The quality of the Initial Conditions is the most important parameter for the high resolution numerical weather prediction. Generally, localized mesoscale features are not well reproduced into the analysis therefore, assimilation approaches that ingest lo-cal observations are important to improve the forecast. During the last decade, highresolutionmesoscale models initialized using variational data assimilation techniques(3DVAR/4DVAR) are being increasingly applied for studying meteorological phenomena
(Kalnay, 2003). One of the reasons for the variational analysis becoming more and
more popular is the ability to directly incorporate non conventional data such as satellite
radiance, radar reflectivity and radial velocity into numerical models (Kalnay, 2003;
Barker et al., 2004).
Doppler Weather radar (DWR) observations are an important data source for
weather analyses and forecasting because of the high temporal and spatial resolutions.
These high-resolution data, together with a sophisticated technique of data assimilation
and a high-resolution mesoscale model, have been chosen in the last decade for
improving the predictability both of convective cells and mesoscale convective systems.
Furthermore, the assimilation of radar reflectivity and radial velocity have shown potential
for very-short-range numerical prediction of rapidly developing convective systems.
It is well known that the reflectivity is related to the amount of precipitation, size and
water phase of the hydrometeors, whereas the radial velocity contains information on
vertical atmospheric motions which are both important for the onset and development
of convection.