution of 2.2 km with a temporal coverage of 26 years . SPHERA reanalysis, validated against ERA5 by Giordani et al (2023), shows added value for the description of moderate to severe local precipitation events and extreme rainfall. The performance of SPHERA 125 demonstrates that it can be a valuable resource for improving climate monitoring by providing insights into regional climate change impacts (Giordani et al, 2023).…”

Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe

“…New generations of automatic hail recorders (HARE) are in operation in some regions, for example, in parts of Switzerland (Löffler-Mang et al, 2011;Kopp et al, 2022), but unfortunately these systems are very expensive and can not be implemented over extended areas. Ground-based hailpad networks can provide homogeneous long-term records and have been deployed in several parts of the world, such as in parts of China, France, Italy, or Croatia (e.g., Changnon Jr, 1970;Giaiotti et al, 2003;Xie et al, 2008;Sánchez et al, 2009;Palencia et al, 2010;Manzato, 2012;Dessens et al, 2015). However, they cover only smaller regions and require significant resources for their maintenance.…”

“…Particularly, CP models have provided added value for the representation of tornadic or large-hail environments (Clark et al, 2013;Adams-Selin and Ziegler, 2016;Labriola et al, 2019;Gagne II et al, 2019;Manzato et al, 2020;Malečić et al, 2022). The need for an enhanced description of high-impact, rapidly-evolving and sharply-localized atmospheric phenomena prompted the development of a new CP regional reanalysis over south-central Europe: SPHERA (High rEsolution ReAnalysis over Italy - Cerenzia et al, 2022;Giordani et al, 2023). Based on dynamical downscaling of ERA5 and driven by a non-hydrostatic model COSMO (Schättler et al, 2018) at 0.02°grid spacing, SPHERA has shown to enhance the description of weather extremes such as severe precipitation over Italy and neighboring countries (Giordani et al, 2023).…”