A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy

Abstract

This study describes the implementation and performances of a weather hindcast obtained by dynamically downscaling the ERA5 data across the period 1979–2019. The limited-area models used to perform the hindcast are BOLAM (with a grid spacing of 7 km over the Mediterranean domain) and MOLOCH (with a grid spacing of 2.5 km over Italy). BOLAM is used to provide initial and boundary conditions to the inner grid of the MOLOCH model, which is set in a convection-permitting configuration. The performances of such limited-area, high-resolution and long-term hindcast are evaluated comparing modelled precipitation data against two high-resolution gridded observational datasets. Any potential added-value of the BOLAM/MOLOCH hindcast is assessed with respect to ERA5-Land data, which are used as benchmark. Results demonstrate that the MOLOCH hindcast, compared to observations, provides a lower bias than ERA5-Land as regards both the mean annual rainfall ($$-$$ - 1.3% vs 8.7%) and the 90th percentile of summer daily precipitation, although a wet bias is found in southern Italy (bias $$\simeq$$ ≃ 17.1%). Improvements are also gained in the simulation of the 90th percentile of hourly precipitations both in winter and, to a minor extent, in summer. The diurnal cycle of summer precipitations is found to be better reconstructed in the Alps than in the hilly areas of southern Italy. This study also presents the rainfall peaks obtained in the simulation of two well-known severe precipitation events that caused floods and damages in north-western Italy in 1994 and 2011. Finally, it is discussed how the demonstrated reliability of the BOLAM and MOLOCH models associated to the documented low computational cost, promote their use as a valuable tool for downscaling not only reanalyses but also climate projections.

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

“…Furthermore, the results obtained are in line with those of recently produced CP hindcasts over Italy, sharing similar characteristics with SPHERA, and obtained by downscaling ERA5 with the BOLAM/-MOLOCH model (Capecchi et al, 2022) or COSMO model (Raffa et al, 2021;Reder et al, 2022). In Capecchi et al (2022), similar wet frequency biases in reproducing the 90th percentiles of annual, daily and hourly rainfalls are detected for MOLOCH simulations at 2.5 km grid spacing, as opposed to the dry biases detected with the BOLAM run at 7 km. Further, from the analysis of two severe-precipitation events, a higher level of detail in the spatial characterization and less deviation from maximum intensities is maintained with the CP hindcast, despite underestimating the most extreme rainfall observations (by 64% in one case).…”

“…Previous studies reported multiple benefits related to increased grid resolutions in numerical simulations, allowing an adequate representation of local dynamical features and forcings leading to or intensifying precipitation events (Buzzi et al, 2014;Cassola et al, 2015;Clark et al, 2016;Wahl et al, 2017;Klasa et al, 2018;Cerenzia et al, 2020;Capecchi, 2021). Furthermore, the results obtained are in line with those of recently produced CP hindcasts over Italy, sharing similar characteristics with SPHERA, and obtained by downscaling ERA5 with the BOLAM/-MOLOCH model (Capecchi et al, 2022) or COSMO model (Raffa et al, 2021;Reder et al, 2022). In Capecchi et al (2022), similar wet frequency biases in reproducing the 90th percentiles of annual, daily and hourly rainfalls are detected for MOLOCH simulations at 2.5 km grid spacing, as opposed to the dry biases detected with the BOLAM run at 7 km.…”

“…In this context, several recent European efforts demonstrated the potential that multi-model CP regional climate simulations have for numerous aspects: better understanding the response of convection extremes to human-induced climate change and providing critical added value to decision-makers due to the enhanced confidence in simulating convection extremes (Coppola et al, 2020), more realistic representation of heavy precipitation with a significant reduction of the hourly summer bias and a reduction of their temporal uncertainty (Ban et al, 2021), and improved representation of fine-scale details of seasonal, daily and hourly heavy rainfall (Pichelli et al, 2021). In light of this, we believe that the combination of the efforts leading to the recent development of similar CP reanalysis/hindcast datasets over Italy (such as SPHERA or Capecchi et al (2022) and Reder et al, 2022) is of paramount importance. Hence, the proposal is to jointly develop the first Italian multi-model high-resolution reanalysis/hindcast ensemble.…”

“…When coming to Italy, the considerable interest in developing highly resolved re-forecast datasets is demonstrated by the recent production of two CP regional hindcasts. These are obtained by downscaling ERA5 using the COSMO model at 2.2 km grid spacing (Raffa et al, 2021;Reder et al, 2022) or the model MOLOCH at 2.5 km (Capecchi et al, 2022). Anyhow, in neither case is the additional assimilation of regional observations included in the production of the datasets.…”