This paper describes a 2-month dataset of ground-based triple-frequency (X,
Ka, and W band) Doppler radar observations during the winter season obtained
at the Jülich ObservatorY for Cloud Evolution Core Facility (JOYCE-CF),
Germany. All relevant post-processing steps, such as re-gridding and offset and
attenuation correction, as well as quality flagging, are described. The
dataset contains all necessary information required to recover data at
intermediate processing steps for user-specific applications and corrections
(10.5281/zenodo.1341389; ). The large number of ice clouds included in the dataset
allows for a first statistical analysis of their multifrequency radar
signatures. The reflectivity differences quantified by dual-wavelength ratios
(DWRs) reveal temperature regimes where aggregation seems to be triggered.
Overall, the aggregation signatures found in the triple-frequency space agree
with and corroborate conclusions from previous studies. The combination of
DWRs with mean Doppler velocity and linear depolarization ratio enables us to
distinguish signatures of rimed particles and melting snowflakes. The riming
signatures in the DWRs agree well with results found in previous
triple-frequency studies. Close to the melting layer, however, we find very
large DWRs (up to 20 dB), which have not been reported before. A combined
analysis of these extreme DWR with mean Doppler velocity and a linear
depolarization ratio allows this signature to be separated, which is most likely
related to strong aggregation, from the triple-frequency characteristics of
melting particles.
Introduction
The combined observation of clouds and precipitation at
different radar frequencies is used to improve retrievals of hydrometeor
properties. All methods exploit frequency-dependent hydrometeor scattering
and absorption properties governed by their microphysical characteristics.
Multifrequency retrievals are already well developed for liquid
hydrometeors. For example, used differential radar
attenuation at 35 and 94 GHz to retrieve vertical profiles of cloud liquid
water. Improved precipitation rate retrievals on a global scale are provided
by the core satellite of the Global Precipitation Mission which operates a
Ku–Ka band dual-frequency radar . For frequencies below
≈10 GHz, attenuation effects are negligible (except for heavy
rainfall or hail), and the sensitivity to non-precipitating particles, such as
ice crystals, is relatively weak. Therefore, the majority of multifrequency
applications for cold clouds focus on cloud radar systems operating at 35
or 94 GHz. At these frequencies, the radars are sensitive enough to detect
even sub-millimeter ice particles and cloud droplets. The sizes of large ice
crystals, snowflakes, graupel, and hail are on the order of the wavelengths
used to observe them (3 mm, 8 mm, and 3 cm for W, Ka, and X band, respectively).
Thus, non-Rayleigh scattering becomes important and can be used to constrain
particle size distributions, improving ice and snow water content retrievals
.
Recent modeling studies
revealed that different ice particle classes like graupel, single crystals,
or aggregates can be distinguished using a combination of three radar
frequencies (13, 35, and 94 GHz). Triple-frequency radar datasets from
airborne campaigns and satellites
confirmed distinct signatures in the triple-frequency space.
Ground-based triple-frequency radar measurements in combination with in situ
observations provided the first experimental evidence for
a close relation between triple-frequency signatures and the characteristic
particle size, as well as the bulk density of snowfall. These early results
were corroborated and refined by coinciding in situ observations in aircraft
campaigns as well as by ground-based observations
. A better understanding of the relations between
triple-frequency signatures and snowfall properties is key for
triple-frequency radar retrieval development. The connection between
scattering and microphysical properties is currently addressed by novel
ground-based in situ instrumentation and triple-frequency
Doppler spectra . Long-term triple-frequency datasets from
various sites and radar systems are, however, needed to better understand the
relations between triple-frequency signatures and clouds.
We present a first analysis of triple-frequency (X, Ka, and W band) radar
observations collected over two winter months at the Jülich Observatory
for Cloud Evolution Core Facility, Germany . The data were
corrected for known offsets and attenuation effects and re-gridded for
multifrequency studies. Section 2 describes the experimental setup and the
characteristics of the X, Ka, and W band radars. Section 3 details the data
processing and corrections applied. Section 4 gives a general overview of the
dataset and its limitations. Section 5 presents a statistical analysis of the
data with a focus on the temperature dependency of the triple-frequency
properties, signatures of riming, intense aggregation, and melting snow
particles. We summarize and discuss our results in Sect. 6.
Measurement site and instruments
The TRIple-frequency and Polarimetric radar Experiment for improving
process observation of winter precipitation (TRIPEx) was a joint field
experiment of the University of Cologne, the University of Bonn, the
Karlsruhe Institute of Technology (KIT), and the Jülich Research Centre
(Forschungszentrum Jülich, FZJ). TRIPEx took place at the Jülich
Observatory for Cloud Evolution Core Facility (JOYCE-CF
50∘54′31′′ N, 6∘24′49′′ E; 111 m above mean sea
level) from 11 November 2015 until 4 January 2016. The core instruments
deployed during TRIPEx were three vertically pointing radars providing a
triple-frequency (X, Ka, and W band) column view of the hydrometeors aloft.
All three radars were calibrated by the manufacturers before the campaign.
Figure sketches the positions of the instruments relative
to each other and the ground surface. A large number of additional
permanently installed remote sensing and in situ observing instruments are
available at the JOYCE-CF site (see , for a detailed
overview).
Sketch (not to scale) of the horizontal and vertical distances
between the three zenith-pointing radars operated during TRIPEx. The JOYCE-CF
platform with all auxiliary instruments is located on the roof of a 17 m
tall building. The mobile X band radar was placed on the ground close to the
other two radars.
Precipitation radar KiXPol (X band)
KiXPol, hereafter referred to as the X band, is a pulsed 9.4 GHz Doppler
precipitation radar, usually integrated into the KITcube platform
. The mobile Meteor 50DX radar, manufactured by Selex ES
(Gematronik), is mounted on a trailer and placed next to the JOYCE-CF
building in order to position it as close as possible to the other two
radars, which were installed on the JOYCE-CF roof platform (see
Fig. ). The radar operates in a simultaneous transmit and
receive (STAR) mode and is thus capable of measuring standard polarimetric
variables like differential reflectivity Zdr and differential
phase shift Φdp. The linear depolarization ratio (LDR) is not
provided because it requires the emission of single-polarization pulses in
order to allow for independent measurements of the cross-polarized component
of the returning signal. During the campaign, the X band was set to a pulse
duration of 0.3 µs; a slight oversampling was applied to achieve
a radial resolution of 30 m in order to match the resolution of the other
radars as close as possible (see Table ). The X band
radar is designed for operational observations of precipitation via volume
scans (series of azimuth scans at several fixed elevation angles). KiXPol was
operated at JOYCE in this mode during the HOPE campaign
. The standard software requires the
antenna to be rotated in azimuth in order to record data. Hence, we constantly rotated the
antenna at zenith elevation with a slow rotation speed (2∘ s-1)
in order to enhance the sensitivity through longer time averaging. After each
complete rotation, the radar stops the measurements for a few seconds before
the next scan starts, thus introducing a small measurement gap in each scan
routine. Further technical specifications of the X band are listed in
Table .
