Amongst all meteorological phenomena, precipitation has one of the largest impacts on the human society. It affects not onldy our daily lives, it has also a large impact on (urban and rural) hydrology, agriculture, water supply, industrial activities etc. Moreover, severe precipitation events can lead to human, economical and ecological disasters with huge economical consequences. The forecast of precipitation is, however, a most challenging task. Since it is governed by complex microphysical processes, the exact circumstances for a cloud to form precipitation are still not fully understood. But also on a macro-scale, the formation of precipitation is linked to very complex dynamical processes of the atmosphere. Moreover, precipitation can be a very local phenomenon, e.g. in the case of an isolated convective event, acting on scales much smaller than the mesoscale, and sometimes even smaller than the grid scale of the most recent NWP models (note that also very large widespread precipitation structures exist, e.g. in the case of the slow passage of a cold front). Due to these incompatible scales of precipitating structures and (most) NWP models, the capabilities of the existing NWP models in the prediction of precipitation at a specific location are rather limited. Moreover, an incomplete data assimilation in the stage of the initialisation of an NWP model can lead to unreliable precipitation predictions for short lead times of 0-6 h. In other words, an incomplete assimilation in an NWP model limits the skill of NWP precipitation forecasts for short lead times considerably, since the model does not cover adequately enough the present situation.