Assimilation of Nowcast Objects in the Regional Forecast Model ICON-LAM

A primary goal of the upcoming Rapid Update Cycle (RUC) at the German weather service is to close the gap between nowcasts (NWC) and numerical weatherp prediction (NWP) by adding cloud- and precipitation-related observations to the operational data assimilation. However, the NWP and NWC worlds differ not just by timescale but also more fundamentally in their approach:  while NWCs deal with individual convective cells, i.e. coherent objects whose positions and physical features are tracked, NWP systems and their associated data assimilation deal with gridded information, i.e. pixels of data.

To bridge these two worlds, we have developed a unique aproach of assimilating nowcast objects into an NWP model. The crux of the idea is to identify objects first, and then map the individual physical features of each object onto a regular model grid. In this talk we explore two ways of implementing this idea: The first defines objects simply by whether or not the observed radar reflectivity exceeds a given threshold, and then assimilates the gridded fraction of gridpoints that meet this criterion within a given spatial scale. The second approach defines objects using a more complex cell identification and tracking algorithm, and then grids the associated cell attributes (e.g. cell area) based on the distance of each gridpoint from the object centroid. We then go on to show how both of these approaches allow us to assimilate object-based information into an ensemble filter, focusing in particular on the difficulties of such an unconventional observation operator, as well as the possible complimentarity to conventional radar reflectivity assimilation.