Articles | Volume 34, issue 3
https://doi.org/10.5194/angeo-34-347-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-34-347-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Regular paper
| 
21 Mar 2016
Regular paper |
| 21 Mar 2016
Three-model ensemble wind prediction in southern Italy
Rosa Claudia Torcasio
CNR-ISAC, Zona Industriale Comparto 15, 88046 Lamezia Terme, Italy
CNR-ISAC, Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Claudia Roberta Calidonna
CNR-ISAC, Zona Industriale Comparto 15, 88046 Lamezia Terme, Italy
Elenio Avolio
CNR-ISAC, Zona Industriale Comparto 15, 88046 Lamezia Terme, Italy
Oxana Drofa
CNR-ISAC, Via Gobetti 101, Bologna, Italy
Tony Christian Landi
CNR-ISAC, Via Gobetti 101, Bologna, Italy
Piero Malguzzi
CNR-ISAC, Via Gobetti 101, Bologna, Italy
Andrea Buzzi
CNR-ISAC, Via Gobetti 101, Bologna, Italy
Paolo Bonasoni
CNR-ISAC, Via Gobetti 101, Bologna, Italy
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Stefano Federico, Marco Petracca, Giulia Panegrossi, Claudio Transerici, and Stefano Dietrich
Adv. Sci. Res., 14, 187–194, https://doi.org/10.5194/asr-14-187-2017, https://doi.org/10.5194/asr-14-187-2017, 2017
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This study investigates the impact of using lightning data on the precipitation forecast at different forecast ranges (3–24 h). Twenty case studies, occurred over Italy in fall 2012, are selected to show the impact.
Results show the important and positive impact of using lightning data to improve the precipitation forecast. The time range, however, is very important because the performance decreases steadily and substantially with forecasting time.
Stefano Federico, Rosa Claudia Torcasio, Paolo Sanò, Daniele Casella, Monica Campanelli, Jan Fokke Meirink, Ping Wang, Stefania Vergari, Henri Diémoz, and Stefano Dietrich
Atmos. Meas. Tech., 10, 2337–2352, https://doi.org/10.5194/amt-10-2337-2017, https://doi.org/10.5194/amt-10-2337-2017, 2017
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Alfonso Ferrone, Daniele Mastrangelo, and Piero Malguzzi
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Daniele Mastrangelo and Piero Malguzzi
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Stefano Federico, Marco Petracca, Giulia Panegrossi, and Stefano Dietrich
Nat. Hazards Earth Syst. Sci., 17, 61–76, https://doi.org/10.5194/nhess-17-61-2017, https://doi.org/10.5194/nhess-17-61-2017, 2017
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Davide Putero, Paolo Cristofanelli, Michael Sprenger, Bojan Škerlak, Laura Tositti, and Paolo Bonasoni
Atmos. Chem. Phys., 16, 14203–14217, https://doi.org/10.5194/acp-16-14203-2016, https://doi.org/10.5194/acp-16-14203-2016, 2016
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Atmos. Chem. Phys., 16, 12849–12859, https://doi.org/10.5194/acp-16-12849-2016, https://doi.org/10.5194/acp-16-12849-2016, 2016
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E. D. Sofen, D. Bowdalo, M. J. Evans, F. Apadula, P. Bonasoni, M. Cupeiro, R. Ellul, I. E. Galbally, R. Girgzdiene, S. Luppo, M. Mimouni, A. C. Nahas, M. Saliba, and K. Tørseth
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D. Putero, P. Cristofanelli, A. Marinoni, B. Adhikary, R. Duchi, S. D. Shrestha, G. P. Verza, T. C. Landi, F. Calzolari, M. Busetto, G. Agrillo, F. Biancofiore, P. Di Carlo, A. K. Panday, M. Rupakheti, and P. Bonasoni
Atmos. Chem. Phys., 15, 13957–13971, https://doi.org/10.5194/acp-15-13957-2015, https://doi.org/10.5194/acp-15-13957-2015, 2015
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The aim of this paper is to present a full-year analysis of simultaneous measurements of ozone, black carbon, and aerosol number concentration at Paknajol, in the Kathmandu Valley, one of the global “hot spots” in terms of air pollution. Results indicate persisting poor air quality conditions throughout the measurement period, and suggest that the pollutants' variability is mainly driven by local pollution source activity, local- and large-scale dynamics, photochemistry, and vegetation fires.
M. Rinaldi, S. Gilardoni, M. Paglione, S. Sandrini, S. Fuzzi, P. Massoli, P. Bonasoni, P. Cristofanelli, A. Marinoni, V. Poluzzi, and S. Decesari
Atmos. Chem. Phys., 15, 11327–11340, https://doi.org/10.5194/acp-15-11327-2015, https://doi.org/10.5194/acp-15-11327-2015, 2015
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This work highlights the important contribution of organic aerosols to the composition of submicron particles at remote mountain sites. Moreover, it confirms the importance of regional-scale physical and chemical processes and of transboundary transport in determining the background aerosol composition at rural European sites.
