The accurate representation of ice particles is essential for both remotely sensed estimates of clouds and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals ...to assume that all snow is composed of aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to interrogate the internal structure of aggregate snowflakes and to distinguish more dense and homogeneous rimed particles from aggregates.
The dendritic growth layer (DGL), defined as the temperature region between -20 and -10 .sup." 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.
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.
Plain Language Summary
To better predict precipitation by numerical models and quantify precipitation by observations, it is important to improve the understanding of processes in the melting layer (ML). The ML is the part of clouds where ice particles melt and become rain. We use an approach that assesses whether a tendency toward either growth or shrinking processes is evident in the ML. We assess the uncertainty of the approach, correct for different factors, and apply it to a large data set to derive robust statistics separately for profiles with different characteristic ice particle shapes above the ML. These statistics are surprisingly similar for the different characteristic ice particle shapes and suggest that either growth and shrinking processes are not important in the ML or strongly compensate for each other.
Key Points
We investigated the growth or shrinking of snowflakes in the melting layer using statistics of multi‐frequency Doppler radar observations
Reflectivity flux analysis indicates only slight differences for unrimed or rimed particles
Growth or shrinking processes either compensate each other or have, on average, only a small impact on the reflectivity flux
Vertically pointing radar observations combining multiple frequencies and Doppler measurements have been recently shown to contain valuable information about ice particle growth processes, such as ...aggregation and riming. In this study, we use a two‐months X, Ka, W‐Band Doppler radar dataset of midlatitude winter clouds to infer statistical growth signatures of ice and snow particles. The observational statistics are compared to forward‐simulated radar moments based on simulations of the campaign time period with a high‐resolution version of the ICON model and a two‐moment microphysical scheme. The statistical comparison shows very good agreement of the simulated vertical structure of radar reflectivity and surface precipitation rate. The dual‐wavelength ratios, which are closely related to the mean particle size, also show consistently a major increase at temperatures higher than –15 °C. However, at temperatures higher than –7 °C, ICON increasingly overestimates the mean particle size. The statistics of mean Doppler velocities also reveal that the model overestimates the terminal velocity of snow particles, especially at larger sizes. We discuss possible reasons for the identified discrepancies, such as an unrealistic temperature dependence of the sticking efficiency or the non‐saturation of terminal velocities at larger sizes caused by the implemented power law relations. Our study demonstrates examples of the importance of combining various radar techniques for identifying issues in simulated microphysical processes, which can otherwise be hidden due to compensating errors.
Numerical weather prediction model output is compared with multi‐frequency Doppler radar observations. The statistical comparison reveals discrepancies in the cloud structure that suggest the presence of some inaccuracies in the modelled ice properties. The study demonstrates the importance of combining various radar techniques (such as multi‐frequency and Doppler) for revealing these discrepancies which otherwise might remain hidden by the effect of compensating errors.
This study investigates the link between rain and ice microphysics across the melting layer in stratiform rain systems using measurements from vertically pointing multi-frequency Doppler radars.
A ...novel methodology to examine the variability of the precipitation rate and the mass-weighted melted diameter (Dm) across the melting region is proposed and applied to a 6 h long case study, observed during the TRIPEx-pol field campaign at the Jülich Observatory for Cloud Evolution Core Facility and covering a gamut of ice microphysical processes.
The methodology is based on an optimal estimation (OE) retrieval of particle size distributions (PSDs) and dynamics (turbulence and vertical motions) from observed multi-frequency radar Doppler spectra applied both above and below the melting layer.
First, the retrieval is applied in the rain region; based on a one-to-one conversion of raindrops into snowflakes, the retrieved drop size distributions (DSDs) are propagated upward to provide the mass-flux-preserving PSDs of snow. These ice PSDs are used to simulate radar reflectivities above the melting layer for different snow models and they are evaluated for a consistency with the actual radar measurements.
Second, the OE snow retrieval where Doppler spectra are simulated based on different snow models, which consistently compute fall speeds and electromagnetic properties, is performed. The results corresponding to the best-matching models are then used to estimate snow fluxes and Dm, which are directly compared to the corresponding rain quantities.
For the case study, the total accumulation of rain (2.30 mm) and the melted equivalent accumulation of snow (1.93 mm) show a 19 % difference. The analysis suggests that the mass flux through the melting zone is well preserved except the periods of intense riming where the precipitation rates were higher in rain than in the ice above. This is potentially due to additional condensation within the melting zone in correspondence to high relative humidity and collision and coalescence with the cloud droplets whose occurrence is ubiquitous with riming.
It is shown that the mean mass-weighted diameter of ice is strongly related to the characteristic size of the underlying rain except the period of extreme aggregation where breakup of melting snowflakes significantly reduces Dm.
The proposed methodology can be applied to long-term observations to advance our knowledge of the processes occurring across the melting region; this can then be used to improve assumptions underpinning spaceborne radar precipitation retrievals.
Cloud and precipitation processes are still a main source of
uncertainties in numerical weather prediction and climate change
projections. The Priority Programme “Polarimetric Radar Observations meet
...Atmospheric Modelling (PROM)”, funded by the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that
many uncertainties relate to the lack of observations suitable to challenge
the representation of cloud and precipitation processes in atmospheric
models. Such observations can, however, at present be provided by the
recently installed dual-polarization C-band weather radar network of the
German national meteorological service in synergy with cloud radars and
other instruments at German supersites and similar national networks
increasingly available worldwide. While polarimetric radars potentially
provide valuable in-cloud information on hydrometeor type, quantity,
and microphysical cloud and precipitation processes, and atmospheric models
employ increasingly complex microphysical modules, considerable knowledge
gaps still exist in the interpretation of the observations and in the
optimal microphysics model process formulations. PROM is a coordinated
interdisciplinary effort to increase the use of polarimetric radar
observations in data assimilation, which requires a thorough evaluation and
improvement of parameterizations of moist processes in atmospheric models.
As an overview article of the inter-journal special issue “Fusion of radar
polarimetry and numerical atmospheric modelling towards an improved
understanding of cloud and precipitation processes”, this article outlines
the knowledge achieved in PROM during the past 2 years and gives
perspectives for the next 4 years.
More detailed observational capabilities in the microwave (MW) range and advancements in the details of microphysical schemes for ice and snow demand increasing complexity to be included in ...scattering databases. The majority of existing databases rely on the discrete dipole approximation (DDA) whose high computational costs limit either the variety of particle types or the range of parameters included, such as frequency, temperature, and particle size.
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