Funded by the German Research Foundation, the research initiative PROM exploits polarimetric precipitation radars in synergy with measurements from cloud radars and other instrumentation available at the German Meteorological Service (DWD), supersites and research institutions to improve our understanding of moist processes and current microphysical parameterisations. A thorough evaluation and improvement of the representation of precipitation generating processes is seen as a mandatory prerequisite for assimilation of radar data and thus, the ultimate fusion of polarimetric information and NWP-models. This presentation introduces a selection of results from PROM achieved so far.
In project Operation Hydrometeors, the polarimetric forward operator EMVORADO applying scattering properties of oriented, oblate spheroidal hydrometeors from the T-Matrix approach was developed with online implementations in COSMO and ICON and a stand-alone framework. Together with project PARA, a dual-strategy for model evaluation is applied including 1) the comparison of measured polarimetric variables and signatures with their simulated counterparts via EMVORADO applied to the model output, and 2) the comparison of simulated state variables with their radar-derived counterparts using microphysical retrievals (like ice water content and number concentrations). Deficiencies of simulated polarimetric variables in the melting and the dendritic growth layers caused by ICON’s 2-moment microphysics and by assumptions in the radar forward operator EMVORADO have been identified. Within Operation Hydrometeors-2, the identified model issues are tackled (e.g. freezing process, excessive riming, ice/snow partitioning), while project PRISTINE is now using more realistic hydrometeor morphologies and the DDA scattering approach to further improve EMVORADO.
Combined multi-frequency and polarimetric cloud radar observations obtained within the IMPRINT project provide a statistical analysis of signatures of ice particle growth and subsequent aggregation close to the dendritic growth layer (DGL). Experiments with a Lagrangian super-particle model with habit prediction and a polarimetric forward operator help to better understand the potential role of secondary ice processes for the radar signatures observed in the DGL.
Within the project PICNICC, an artificial neural network retrieval to detect riming from cloud radar Doppler spectra is developed. Not relying on mean Doppler velocity values, the detection method can also be applied in convective conditions. PICCNIC also provided the framework for the development of the Vertical Distribution of Particle Shape (VDPS) retrieval for cloud hydrometeor shape and orientation. The VDPS technique is based on range-height-indicator (RHI) scans of the polarimetric properties Slanted LDR and co-cross correlation coefficient, from which profiles of the two parameters polarizability ratio (density-weighted axis ratio) and degree of orientation are derived.
A technique similar to the VDPS retrieval was developed within the project SPOCC, with the focus set on the utilization of RHI scans of Doppler spectra of ZDR and horizontal-vertical correlation coefficient. Based on the Doppler spectra of these two quantities up to 5 species of hydrometeors can be identified by means of polarizability ratio and degree of orientation. Parallel to the extended retrieval of particle shape, a model framework was developed within SPOCC, which provides spectral-bin simulations of cloud hydrometeors. Basis for the model simulations is the Advanced Microphysics Prediction System (AMPS) Model, which was coupled to the radar forward operator PAMTRA. First studies related to this modeling framework were used to demonstrate that aerosol-related impacts on the microphysical structure of clouds should be well observable with conventional and polarimetric cloud radar systems.
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