Lightning is a natural phenomenon that can be dangerous to humans. It is therefore challenging to study thunderstorm clouds using direct observations since it can be dangerous to fly into them. In this study, a cloud radar at 35 GHz with 45° elevation is used to study the properties and dynamics of thunderstorm clouds. It is based on a thunderstorm case on 18 June 2021 from 16:10 to 17:45 UTC near Cabauw, the Netherlands. The observed thunderstorm was associated with severe weather conditions over The Netherlands, attributed to the remnants of storm “Bill”. The time and location of individual lightning strikes are determined using the BLIDS system, operated by Siemens, which is based on the time-of-arrival principle. Concurrently, spectral polarimetry in the millimetre band – an innovative technique not previously applied in thunderstorm cloud studies – is employed to elucidate the behaviour of various particle types within a radar resolution volume. Spectral polarimetric radar variables are also used to look for vertical alignment of ice crystals that is expected due to electric torque. Due to challenges posed by non-Rayleigh scattering, scattering simulations are carried out to aid the interpretation of spectral polarimetric variables. It is shown that the start of the Mie regime in the Doppler spectrum can be clearly identified by the use of the spectral differential phase. Furthermore, variations in the location of the first Mie minimum across different spectral polarimetric variables may be attributed to different sensitivities of these variables to particle shape and ice fraction. From the results, there is a high chance that supercooled liquid water and conical graupel are present in the investigated thunderstorm clouds. There is also a possibility of ice crystals arranged in chains at the cloud top. Ice crystals become vertically aligned a few seconds before lightning and return to their usual horizontal alignment afterwards. However, this phenomenon has been witnessed in only a few cases of cloud-to-cloud lightning, specifically when the lightning strike is in close proximity to the radar's line of sight or when the lightning is strong. Doppler analyses show that updrafts are found near the core of the thunderstorm cloud, while downdrafts are observed at the edges. Strong turbulence is also observed as shown by the large Doppler spectrum width.@en

In this study, we take a closer look at the important issue of μ–Λ relationships in raindrop size distributions (DSDs) by conducting a systematic analysis of 20 months of data collected by disdrometers in the Netherlands. A new power-law model for representing μ–Λ relationships based on the double normalization framework is proposed and used to derive separate μ–Λ relationships for stratiform and convective rain events. The sensitivity of the obtained relationships to measurement uncertainty is studied by applying two different quality control filters based on the mass-weighted mean drop diameter (Dm) and liquid water content (LWC). Our results show that there are significant differences in μ–Λ relationships between convective and stratiform rainfall types. However, the retrieved relationships appear to be quite robust to measurement noise and there is good agreement with other reference relations for similar climatological conditions.@en

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.

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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.

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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.

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This paper introduces an experimental setup for retrieving horizontal wind speed and direction profiles with a high temporal and vertical resolution for process studies and validation of convection-permitting model simulations. The CMTRACE (tracing convective momentum transport in complex cloudy atmospheres) campaign used collocated wind lidar and cloud radar measurements to retrieve seamless wind profiles from near the surface up to cloud tops. It took place in Cabauw, the Netherlands, between 13 September and 3 October 2021. The intermediate processing steps for generating the level 1 and level 2 data, such as second trip echoes filtering, offset correction, wind retrieval, re-gridding, and flagging, are described. In level 1 (https://doi.org/10.5281/zenodo.6926483, Dias Neto, 2022a), the data from lidar and radars are kept in the original spatial and temporal resolution, while in level 2 (https://doi.org/10.5281/zenodo.6926605, Dias Neto, 2022b), they are regridded to a common spatial and temporal resolution. Statistical analyses of the lidar's and radar's wind speed and direction profiles indicate a correlation higher than 0.95 for both variables. The bias of wind direction and speed calculated between radar's and lidar's observations are 0.24∘ and −0.16 m s−1, respectively. The foreseen initial application of the datasets includes the study of convective momentum transport and its validation in regional weather forecasts and large-eddy simulation hindcasts.@en

