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

lidarwind is an open-source Python project to retrieve wind speed and direction profiles from Doppler lidar observations from the WindCube-200s, and it was developed to be easy to use. It can retrieve wind profiles from the 6-beam and DBS scanning strategies and allow users to set the signal-to-noise ratio threshold to reduce the noise. It also calculates the Reynolds stress tensor matrix elements from the 6-beam observations. lidarwind is a result of an effort to create an environment where it would be flexible and easy to process the observations from the WindCube Doppler lidar.@en

The dendritic growth layer (DGL), defined as the temperature region between -20 and -10 °C, plays an important role for ice depositional growth, aggregation and potentially secondary ice processes. The DGL has been found in the past to exhibit specific observational signatures in polarimetric and vertically pointing radar observations. However, consistent conclusions about their physical interpretation have often not been reached. In this study, we exploit a unique 3-months dataset of mid-latitude winter clouds observed with vertically pointing triple-frequency (X-, Ka-, W-band) and polarimetric W-band Doppler radars. In addition to standard radar moments, we also analyse the multi-wavelength and polarimetric Doppler spectra. New variables, such as the maximum of the spectral differential reflectivity (ZDR) (sZDRmax), allows us to analyse the ZDR signal of asymmetric ice particles independent of the presence of low ZDR producing aggregates. This unique dataset enables us to investigate correlations between enhanced aggregation and evolution of small ice particles in the DGL. For this, the multi-frequency observations are used to classify all profiles according to their maximum average aggregate size within the DGL. The strong correlation between aggregate class and specific differential phase shift (KDP) confirms the expected link between ice particle concentration and aggregation. Interestingly, no correlation between aggregation class and sZDRmax is visible. This indicates that aggregation is rather independent of the aspect ratio and density of ice crystals. A distinct reduction of mean Doppler velocity in the DGL is found to be strongest for cases with largest aggregate sizes. Analyses of spectral edge velocities suggest that the reduction is the combined result of the formation of new ice particles with low fall velocity and a weak updraft. It appears most likely that this updraft is the result of latent heat released by enhanced depositional growth. Clearly, the strongest correlations of aggregate class with other variables are found inside the DGL. Surprisingly, no correlation between aggregate class and concentration or aspect ratio of particles falling from above into the DGL could be found. Only a weak correlation between the mean particle size falling into the DGL and maximum aggregate size within the DGL is apparent. In addition to the correlation analysis, the dataset also allows study of the evolution of radar variables as a function of temperature. We find the ice particle concentration continuously increasing from -18 °C towards the bottom of the DGL. Aggregation increases more rapidly from -15 °C towards warmer temperatures. Surprisingly, KDP and sZDRmax are not reduced by the intensifying aggregation below -15 °C but rather reach their maximum values in the lower half of the DGL. Also below the DGL, KDP and sZDRmax remain enhanced until -4 °C. Only there, additional aggregation appears to deplete ice crystals and therefore reduce KDP and sZDRmax. The simultaneous increase of aggregation and particle concentration inside the DGL necessitates a source mechanism for new ice crystals. As primary ice nucleation is expected to decrease towards warmer temperatures, secondary ice processes are a likely explanation for the increase in ice particle concentration. Previous laboratory experiments strongly point towards ice collisional fragmentation as a possible mechanism for new particle generation. The presence of an updraft in the temperature region of maximum depositional growth might also suggest an important positive feedback mechanism between ice microphysics and dynamics which might further enhance ice particle growth in the DGL.

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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

Comparing the reflectivity flux at the top and bottom of the melting layer (ML) reveals the overall effect of the microphysical processes occurring within the ML on the particle population. If melting is the only process taking place and all particles scatter in the Rayleigh regime, the reflectivity flux increases in the ML by a constant factor given by the ratio of the dielectric factors. Deviations from this constant factor can indicate that either growth or shrinking processes (breakup, sublimation, and evaporation) dominate. However, inference of growth or shrinking dominance from the increase in reflectivity flux is only possible if other influences (e.g., vertical wind speed) are negligible or corrected. By analyzing radar Doppler spectra and multi-frequency observations, we correct the reflectivity fluxes for vertical wind and categorize the height profiles by the riming degree at the ML top. We apply this reflectivity flux ratio (ZFR) approach to a multi-month mid-latitude winter data set that contains mostly stratiform clouds. The profiles of radar variables in the ML are found to be surprisingly similar for both unrimed and rimed profiles with slight differences, for example, in the absolute values of the reflectivity flux. Statistical analysis of the ZFR suggests that either microphysical processes other than melting are not important or strongly compensate for each other. The results seem to confirm that at least for moderately precipitating stratiform clouds, the melting-only assumption applied in several retrievals and microphysical schemes is reasonable.

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