In this work, the T-matrix approach is exploited to produce simulations of spectral polarimetric variables (spectral differential reflectivity, sZDR, spectral differential scattering phase, sHV, and spectral correlation coefficient, sρHV) for observations of rain acquired from slant-looking W-band cloud radar. The spectral polarimetric variables are simulated with two different methodologies, taking into account instrument noise and the stochastic movement of the raindrops, introduced by raindrop oscillations and by turbulence. The simulated results are then compared with rain Doppler spectra observations from W-band radar for moderate rain rate conditions. Two cases, differing in levels of turbulence, are considered. While the comparison of the simulations with the measurements presents a reasonable agreement for equi-volume diameters less than 2.25 mm, large discrepancies are found in the amplitude (but not the position) of the maxima and minima of sZDR and, more mildly, of sIHV. This pinpoints a general weakness in approximating raindrop as spheroids to simulate radar backscattering properties at the W-band.

Dual-polarization weather radars have improved the accuracy of precipitation estimates. However, challenges persist in evaluating hydrometeor classification (HMC) algorithms, thereby impacting the accuracy of precipitation estimates. This study proposes to use full Doppler spectra in both polarizations from a Ka- and W-band Doppler-polarimetric profiler with a 45° elevation angle to provide insights into hydrometeor characteristics. A novel methodology was developed to link the observed spectra with the output of an HMC scheme. We applied the wradlib HMC scheme using C-band weather radar data from the Netherlands for six cases (2021–2022). The HMC scheme output is used to calculate mixing ratios that are combined with the corresponding scattering properties using the Atmospheric Radiative Transfer Simulator microwave single scattering properties database (frozen hydrometeors) and T-matrix calculations (liquid hydrometeors) to simulate Doppler spectra of polarimetric variables that would be measured by the profiler. Comparing these simulations with actual profiler measurements enables a quality assessment. The method works in stratiform cases, but convective cases reveal the influence of turbulence and wind variability. Uncertainty arises from the selection of specific parameterizations for the particle size distribution and the relationship between hydrometeor size and terminal fall velocity as well as from the derived mixing ratios. Additionally, the 45° angle complicates separating horizontal wind from hydrometeor fall velocities, although the Mie notch in the dual-wavelength ratio can be effectively used to remove the radial wind component. Our results underline limitations that must be addressed but also show that integrating spectral and dual-frequency observations could yield valuable insights into hydrometeor characteristics.

Accurate precipitation characterization relies on the estimation of raindrop size distribution (RDSD) from observations. While various techniques using centimeter-wavelength radars have been proposed for RDSD retrieval, the potential of millimeter-wavelength polarimetric radars, offering enhanced spatial and temporal resolution while capturing light to moderate rain, remains unexplored. This study focuses on retrieving the mass-weighted mean diameter D_m using a dual-frequency cloud radar. Since the differential reflectivity Z_dr is ineffective for D_m retrieval at 94 GHz, and simulations demonstrate a strong dependence of the differential backscatter phase d_co on D_m, the estimation of d_co takes precedence in this paper. Notably, d_co remains unaffected by attenuation and polarimetric calibration. Addressing the initial require-ment of disentangling backscattering and propagation effects at millimeter wavelength, an automatic algorithm is proposed to detect Rayleigh plateaus in the spectral domain. Subsequently, a methodology for estimating d_co and its associated error is presented. Leveraging simulation results, confidence intervals for D_m that align with d_co confidence intervals are re-trieved. The assessment of D_m and its confidence interval at 35 and 94 GHz is conducted employing disdrometer-derived D_m. The results demonstrate a comprehensive concordance within a margin of 0.2 mm, underscoring the cloud radar’s efficacy in delineating nuanced variations in the raindrop mean diameter versus altitude. The validation process encounters difficulties for D_m below 1 mm, as the disdrometer-derived D_m may exhibit an overestimation, while the cloud-radar-derived D_m may exhibit an underestimation. The combination of 35 and 94 GHz serves to diminish the confidence interval associated with the retrieved D_m.

This paper is devoted to discussing peculiarities of multi-instrument measurements of rain using millimeter band radar and laser optical disdrometers as basic sensors with application of weather station and radiometer as sources of additional information. After brief discussion of meteorological radar application for quantitative information obtaining, the paper considers the problems and their possible solutions in respect to data fusion and comparison the results of measurements with sensors of different physical nature. 94 GHz radar, laser optical disdrometers, weather station and potentially the radiometer are considered as information sources. Experimental part of the research is based on measurements of rain provided during several years at the experimental range located in Cabauw, the Netherlands.

This paper describes the results of the research fulfilled in TU-Delft by joint Ukrainian and Dutch team. It analyzes multi-instrument rain observations, using the instrument set, which includes W-band cloud radar, laser optical disdrometers, weather station, and microwave radiometer. New friendly interface software is developed, presented, and used as a tool for comparison and fusion of diverse sensors datasets. The results obtained demonstrate the synergy of multi-instrument measurements and corresponds to the overarching trends of big data analysis. The intricacies of combining data from various sources to enhance calibration and improve the accuracy of atmospheric studies is discussed. In particular, analysis of 94 GHz cloud radar calibration based on disdrometer measurements with application of additional multi-instrument measurements is performed.

