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

Abstract

<p>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.</p><p>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.</p>