On the assimilation of dual-polarization radar observations via estimators of hydrometeor mixing ratios in Germany

Dual-polarization radar (DPR) observations provide additional information about bulk properties of clouds and precipitation such as hydrometeor size, shape, orientation and composition compared to single-polarization radar data. Thus, the use of DPR data for model evaluation and data assimilation has the potential to improve the representation of cloud-precipitation microphysical processes in numerical weather prediction (NWP) models, weather analyses and short-term quantitative precipitation forecasts (QPFs). Two frequently used approaches for radar data assimilation are 1) the use of radar forward operators and 2) the use of radar-estimated microphysical model state variables. Approach 1 is challenging as particle size, shape and orientation distributions and the composition of mixed-phase particles, which all impact polarimetric radar observables, are still rather rudimentarily represented in NWP models. Approach 2 circumvents these difficulties but may suffer from uncertainties in the retrievals. Here, we present first results of the latter approach.

Estimates of liquid water content (LWC) and ice water content (IWC) derived from observations of the operational dual-polarimetric C-band radar network of the German national meteorological service (DWD, Deutscher WetterDienst) are assimilated into DWD’s operational convective-scale NWP model ICON-D2 using the KENDA (Kilometre-sale ENsemble Data Assimilation) system. We compare the results of assimilating A) only conventional observations, B) conventional and radar reflectivity observations (approach 1 for single-polarization radar observations), and C) conventional and radar reflectivity observations as in B) and additionally LWC-/IWC-estimates below/above the melting layer. We focus on predicted hourly precipitation accumulations resulting from the three assimilation configurations for an intense three-day stratiform and a two-day convective precipitation period in summer 2017. Configurations B and C, which include radar observations, clearly improve both the deterministic and ensemble first guess QPFs for both precipitation periods compared to configuration A. Configuration C shows better results than configuration B only in some situations. Results also suggest that the assimilation of LWC is superior to the assimilation of IWC, possibly due to larger observation errors above the melting layer and/or the fact that the LWC estimator has been adjusted to the central European climatology. Investigation of the impact of the LWC/IWC-assimilation on QPFs with longer lead times and more events is in progress.