Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics

Towards the connection between snow microphysics and melting layer : insights from multifrequency and dual-polarization radar observations during BAECC

In stratiform rainfall, the melting layer (ML) is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the ML and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured ML properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide new insights into radar signatures of snow growth processes, we have investigated this link This study is divided into two parts. Firstly, surface-based snowfall measurements are used to develop a new method for identifying rimed and unrimed snow from X- and Ka-band Doppler radar observations. Secondly, this classification is used in combination with multifrequency and dual-polarization radar observations collected during the Biogenic Aerosols - Effects on Clouds and Climate (BAECC) experiment in 2014 to investigate the impact of precipitation intensity, aggregation, riming and dendritic growth on the ML properties. The results show that the radar-observed ML properties are highly related to the precipitation intensity. The previously reported bright band "sagging" is mainly connected to the increase in precipitation intensity. Ice particle riming plays a secondary role. In moderate to heavy rainfall, riming may cause additional bright band sagging, while in light precipitation the sagging is associated with unrimed snow. The correlation between ML properties and dual-polarization radar signatures in the snow region above appears to be arising through the connection of the radar signatures and ML properties to the precipitation intensity. In addition to advancing our knowledge of the link between ML properties and snow processes, the presented analysis demonstrates how multifrequency Doppler radar observations can be used to get a more detailed view of cloud processes and establish a link to precipitation formation.Peer reviewe

Highly supercooled riming and unusual triple-frequency radar signatures over Antarctica

Riming of ice crystals by supercooled water droplets is an efficient ice growth process, but its basic properties are still poorly known. While it has been shown to contribute significantly to surface precipitation at mid-latitudes, little is known about its occurrence at high latitudes. In Antarctica, two competing effects can influence the occurrence of riming: the scarcity of supercooled liquid water clouds due to the extremely low tropospheric temperatures and the low aerosol concentration, which may lead to the formation of fewer and larger supercooled drops potentially resulting in an enhanced riming efficiency. In this work, by exploiting the deployment of an unprecedented number of multi-wavelength active and passive remote sensing systems (including triple-frequency radar measurements) in West Antarctica, during the Atmospheric Radiation Measurements West Antarctic Radiation Experiment (AWARE) field campaign, we evaluate the importance of riming incidence in Antarctica and find that riming occurs at much lower temperatures compared to the mid-latitudes. We then focus on a case study featuring a persistent layer of unexpectedly pronounced triple-frequency radar signatures but only a relatively modest amount of supercooled liquid water. In-depth analysis of the radar observations suggests that such signatures can only be explained by the combined effects of moderately rimed aggregates or similarly shaped florid polycrystals and a narrow particle size distribution (PSD). Simulations of this case study performed with a 1D bin model %by introducing an additional class corresponding to rimed ice indicate that similar triple frequency radar observations can be reproduced when narrow PSDs are simulated. Such narrow PSDs can in turn be explained by two key factors: (i) the presence of a shallow homogeneous droplet or humidified aerosol freezing layer aloft seeding an underlying supercooled liquid layer, and (ii) the absence of turbulent mixing throughout a stable polar atmosphere that sustains narrow PSDs, as hydrometeors grow from the nucleation region aloft to several millimeter ice particles, by vapor deposition and then riming

First Observations of G-Band Radar Doppler Spectra

The first Doppler spectra ever acquired by an atmospheric radar at 200 GHz (G-band) are presented. The observations were taken during a light precipitation event in May (rain rates <2 mm hr−1) at Chilbolton Observatory, UK, with coincident Ka-band and W-band Doppler radar measurements. The collected rain spectra agree with Mie theory predictions: at G-band they show significant reductions in the spectral power return—as compared to theoretical Rayleigh scattering—corresponding to high Doppler velocities (i.e., large raindrops) with the presence of multiple peaks and “Mie notches” in correspondence to the maxima and minima of the raindrop backscattering cross sections. The first two G-band Mie troughs correspond to smaller velocities/sizes than the first W-band Mie notch. These features offered by G-band radars pave the way toward applying, in rain, Mie notch vertical wind retrievals and multifrequency drop size distribution microphysical retrievals to smaller rain rates and smaller characteristic sizes than ever before

Improving the Understanding and Simulation of Precipitation Forming Processes through Combined Analysis of Microphysical Models and Multi-Frequency Doppler Radar Observations

