Articles | Volume 2
Adv. Geosci., 2, 65–71, 2005
Adv. Geosci., 2, 65–71, 2005

  29 Mar 2005

29 Mar 2005

Simulations of deep convection in the Mediterranean area using 3DVAR of conventional and non-conventional data

R. Ferretti1,2, C. Faccani1, D. Cimini1, F. S. Marzano2,3, A. Memmo2, L. Cucurull4, and R. Pacione5 R. Ferretti et al.
  • 1Department of Physics, University of L’Aquila, Italy
  • 2CETEMPS, University of L’Aquila, Italy
  • 3Department of Electrical Engineering, University of L’Aquila, Italy
  • 4COSMIC Project SCSDA, Suitland, MD 20746, USA
  • 5Telespazio SPA, Centro di Geodesia Spaziale, Matera, Italy

Abstract. In autumn deep convection in the Mediterranean region is a common phenomenon. The local events characterized by deep convection are still a difficult task even for high resolution numerical weather prediction. Three flood cases, produced by convection either embedded in a large scale system or locally developed, occurring in Italy, are presented. All these case were not correctly forecasted: Sardinia (Cagliari, 13 November 1999); Calabria (Soverato, 7 September 2000) and Sicily (Catania, 16 September 2003). The first case occurred during the Mesoscale Alpine Programme (MAP) campaign, therefore a lot of data are available; for the second one only data from SSM/I and local rain-gauge are available; the third one occurred during the operational experimentation of the TOUGH project. The last one was not well predicted even using the operational assimilation of ground based GPS. To improve the forecast of these cases the assimilation of several data is tested. The variational assimilation performed using 3DVAR of GPS, SSM/I and surface and upper air data is applied to improve the Initial Conditions of the Sicily case. The Sardinia case is improved using either GPS and surface data, whereas for the Soverato case only ZTD is assimilated. The experiments are performed using the MM5 model from Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR); the model is initialized using the new Initial Conditions produced by the variational assimilation of conventional and non conventional data. The results show that the assimilation of the retrieved quantities does produces large improvement in the precipitation forecast. Large sensitivity to the assimilation of surface data and brightness temperature from SSM/I is found.