Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Valerio Capecchi
* ORCID logo ,
Andrea Antonini
,
Riccardo Benedetti
,
Luca Fibbi
,
Samantha Melani
ORCID logo ,
Luca Rovai
,
Antonio Ricchi
,
Diego Cerrai
ORCID logo
Version 1 : Received: 3 June 2021 / Approved: 4 June 2021 / Online: 4 June 2021 (12:54:12 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood 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. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

Keywords

WRF model; 3D-Var data assimilation; radar data; short-range prediction; heavy precipitation event

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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