The society is strongly influenced by precipitation, which forms by cloud microphysical processes, e.g., sedimentation and aggregation. These processes determine where and how clouds precipitate relevant for the global water cycle, freshwater availability, and flooding. However, the precipitation forming processes are poorly understood and pose a significant challenge to earth system modeling. Challenges arise from the difficulties of deriving parameterizations from laboratory experiments or observations. Even if accurate process parameterizations could be derived, implementing them into numerical models poses additional challenges due to computational cost and unresolved scales. In the last decades, rapid progress has been made in modeling and observing microphysical processes, which enables or even necessitates further studies that exploit the synergy between both fields. In this thesis, microphysical models are employed that either resolve the microphysical processes up to the single particle level (3D snowflake model and Lagrangian particle model) or are computationally efficient (bulk scheme). The explicit models are used to derive parameterizations and provide detailed insights into the processes that can be used in the less explicit models. Improving the less explicit but computationally efficient bulk schemes is particularly important, as they are indispensable for weather and climate prediction. Output from all models is compared to observations that provide information either on individual particle properties (in situ particle observations) or average properties of large particle ensembles (multi-frequency Doppler radar observations). These model-observation combinations are used to improve the knowledge about the microphysical processes and their representation in the microphysical models. 3D snowflake models simulate the complex shape of ice particles, the representation of which presents a major difficulty for microphysical schemes. In Study I, such a 3D snowflake model is used to derive parameterizations of particle properties, such as mass as a function of size, monomer number and shape. Hydrodynamic models are used to additionally derive the particle velocity. The most detailed parameterizations are used to assess the effect of aggregate composition on the particle properties, which is challenging to do with observations alone. It is found that aggregate properties change smoothly with increasing monomer number but differ substantially depending on the monomer shapes that constitute the aggregates. Other, less detailed parameterizations can be readily applied in bulk microphysical schemes to improve the physical consistency of these schemes. In simulations with a Lagrangian particle model, it can be shown that these less detailed parameterisations are very accurate even if they only distinguish between the two classes of monomers and aggregates. Comparing the parameterization with in situ observations ensures that they are physically realistic in size ranges where observations are available. In addition, the physical principles of the 3D snowflake and hydrodynamic models help to ensure that the parameterizations are realistic even in size ranges for which it is difficult to obtain observations. In Study II, parameters that are important for the microphysical description of sedimentation and aggregation in a two-moment scheme bulk microphysics scheme are constrained by observations. Traditionally, microphysical parameterizations are tuned to improve the prediction of few variables of interest, such as the precipitation rate. This procedure likely introduces compensating errors, since adjusting one parameter may improve the prediction of these variables even if that change leads away from the most physically meaningful value of the parameters. Therefore, a different approach is used in this study that uses several variables from multi-frequency Doppler radars simultaneously and focuses on single or few processes to avoid this issue of underdetermined parameters. First, the observed statistics are used to evaluate microphysical parameters in an idealized 1D model, which allows efficient testing of all key parameters. These simulations reveal that the simulation of aggregation is most sensitive to the aggregate particle properties, the aggregation kernel formulation and the size distribution width and less sensitive to the monomer habit and the sticking efficiency. A statistical comparison between 3D large-eddy simulations with the default and the new scheme setup and the observations show that previously existing large biases of too fast and too large particles in the scheme could be substantially reduced. This bias reduction can be attributed to the improved simulation of sedimentation and aggregation. Since a large portion of precipitation reaches the ground as rain but forms in the ice phase, processes in the melting layer are an essential part of precipitation modeling. In Study III, an approach is used to infer the dominance of growth or shrinkage processes through the relationship of reflectivity flux at the melting layer boundaries. In addition, radar Doppler spectra and multi-frequency observations are used to evaluate assumptions of the approach and to classify profiles according to the degree of riming. For unrimed profiles, growth processes increase the mean mass only slightly. For rimed profiles, shrinking processes lead to a substantial decrease the mean mass probably caused by particle breakup. Simulations using a Lagrangian particle model reveal that breakup processes for which parameterizations are available can not reproduce the observed decrease of the mean mass for rimed profiles and suggest that further laboratory studies of collisional breakup of melting particles are needed

Triple frequency radar retrieval of microphysical properties of snow

An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.</p

investigating ice microphysical processes by combining multi-frequency and polarimetric Doppler radar observations with Lagrangian Monte-Carlo particle modelling

