The Micro Rain Radar 2 (MRR) is a compact Frequency Modulated Continuous Wave (FMCW) system that operates at 24 GHz. The MRR is a low-cost, portable radar system that requires minimum supervision in ...the field. As such, the MRR is a frequently used radar system for conducting precipitation research. Current MRR drawbacks are the lack of a sophisticated post-processing algorithm to improve its sensitivity (currently at +3 dBz), spurious artefacts concerning radar receiver noise and the lack of high quality Doppler radar moments. Here we propose an improved processing method which is especially suited for snow observations and provides reliable values of effective reflectivity, Doppler velocity and spectral width. The proposed method is freely available on the web and features a noise removal based on recognition of the most significant peak. A dynamic dealiasing routine allows observations even if the Nyquist velocity range is exceeded. Collocated observations over 115 days of a MRR and a pulsed 35.2 GHz MIRA35 cloud radar show a very high agreement for the proposed method for snow, if reflectivities are larger than -5 dBz. The overall sensitivity is increased to -14 and -8 dBz, depending on range. The proposed method exploits the full potential of MRR's hardware and substantially enhances the use of Micro Rain Radar for studies of solid precipitation.
The potential of multifrequency Doppler spectra to constrain precipitation microphysics has so far only been exploited for dual‐frequency spectra in rain. In this study, we extend the dual‐frequency ...concept to triple‐frequency Doppler radar spectra obtained during a snowfall event which included rimed and unrimed snow aggregates. A large selection of spectra obtained from low‐turbulence regions within the cloud reveals distinctly different signatures of the derived dual spectral ratios. Due to the third frequency, a characteristic curve can be derived which is almost independent of the underlying particle size distribution and velocity‐size relation. This approach provides new opportunities for validating existing and future snow scattering models and reveals how the information content of triple‐frequency radar data sets can be further exploited for snowfall studies.
Key Points
First analysis of triple‐frequency Doppler radar spectra of rimed and unrimed snowfall
Unique signature can be derived which characterized snowfall scattering properties
Signature of riming can be well reproduced by recent calculations with discrete dipole approximation
A new comprehensive cloud–precipitation–meteorological observatory has been established at Princess Elisabeth base, located in the escarpment zone of Dronning Maud Land (DML), East Antarctica. The ...observatory consists of a set of ground-based remote-sensing instruments (ceilometer, infrared pyrometer and vertically profiling precipitation radar) combined with automatic weather station measurements of near-surface meteorology, radiative fluxes, and snow height. In this paper, the observatory is presented and the potential for studying the evolution of clouds and precipitating systems is illustrated by case studies. It is shown that the synergetic use of the set of instruments allows for distinguishing ice, liquid-containing clouds and precipitating clouds, including some information on their vertical extent. In addition, wind-driven blowing snow events can be distinguished from deeper precipitating systems. Cloud properties largely affect the surface radiative fluxes, with liquid-containing clouds dominating the radiative impact. A statistical analysis of all measurements (in total 14 months mainly during summer–beginning of winter) indicates that these liquid-containing clouds occur during as much as 20% of the cloudy periods. The cloud occurrence shows a strong bimodal distribution with clear-sky conditions 51% of the time and complete overcast conditions 35% of the time. Snowfall occurred during 17% of the cloudy periods with a predominance of light precipitation and only rare events with snowfall >1 mm h−1 water equivalent (w.e.). Three of such intense snowfall events occurred during 2011 contributing to anomalously large annual surface mass balance (SMB). Large accumulation events (>10 mm w.e. day−1) during the radar-measurement period of 26 months were always associated with snowfall, but at the same time other snowfall events did not always lead to accumulation. The multiyear deployment of a precipitation radar in Antarctica allows for assessing the contribution of the snowfall to the local SMB and comparing it to the other SMB components. During 2012, snowfall rate was 110 ± 20 mm w.e. yr−1, from which surface and drifting snow sublimation removed together 23%. Given the measured yearly SMB of 52 ± 3 mm w.e., the residual term of 33 ± 21 mm w.e. yr−1 was attributed to the wind-driven snow erosion. In general, this promising set of robust instrumentation allows for improved insight into cloud and precipitation processes in Antarctica and can be easily deployed at other Antarctic stations.
JOYCE Löhnert, U.; Schween, J. H.; Acquistapace, C. ...
Bulletin of the American Meteorological Society,
07/2015, Letnik:
96, Številka:
7
Journal Article
Recenzirano
Odprti dostop
The Jülich Observatory for Cloud Evolution (JOYCE), located at Forschungszentrum Jülich in the most western part of Germany, is a recently established platform for cloud research. The main objective ...of JOYCE is to provide observations, which improve our understanding of the cloudy boundary layer in a midlatitude environment. Continuous and temporally highly resolved measurements that are specifically suited to characterize the diurnal cycle of water vapor, stability, and turbulence in the lower troposphere are performed with a special focus on atmosphere–surface interaction. In addition, instruments are set up to measure the micro- and macrophysical properties of clouds in detail and how they interact with different boundary layer processes and the large-scale synoptic situation. For this, JOYCE is equipped with an array of state-of-the-art active and passive remote sensing and in situ instruments, which are briefly described in this scientific overview. As an example, a 24-h time series of the evolution of a typical cumulus cloud-topped boundary layer is analyzed with respect to stability, turbulence, and cloud properties. Additionally, we present longer-term statistics, which can be used to elucidate the diurnal cycle of water vapor, drizzle formation through autoconversion, and warm versus cold rain precipitation formation. Both case studies and long-term observations are important for improving the representation of clouds in climate and numerical weather prediction models.
