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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2020-406
https://doi.org/10.5194/nhess-2020-406

  06 Jan 2021

06 Jan 2021

Review status: this preprint is currently under review for the journal NHESS.

Investigating 3D and 4D Variational Rapid-Update-Cycling Assimilation of Weather Radar Reflectivity for a Flash Flood Event in Central Italy

Vincenzo Mazzarella1, Rossella Ferretti1, Errico Picciotti1, and Frank S. Marzano1,2 Vincenzo Mazzarella et al.
  • 1Centre of Excellence CETEMPS, Department of Physical and Chemical Sciences – University of L’Aquila, L’Aquila, 67100, Italy
  • 2Department of Information Engineering, Sapienza University of Rome, Rome, 00185, Italy

Abstract. The precipitation forecast over the Mediterranean basin is still a challenge because of the complex orographic region which amplifies the need for local observation to correctly initialize the forecast. In this context the data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of precipitation pattern. For the first time, the ability of a cycling 4D-Var to reproduce a severe weather event in central Italy, as well as to provide a comparison with the largely used cycling 3D-Var, is evaluated in this study. The radar reflectivity measured by the Italian ground radar network is assimilated in the WRF model to simulate an event occurred on May 3, 2018 in central Italy. In order to evaluate the impact of data assimilation, several simulations are objectively compared by means of a Fraction Skill Score (FSS), which is calculated for several threshold values, and a Receiver Operating Characteristic (ROC) curve. The results suggest that both assimilation methods in cycling mode improve the 1, 3 and 6-hourly quantitative precipitation estimation. More specifically, the cycling 4D-Var with a warm start initialization shows the highest FSS values in the first hours of simulation both with light and heavy precipitation. Finally, the ROC curve confirms the benefit of 4D-Var: the area under the curve is 0.91 compared to the 0.88 of control experiment without data assimilation.

Vincenzo Mazzarella et al.

Status: open (until 19 Feb 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Vincenzo Mazzarella et al.

Vincenzo Mazzarella et al.

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