Cloud radar JOYRAD-35 (Ka band)
JOYRAD-35, hereafter referred to as the Ka band, is a scanning 35.5 GHz Doppler
cloud radar of the type MIRA-35 manufactured by Metek
(Meteorologische Messtechnik GmbH), Germany. An overview of its main
technical characteristics and settings used during TRIPEx is provided in
Table . The radar transmits linearly polarized pulses at
35.5 GHz and receives the co- and cross-polarized returns simultaneously.
This allows derivation of the LDR, which is used by the Metek processing software
to filter out signals from insects and to detect the melting layer. From the
measured Doppler spectra, standard radar moments such as the effective
reflectivity factor Ze, mean Doppler velocity (MDV) and Doppler spectral
width (SW) are computed. Since March 2012, the Ka band radar has been a permanent
component of JOYCE-CF , and its zenith observations are
used as input for generating CloudNet products . The
radar was vertically pointing most of the time because the major scientific
focus during TRIPEx was to collect combined triple-frequency observations.
Every 30 min, a sequence of range height display (RHI) scans in different
azimuth directions (duration ≈4 min) was performed in order to
capture a snapshot of the spatial cloud field and also to derive the radial
component of the horizontal wind inside the cloud. The scanning data have not
been processed yet; thus, the dataset described here only includes the zenith
observations; the RHI scans will be included in a future release. The Ka band
radar operated almost continuously during the TRIPEx campaign, except
for a gap from 25 November to 2 December 2015 due to a failure of the storage
unit.
Cloud radar JOYRAD-94 (W band)
JOYRAD-94, hereafter referred to as the W band, is a 94 GHz frequency-modulated continuous-wave (FMCW) radar, combined with a radiometric channel at 89 GHz.
The instrument is manufactured by Radiometer Physics GmbH (RPG), Germany.
Unlike the X and Ka band radar, the W band radar is a non-polarimetric,
non-scanning, and non-pulsed system. The W band started measurements at JOYCE-CF
in October 2015; a detailed description of the radar performance, hardware,
signal processing, and calibration can be found in . The W
band radar has a similar beam width, range, and temporal resolution as the Ka
band (Table ). The FMCW system allows the user to set
different range resolutions for different altitudes by acting on the frequency
modulation settings (chirp sequence). During TRIPEx the standard chirp
sequence (Table ) was used. After correcting the
Doppler spectra for aliasing using the method described in
, standard radar moments such as the equivalent Ze, MDV, and SW are derived.
Technical specifications and settings of the three vertically
pointing radars operated during TRIPEx at JOYCE-CF.
SpecificationsX bandKa bandW bandFrequency (GHz)9.435.594.0Pulse repetition frequency (kHz)1.25.05.3–12bDoppler velocity bins1200512512Number of spectral average1208–18b3dB beam width (∘)1.30.60.5Sensitivity at 5 km (dBZ)a-10-39-33Nyquist velocity (± m s-1)9104.2–9.7bRange resolution (m)30.028.816–34.1bTemporal sampling (s)123Lowest clutter-free range (m)700400370RadomeYesNoYes
a Minimum sensitivities have been derived from the
reflectivity histograms shown in Fig. . b Pulse
repetition frequency, number of spectral average, Nyquist velocity, and range
resolution depend on the chirp definition; those values are indicated in
Table .
Main settings of the chirp sequence used during TRIPEx for the W
band radar. See for a detailed
description.
AttributesChirp sequence 1234Integration time (s)0.3380.4020.5301.769Range interval (m)100–400400–12001200–30003000–12 000Range resolution (m)16.021.326.934.1Nyquist velocity (± m s-1)9.78.16.24.2Doppler velocity bins512512512512Number of spectral average88818Chirp repetition frequency (kHz)12.210.27.85.3Data processing
The full TRIPEx dataset is structured on three processing levels. Level 0
contains the original data from the X, Ka, and W band. For Level 1, the
measurements are corrected for known instrument problems and sampled into a
common time–height grid. At this stage, the data can still be considered raw;
further processing steps that are either dependent on radar frequency or
atmospheric conditions are applied to the Level 2 dataset. These processing
steps include the detection and removal of measurements affected by ground
clutter, an offset correction of the radars based on independent sources, the
compensation for estimated differential attenuation caused by atmospheric
gases, adjustment of the DWRs by cross calibrations between the three radars
and the addition of data quality flags. These steps are meant to remove
spurious multifrequency signals that are not produced by cloud properties.
The processing is performed to the best of our knowledge; however,
intermediate steps are included in the dataset in order to allow the original data to be recovered at any stage and different processing techniques to be applied.
Figure illustrates the work chain from Level 0 to
Level 2. The following sections provide a detailed description of each step.
Flowchart of the TRIPEx data processing. The upper part describes
the steps producing data Level 1 and the bottom part those producing data
Level 2.
Spatiotemporal re-gridding and offset correction
Since the range and temporal resolutions of the three radars are slightly
different (Table ), the data are re-gridded at a common
time and space resolution in order to allow for the calculation of dual
wavelength ratios (DWRs) defined for two wavelengths
λ1 and λ2 as
DWR=Zeλ1-Zeλ2,
with Zeλ in dBZ. The reference grid has a temporal resolution of
of 4 s and a vertical resolution of 30 m, which is the resolution of
the W band. The data are interpolated using a nearest-neighbor approach, with the
maximum data displacement limited to ±17 m in range and ±2 s in
time. This method preserves the high-resolution information of the original
radar observations. Limiting the interpolation displacement avoids spurious
multifrequency features that may result from nonmatching radar volumes.
Residual volume mismatches may occur at cloud boundaries where
heterogeneities are largest. For the Ka band, two corrections are applied to the
original reflectivity as suggested by the manufacturer (Matthias
Bauer-Pfundstein, Metek GmbH, personal communication, 2015). An offset of 2 dB is added to account for power loss
caused by the finite receiver bandwidth; another 3 dB offset is added to
correct for problems in the digital signal processor used in older MIRA
systems. These corrections are applied for processing of the Level 1 data.
Clutter removal
Following the corrections for radar offsets and re-gridding, the first step
in the Level 2 processing is the removal of the range gates affected by
ground clutter. Considering the different radar installation locations (roof
mount or ground surface) and antenna patterns, the clutter contamination
affects each type of radar data differently. The thresholds for the lowest usable
range gates are determined empirically and are reported in
Table .
Evaluation of the Ka band calibration with PARSIVEL disdrometer measurements
The three radars have been individually calibrated by their respective
manufacturers; however, radar components might experience drifts over time,
which can lead to biases of several dB. The JOYCE site is equipped with a
PARSIVEL optical disdrometer , which provides the drop
size distribution (DSD) with a temporal resolution of 1 min. For
rainfall events, the DSD can be used to calculate the associated radar
reflectivity factor. In this study, the scattering properties of raindrops
are calculated using the T-matrix approach with a
drop shape model that follows and assuming drop canting
angles that follow a Gaussian distribution with zero mean and 7∘
standard deviation . Unfortunately, the lowest usable radar
range gates are 500–600 m above the PARSIVEL; thus we have to assume a
constant DSD over this altitude range in order to compare with the radar
reflectivities. Time lags and wind shear effects raise further problems in
the direct comparisons between radar-measured Ze and the one calculated with
PARSIVEL. For this reason, we only compare the statistical distribution of
reflectivities at the lowest range gates measured over several hours with the
corresponding distribution calculated at the ground level. Of course,
systematic differences caused by rain evaporation, drop breakup, or drop
growth due to accretion towards the ground may affect such comparisons.