H.-W. Jacobi, S. Lim, M. Ménégoz, P. Ginot, P. Laj, P. Bonasoni, P. Stocchi, A. Marinoni, and Y. Arnaud
The Cryosphere, 9, 1685–1699, https://doi.org/10.5194/tc-9-1685-2015, https://doi.org/10.5194/tc-9-1685-2015, 2015
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We detected up to 70 ppb of black carbon (BC) in surface snow in the upper Khumbu Valley, Nepal. With an upgraded snowpack model, including radiative transfer inside the snow, we studied the impact of BC on snow albedo, melting and radiative forcing for the sensitive high altitude regions of the Himalayas. We found that due to BC, the melting of the snow can be shifted by several days up to several weeks depending on meteorological conditions. The impact of BC is larger in dirty snow.
L. Tiriolo, R. C. Torcasio, S. Montesanti, A. M. Sempreviva, C. R. Calidonna, C. Transerici, and S. Federico
Adv. Sci. Res., 12, 37–44, https://doi.org/10.5194/asr-12-37-2015, https://doi.org/10.5194/asr-12-37-2015, 2015
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We show a study of the prediction of power production of a wind farm located in Central Italy using RAMS model for wind speed forecast.
G. Curci, L. Ferrero, P. Tuccella, F. Barnaba, F. Angelini, E. Bolzacchini, C. Carbone, H. A. C. Denier van der Gon, M. C. Facchini, G. P. Gobbi, J. P. P. Kuenen, T. C. Landi, C. Perrino, M. G. Perrone, G. Sangiorgi, and P. Stocchi
Atmos. Chem. Phys., 15, 2629–2649, https://doi.org/10.5194/acp-15-2629-2015, https://doi.org/10.5194/acp-15-2629-2015, 2015
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S. Mariani, M. Casaioli, E. Coraci, and P. Malguzzi
Nat. Hazards Earth Syst. Sci., 15, 1–24, https://doi.org/10.5194/nhess-15-1-2015, https://doi.org/10.5194/nhess-15-1-2015, 2015
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In the HyMeX framework, an intercomparison study of two different configurations of the ISPRA hydro-meteo-marine forecasting system (SIMM) has been performed to assess the forecast skill in predicting high precipitations and very intense storm surges over the northern Adriatic Sea (acqua alta). The results show an objective added value of the use of high-resolution meteorological models, and they suggest the opportunity to develop a time-lagged multi-model ensemble for the acqua alta prediction.
S. Federico, E. Avolio, M. Petracca, G. Panegrossi, P. Sanò, D. Casella, and S. Dietrich
Nat. Hazards Earth Syst. Sci., 14, 2933–2950, https://doi.org/10.5194/nhess-14-2933-2014, https://doi.org/10.5194/nhess-14-2933-2014, 2014
Short summary
Short summary
This paper shows the implementation of a simple model for simulating lightning into the RAMS model.
The methodology is applied to six case studies that occurred in central Italy and the results are verified against LINET observations.
Advantages and weaknesses of the methodology are discussed.
M. Maione, F. Graziosi, J. Arduini, F. Furlani, U. Giostra, D. R. Blake, P. Bonasoni, X. Fang, S. A. Montzka, S. J. O'Doherty, S. Reimann, A. Stohl, and M. K. Vollmer
Atmos. Chem. Phys., 14, 9755–9770, https://doi.org/10.5194/acp-14-9755-2014, https://doi.org/10.5194/acp-14-9755-2014, 2014
P. Ginot, M. Dumont, S. Lim, N. Patris, J.-D. Taupin, P. Wagnon, A. Gilbert, Y. Arnaud, A. Marinoni, P. Bonasoni, and P. Laj
The Cryosphere, 8, 1479–1496, https://doi.org/10.5194/tc-8-1479-2014, https://doi.org/10.5194/tc-8-1479-2014, 2014
A. Jansa, P. Alpert, P. Arbogast, A. Buzzi, B. Ivancan-Picek, V. Kotroni, M. C. Llasat, C. Ramis, E. Richard, R. Romero, and A. Speranza
Nat. Hazards Earth Syst. Sci., 14, 1965–1984, https://doi.org/10.5194/nhess-14-1965-2014, https://doi.org/10.5194/nhess-14-1965-2014, 2014
A. Buzzi, S. Davolio, P. Malguzzi, O. Drofa, and D. Mastrangelo
Nat. Hazards Earth Syst. Sci., 14, 1325–1340, https://doi.org/10.5194/nhess-14-1325-2014, https://doi.org/10.5194/nhess-14-1325-2014, 2014
M. Casaioli, F. Catini, R. Inghilesi, P. Lanucara, P. Malguzzi, S. Mariani, and A. Orasi
Adv. Sci. Res., 11, 11–23, https://doi.org/10.5194/asr-11-11-2014, https://doi.org/10.5194/asr-11-11-2014, 2014
S. Federico
Atmos. Meas. Tech., 6, 3563–3576, https://doi.org/10.5194/amt-6-3563-2013, https://doi.org/10.5194/amt-6-3563-2013, 2013
Short summary
Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market.
This work shows the application of a technique to improve wind forecasting. The study area is southern Italy.
Quality of wind prediction is of great importance since a good wind forecast allows the...