An effective filtering technique is required for rainfall rate measurement by weather radar. A Jensen–Shannon distance (JSD)-based thresholding filter is proposed to mitigate nonmeteorological signals, either in clear air or rain situations. This algorithm classifies range-Doppler bins into two classes, hydrometeors and nonhydrometeors, based on spectral polarimetric variable features. The result is a mask to be applied on the spectrograms. The variable selected here is the spectral co-polar correlation coefficient, available in dual-polarization and full polarimetric radars. The algorithm first does global thresholding by finding an optimized threshold value based on the averaged clear-air spectral polarimetric variable distribution. Next, classical filtering steps are carried out like a ground clutter notch filter around 0 ms−1, a mathematical morphology to fill gaps in the hydrometeor areas, and a removal of narrow Doppler power spectra. The second part of this article is the assessment of filtering techniques without ground truth. An assessment without ground truth is useful to select optimal algorithm configurations from a large solution space. Criteria of good filtering are defined both in the spectral and time domain. Based on those criteria, subjective and objective unsupervised evaluation metrics are derived, with a focus on the objective ones. Data, including clear air and rain collected from a full polarimetric Doppler X-band radar in the urban area, are used. With the proposed unsupervised evaluation metrics, the JSD-based thresholding filter is compared to two spectral polarimetric filters. Overall, the JSD-based filter performs very well considering both the subjective and the objective evaluation metrics.@en

Convective clouds may be associated with substantial transport of momentum. Much of what we know about convective momentum transport stems from high-resolution simulations because high-resolution measurements of the wind profile are rare. This study exploits ground-based remote sensing techniques to visualize wind below and within clouds and their surroundings, to assess momentum transport. The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment (CMTRACE) took place at the experimental Cabauw site (The Netherlands) between 13.09.2021 and 03.10.2021. The goal of CMTRACE was to provide continuous profiles of horizontal and vertical wind with a temporal resolution of ~1 minute and vertical resolution of ~50 m within the cloud and sub-cloud layers to improve our understanding of the role of momentum transport from cloud- to mesoscales. A scanning wind lidar provided the observations in the sub-cloud layer, while in the cloud layer, one scanning and one vertically pointing cloud radar provided observations. During CMTRACE, we sampled various cloud regimes including non-precipitating shallow cumulus clouds, deep convective clouds and stratiform clouds. In this study, we illustrate some of the most interesting CMTRACE observations that reveal the circulations (winds) near clouds and present statistical analyses as a function of different cloud regimes. Specifically, we calculate profiles of wind fluctuations and their cross-correlations to address the momentum flux carried on cloud- and mesoscale scales. The observations from different cloud regimes (e.g. clear sky, shallow convection and frontal passage) are compared to momentum fluxes and wind variability in the Dutch Large-Eddy Simulations nested on the experimental site for the selected days.@en

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.

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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.

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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.

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A Sentinel-5p/TROPOMI validation campaign was held in the Netherlands based at the Cabauw Experimental Site for Atmospheric Research during September 2019. The TROpomi vaLIdation eXperiment (TROLIX) consisted of active and passive remote sensing platforms in conjunction with several balloon-borne, airborne and surface chemical measurements. The goal of this geophysical validation campaign was to make intensive observations to establish the quality of TROPOMI L2 main data products (UVAI, Aerosol Layer Height, NO2, O3, HCHO, Clouds) under realistic non-idealized conditions with varying cloud cover and a wide range of atmospheric conditions. Since TROPOMI is a hyperspectral imager with a very high spatial resolution of 3.5x7 km2, understanding local effects such as inhomogeneous sources of pollution, sub-pixel clouds and variations in ground albedo is important to interpret TROPOMI results. Therefore, the campaign included sub-pixel resolution local networks of sensors, involving Pandora and MAXDOAS instruments, around Cabauw (51.97° N, 4.93° E) and within the city of Rotterdam. Cabauw is considered rural while Rotterdam is densely populated and industrialized. These focal areas were connected through airborne as well as ground based mobile observations. Cabauw, using its comprehensive in-situ and remote sensing observation program in and around the 213 m meteorological tower, was the main site of the campaign with focus on vertical profiling using lidar instruments for aerosols, clouds, water vapor, tropospheric and stratospheric ozone, as well as balloon-borne sensors for NO2 and ozone. The data set collected can be directly compared to the TROPOMI L2 data products, while measurements of parameters related to a-priori data and auxiliary parameters that influence the quality of the L2 products such as aerosol and cloud profiles and in-situ aerosol and atmospheric chemistry were also collected. This paper gives an overview of the campaign, and an overview of the participating main and ancillary instrumentation. Furthermore, an overview of the meteorological and atmospheric conditions observed during the campaign is given from the respective perspectives of the participating instruments, including satellite observations and the support by atmospheric modeling (CAMS). @en

