VHR-REA_IT Dataset: Very High Resolution Dynamical Downscaling of ERA5 Reanalysis over Italy by COSMO-CLM

Raffa, Mario; Reder, Alfredo; Marras, Gian Franco; Mancini, Marco; Scipione, Gabriella; Santini, Monia; Mercogliano, Paola

doi:10.3390/data6080088

Cited by 26 publications

(18 citation statements)

References 30 publications

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“…Our work is a first assessment about the reliability, affordability and ease of implementation of the BOLAM/MOLOCH suite for downscaling reanalyses and thus, potentially, climate projections. Furthermore, thanks to the numerous ongoing activities regarding the refinement of ERA5 data at the local scale, the BOLAM/MOLOCH hindcast can complement similar datasets (Bonanno et al, 2019;Raffa et al, 2021b) to create a multi-model and convection-permitting ensemble, aimed at assessing the uncertainty of past climate in Italy. Fig.…”

Section: Discussionmentioning

confidence: 99%

“…By comparing rainfall outputs with the E-OBS (Cornes et al, 2018) gridded dataset as ground truth, the authors argued that the direct nesting should be preferred. In a second paper (Raffa et al, 2021b), the authors presented the dataset, which covers the Italian domain, for the period 1989-2020. More recently, Reder et al (2022), demonstrated that this dataset provides more reliable data than global reanalyses in characterising extreme precipitations at the urban scale.…”

Section: Introductionmentioning

confidence: 99%

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

Capecchi¹,

Pasi

Gozzini³

et al. 2022

Preprint

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We describe 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 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. We also analyse 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. We finally discuss how the demonstrated reliability of the BOLAM and MOLOCH models associated to the relatively low computational cost, promote their use as a valuable tool for downscaling not only reanalyses but also climate projections.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Capecchi¹,

Pasi

Gozzini³

et al. 2022

Preprint

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“…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 .…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Giordani

Cerenzia

Paccagnella

et al. 2023

Quart J Royal Meteoro Soc

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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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“…Recently, as part of the Highlander project ( https://highlanderproject.eu/ ), Raffa et al . 15 released VHR-REA_IT (Very High-Resolution REAnalysis for ITaly), a gridded dataset for the recent past thirty years (1989–2020) over Italy. Such a dataset has been obtained by dynamically downscaling ERA5 reanalysis 16 from its native resolution (≃31 km) to a resolution of ≃2.2 km using the regional climate model COSMO-CLM 17 .…”

Section: Background and Summarymentioning

confidence: 99%

Very High Resolution Projections over Italy under different CMIP5 IPCC scenarios

et al. 2023

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This paper introduces VHR-PRO_IT (Very High-Resolution PROjections for ITaly), an open access hourly climate projection with a resolution of ≃2.2 km (i.e., Convection Permitting Scale) up to 2050, covering the Italian peninsula and some neighbouring areas. VHR-PRO_IT is produced within the Highlander project (https://highlanderproject.eu/) by dynamically downscaling the Italy8km-CM climate projection (spatial resolution ≃8 km; output frequency = 6 h; driven CMIP5 GCM = CMCC-CM) with the Regional Climate Model COSMO-CLM under the IPCC RCP4.5 and RCP8.5 scenarios. It covers the 60-year period 1989–2050. VHR-PRO_IT is intended for research purposes in the field of climate studies. For example, it may be included in the ongoing activities to clarify the added value of running climate simulation at the convection-permitting scale.

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VHR-REA_IT Dataset: Very High Resolution Dynamical Downscaling of ERA5 Reanalysis over Italy by COSMO-CLM

Cited by 26 publications

References 30 publications

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

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

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

Very High Resolution Projections over Italy under different CMIP5 IPCC scenarios

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