Dynamical Downscaling of ERA5 Data on the North-Western Mediterranean Sea: From Atmosphere to High-Resolution Coastal Wave Climate

Vannucchi, Valentina; Taddei, Stefano; Capecchi, Valerio; Bendoni, Michele; Brandini, Carlo

doi:10.3390/jmse9020208

Cited by 32 publications

(17 citation statements)

References 62 publications

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“…Wave data were extracted from the LaMMA Consortium wave hindcast, covering the period 1990-2018 [27,35]. This wave hindcast was produced for the Mediterranean Sea at high-resolution, up to 500 m on some coasts of the Ligurian and Tyrrhenian Seas, and around 6 km on the rest of the Mediterranean coasts, while the minimum offshore resolution is of 30 km.…”

Section: Wavewatch III Hindcastmentioning

confidence: 99%

“…The wave spectrum has a resolution of 10 • (36 directional bands) with 30 frequency intervals, ranging from 0.0418 Hz to 1.1181 Hz. A wave model calibration was performed in a previous work [27,35] comparing the numerical results for twelve storms with data from seven buoys, in the area with higher resolution.…”

Section: Wavewatch III Hindcastmentioning

confidence: 99%

“…The time series of the significant wave height at the buoy position in Figure 2a, measured by the buoy and by our hindcast, shows a good overall agreement for the year 2010, quantified in Figure 2b with a scatterplot between observed and model significant wave height, resulting in a correlation coefficient of r = 0.95, and in a mean bias error of MBE = 0.09. For a thorough assessment of hindcast performances, we remind the reader to refer to [27]. It is worth noting that the hindcast underestimates the significant wave height at the time of the accident H s ≈ 4.1 m with respect to buoy observations H s ≈ 5 m. Moreover, a different expected significant wave height also with respect to the analysis of Cavaleri et al [11] denotes a certain variability between the hindcasts, which could lead to slightly different results in absolute terms.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…A similar coupled WWIII/HOS methodology has been used in this work, coupling wave spectra from a wave hindcast produced by the Environmental Modelling and Monitoring Laboratory for the Sustainable Development (LaMMA) Consortium [27] with local HOS phase-resolving simulations, without interaction with winds, currents, and topography, in order to evaluate the sole effect of nonlinear interactions. With phase-resolving simulations, it would then be possible to follow a statistical approach for the description of the wave system, evaluating crest height distributions as well as integral statistics (skewness, kurtosis) of surface elevation.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Dangerous Sea States in the Northwestern Mediterranean Area

Innocenti¹,

Onorato

Brandini

2021

JMSE

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Extreme sea waves, although rare, can be notably dangerous when associated with energetic sea states and can generate risks for the navigation. In the last few years, they have been the object of extensive research from the scientific community that helped with understanding the main physical aspects; however, the estimate of extreme waves probability in operational forecasts is still debated. In this study, we analyzed a number of sea-states that occurred in a precise area of the Mediterranean sea, near the location of a reported accident, with the objective of relating the probability of extreme events with different sea state conditions. For this purpose, we performed phase-resolving simulations of wave spectra obtained from a WaveWatch III hindcast, using a Higher Order Spectral Method. We produced statistics of the sea-surface elevation field, calculating crest distributions and the probability of extreme events from the analysis of a long time-series of the surface elevation. We found a good matching between the distributions of the numerically simulated field and theory, namely Tayfun second- and third- order ones, in contrast with a significant underestimate given by the Rayleigh distribution. We then related spectral quantities like angular spreading and wave steepness to the probability of occurrence of extreme events finding an enhanced probability for high mean steepness seas and narrow spectra, in accordance with literature results, finding also that the case study of the reported accident was not amongst the most dangerous. Finally, we related the skewness and kurtosis of the surface elevation to the wave steepness to explain the discrepancy between theoretical and numerical distributions.

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Section: Wavewatch III Hindcastmentioning

confidence: 99%

Section: Wavewatch III Hindcastmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Dangerous Sea States in the Northwestern Mediterranean Area

Innocenti¹,

Onorato

Brandini

2021

JMSE

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“…ERA5 has a better performance than other reanalysis data, 29 contains global long-term data from 1979 to the present, and is easily accessible. Therefore, many previous downscaling studies 17,[30][31][32] have used ERA5 as…”

Section: Low-resolution Wind Resource Datamentioning

confidence: 99%

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Lee

Kim

et al. 2022

Wind Energy

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The need for high-resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography-derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi-resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.

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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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Dynamical Downscaling of ERA5 Data on the North-Western Mediterranean Sea: From Atmosphere to High-Resolution Coastal Wave Climate

Cited by 32 publications

References 62 publications

Analysis of Dangerous Sea States in the Northwestern Mediterranean Area

Analysis of Dangerous Sea States in the Northwestern Mediterranean Area

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

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

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