In this article, five velocity-based energy dissipation rate (EDR) retrieval techniques are assessed. The EDR retrieval techniques are applied to Doppler measurements from Transportable Atmospheric Radar (TARA)—a precipitation profiling radar—operating in the vertically fixed-pointing mode. A generalized formula for the Kolmogorov constant is derived, which gives potential for the application of the EDR retrieval techniques to any radar line of sight (LOS). Two case studies are discussed that contain rain events of about 2 and 18 h, respectively. The EDR values retrieved from the radar are compared to in situ EDR values from collocated sonic anemometers. For the two case studies, a correlation coefficient of 0.79 was found for the wind speed variance (WSV) EDR retrieval technique, which uses 3D wind vectors as input and has a total sampling time of 10 min. From this comparison it is concluded that the radar is able to measure EDR with a reasonable accuracy. Almost no correlation was found for the vertical wind velocity variance (VWVV) EDR retrieval technique, as it was not possible to sufficiently separate the turbulence dynamics contribution to the radar Doppler mean velocities from the velocity contribution of falling raindrops. An important cause of the discrepancies between radar and in situ EDR values is thus due to insufficient accurate estimation of vertical air velocities.@en

Weather radar is well recognized as an effective sensor for obtaining the microphysical and dynamical properties of precipitation at high spatial and temporal resolution. Radar calibration is one of the most important prerequisites for achieving accurate observations. In this article, a portable, cost-effective and repeatable radar calibration technique, namely, unmanned aerial vehicle (UAV)-aided radar calibration, is proposed. A UAV serves as the stable aerial platform carrying a metal sphere, flying over the radar illumination areas to complete the calibration process. The flying routine of the UAV can be pre-programmed, and thus, the antenna pattern regarding different elevation and azimuth angles can be retrieved. To obtain the position of the sphere, the real-time single-frequency precise point positioning-type global navigation satellite system solution is developed. In addition, the radar constant is calculated in the range-Doppler domain, and only the data where the metal sphere separates from clutter and other objects are selected. The S-band polarimetric Doppler transportable atmospheric radar (TARA) is used in the calibration campaign. The experiments demonstrate the following results: 1) antenna pointing calibration can be completed and 2) antenna pattern can be retrieved and weather radar constant can be accurately calculated.@en

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.

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One of the most beneficial polarimetric variables may be the specific differential phase K_DP because of its independence from power attenuation and radar miscalibration. However, conventional K_DP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ψ_DP. In addition, the backscatter differential phase δ_hv component of ψ_DP, significant at C- and X-band frequency, may lead to inaccurate K_DP estimates. In this work, an adaptive approach is proposed to obtain accurate K_DP estimates in rain from noisy ψ_DP, whose δ_hv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δ_hv from ψ_DP while maintaining its spatial variability. In addition, the standard deviation of the proposed K_DP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between K_DP and reflectivity, and K_DP standard deviation reduction.

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In radar polarimetry, the differential phase CDP consists of the propagation differential phase FDP and the backscatter differential phase dhv. While FDP is commonly used for attenuation correction (i.e., estimation of the specific attenuation A and specific differential phase KDP), recent studies have demonstrated that dhv can provide information concerning the dominant size of raindrops. However, the estimation of FDP and dhv is not straightforward given their coupled nature and the noisy behavior of CDP, especially over short paths. In this work, the impacts of estimating FDP on the estimation of A over short paths, using the extended version of the ZPHI method, are examined. Special attention is given to the optimization of the parameter a that connects KDP and A. In addition, an improved technique is proposed to compute dhv from CDP and FDP in rain. For these purposes, diverse storm events observed by a polarimetric X-band radar in the Netherlands are used. Statistical analysis based on the minimum errors associated with the optimization of a and the consistency between KDP and A showed that more accurate and stable a and A are obtained if FDP is estimated at range resolution, which is not possible by conventional range filtering techniques. Accurate dhv estimates were able to depict the spatial variability of dominant raindrop size in the observed storms. By following the presented study, the ZPHI method and its variations can be employed without the need for considering long paths, leading to localized and accurate estimation of A and dhv.@en

