The implementation of dual-polarization weather radars has improved the accuracy of precipitation estimates. However, identifying hydrometeor types and evaluating related algorithms remains challenging. This study introduces a novel method to evaluate hydrometeor classification (HMC) schemes using Doppler spectra from a mm-wave dual-frequency Doppler-polarimetric slanted profiler. The output of the HMC scheme is used to calculate mixing ratios that are then combined with the ARTS Microwave Single Scattering Properties Database to simulate Doppler spectra of polarimetric variables that would be measured by the profiler. Comparing these with actual profiler measurements provides valuable validation information. We have tested this method on the wradlib HMC scheme using C-band weather radar data from the Netherlands (2021–2022) and a Ka- and W-band Doppler-polarimetric profiler operating at an elevation angle of 45°. The method works well in stratiform cases, but convective cases reveal the influence of turbulence and wind variability, highlighting the need for improved approaches. Uncertainty arises from selecting specific parameterizations for the particle size distribution (PSD) and for the relationship between hydrometeor size and terminal fall velocity, as well as from the derived mixing ratios. Additionally, observing at a 45° angle complicates separating horizontal wind from hydrometeor fall velocities, though the Mie notch in the dual-wavelength ratio can be effectively used to remove the radial wind component. Our results highlight both the potential and limitations of using this profiler configuration to evaluate HMC schemes.

We have used the LOw-Frequency ARray (LOFAR) to image a few lightning flashes during a particularly severe thunderstorm. The images show an exceptional amount of VHF activity at altitudes above 10 km. Much of this is in the form of small-scale discharges occurring seemingly randomly around the centers of active storm cells. Because of their small and incidental structure we refer to these as ‘speckles’. A detailed investigation shows strong evidence that these speckles are indicative of positive leader channels and that they are equivalent to the needle activity seen around positive leader tracks at lower altitudes.

Radiance measurements from several types of passive microwave (PMW) sensors are combined in the Global Precipitation Measurement mission (GPM) to increase the temporal and spatial coverage of precipitation observations. The measurements of these sensors are converted to precipitation estimates by the GPM Profiling Algorithm (GPROF). High frequency PMW-channels are used to retrieve precipitation estimates over land, as these frequencies can measure the radiance scattered by ice particles in rain clouds. Scattering related to shallow and low-intensity precipitation events, however, is limited. Hence, radiometric signals associated with these events are hard to distinguish from the naturally emitted radiation from the Earth’s surface, especially since this so-called background radiation is dependent on the surface type. A better understanding of the physical processes that occur during precipitation events can help to identify possible weaknesses in the GPROF algorithm. Hence, this study couples overpasses of GPM radiometers over the Netherlands to two dual-polarization radars from the Royal Netherlands Meteorological Institute (KNMI) in 2019. All rainy overpasses (>0.1 mm/hr) within a 75 km radius around one of the radars are selected. This coupling provides the opportunity to relate GPROFs performance to physical characteristics of precipitation events, such as the vertical reflectivity profile and dual-polarization information on the melting layer. Additionally, simultaneous observations from both the PMW sensor and the dual-frequency precipitation radar (DPR, used as a-priori database in GPROF) aboard the GPM core satellite are available. Hence, space-based and ground-based reflectivity profiles can be compared and coupled to discrepancies of the GPROF algorithm.