Clouds and precipitation strongly impact society and the earth system by influencing the water cycle, determining fresh water availability or causing natural disasters such as floods or droughts. However, many aspects of precipitation formation are still poorly understood, causing large uncertainties in the prediction of precipitation. Especially the microphysical processes, which describe the nucleation of cloud particle and their growth into precipitation lack understanding. As globally 63% of precipitation originates from the ice phase, increasing the understanding of ice microphysical processes is crucial to improve precipitation forecast. The dendritic growth layer (DGL), located at temperatures between −20 and −10 ° C, plays an important role in the formation of precipitation. Previous studies have found an in particle size and number concentration through depositional growth, aggregation and secondary ice processes. This dissertation investigates ice microphysical processes in the DGL by combining polarimetric and multi-frequency Doppler cloud radar observations with Monte-Carlo Lagrangian particle modelling. Study I presents a statistical analysis of a three-month polarimetric and multi-frequency Doppler radar dataset. This combination of radar measurements allows to observe the full evolution of ice particle growth, as the polarimetric measurements are indicators of depositional growth and possible secondary ice processes, while the multi-frequency approach gives an indication of the increase particle in size through aggregation and riming. The statistical analysis revealed an increase of aggregate size at −15 ° C. The mean size of aggregates is found to be correlated to an updraft with a maximum of approximately 0.1 m s −1 at −14 ° C. The radar observations further indicate the growth of plate-like ice crystals at −15 ° C. Unexpectedly, aggregation is found to increase in the DGL alongside an increase in ice particle number concentration. This simultaneous increase necessitates a source of new ice particles, as aggregation is expected to decrease the total number of ice particles. Secondary ice processes, such as collisional fragmentation provide one explanation for this increase in ice particle size. Another possible explanation might be that small ice particles sediment from colder temperatures into the DGL and enhance the number concentration locally. The third explanation is linked to the observed updraft, as this updraft might increase the super-saturation with respect to ice at −15 ° C, leading to the activation of ice nucleating particles and a subsequent increase in ice particle number and growth of plate-like particles. Unfortunately, radar observations do not observe the formation of particles directly, it is difficult to predict the origin of the particles responsible for the increase in particle concentration and observed polarimetric signatures further. With the observational dataset as a constrain, Study II uses the Monte-Carlo Lagrangian particle model McSnow to investigate the origin of the increase in ice particle number concentration in the DGL further. The comparison of the observations and McSnow simulations indicate that the particles responsible for the polarimetric signatures and increase in number concentration need to be nucleated at temperatures close to −15 ° C. This might indicate that in the observed clouds, sedimenting ice particles into the DGL play a lesser role. The McSnow simulations further indicate that neither collisional fragmentation nor new ice particles due to activation of ice nucleating particles can explain the observed multi-frequency and polarimetric observations. A combination of both processes might explain the observed signatures. This dissertation shows the potential of a combination of radar observations and modelling for increasing the understanding of microphysical processes in clouds. However, further laboratory studies are needed in order to further constrain the processes in the DGL and validate the findings of this dissertation

Characterization of snowfall using ground-based passive and active remote sensors.

Snowfall is a key quantity in the global hydrological cycle and has an impact on the global energy budget as well. In sub-polar and polar latitudes, snowfall is the predominant type of precipitation and rainfall is often initiated via the ice phase. Currently, the spatial distribution of snowfall is poorly captured by numerical weather prediction and climate models. In order to evaluate the models and to improve our understanding of snowfall microphysics, global observations of snowfall are needed. This can only be obtained by space-borne active and passive remote sensors. In order to be able to penetrate even thick snow clouds, sensors operating in the microwave frequency region are favoured. The challenge for snowfall retrieval development lies first in the complexity of snowfall microphysics and its interactions with liquid cloud water. Secondly, comprehensive knowledge is needed about the interaction of electromagnetic radiation with snowfall in order to finally relate the radiative signatures to physical quantities. A general advantage of ground-based observations is that simultaneous measurements of in-situ and remote sensing instruments can be obtained. Such a six-month dataset was collected within this thesis at an alpine site. The instrumentation included passive microwave radiometers that covered the frequency range from 22 up to \unit[150]{GHz} as well as two radar systems operating at 24.1 and 35.5 GHz. These data were complemented by optical disdrometer, ceilometer and various standard meteorological measurements. State-of-the-art single scattering databases for pristine ice crystals and complex snow aggregates were used within this thesis to investigate the sensitivity of ground--based passive and active remote sensors to various snowfall parameters such as vertical snow and liquid water distribution, snow particle habit, snow size distribution and ground surface properties. The comparison of simulations with measurements within a distinct case study revealed that snow particle scattering can be measured with ground--based passive microwave sensors at frequencies higher than 90 GHz. Sensitivity experiments further revealed that ground-based sensors have clear advantages over nadir measuring instruments due to a stronger snow scattering signal and lower sensitivity to variable ground surface emissivity. However, passive sensors were also found to be highly sensitive to liquid cloud water that was frequently observed during the entire campaign. The simulations indicate that the uncertainties of sizes distribution and snow particle habit are not distinguishable with a passive-only approach. In addition to passive microwave observations, data from a low-end radar system that is commonly used for rainfall were investigated for its capabilities to observe snowfall. For this, a snowfall specific data processing algorithm was developed and the re-processed data were compared to collocated measurements of a high-end cloud radar. If the focus can be narrowed down to medium and strong snowfall within the lowest 2-3 km height, the reflectivity and fall velocity measurements of the low-end system agree well with the cloud radar. The cloud radar dataset was used to estimate the uncertainty of retrieved snowfall rate and snow accumulation of the low-end system. Besides the intrinsic uncertainties of single-frequency radar retrievals the estimates of total snow accumulation by the low-end system lay within 7% compared to the cloud radar estimates. In a more general approach, the potential of multi-frequency radar systems for derivation of snow size distribution parameters and particle habit were investigated within a theoretical simulation study. Various single-scattering databases were combined to test the validity of dual-frequency approaches when applied to non-spheroid particle habits. It was found that the dual-frequency technique is dependent on particle habit. It could be shown that a rough distinction of snow particle habits can be achieved by a combination of three frequencies. The method was additionally tested with respect to signal attenuation and maximum particle size. The results obtained by observations and simulations within this thesis strongly suggest the further development of simultaneous ground-based in-situ and remote sensing observations of snowfall. Extending the sensitivity studies of this study will help to define the most suitable set of sensors for future studies. A combination of these measurements with a further development of single-scattering databases will potentially help to improve our understanding of snowfall microphysics