Aiming at removing stationary and moving clutter while retaining precipitation for dual-polarization weather radar, a new clutter suppression method, named the object-orientated spectral polarimetric (OBSpol) filter, is put forward in this paper. Based on the spectral polarimetric feature and the range-Doppler continuity of precipitation, the OBSpol filter generates a filtering mask implemented on the raw range-Doppler spectrogram to mitigate the clutter and noise. The procedures are as follows. After the spectral polarimetric filtering and the mathematical morphology method, one binary mask where “1” indicates the precipitation is obtained. Then, the contiguous bins of the same value “1” in the range-Doppler domain are grouped in areas termed as objects. Whether the produced objects are precipitation or not will be further judged based on appropriate weather radar observable. Thus, combining all the filtered separate objects, a filtering mask can be obtained. In this paper, data collected by the polarimetric Doppler IRCTR drizzle radar are used to demonstrate and assess the performance of the proposed technique in the case of ground clutter, narrowband moving clutter, and noise. Two precipitation cases are examined: 1) moderate precipitation with large scale and 2) light precipitation with severe clutter contamination. In the range-Doppler spectrogram, both stationary and narrowband moving clutters are mitigated, while maintaining nonoverlapping precipitation signal. This helps to solve the problem when clutter and precipitation overlap in the time domain. In addition, the OBSpol filter is proven to be effective with different Doppler velocity resolutions. This technique can be applied in real-time due to its low computation complexity. Moreover, the spectral polarimetric filtering can be designed using the measurements of dual-polarization radar systems which do not have cross-polar measurements. Hence, the proposed clutter mitigation technique can be implemented for operational dual-polarization weather radar. @en

A novel algorithm is put forward to separate radar target and moving clutter based on a combination of the low-rank matrix optimization (LRMO) and the decision tree. Similar to the moving object detection in the field of automated video analysis, the proposed separation method, which is carried out in the range-Doppler domain, makes use of different motion variations of radar target and clutter in the spectrogram sequence. The technique is very general, but the focus of this paper is on narrowband moving clutter suppression in a weather radar. The first step in implementing this method is the generation of a range-Doppler spectrogram sequence. Then, the LRMO is applied to the obtained sequence to divide target and moving clutter into foreground and background. From the foreground sequence which is obtained by solving the LRMO, foreground frequency and spectral width are combined in a decision tree to obtain a filtering mask to mitigate the narrowband moving clutter and noise. Data collected by a polarimetric Doppler weather radar known as the IRCTR Drizzle Radar are used to validate the performance of the proposed algorithm. Moreover, its effectiveness in removing narrowband moving clutter is quantitatively assessed through comparisons with another clutter mitigation method.

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In this paper, a new clutter suppression method, named the moving double spectral linear depolarization ratio (MDsLDR) filter, is put forward to mitigate narrow-band clutter in weather radars. The narrow-band clutter observed in the Doppler domain includes: 1) stationary clutter such as ground clutter and 2) nonstationary clutter such as artifacts caused by the radar system itself or external sources. These artifacts are difficult to remove, because they are not confined to specific azimuth and range bins. Based on the difference of the spectral-polarization feature and the spectral continuity of precipitation and clutter, the MDsLDR filter can remove ground clutter, artifacts, and noise. The performance of the newly proposed filter is assessed by data collected by the Doppler-polarimetric IRCTR Drizzle Radar. Three precipitation cases are considered in this paper: moderate/light precipitation, convective precipitation with hook-echo signature, and light precipitation with severe artifact contamination. Furthermore, the implementation of the MDsDLR filter requires relatively low computation complexity, so that the MDsLDR filter can be operated in real time.@en