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| Titel |
Simulations of deep convection in the Mediterranean area using 3DVAR of conventional and non-conventional data |
| VerfasserIn |
R. Ferretti, C. Faccani, D. Cimini, F. S. Marzano, A. Memmo, L. Cucurull, R. Pacione |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: 6th Plinius Conference on Mediterranean Storms (2004) ; Nr. 2 (2005-03-29), S.65-71 |
| Datensatznummer |
250000298
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| Publikation (Nr.) |
 copernicus.org/adgeo-2-65-2005.pdf |
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| Zusammenfassung |
| 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. |
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