However, the changes in the Ze profile are very close to the ones predicted
by attenuation and constant DSD from three light rainfall cases. The
reflectivity distributions from PARSIVEL and the Ka band
(Fig. ) of those periods are very similar but differ by
approximately 3.6 dB, with the Ka band having the lower reflectivities. For these
comparisons, periods before and after the TRIPEx campaign had to be used because PARSIVEL had a hardware failure during the campaign. The similarity
of the results gives us an indication that this method is reliable; however,
a large number of cases are still needed in other to draw a final conclusion
on this method. Unfortunately, only the Ka band was available because the
other two radars did not measure during the selected rainfall events.
Histograms of radar reflectivities from the Ka band (gray) and results
from T-matrix calculations with the raindrop size distribution provided by
PARSIVEL (red) for three long-lasting stratiform rain cases before and after
the TRIPEx campaign (a 16 August 2015, b 27 August 2015,
c 11 August 2016). Ka band reflectivities are taken from the lowest
clutter-free range gates between 500 and 600 m. The vertical dashed line
indicates the median of the distribution; the offset is calculated as the
difference between Ka band and T-matrix results.
Correction for atmospheric gas attenuation
Hydrometeors and atmospheric gases cause considerable attenuation at cloud
radar frequencies. The reflectivities from the X, Ka, and W band are corrected
for estimated attenuation due to atmospheric gases
(Fig. ) by means of the Passive and Active Microwave
TRAnsfer model (PAMTRA) . PAMTRA calculates specific
attenuation due to molecular nitrogen, oxygen, and water vapor based on the
gas absorption model from
. Input parameters are
the vertical profiles of atmospheric temperature, pressure, and humidity
provided by the CloudNet products , which are
generated operationally at the JOYCE-CF site. The two-way path-integrated
attenuation (PIA) at the radar range gates is derived from the specific
attenuation integrated along the vertical. Table lists the
minimum and maximum two-way attenuation values at ≈12 km (height of
the maximum range gate in Level 2 data) for the three radars during the
entire campaign. The highest attenuation of ≈2.6 dB occurs at
94 GHz and is mainly caused by water vapor. Conversely, the 9.4 GHz
maximum attenuation of ≈0.1 dB is the lowest among the three radars,
and it is mainly produced by oxygen continuum absorption. At 35.5 GHz,
attenuation is governed by both oxygen and water vapor. The maximum
attenuation value found at this frequency is ≈0.7 dB.
Calculated minimum and maximum two-way path-integrated attenuation
(PIA) at a height of ≈12 km for the X, Ka, and W band during
TRIPEx.
FrequencyMinimum attenuationMaximum attenuation(GHz)(dB)(dB)9.40.0770.10435.50.3650.728940.6502.675DWR calibration and generation of quality flags
Spurious multifrequency signals can arise from attenuation effects due to
particulate atmospheric components (e.g., liquid water, melting layer, and snow)
but also from instrument-specific effects such as a wet radome, snow on the
antenna, and remaining relative offsets due to radar miscalibration. With
this processing step, the reflectivity measurements are adjusted in order to
take into account the cumulative effects of the aforementioned bias
mechanisms at the top of the clouds. By doing so, the effects of the cloud
microphysical processes on the DWR signals are recovered.
The Ka band is used as a reference because of its better sensitivity level and
larger dynamic range compared to the other radars (up to high altitudes) and
its lower signal attenuation compared to the W band. Moreover, the Ka band is the
only system not equipped with a radome which might collect raindrops on its
surface and cause additional attenuation. The signal attenuation due to
antenna wetness on the Ka band is expected to be lower compared to other radars'
radome attenuation because of the periodic antenna tilts during RHI scans
(every 30 min). The processing is complemented by the generation of
quality flags categorized as errors and warnings. Error flags mark data of
poor quality based on the applied correction procedure, while warnings
indicate the detection of potential sources of DWR offsets that have not been
accounted for in the procedures described below. An additional error flag is
raised if spurious multifrequency signals due to radar volume mismatch are
suspected. A list of all the quality flags (both errors and warnings) is
provided in Table .
The small ice particles in the upper parts of clouds are mostly Rayleigh
scatterers ; thus, their reflectivities should
not be frequency-dependent . The reflectivity range, at
which the Rayleigh approximation can be assumed, is estimated by
investigating the behavior of the observed DWRs as a function of
ZeKa. Within the Rayleigh regime, the measured DWRs are expected
to remain constant at a value that accounts for all the integrated
differential attenuation and radar miscalibration effects. As the ice
particles grow larger, the DWRs start to deviate from that constant value, and
this deviation affects the higher-frequency radars first. Because of that,
the Rayleigh data have been isolated by means of two different reflectivity
thresholds for X and W band radars. In addition, the sensitivity of the X band is
much lower; thus, a higher reflectivity threshold is accepted for the offset
estimate between the X and Ka band compared to the Ka and W band. For the
determination of the relative offset for the W band, we found an optimal range of
-30<ZeKa<-10 dBZ and -20<ZeKa<-5 dBZ for the X band. In order to safely exclude partially melted particles,
only reflectivities from at least 1 km above the 0 ∘C isotherm are
used.
The relative offset correction is estimated for each measuring time from the
data inside a moving time window of 15 min. The selected data are restricted
to the reflectivity pairs, which are within threshold values defined above.
The mean value of the DWR computed for these reflectivity pairs constitutes
the DWR offset. The quality of this offset estimation strongly depends on the
quality and quantity of the reflectivity data included in the average.
Empirical analysis showed that at least 300 data points spanning a wide
reflectivity range are required in order to have acceptable sampling errors.
The data that present smaller sampling statistics are marked with an error
flag.
Whenever cloud edges are included in the sampling volume, and/or when the
measured Ze is close to the sensitivity limits of the instruments, the
correlation between the reflectivities of two radars might strongly
deteriorate. In order to help the user identify these potential sources of
errors, the data profiles presenting a correlation lower than 0.7 are marked
with an additional error flag.
Despite the matching procedure of the different frequency radar volumes
(Sect. ), mismatches are unavoidable due to the horizontal
distances between the radars (Fig. ) and the different radar
range resolutions and beam widths (Table ). At cloud
edges and close to the melting layer, where the largest spatial cloud
inhomogeneities are expected, the effects of the remaining radar volume
mismatches will be maximized. The temporal DWR variability during 2 min
moving windows is used as an indicator for a potential volume mismatch; cloud
regions with variances above 2 dB2 are flagged accordingly.
