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Turbulent mixing processes are important in determining the evolution of convective clouds,and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convection‐permitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, ϵ. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beam‐width) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow “shower” case, and a “deep convection” case, exhibiting stronger and deeper updraughts. In our observations, ϵ generally ranges from 10−3 to 10−1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of ϵ suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02–0.03 m2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of ϵ has significant (p < 10−3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The ϵ‐retrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using high‐resolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models.

Pristine ice crystals typically have high aspect ratios (>>1), have a high density and tend to fall preferentially with their major axis aligned horizontally. Consequently, they can, in certain circumstances, be readily identified by measurements of differential reflectivity (ZDR), which is related to their average aspect ratio. However, because ZDR is reflectivity-weighted, its interpretation becomes ambiguous in the presence of even a few, larger aggregates or irregular polycrystals. An example of this is in mixed-phase regions that are embedded within deeper ice cloud. Currently, our understanding of the microphysical processes within these regions is hindered by a lack of good observations. In this paper, a novel technique is presented that removes this ambiguity\ud using measurements from the 3 GHz Chilbolton Advanced Meteorological Radar in Southern England. By combining measurements of ZDR and the co-polar correlation coefficient (ρhv), we show that it is possible to retrieve both the relative contribution to the radar signal and “intrinsic” ZDR (ZPDRI) of the pristine oriented crystals, even in circumstances where their signal is being masked by the presence of aggregates. Results from two case studies indicate that enhancements in ZDR embedded within deep ice clouds are typically\ud produced by pristine oriented crystals with ZPDRI values between 3 and 7 dB (equivalent to 5-9 dB at horizontal incidence) but with varying contributions to the radar reflectivity. Vertically pointing 35 GHz cloud radar Doppler spectra and in-situ particle images from the FAAM BAe-146 aircraft support the conceptual model used and are consistent with the retrieval interpretation

The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for.\ud \ud The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.

Radar reflectivity measurements from three different wavelengths are used to retrieve information about the shape of aggregate snowflakes in deep stratiform ice clouds. Dual-wavelength ratios are calculated for different shape models and compared to observations at 3, 35 and 94 GHz. It is demonstrated that many scattering models, including spherical and spheroidal models, do not adequately describe the aggregate snowflakes that are observed. The observations are consistent with fractal aggregate geometries generated by a physically-based aggregation model. It is demonstrated that the fractal dimension of large aggregates can be inferred directly from the radar data. Fractal dimensions close to 2 are retrieved, consistent with previous theoretical models and in-situ observations.

The use of pulse compression techniques to improve the sensitivity of meteorological radars has\ud become increasingly common in recent years. An unavoidable side-effect of such techniques is the\ud formation of ‘range sidelobes’ which lead to spreading of information across several range gates.\ud These artefacts are particularly troublesome in regions where there is a sharp gradient in the power\ud backscattered to the antenna as a function of range.\ud In this article we present a simple method for identifying and correcting range sidelobe artefacts.\ud We make use of the fact that meteorological targets produce an echo which fluctuates at random,\ud and that this echo, like a fingerprint, is unique to each range gate. By cross-correlating the\ud echo time series from pairs of gates therefore we can identify whether information from one gate\ud has spread into another, and hence flag regions of contamination. In addition we show that the\ud correlation coefficients contain quantitative information about the fraction of power leaked from one\ud range gate to another, and we propose a simple algorithm to correct the corrupted reflectivity profile.