Aerosols that serve as ice nucleating particles (INPs) have the potential to
modulate cloud microphysical properties and can therefore impact cloud
radiative forcing (CRF) and precipitation formation ...processes. In remote
regions such as the Arctic, aerosol–cloud interactions are severely
understudied yet may have significant implications for the surface energy
budget and its impact on sea ice and snow surfaces. Further, uncertainties in
model representations of heterogeneous ice nucleation are a significant
hindrance to simulating Arctic mixed-phase cloud processes. We present
results from a campaign called INPOP (Ice Nucleating Particles at Oliktok
Point), which took place at a US Department of Energy Atmospheric Radiation
Measurement (DOE ARM) facility in the northern Alaskan Arctic. Three time-
and size-resolved aerosol impactors were deployed from 1 March to 31 May 2017
for offline ice nucleation and chemical analyses and were co-located with
routine measurements of aerosol number and size. The largest particles (i.e.,
≥ 3 µm or “coarse mode”) were the most efficient INPs by
inducing freezing at the warmest temperatures. During periods with snow- and
ice-covered surfaces, coarse mode INP concentrations were very low (maximum
of 6 × 10−4 L−1 at −15 ∘C), but higher
concentrations of warm-temperature INPs were observed during late May
(maximum of 2 × 10−2 L−1 at −15 ∘C). These
higher concentrations were attributed to air masses originating from over
open Arctic Ocean water and tundra surfaces. To our knowledge, these results
represent the first INP characterization measurements in an Arctic oilfield
location and demonstrate strong influences from mineral and marine sources
despite the relatively high springtime pollution levels. Ultimately, these
results can be used to evaluate the anthropogenic and natural influences on
aerosol composition and Arctic cloud properties.
This study investigates the interactions between cloud dynamics and aerosols in idealized large-eddy simulations (LES) of Arctic mixed-phase stratocumulus clouds (AMPS) observed at Oliktok Point, ...Alaska, in April 2015. This case was chosen because it allows the cloud to form in response to radiative cooling starting from a cloud-free state, rather than requiring the cloud ice and liquid to adjust to an initial cloudy state. Sensitivity studies are used to identify whether there are buffering feedbacks that limit the impact of aerosol perturbations. The results of this study indicate that perturbations in ice nucleating particles (INPs) dominate over cloud condensation nuclei (CCN) perturbations; i.e., an equivalent fractional decrease in CCN and INPs results in an increase in the cloud-top longwave cooling rate, even though the droplet effective radius increases and the cloud emissivity decreases. The dominant effect of ice in the simulated mixed-phase cloud is a thinning rather than a glaciation, causing the mixed-phase clouds to radiate as a grey body and the radiative properties of the cloud to be more sensitive to aerosol perturbations. It is demonstrated that allowing prognostic CCN and INPs causes a layering of the aerosols, with increased concentrations of CCN above cloud top and increased concentrations of INPs at the base of the cloud-driven mixed layer. This layering contributes to the maintenance of the cloud liquid, which drives the dynamics of the cloud system.
In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations on the morphology and abundance of ice particles in mixed-phase ...clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INPs affect radar reflectivities.
Riming is a key precipitation formation process in mixed-phase clouds which efficiently converts cloud liquid to ice water. Here, we present two methods to quantify riming of ice particles from ...airborne observations with the normalized rime mass, which is the ratio of rime mass to the mass of a size-equivalent spherical graupel particle. We use data obtained during the HALO-(AC)3 aircraft campaign, where two aircraft collected radar and in situ measurements that were closely spatially and temporally collocated over the Fram Strait west of Svalbard in spring 2022. The first method is based on an inverse optimal estimation algorithm for the retrieval of the normalized rime mass from a closure between cloud radar and in situ measurements during these collocated flight segments (combined method). The second method relies on in situ observations only, relating the normalized rime mass to optical particle shape measurements (in situ method). We find good agreement between both methods during collocated flight segments with median normalized rime masses of 0.024 and 0.021 (mean values of 0.035 and 0.033) for the combined and in situ method, respectively. Assuming that particles with a normalized rime mass smaller than 0.01 are unrimed, we obtain average rimed fractions of 88 % and 87 % over all collocated flight segments. Although in situ measurement volumes are in the range of a few cubic centimeters and are therefore much smaller than the radar volume (about 45 m footprint diameter at an altitude of 500 m above ground, with a vertical resolution of 5 m), we assume they are representative of the radar volume. When this assumption is not met due to less homogeneous conditions, discrepancies between the two methods result. We show the performance of the methods in a case study of a collocated segment of cold-air outbreak conditions and compare normalized rime mass results with meteorological and cloud parameters. We find that higher normalized rime masses correlate with streaks of higher radar reflectivity. The methods presented improve our ability to quantify riming from aircraft observations.
Riming, i.e., the accretion and freezing of supercooled liquid water (SLW) on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying ...riming using ground-based cloud radar observations is of great interest; however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based, zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka- and W-band cloud radar and in situ observations of snowfall by a Precipitation Imaging Package from which the rime mass fraction (FRPIP) is retrieved. ANNs are trained separately either on the Ka-band radar or the W-band radar data set to predict the rime fraction FRANN. We focus on two configurations of input variables. ANN 1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width – SEW), and the skewness as input features. ANN 2 only uses Ze, SEW, and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (FRANN > 0.7) and yield low values (FRANN ≤ 0.4) for unrimed snow. In general, the predictions of ANN 1 and 2 are very similar, advocating the capability of predicting riming without the use of MDV. The predictions of both ANNs for a wintertime convective cloud fit with coinciding in situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN 2 to an orographic case yields high FRANN values coinciding with observations of solid graupel particles at the ground.
The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED ...backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
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