Quality flags included in the data Level 2 product (bit coded in a
16-bit integer value). The flags indicate the reliability of the data and in
relation to the quality of the relative offset estimate for X-Ka and W–Ka
band reflectivities. Note that offsets are not calculated when the number of
reflectivity pairs is below 300.
Bits Criteria0–5Reserved for future warning flagsWarning6LWP >200 g m-27Rain detected by CloudNetErrors8–12Reserved for future error flags13Variance in time of DWR >2 dB214Correlation of data points is poor (<0.7)15Number of valid measurements <300
The described adjustment technique accounts for all processes that affect
relative offsets of the radars in the upper and frozen part of clouds. These
processes include possible frequency-dependent attenuation effects from lower
levels, radar miscalibration, and radome and antenna attenuation. Since the
estimated correction is applied to the entire profile, inevitably
overcompensations might occur in the lower, possibly rainy parts of clouds.
This limitation is necessary in order to increase the quality of the data in
the ice part of the clouds, which is the main focus area of the presented
study.
The lack of information about vertical hydrometeor distribution prevents
reliable reflectivity corrections by differential attenuation. As a
consequence of the presented DWR calibration and the fact that hydrometeor
attenuation is hitting the higher frequencies more, the computed DWRs are
expected to be increasingly underestimated towards the ground. A refined
correction should be applied for rain and melting layer studies. Possible
sources of information about the amount and position of supercooled liquid
water could be collocated lidar or analysis of radar Doppler spectra
measurements. Those data are available at JOYCE-CF, but they are not included
in the current dataset. However, an additional warning flag indicates periods
with large liquid water paths derived from the collocated microwave
radiometer. Lastly, the occurrence of rainfall and/or a melting layer from
the CloudNet classification and indicated by the precipitation gauge is
marked with an additional warning flag (Table ).
Overview of the dataset
The Level 2 of the TRIPEx dataset contains radar moments, polarimetric
variables, integrated attenuation, and atmospheric state variables. The
polarimetric variables are included as they are provided by the radar
software, and no additional processing or quality check is applied to them.
Zdr, ϕdp, and ρhv
from the X band might be a useful additional source of information for melting
layer studies . We are not confident about the
quality of Kdp provided by the X band software, and
therefore, this variable is not included in the dataset but can be calculated
by the user. Table lists all variables available in Level 2.
Variables available in the TRIPEx dataset
Level 2.
Radar variablesX bandKa bandW bandReflectivity (dBZ)xxxMean Doppler velocity (m s-1)xxxSpectral width (m s-1)xxxDifferential reflectivity (dB)x––Differential propagation phase shift (∘)x––Co-polar correlation coefficientx––Linear depolarization ratio (dB)–x–Two-way path-integrated attenuation (dB)xxxAtmospheric variablesCloudNet Air temperature (∘C)x Air pressure (Pa)x Relative humidity (%)x
The dataset contains 47 days of measurements. For each day,
Table lists the atmospheric conditions such as
temperature at 2 m (T2m), rain rate (RR),
accumulated rain (AR), liquid water path (LWP), and integrated water vapor
(IWV). The duration of four empirically classified predominant types of cloud
and precipitation is provided for each day
(Table ). The
two most frequent cloud types are ice clouds (IC) with 377 h and shallow
mixed-phase clouds with 222 h of observations. Stratiform rainfall (SR)
occurred during 137 h, while rain showers (SR) were only observed during
47 h. The average rain rate (RR) for all rainy periods over the whole period
(mean rain intensity) is 0.078 mm h-1, with a maximum instantaneous RR
of 8.07 mm h-1. DWR signatures and radar Doppler information suggest
that the ice part of clouds is dominated by depositional growth and
aggregation. Riming only seems to occur during a few short events. Although
the dataset spans the main winter season, no snowfall was recorded at the
surface. In the following, we will demonstrate the effect of applying data
quality flags and discuss remaining limitations as well as the effects of the
different radar sensitivities.
Characterization of the atmospheric conditions and estimated
duration of cloud and precipitation events during TRIPEx. T2m
is the air temperature at 2 m from a nearby weather station. RR and AR are
the rain rate and the accumulated rain measured by a Pluvio disdrometer; mean
RR is calculated using all RR values larger than 0 mm h-1. Liquid
water path (LWP) and integrated water vapor (IWV) are derived from the
collocated 14-channel microwave radiometer; mean LWP is calculated using all
LWP values larger than 0.03 kg m-1 in order to exclude clear-sky
periods. The columns with IC, SR, RS, and MP indicate the approximate
duration in hours of non-precipitating ice clouds, stratiform rain, rain
showers, and shallow mixed-phase clouds, respectively.
DateT2m (∘C)RR (mm h-1)ARLWP (kg m-2)IWV (kg m-2)ICSRRSMP(yyyy.mm.dd)max/minmax/mean(mm)max/meanmax/mean(h)(h)(h)(h)2015.11.1112.85/11.130.00/0.000.000.42/0.1025.76/17.50900242015.11.1212.81/10.250.00/0.000.000.29/0.0720.58/17.341800182015.11.1313.89/7.520.66/0.270.591.61/0.1523.72/15.82130862015.11.1410.86/6.460.33/0.120.790.38/0.1019.34/12.231210002015.11.1515.99/10.150.15/0.050.080.63/0.1128.27/20.871100212015.11.1613.74/11.452.16/0.402.162.64/0.1528.65/18.99443122015.11.1715.83/11.945.97/0.828.311.68/0.1629.39/19.231001002015.11.1814.60/11.418.07/1.884.401.65/0.1327.71/15.02600142015.11.1911.76/8.415.64/1.1612.821.70/0.2023.51/17.221312202015.11.209.45/4.871.08/0.271.020.98/0.1319.02/13.63103062015.11.215.66/2.170.30/0.110.231.38/0.1215.38/8.82040762015.11.225.33/-0.097.35/3.802.540.84/0.0711.11/8.1740522015.11.235.32/-0.420.00/0.000.000.52/0.089.81/7.8370022015.11.244.51/0.191.26/0.281.300.53/0.1716.71/12.571012002015.12.0311.90/6.630.00/0.000.000.03/0.0315.38/13.59100052015.12.0411.39/5.872.67/0.563.380.57/0.2124.09/10.9844022015.12.0510.20/4.470.00/0.000.00–9.77/7.19160002015.12.0612.86/3.340.00/0.000.000.39/0.1124.14/15.63200122015.12.0714.53/8.740.03/0.030.000.51/0.1324.31/18.8190482015.12.0814.66/7.922.67/0.844.060.84/0.1823.01/14.6725002015.12.099.34/2.200.06/0.030.040.48/0.0818.89/8.9604012015.12.108.81/0.770.00/0.000.00–11.86/6.4970002015.12.118.61/4.772.16/0.579.340.41/0.1719.81/16.18220002015.12.1210.42/4.70.03/0.030.020.36/0.0921.10/15.73160002015.12.1310.08/6.183.09/0.375.501.07/0.3822.73/19.1070082015.12.149.24/3.360.03/0.030.020.17/0.0816.00/12.9560002015.12.1510.3/3.890.39/0.160.160.57/0.1523.55/17.51122302015.12.1613.04/8.902.49/0.396.02–—010072015.12.1716.28/12