This paper is devoted to discussing peculiarities of W-band cloud radar calibration. After a brief overview of meteorological radar calibration methods for quantitative information retrieval, we focus on problems and their possible solutions with respect to mm-wave radar calibration. The experimental part of the research is based on multi-instrument measurements performed during several years in the Cabauw experimental meteorological site in the Netherlands. The accumulated data are used for comparison of 94 GHz radar rain measurements with non-radar droplet size distribution measurements, provided by laser disdrometers. Calculations are done taking into account data of other in situ meteorological measurements. A specialized MATLAB software tool for processing such complex data and radar calibration is developed and demonstrated.

Raindrop size distributions (DSDs) play a crucial role in quantitative rainfall estimation using weather radar. Thanks to dual polarization capabilities, crucial information about the DSD in a given volume of air can be retrieved. One popular retrieval method assumes that the DSD can be modeled by a constrained gamma distribution in which the shape (μ) and rate (Λ) parameters are linked together by a deterministic relationship. In the literature, μ-Λ relationships are often taken for granted and applied without much critical discussion. In this study, we take another look at this important issue by conducting a detailed analysis of μ-Λ relations in stratiform rain and quantifying the accuracy of the associated DSD retrievals. Crucial aspects of our research include the sensitivity of μ-Λ relations to the temporal aggregation scale, drop concentration, inter-event variability, and adequacy of the gamma distribution model. Our results show that μ-Λ relationships in stratiform rain are surprisingly robust to the choice of the sampling resolution, sample size, and adequacy of the gamma model. Overall, the retrieved DSDs are in a rather decent agreement with ground observations (correlation coefficient of 0.57 and 0.74 for μ and Dm). The main sources of errors and uncertainty during the retrievals are calibration offsets in reflectivity (Zhh) and differential reflectivity (Zdr). Measurement noise and differences in scale between radars and disdrometers also play a minor role. The raindrop concentration (NT) remains the most difficult parameter to retrieve, which can be off by several orders of magnitude. After careful data filtering and removal of problematic Zhh/Zdr pairs, the correlation coefficient for the retrieved NT values remained low, only slightly increasing from 0.12 into 0.24.

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

The adequacy of the gamma model to describe the variability of raindrop size distributions (DSD) is studied using observations from an optical disdrometer. Model adequacy is checked using a combination of Kolmogorov–Smirnov goodness-of-fit test and Kullback–Leibler divergence and the sensitivity of the results to the sampling resolution is inves-tigated. A new adaptive DSD sampling technique capable of determining the highest possible temporal sampling resolution at which the gamma model provides an adequate representation of sampled DSDs is proposed. The results show that most DSDs at 30 s are not strictly distributed according to a gamma model, while at the same time they are not far away from it either. According to the adaptive DSD sampling algorithm, the gamma model proves to be an adequate choice for the majority (85.81%) of the DSD spectra at resolutions up to 300 s. At the same time, it also reveals a considerable number of DSD spectra (5.55%) that do not follow a gamma distribution at any resolution (up to 1800 s). These are attributed to transitional periods during which the DSD is not stationary and exhibits a bimodal shape that cannot be modeled by a gamma distribution. The proposed resampling procedure is capable of automatically identifying and flagging these periods, providing new valuable quality control mechanisms for DSD retrievals in disdrometers and weather radars.

The growth of ice crystals in presence of supercooled liquid droplets represents the most important process for precipitation formation in the mid-latitudes. However, such mixed-phase interaction processes remain relatively unknown, as capturing the complexity in cloud dynamics and microphysical variabilities turns to be a real observational challenge. Ground-based radar systems equipped with fully polarimetric and Doppler capabilities in high temporal and spatial resolutions such as the S-band transportable atmospheric radar (TARA) are best suited to observe mixed-phase growth processes. In this paper, measurements are taken with the TARA radar during the ACCEPT campaign (analysis of the composition of clouds with extended polarization techniques). Besides the common radar observables, the 3-D wind field is also retrieved due to TARA unique three beam configuration. The novelty of this paper is to combine all these observations with a particle evolution detection algorithm based on a new fall streak retrieval technique in order to study ice particle growth within complex precipitating mixed-phased cloud systems. In the presented cases, three different growth processes of ice crystals, plate-like crystals, and needles are detected and related to the presence of supercooled liquid water. Moreover, TARA observed signatures are assessed with co-located measurements obtained from a cloud radar and radiosondes. This paper shows that it is possible to observe ice particle growth processes within complex systems taking advantage of adequate technology and state of the art retrieval algorithms. A significant improvement is made towards a conclusive interpretation of ice particle growth processes and their contribution to rain production using fall streak rearranged radar data.