Hydrometeor Classification and Quantitative Precipitation Estimation from Quality Assured Radar Data for the DWD Polarimetric C-Band Weather Radar Network

In 2010 Deutscher Wetterdienst (DWD) has started the exchange of its Doppler C-Band weather radar network with 17 dual-polarimetric radars. Attached to this procedure, DWD has launched an internal project with the aim to implement state-of-the-art dual-polarimetric algorithms and to redesign or improve existing methods for data quality assurance (QA), hydrometeor classification (Hymec), and quantitative precipitation estimation (QPE). These new techniques are realized in a convenient software framework POLARA (Polarimetric Radar Algorithms), which is also newly developed and integrated into DWD's operational 24/7 radar data processing environment in the course of this project. In this paper, focus is placed on the realization of the building blocks QA, Hymec, and QPE within POLARA. Each of the three parts consists of several algorithms, which are, altogether, organized in a special workflow. The fact that some of the schemes are mutually dependent represents a common problem. One example is the propagation path attenuation correction, a typical QA algorithm, that may draw benefit from some information about the hydrometeor phase along the radar beam. This input can be provided by a previous Hymec scheme, which itself, however, shows a better performance if it is run on attenuation corrected input data. Therefore, the algorithm workflow is partly realized in a cyclic fashion. The chain of algorithms is run at DWD's central office and applied to the data sweeps (one full antenna revolution) of each radar device in the network, complemented by selected supplementary data, e.g., from the NWP model COSMO. The results produced in the different stages, QA, Hymec, and QPE, then serve as input for subsequent purposes like mosaicing, now-casting, or hydrological applications. The goal of this paper is to illustrate in detail the QA/Hymec/QPE workflow with the used input and the corresponding output parameters for the individual schemes. Furthermore, the fallback strategy, used to handle the partial absence of input data (e.g., missing dual-polarimetric data), is addressed. Additionally, the functionality of the different algorithms and their interplay will be highlighted by an application to characteristic weather cases.