.533.60/0.480.721.12/0.1525.61/20.0180062015.12.1813.11/8.740.27/0.170.080.71/0.1226.64/16.45100122015.12.1913.21/9.930.00/0.000.000.27/0.0925.11/22.7080002015.12.2013.22/11.310.00/0.000.000.44/0.1023.15/20.99221002015.12.2112.17/9.520.72/0.180.450.84/0.1323.52/14.4933162015.12.2214.75/10.412.19/0.411.450.61/0.0826.53/22.00162082015.12.2313.00/4.380.45/0.210.420.23/0.0714.21/11.2440082015.12.2414.51/4.385.34/0.681.821.14/0.1122.91/15.4060132015.12.2513.35/7.783.27/0.814.720.60/0.1324.76/18.32158042015.12.2615.78/7.170.00/0.000.000.20/0.0822.51/17.5540042015.12.2714.40/6.130.00/0.000.00–18.71/14.20120002015.12.2811.07/5.120.00/0.000.00–9.56/8.57110002015.12.2911.87/4.350.00/0.000.000.34/0.0819.78/13.8023002015.12.309.40/3.770.00/0.000.000.05/0.0417.80/10.9330002015.12.3110.31/3.530.69/0.200.471.01/0.2224.39/11.8243202016.01.018.45/3.460.30/0.130.100.83/0.1315.42/9.85130062016.01.025.94/4.112.88/0.724.690.42/0.1417.80/12.8967082016.01.038.29/4.841.86/0.442.950.93/0.2319.85/14.45614042016.01.047.74/3.663.57/0.817.06––01009Total37713747222Effects of data filtering based on quality flags
The effects of data filtering on DWRXKa and DWRKaW are
demonstrated for clouds observed on 20 November 2015 in
Figs. and . In order to give a
better visual impression of these effects, the filtering steps are applied
sequentially and cumulatively. Figure a–c show the
unfiltered Level 2 data. The time–height plots (Fig. a
and b) reveal a stratiform cloud passing over the site from 01:00 to
17:00 UTC, followed by a series of low-level, shallow, most likely
mixed-phase clouds. The short periodic gaps result from interruptions of
zenith observations caused by range-height indicator (RHI) scans of the Ka band,
and the large gap in DWRKaW between 09:00 and 10:00 UTC is
caused from missing W band observations. The -15∘C isotherm
(dashed line in the time–height plots) separates DWRs around 0 dB for
temperatures below -15∘C from rapid increases with reflectivity
for higher temperatures.
Figure 4c displays a scatter density plot of DWRXKa versus
DWRKaW (hereafter called the triple-frequency plot). The position in
the triple-frequency plot is mainly driven by the respective hydrometeors'
bulk density ρ and their mean volume diameter D0. This plot allows discrimination between the two
processes: rimed particles follow the flat curve (low DWRXKa) due
to their higher density, while aggregated particles give rise to a bending-up
signature (increase in DWRXKa, while DWRKaW saturates
or even decreases) due to their lower density, which is nicely shown in
Fig. c.
Time–height plots of DWRKaW(a) and
DWRXKa(b) using the Level 2 data of 20 November 2015 without
applying any filtering. The continuous line and dashed line are the 0 and
-15∘C isotherms (provided by the CloudNet products),
respectively. The triple-frequency signatures for the ice part of the clouds
are shown in (c). Panels (d–f) show the remaining data
after applying the offset quality flags and the restriction to data pairs
with sufficient correlation. N in (c, f) indicates the respective
number of data pairs in the ice part of the clouds. Note the log scale on the
color bars in (c, f).
A large number of points in Fig. c populate areas
which are unrealistic from a microphysical point, such as negative DWRs. Some
of those originate from time periods when the offset cannot be calculated
properly or when the correlation between the three radars is poor.
Figure d and e show the results after removing those
points (bits 14 and 15 in the quality flag; see Table ),
an effect best visible between 17:00 and 20:00 for DWRKaW and
between 17:00 and 23:00 for DWRXKa. The triple-frequency plot
(Fig. f) shows a strong reduction of outliers when
compared to the unfiltered triple-frequency plot
(Fig. c).
Same as Fig. , but here the effects of
cumulative data filtering subject to different quality flags and averaging are
illustrated. Panels (a–c) display the effect of filtering based on
the DWR variance in time, which removes areas potentially affected by poor
radar volume matching. The effect of the additional temporal averaging over
3 min is shown in (d–f). The effects of the removal of time periods
with rain as identified by CloudNet or large liquid water paths measured by
the nearby microwave radiometer are displayed in (g–i). Note the
log scale on the color bars in (c, f, i).
Despite the data filtering described in the previous paragraph, the scatter
around the main signature is still large. Figure a and b
show the time–height plots after removing observations flagged with the DWR
2 min temporal variance flag (bit 13 in the quality flag; see
Table ). This filtering step removes most of the outliers
from the aggregation signature in the triple-frequency plot
(Fig. c). It is worth noting that the removal of such data
reduces the scatter in the triple-frequency space but might also remove
interesting measurements from regions with strong reflectivity gradients. Additional 3 min running-window averaging of the reflectivities keeps the
most stable signatures (Fig. d and e), further removes
scatter, and thus accentuates the aggregation signature in triple-frequency
plot (Fig. f). The averaged reflectivities calculated in
this procedure are not included in the TRIPEx dataset because it would not
be possible to retrieve the original data. The last two quality flags (bits 7
and 6; see Table ) mark data acquired during rainfall
according to the CloudNet product and times with total liquid water path
larger than 200 g m-2 as estimated by the microwave radiometer. The
latter filtering significantly reduces the amount of usable data
(Fig. g and h) but preserves the main aggregation
signature surprisingly well (Fig. i).
Limitations of the current dataset
Despite the filtering steps discussed in Sect. , some
limitations remain. As an example, on 23 November 2015 between 16:00 and
23:00 UTC we observe enhanced values of ZeX (-20 up to
10 dBZ) (Fig. a), while ZeKa and
ZeW remain very low. The mean Doppler velocity of that structure
is very small (MDV between 0 and 0.5 m s-1) and is associated with
a strongly enhanced LDR from the Ka band (Fig. b). Large
Zdr values are observed by the nearby weather polarimetric X band
radars JuXPol and BoXPol (see , for a detailed
characterization of the radars) that were performing RHI scans over the
TRIPEx site at that time. The most likely explanation based on the
polarimetric signature and the fall velocity is fall streaks of chaff
deployed by military aircraft during a training session. We recommend avoiding this period in cloud microphysical studies.
Time–height plots of the ZeX and LDRKa of
23 November 2015 between 16:00 and 23:59. The region where the LDR is
≈-5 dB is most probably the result of chaff. The Ka band software
applies a filtering for non-meteorological targets which removes most of the
chaff; only the filtered Ka band data are included in the TRIPEx dataset.
