How can data assimilation of visible satellite data assist seamless prediction?

In the framework of the project SINFONY at Deutscher Wetterdienst, we work towards seamless prediction at the very-short range blending over from observation-based nowcasting to numerical weather prediction. The key goals that we pursue in this context are:
1.    To deliver forecasts earlier to be displayed in the meteorological workstation NinJo of our forecasters, which is realized by hourly forecast initialization in our newly-developed Rapid Update Cycle (RUC) and shorter latency for observation arrival ahead of data assimilation. 
2.    To provide seamlessly combined products integrating nowcasted and forecasted radar reflectivities as well as precipitation from both forecasting systems.
3.    To achieve a better representation of precipitation processes and convective cells in our NWP model to allow for the seamless blending with nowcasts. For this purpose, we use a two-moment microphysics scheme that predicts not only mixing ratios of hydrometeor species, but also their particle size distribution. This is also of great importance for the data assimilation of geostationary all-sky satellite data assimilation, for data assimilation of lightning data and essentially radar reflectivities.

In this presentation, we explain how data assimilation of cloudy visible satellite data can help to improve the accuracy of clouds and precipitation processes in NWP forecasts to assist a seamless blending of nowcasting and NWP in terms of radar reflectivities mentioned in 2) and 3). 

Visible satellite data are directly sensitive to liquid water path, ice water path and specific humidity  which are integral quantities related to precipitation processes. Moreover, cloud positioning can be improved by deleting false alarm clouds and convective cells and introducing missing ones to the forecast. A key advantage is that visible data are particularly sensitive to water clouds, which allows to constrain convective cells already at their state of initiation in the initial conditions of our RUC forecasts.

We elaborate on the basic principles of satellite data assimilation in our ICON-D2-KENDA system making use of an ensemble Kalman filter. Case studies will be shown to demonstrate how data assimilation of all-sky satellite data reduces analysis and forecast error of clouds and precipitation. Finally, we show the impact in our rapid update pre-operational system over longer periods of time.