A time-series method to identify and correct range sidelobes in meteorological radar data

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

High-precision measurements of the co-polar correlation coefficient: non-Gaussian errors and retrieval of the dispersion parameter µ in rainfall

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. 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

A robust automated technique for operational calibration of ceilometers using the integrated backscatter from totally attenuating liquid clouds

A simple and robust method for calibrating ceilometers has been tested in an operational environment demonstrating that the calibrations are stable to better than ± 5 % over a period of a year. The method relies on using the integrated backscatter (B) from liquid clouds that totally extinguish the ceilometer signal; B is inversely proportional to the lidar ratio (S) of the backscatter to the extinction for cloud droplets. The calibration technique involves scaling the observed backscatter so that B matches the predicted value for S of 18.8 ± 0.8 sr for cloud droplets, at ceilometer wavelengths. For accurate calibration, care must be taken to exclude any profiles having targets with different values of S, such as drizzle drops and aerosol particles, profiles that do not totally extinguish the ceilometer signal, profiles with low cloud bases that saturate the receiver, and any profiles where the window transmission or the lidar pulse energy is low. A range dependent multiple scattering correction that depends on the ceilometer optics should be applied to the profile. A simple correction for water vapour attenuation for ceilometers operating at around 910 nm wavelength is applied to the signal using the vapour profiles from a forecast analysis. For a generic ceilometer in the UK the 90-day running mean of the calibration coefficient over a period of 20 months is constant to within 3 % with no detectable annual cycle, thus confirming the validity of the humidity and multiple scattering correction. For Gibraltar, where cloud cover is less prevalent than in the UK, the 90-day running mean calibration coefficient was constant to within 4 %. The more sensitive ceilometer model operating at 1064 nm is unaffected by water vapour attenuation but is more prone to saturation in liquid clouds. We show that reliable calibration is still possible, provided the clouds used are above a certain altitude. The threshold is instrument dependent but is typically around 2 km. We also identify a characteristic signature of saturation, and remove any profiles with this signature. Despite the more restricted sample of cloud profiles, a robust calibration is readily achieved, and, in the UK, the running mean 90-day calibration coefficients varied by about 4 % over a period of one year. The consistency of profiles observed by nine pairs of co-located ceilometers in the UK Met Office network operating at around 910 nm and 1064 nm provided independent validation of the calibration technique. EUMETNET is currently networking 700 European ceilometers so they can provide ceilometer profiles in near real time to European weather forecast centres and has adopted the cloud calibration technique described in this paper for ceilometers with a wavelength of around 910 nm

Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations

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

Drag coefficient prediction of complex-shaped snow particles falling in air beyond the Stokes regime

This study considers complex ice particles falling in the atmosphere: predicting the drag of such particles is important for developing of climate models parameterizations. A Delayed-Detached Eddy Simulation model is developed to predict the drag coefficient of snowflakes falling at Reynolds number between 50 and 2200. We first consider the case where the orientation of the particle is known a posteriori, and evaluate our results against laboratory experiments using 3D-printed particles of the same shape, falling at the same Reynolds number. Close agreement is found in cases where the particles fall stably, while a more complex behavior is observed in cases where the flow is unsteady. The second objective of this study is to evaluate methods for estimating the drag coefficient when the orientation of the particles is not known a posteriori. We find that a suitable average of two orientations corresponding to the minimum and maximum eigenvalues of the inertia tensor provides a good estimate of the particle drag coefficient. Meanwhile, existing correlations for the drag on non-spherical particles produce large errors ( 50%). A new formula to estimate snow particles settling velocity is also proposed. Our approach provides a framework to investigate the aerodynamics of complex snowflakes and is relevant to other problems that involve the sedimentation of irregular particles in viscous fluids

An accurate and computationally cheap microwave scattering method for ice aggregates: the Independent Monomer Approximation

The Discrete Dipole Approximation (DDA) is widely used to simulate scattering of microwaves by snowflakes, by discretising the snowflake into small “dipoles” which oscillate in response to (i) the incident wave and (ii) scattered waves from all the other dipoles in the particle. It is this coupling between all dipole pairs which makes solving the DDA system computationally expensive, and that cost grows non‐linearly as the number of crystals n within an aggregate is increased. Motivated by this, many studies have ignored the dipole coupling (the Rayleigh‐Gans Approximation, RGA). However, use of RGA leads to systematic underestimation of both scattering and absorption, and an inability to predict polarimetric properties. To address this, we present a new approach (the Independent Monomer Approximation, IMA) which solves the DDA system for each crystal “monomer” separately, then combines them to construct the full solution. By including intra‐monomer coupling, but neglecting inter‐monomer coupling, we save a factor of n in computation time over DDA. Benchmarking IMA against DDA solutions indicates that its accuracy is greatly superior to RGA, and provides ensemble scattering cross sections which closely agree with their more expensive DDA counterparts, particularly at size parameters smaller than ∼5. Addition of rime to the aggregates does not significantly degrade the results, despite the increased density. The use of IMA for radar remote sensing is evaluated, and we show that multi‐wavelength and multi‐polarisation parameters are successfully captured to within a few tenths of a dB for aggregates probed with frequencies between 3 and 200GHz, in contrast to RGA where errors of up to 2.5dB are observed. Finally we explore the realism of the IMA solutions in greater detail by analysing internal electric fields, and discuss some broader insights that IMA provides into the physical features of aggregates that are important for microwave scattering

Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling

In situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of a line of small cumulus clouds, using Radar and Lidar, as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE) project. A narrow but extensive line (~100 km long) of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than −8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN) numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed, near cloud top, temperatures (−7.5 °C). The role of mineral dust particles, consistent with concentrations observed near the surface, acting as high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L−1) could be produced by secondary ice particle production providing the observed small amount of primary ice (about 0.01 L−1) was present to initiate it. This emphasises the need to understand primary ice formation in slightly supercooled clouds. It is shown using simple calculations that the Hallett-Mossop process (HM) is the likely source of the secondary ice. Model simulations of the case study were performed with the Aerosol Cloud and Precipitation Interactions Model (ACPIM). These parcel model investigations confirmed the HM process to be a very important mechanism for producing the observed high ice concentrations. A key step in generating the high concentrations was the process of collision and coalescence of rain drops, which once formed fell rapidly through the cloud, collecting ice particles which caused them to freeze and form instant large riming particles. The broadening of the droplet size-distribution by collision-coalescence was, therefore, a vital step in this process as this was required to generate the large number of ice crystals observed in the time available. Simulations were also performed with the WRF (Weather, Research and Forecasting) model. The results showed that while HM does act to increase the mass and number concentration of ice particles in these model simulations it was not found to be critical for the formation of precipitation. However, the WRF simulations produced a cloud top that was too cold and this, combined with the assumption of continual replenishing of ice nuclei removed by ice crystal formation, resulted in too many ice crystals forming by primary nucleation compared to the observations and parcel modelling

Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling

In situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of a line of small cumulus clouds, using Radar and Lidar, as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE) project. A narrow but extensive line (~100 km long) of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than −8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN) numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed, near cloud top, temperatures (−7.5 °C). The role of mineral dust particles, consistent with concentrations observed near the surface, acting as high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L−1) could be produced by secondary ice particle production providing the observed small amount of primary ice (about 0.01 L−1) was present to initiate it. This emphasises the need to understand primary ice formation in slightly supercooled clouds. It is shown using simple calculations that the Hallett-Mossop process (HM) is the likely source of the secondary ice. Model simulations of the case study were performed with the Aerosol Cloud and Precipitation Interactions Model (ACPIM). These parcel model investigations confirmed the HM process to be a very important mechanism for producing the observed high ice concentrations. A key step in generating the high concentrations was the process of collision and coalescence of rain drops, which once formed fell rapidly through the cloud, collecting ice particles which caused them to freeze and form instant large riming particles. The broadening of the droplet size-distribution by collision-coalescence was, therefore, a vital step in this process as this was required to generate the large number of ice crystals observed in the time available. Simulations were also performed with the WRF (Weather, Research and Forecasting) model. The results showed that while HM does act to increase the mass and number concentration of ice particles in these model simulations it was not found to be critical for the formation of precipitation. However, the WRF simulations produced a cloud top that was too cold and this, combined with the assumption of continual replenishing of ice nuclei removed by ice crystal formation, resulted in too many ice crystals forming by primary nucleation compared to the observations and parcel modelling

A radar backscatter simulation of a forest canopy using 3D physical structures derived from LiDAR scanning

This study aims to explore the forest aboveground biomass relationship to C-band backscatter. A one-hectare area in Wytham Woods, located west of Oxford, was selected for this research. The area has a total of 525 trees of seven different tree species. The Michigan Microwave Canopy Scattering Model (MIMICS), a two-layer radiative transfer model, was applied to simulate canopy backscatter responses in this deciduous UK forest. Model parameters related to forest structure were derived from previously published terrestrial laser (LiDAR) data. Simulated backscatter was performed for co-polarized and cross-polarized modes at a C-band frequency range and incidence angles (20° to 45° at 5° increments). The research includes five objectives: i) backscatter sensitivity analysis from the variation of different model parameters; ii) backscatter seasonal effect under spring–summer (leaf-on) and autumn–winter (leaf-off) periods; iii) backscatter comparison between species as well as between simulated and satellite observations in a spatial pattern; iv) relationship between simulated backscatter and aboveground biomass; and v) relationship between MIMICS forest structure parameters and simulated backscatter. Sensitivity analysis results showed significant differences in backscatter from changes in leaf distribution, leaf thickness and water content (leaf, trunk and soil) for both polarization modes in leaf-on scenarios. Leaf-off scenarios presented significant differences from changes in branch distribution but only for the co-polarized mode. Seasonal variations presented significant backscatter differences between spring–summer and autumn–winter scenarios; additionally, backscatter differences among species for each seasonality in the co-polarized mode were observed. The computation of the grid resolution (20 m × 20 m) showed a range of backscatter values depending on the grid and incidence angle. Higher backscatter values were observed for the satellite data than for the simulated data. Finally, the aboveground biomass and the MIMICS input parameters presented high random variability and little systematic co-variation with the total backscatter on both simulated seasons, suggesting that more robust allometric equations for biomass estimation are required