Note that no such filtering is applied to the X band and W band
data.
Time–height plots of the dual mean Doppler velocity using the
Level 2 data of 20 November 2015. The dashed line and the continuous line are
the -15 and 0 ∘C isotherms, respectively. Panel (a) shows
the DDVXKa using the original data from Level 2.
Panel (b) shows the DDVXKa after applying a 3 min
moving average.
As described in Sect. , the X band was operated vertically
pointing while rotating the antenna. Figure illustrates
effects related to imperfect vertical antenna pointing. When looking at
the differences between vertical Doppler velocities observed from low-frequency
and high-frequency radars (dual Doppler velocity, DDV), increases are
expected in the presence of large scatterers .
Large particles, which usually also have greater terminal velocities, give a
lower reflectivity signal at high frequencies due to non-Rayleigh scattering.
This effect also leads to a lower MDV (MDVX>MDVKa>MDVW). Since the ice particles in
the uppermost part of the clouds are expected to be Rayleigh scatterers, the
DDV should be zero. However, DDVXKa (Fig. a)
shows a periodic variation along the entire vertical range, with the period
matching the X band scan duration of 3 min. Obviously, a non-perfect zenith
pointing of the X band antenna introduces these periodic shifts in the mean
Doppler velocity due to the contamination of the vertical Doppler signal by
the horizontal wind component. A temporal average over 3 min minimizes the
standard deviation of DDVXKa relative to other averaging window
sizes (Fig. b). Note that the averaged data are not included
in the Level 2 data product because the optimal averaging window might
depend on the prevailing atmospheric, height-dependent wind conditions, and
original data cannot be recovered after averaging. We can also not completely
rule out a slight mispointing of the other two radars because their DDVs
sometimes show deviations, especially in regions with strong horizontal winds
with maximum DDVs. However, these DDVs are found to be below
0.4 m s-1. An ad hoc estimate of the related relative radar
mispointing of the two radars using the horizontal wind information from
radiosondes for a few extreme cases suggests a potential mismatching of
0.5∘. A correction of the shift requires reliable horizontal wind
profiles, which will be investigated in more detail in the future.
Histograms of reflectivities from the entire TRIPEx campaign Level 2
data for each radar. The red curve is the profile of the minimum retrieved
reflectivity (Eq. ). Panels (a), (b),
and (c) show the histograms for the X, Ka, and W band, respectively; all
error flags (see Table ) were applied to filter the data.
Note the log scale on the color bars.
Radar sensitivity
Figure shows the distribution of reflectivity values
measured by the three radars during the entire campaign filtered with the
error flags (bits 13, 14, and 15 in Table ) and stratified
by height above the site. As already mentioned, the Ka band and W band show
higher sensitivities compared to the X band up to high altitudes. The Ka band
(Fig. b) exhibits the largest dynamic range (Fig. a and c). The step-like
shape of the lowest altitude reflectivities from the W band is caused by
different chirp settings (Table ). A polynomial fit to the
minimum retrieved linear reflectivities (Zelin, in units of
mm6 m-3) as a function of altitude z (units of m),
Zelin(z)=a⋅zb,
results for the X and Ka band in the expected nearly quadratic decrease with
range (Table ). The slower decrease (smaller exponent) for
the W band results from the altitude-dependent sensitivity associated with the
height-varying chirp settings.
The melting layer was mostly observed at altitudes between 1 and 2 km, where
it causes a sharper increase in the reflectivity distribution and the largest
values measured for the X band reflectivities. The X band Ze distribution
shows an enhancement of the largest recorded values at 2 km from ≈30 to ≈40 dBZ. The X band sensitivity limitations did not allow signals above 7 km with reflectivities below -10 dBZ to be observed; however,
dual-wavelength studies of clouds in this region are still possible with the
W band and Ka band included in the Level 2 data. Nonetheless, ice aggregation
and riming, which are most relevant for triple-frequency studies, usually
occur at lower levels and larger reflectivities where all three radars
provide sufficient sensitivity.
Coefficients a and b for the sensitivity fit (Eq. )
obtained for the X, Ka, and W band. The coefficients were calculated using
the Level 2 dataset with filtering according to the error flags applied (see
Table ).
RadarabX band6.25×10-102.19Ka band3.41×10-122.04W band8.36×10-101.53Triple-frequency characteristics of ice and snow clouds
Longer time series of observations are required in order to reliably estimate
the occurrence probabilities of process signatures in the triple-frequency
space. Those statistics might be useful for the development of microphysical
retrievals and to constrain snow particle scattering models. Currently
available datasets are restricted to short time periods or specific cases.
and used observations from airborne
Ku, Ka, and W band radars data collected during the Wakasa Bay campaign
to evaluate aggregate and spheroidal snowflake models. Their
DWRKaW and DWRKuKa values reach up to 10 and 8 dB,
respectively. Although their data are rather noisy due to volume mismatch and
attenuation effects, they were the first observations which confirmed
triple-frequency signatures predicted by complex aggregate scattering models
. The first triple-frequency signatures from ground-based
radars (S, Ka, and W band) were presented by for two case
studies. Similar to the Wakasa Bay studies, they found deviation from
predictions based on simpler spheroidal-based scattering models, but their
aggregates showed a DWRKaW saturation around 8 dB and not the
“hook” or “bending back” feature found in the previous studies. They
attributed this behavior to a snow aggregate fractal dimension of 2.
combined triple-frequency ground-based radar (X, Ka and W
band) with in situ observations and analyzed three cases characterized by
falling snow particles with different degrees of riming. For low-density
aggregates, their DWRKaW also did not exceed the 8 dB limit
reported by previous studies but exhibited a strong bending back feature
(i.e., reduction of DWRKaW for larger particles) with large
DWRXKa up to 15 dB. During riming periods, the triple-frequency
signatures showed a distinctly different behavior: DWRKaW
increases up to 10 dB, while DWRXKa remains constant or slowly
increases up to 3 dB, which appears in triple-frequency plots as an almost
horizontal line.
Two-dimensional histograms (contoured frequency by altitude diagram,
CFAD; see , for more details) of DWR against air
temperature for the entire TRIPEx dataset. The dashed line indicates the
0 ∘C isotherm. The data below the dashed line are only collected
from the cases in which a melting layer is observed. The DWRs were filtered
using the error flags and averaged in time using a 3 min moving window.
Panels (a) and (b) show DWRKaW and
DWRXKa, respectively. Note the log scale of the
color bars.
The TRIPEx dataset is, to the best of our knowledge, one of the longest,
quality-controlled triple-frequency datasets currently available, which
allows for reliable estimations of the occurrence of several triple-frequency
signatures in midlatitude winter clouds. In the following sections, we use
the Level 2 data filtered only with the error quality flag (see
Table ) to analyze the temperature dependence of the
triple-frequency signatures and signatures of riming and melting snow
particles. The extension of the filtering to the warning flags would remove
all melting layer cases and/or observations with larger amounts of
supercooled liquid water, which portray particularly interesting signatures
of partially melted or rimed particles.
