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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 9
Atmos. Meas. Tech., 7, 2919–2935, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Tropospheric profiling (ISTP9)

Atmos. Meas. Tech., 7, 2919–2935, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Sep 2014

Research article | 12 Sep 2014

Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR

I. Maiello1, R. Ferretti1,2, S. Gentile1, M. Montopoli1,3, E. Picciotti1,4, F. S. Marzano1,3, and C. Faccani5 I. Maiello et al.
  • 1Centre of Excellence CETEMPS, L'Aquila, Italy
  • 2Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila, Italy
  • 3Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy
  • 4HIMET s.r.l., L'Aquila, Italy
  • 5ENAV S.p.A. – Academy, Forlì, Italy

Abstract. The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators.

In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used.

The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data.

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