Forecast of precipitation for the 1994 flood in Piedmont: performance of an ensemble system at convection-permitting resolution

Cerenzia, Ines; Pincini, G.; Paccagnella, Tiziana; Minguzzi, Enrico; Gastaldo, Thomas; Poli, Virginia; Tesini, Maria Stefania; Patruno, P.; Cesari, D.

doi:10.1007/s42865-020-00025-2

Cited by 4 publications

(3 citation statements)

References 46 publications

(62 reference statements)

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“…The PIE1994 case was initially analysed by Buzzi et al (1998), who provided an exhaustive description of both the synoptic and small scale meteorological conditions that led to the disastrous flooding of a relatively vast area in northwestern Italy. Recently, several authors (Capecchi, 2020;Cerenzia et al, 2020;Davolio et al, 2020;Garbero and Milelli, 2020;Parodi et al, 2020) produced new reforecasts of the event by using state-of-the-art and limited-area numerical weather models (Meso-NH, COSMO, MOLOCH and WRF), all set in a convection-permitting mode. Although numerical experiments that shortly followed PIE1994 (Buzzi et al, 1998;Ferretti et al, 2000) were able to reproduce the rainfall due to the direct uplift of the flow impinging the Alps, it is by implementing higher-resolution and convection-permitting models that is possible to attain more accurate forecasts.…”

Section: Study Casesmentioning

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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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: Study Casesmentioning

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

View full text Add to dashboard Cite

show abstract

“…The PIE1994 case was initially analysed by Buzzi et al (1998), who provided an exhaustive description of both the synoptic and small scale meteorological conditions that led to the disastrous flooding of a relatively vast area in northwestern Italy. Recently, several authors (Capecchi 2020;Cerenzia et al 2020;Davolio et al 2020;Garbero and Milelli 2020;Parodi et al 2020) produced new reforecasts of the event by using state-of-the-art and limited-area numerical weather models (Meso-NH, COSMO, MOLOCH and WRF), all set in a convection-permitting mode. Although numerical experiments that shortly followed PIE1994 (Buzzi et al 1998;Ferretti et al 2000) were able to reproduce the rainfall due to the direct uplift of the flow impinging the Alps, it is by implementing higher-resolution and convectionpermitting models that is possible to attain more accurate forecasts.…”

Section: Study Casesmentioning

confidence: 99%

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

Capecchi¹,

Pasi²,

Gozzini³

et al. 2022

Clim Dyn

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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.

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“…These are: the finer grid spacing allowing for an enhanced physical and microphysical description of the processes, primarily due to not needing deep convection parametrization, the resulting higher level of topography detailing allowing for a better representation of atmosphere–land interactions, and the better adherence to assimilated regional observations. 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).…”

Section: Discussionmentioning

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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Forecast of precipitation for the 1994 flood in Piedmont: performance of an ensemble system at convection-permitting resolution

Cited by 4 publications

References 46 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

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

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