Temperature dependence of triple-frequency signatures
The relatively large dataset allows us to stratify the occurrence probability
of DWRKaW (Fig. a) and DWRXKa
(Fig. b) according to air temperature, which results in four
main regimes. The regime in which the temperature is smaller than
-20∘C exhibits small DWR values, mostly below 3 dB.
Between -20 and -10∘C, we find a widening of the distribution
to higher values in both DWRs. This DWR increase becomes very rapid at
temperatures warmer than -15∘C, which suggests an increasing
number of larger aggregates caused by stronger aggregation due to
preferential growth of dendritic particles in the -20 to -10∘C
temperature range . Dendrites are well
known to favor snow aggregation due to their branched crystal structure. In
accordance with previous studies, DWRKaW saturates around 7 dB
at -10∘C, with only a small fraction reaching up to 10 dB.
DWRXKa approaches maximum values of 5 to 8 dB; however, the
occurrence probability of enhanced DWRXKa is smaller compared to
those found for DWRKaW. This is an expected behavior since early
aggregation is likely to first enhance the DWRKaW because
particle growth affects the high frequencies early which first transition
out of the Rayleigh regime. Thus the W band radar is the first influenced by this
transition which enhances DWRKaW.
At temperatures between -10 and 0 ∘C, the distribution of
DWRKaW remains almost constant, with the exception of a small peak
with higher values around -5∘C and a widening of the DWR
distributions towards negative values. The latter effect might relate to two
causes. The first is the DWR calibration (Sect. ), derived
for the upper part of the clouds (ice part), which, when applied to the
entire profile, leads to the overestimation of ZeW. The second
possible contributor is the radar volume mismatch, which becomes worse for
observations closer to the radars due to reduced overlap of the radar beams.
Interestingly, DWRXKa grows continuously up to 12 dB for
temperatures warmer than -5∘C, which is in line with intensified
aggregation of the snow particles towards lower heights. The very large
DWRXKa in this regime can be explained by increasing particle
stickiness when approaching the 0 ∘C level. In the fourth regime
between 0 ∘C and the LDR maximum, DWRKaW tends to
further increase, while DWRXKa remains constant or even decreases.
DWRKaW reaches values up to 10 dB, while DWRXKa
attains values up to 15 dB, which could be produced by persistent
aggregation.
Two-dimensional histogram of the triple-frequency signatures for
different temperature regions normalized by the total number of points N.
The color shows the relative frequency. Panel (a) is for
temperatures between -20 and -10∘C. Panel (b) shows
the region between -10 and -1∘C. Note the log scale on the
color bars.
Figure shows the triple-frequency plots for the
temperature ranges -20<T<-10∘C (panel a) and -10<T<-1∘C (panel b). Between -20 and -10∘C (panel a), we
find the typical bending signature in the triple-frequency space saturating
at about a DWRKaW of 8 dB, similar to . This
temperature regime includes the dendritic growth zone (DGZ), which is usually
defined by cloud chamber experiments in the range of temperatures -17 to
-12∘C . It is
worth reminding the reader that the temperature information included in the TRIPEx
dataset has not been obtained from a direct measurement, but it has been
taken from CloudNet. Consequently, it is not surprising that the growth
regimes that we have identified using the signatures observed in the DWR
profiles do not perfectly correspond in temperature to the ones determined in
cloud chamber experiments.
Although we combine observations from different clouds, the variability of
the triple-frequency signatures is relatively small. For warmer temperatures
(-10 to -1∘C, panel b), needle aggregates are likely to be
generated, and ice particles start to become more sticky, leading to a more
pronounced bending feature. For DWRXKa reaching up to 12 dB,
the hook (or bending back) signature also becomes visible
for parts of the dataset (DWRXKa decreases, while
DWRKaW is still increasing). This panel also reveals a secondary
mode with DWRXKa below 3 dB and DWRKaW reaching up
to 12 dB. Following , this mode could hint at rimed
particles, which are still too small to enhance DWRXKa, but due
to their increased density and hence larger refractive index, the
DWRKaW increases. We will investigate this feature in more detail
in the next subsection.
Two-dimensional histogram of the triple-frequency signatures for the
region between 0 ∘C and the LDR maximum in the melting layer
normalized by the total number of points N. The color shows the relative
frequency, and the binning matches what was used for
Fig. . Note the log scale on the
color bar.
Triple-frequency signatures for Level 2 data with temperatures
between -20 and -1∘C and a mean Doppler velocity (MDV) above
1.5 m s-1 in order to select potentially rimed particles.
Panel (a) shows the relative frequency of the observations.
Panel (b) indicates the average MDV of each pixel in the histogram.
Note the log scale on the color bar in (a).
The dataset contains particularly large DWR signatures close to 0 ∘C
and at higher temperatures, which are probably caused by melting snowflakes
or simply by enhanced aggregation. To further investigate this signature we
generated the triple-frequency plot for the data between the 0 ∘C
and the height of the LDR maximum (Fig. ), which we
consider to be a proxy for the center of the melting layer . In this
region, DWRXKa reaches maximum values up to 20 dB already at low
DWRKaW. Overall, the data points are much more scattered than
those in the colder temperature regions. This larger variability might result
from effects of the radar volume mismatch caused by strong vertical gradients
near the melting layer. Another possible explanation is the much lower amount
of data. Latent heat release by melting increases turbulent motion, which
might further enhance the detrimental effects of volume mismatch. We need to
be careful in interpreting these features as triple-frequency signatures of
the melting layer because the temperature information is based on CloudNet
products taken from ECMWF analyses which cannot be expected to represent
small-scale variations of the 0 ∘C isotherm. Moreover, melting can
be delayed depending on the profiles of temperature and humidity and on the
density and size of the particles themselves
. A sagging of the melting layer has been
repeatedly observed with the scanning polarimetric X band radar in Bonn
(BoXPol, also part of JOYCE-CF) for dominant riming processes
. Rimed particles fall with higher terminal
velocities and consequently take more time to melt. In the following
subsection, we will use the LDR and the mean Doppler velocity to better separate
non-melted from melted snow particles.
Signatures of riming and melting snow particles
During riming, supercooled liquid water droplets freeze onto the ice
particles. This strongly increases the particle mass, while its size grows
more slowly, especially during the onset of riming. Since the terminal
velocity is mainly governed by the relation between particle mass
(gravitational force) and its cross section perpendicular to the air stream
(drag force), its terminal velocity observed by the mean Doppler velocity
(MDV) increases due to riming . MDVs above
1.5 m s-1 can be used as a simple indicator of rimed particles as long
as vertical air velocities are small . About 1 % of
triple-frequency data in the temperature range between -20 and
-1∘C have a MDV above 1.5 m s-1
(Fig. ). Interestingly, we find one mode very similar to a
sloped line found for rimed particles in , which coincides
with large MDVs up to 2.4 m s-1 and DWRKaW up to 10 dB.
