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Article

Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

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LaMMA—Laboratorio di Meteorologia e Modellistica Ambientale per lo Sviluppo Sostenibile, 50019 Firenze, Italy
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Istituto per la BioEconomia—Consiglio Nazionale delle Ricerche, 50019 Firenze, Italy
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Department of Physical and Chemical Sciences, Universitá degli Studi dell’Aquila/CETEMPS, 67100 L’Aquila, Italy
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Department of Civil and Environmental Engineering, School of Engineering, University of Connecticut, Storrs, CT 06269-3237, USA
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Eversource Energy Center, University of Connecticut, Storrs, CT 06269-5276, USA
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Author to whom correspondence should be addressed.
Academic Editor: Antonio Parodi
Water 2021, 13(13), 1727; https://doi.org/10.3390/w13131727
Received: 29 April 2021 / Revised: 15 June 2021 / Accepted: 16 June 2021 / Published: 22 June 2021
During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts. View Full-Text
Keywords: WRF model; 3D-Var data assimilation; radar data; short-range prediction; heavy precipitation event WRF model; 3D-Var data assimilation; radar data; short-range prediction; heavy precipitation event
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MDPI and ACS Style

Capecchi, V.; Antonini, A.; Benedetti, R.; Fibbi, L.; Melani, S.; Rovai, L.; Ricchi, A.; Cerrai, D. Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy. Water 2021, 13, 1727. https://doi.org/10.3390/w13131727

AMA Style

Capecchi V, Antonini A, Benedetti R, Fibbi L, Melani S, Rovai L, Ricchi A, Cerrai D. Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy. Water. 2021; 13(13):1727. https://doi.org/10.3390/w13131727

Chicago/Turabian Style

Capecchi, Valerio, Andrea Antonini, Riccardo Benedetti, Luca Fibbi, Samantha Melani, Luca Rovai, Antonio Ricchi, and Diego Cerrai. 2021. "Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy" Water 13, no. 13: 1727. https://doi.org/10.3390/w13131727

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