However, the correlation between enhanced DWRKaW and MDV is less
clear than in the case shown in . A more detailed
investigation showed that TRIPEx only contains short riming periods of a few
minutes' duration, while the period analyzed by was
considerably longer (≈20 min). In general, DWRKaW is
expected to increase for larger particles and strong riming, but detailed
sensitivity studies which clearly characterize these dependencies are still
missing. Another mode in Fig. with larger
DWRXKa of about 3 dB suggests mean particle sizes exceeding
8 mm according to . We speculate that this mode might be
related to only slightly rimed aggregates. A larger number of riming events
are required to better investigate the sensitivities of MDV and
triple-frequency signatures to various degrees of riming, which would also be
a very valuable basis on which to constrain theoretical particle models, as developed,
for example, by .
A particularly interesting signature shown in Fig. is
the very large DWRXKa close to the melting layer. To the best of our
knowledge, these features have not yet been described. It is not clear to us
whether these signatures are caused by very large aggregates or melting
particles. A pure melting of snowflakes should enhance the MDV because of
their decrease in size (and thus cross-sectional area) as well as drag in the
airflow. Early melting can, however, be better detected by the LDR: the much
larger refractive index of liquid water compared to ice and the initially
still asymmetric melting snowflakes result in a much larger depolarization
signal as compared to dry snowflakes. Hence, we replot Fig. to better see the transition from dry snowflakes with
a typical MDV of 1 m s-1 and a LDR around -15 dB to larger MDV
coinciding with a rising LDR as expected for melted snow (Fig. ). Interestingly, the
very large DWRXKa mostly shows MDV and LDR values associated with
unmelted snowflakes. Once the MDV and LDR indicate the onset of melting, the
DWRs, especially DWRXKa, rapidly decrease. As DWRXKa
is strongly related to the mean particle size, the results indicate that the
largest snowflake sizes occur before the melting starts. Once snowflakes are
completely melted, DWRKaW will still be enhanced due to Mie
scattering by the raindrops, while DWRXKa will remain close to
0 dB . However, our corrections for attenuation within the
melting layer are certainly incomplete; thus we leave a deeper analysis of
that feature to future studies.
Triple-frequency diagrams of observations between 0 ∘C and the
LDR maximum in the melting layer (same as Fig. c), but
the color in (a) indicates the average MDV, while in (b)
the color shows the average LDR.
Data availability
The TRIPEx Level 2 data are available for download at the
ZENODO platform (10.5281/zenodo.1341389;
).
Quicklooks of the TRIPEx dataset are freely accessible via a data quicklook
browser
(http://gop.meteo.uni-koeln.de/~Hatpro/dataBrowser/dataBrowser1.html?site=TRIPEX&date=2015-11-20&UpperLeft=3radar_Ze).
The raw and Level 1 data and Kdp can be requested from the
corresponding author.
Conclusions
We present the first 2-month-long dataset of vertically
pointing triple-frequency Doppler radar (X, Ka, and W band) observations of
winter clouds at a midlatitude site (JOYCE-CF, Jülich, Germany). The
dataset includes spatiotemporal re-gridded data including offset and
attenuation corrections. Several quality flags allow the dataset
to be filtered according to the needs of the specific application. The quality flags have
been separated into error and warning flags; we recommend always applying the
error flags, while the warning flags might not be necessary depending on the
application. All corrections applied are stored separately in the data files
in order to allow the user to recover and also work with data at intermediate
processing steps and to potentially apply individual corrections. This might
be necessary because the campaign focus was on the ice and snow part of the
cloud. Consequently, the correction for path-integrated attenuation might be
inappropriate, for example, for studies investigating the melting layer or
rainfall.
The statistical analysis of the ice part of the clouds revealed dominant
triple-frequency signatures related to aggregation (hook or bending up
feature). In agreement with previous studies, DWRKaW mostly
saturates around 7 dB, while DWRXKa reaches values of up to
20 dB in regions of presumably intense aggregation close to the melting
layer. Due to the large dataset, we were able to investigate the relation
between the DWRs and temperature. The first significant increase of
aggregation starts around -15∘C, where dendritic crystals are
known to grow efficiently and favor aggregation. In this zone,
DWRKaW mostly increases up to its saturation value of 7 dB.
DWRXKa increases mainly below -10∘C. Close to the
melting layer, DWRXKa massively increases up to 20 dB, which has
not been reported so far. A deeper investigation using the LDR and MDV revealed
that these extreme DWRXKa are indeed due to large dry aggregates
rather than melting particles. Once melting is indicated by larger MDV and
LDR values, DWRXKa appears to rapidly decrease. Clearly, combined
observational and scattering modeling studies are needed to further
investigate this transition. Although the dataset only contains a few short
riming periods (approximately 1 % of the data between -20 and
-1∘C), a simple MDV threshold reveals the typical riming
signature (flat horizontal line in the triple-frequency space) reported for
riming case studies in . The statistical analysis of
riming is more challenging compared to aggregation. Riming is often connected
to larger amounts of supercooled liquid water, larger vertical air motions,
and turbulence, which deteriorate the signal due to liquid water attenuation
and enhance effects of imperfect radar volume matching. Riming could be
further investigated with this dataset by focusing on single cases, for which
it is possible to apply specific corrections and filtering.
The synergy with nearby polarimetric weather radar observations will be
investigated in future studies by including the vertical polarimetric
profiles matching the JOYCE-CF site based on quasi-vertical profiles (QVPs)
or columnar vertical profiles (CVPs)
. Also a data release including the W and Ka
band Radar Doppler spectra is planned.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-11-845-2019-supplement.
Author contributions
JDN wrote the paper, developed the data processing, and analyzed the data. SK, ST, JH, and CS designed the experiment. SK also supervised the analysis and writing of the paper. DO helped in developing data processing, radar calibration, and analysis. ST, JH, BB, SK, NH, KM, ML, and CS carried out the various radar measurements, set up specific measurement modes, and contributed to the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors acknowledge the funding provided by the German Research
Foundation (DFG) under grant KN 1112/2-1 as part of the Emmy-Noether Group
OPTIMIce. José Dias Neto also
acknowledges support by the Graduate School of Geosciences of the University
of Cologne. We thank the departments S, G and IEK-7 for the technical and
administrative support during the field experiment. The majority of data for
this dataset were obtained at the JOYCE Core Facility (JOYCE-CF) co-funded by
the DFG under DFG research grant LO 901/7-1. Major instrumentation at the
JOYCE site was funded by the Transregional Collaborative Research Center TR32
funded by the DFG and JuXPol by the TERENO (Terrestrial
Environmental Observatories) program of the Helmholtz Association
. For this work, we used products obtained within the
CloudNet project (part of the EU H2020 project ACTRIS, European Research
Infrastructure for the observation of Aerosol, Clouds, and Trace Gases) and
developed during the High Definition Clouds and Precipitation for advancing
Climate Prediction HD(CP)2 project funded by the German Ministry for
Education and Research under grants 01LK1209B and 01LK1502E.
Review statement
This paper was edited by Giulio G. R. Iovine and reviewed by
five anonymous referees.
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