Shallow oceanic precipitation variability is documented using three second-generation radar systems located at the Atmospheric Radiation Measurement (ARM)
Eastern North Atlantic observatory: ARM zenith radar (KAZR2),
the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM
precipitation radar (XSAPR2). First, the radar systems and measurement
post-processing techniques, including sea-clutter removal and calibration
against colocated disdrometer and Global Precipitation Mission (GPM)
observations are described. Then, we present how a combination of profiling
radar and lidar observations can be used to estimate adaptive (in both time
and height) parameters that relate radar reflectivity (Z) to precipitation
rate (R) in the form Z=αRβ, which we use to estimate
precipitation rate over the domain observed by XSAPR2. Furthermore, constant
altitude plan position indicator (CAPPI) gridded XSAPR2 precipitation rate
maps are also constructed.
Hourly precipitation rate statistics estimated from the three radar systems differ because KAZR2 is more sensitive to shallow virga and XSAPR2
suffers from less attenuation than KaSACR2 and as such is best suited for
characterizing intermittent and mesoscale-organized precipitation. Further
analysis reveals that precipitation rate statistics obtained by averaging
12 h of KAZR2 observations can be used to approximate that of a 40 km radius domain averaged over similar time periods. However, it was
determined that KAZR2 is unsuitable for characterizing domain-averaged
precipitation rate over shorter periods. But even more fundamentally, these
results suggest that these observations cannot produce an objective domain
precipitation estimate and that the simultaneous use of forward simulators
is desirable to guide model evaluation studies.
Introduction
Characterizing shallow oceanic precipitation is very important for improving
our understanding of shallow cloud systems since precipitation is related to
a number of cloud processes, all of which may affect cloud properties. For
example, precipitation leads to a reduction in the droplet number via the
collision–coalescence process and of the liquid water path through
sedimentation. Furthermore, a number of modeling studies have suggested that
drizzle organization, intensity and subcloud layer evaporation could play a
role in organizing stratocumulus cloud decks on the mesoscale (Zhou et
al., 2017, 2018; Savic-Jovcic and Stevens, 2008; Wang and Feingold, 2009;
Yamaguchi and Feingold, 2015). Ultimately, these controls
may alter low-cloud radiative properties and climate (Wood, 2012).
Quantification of marine drizzle cell
precipitation rate and environmental conditions over a domain of several kilometers could provide additional
observational constraints for modeling studies. Unfortunately, collecting such
observations remains challenging over the ocean.
Although satellite-based microwave sensors can infer the spatial
distribution of liquid water path (Wood and Hartmann, 2006; Miller and
Yuter, 2013) and precipitation rate (Ellis et al., 2009; Adler et al.,
2009; Rapp et al., 2013), they have poor horizontal resolution and suffer
from surface inference, causing them to under-sample the cloud field
variability and to underreport boundary layer cloud and precipitation
occurrence (Schumacher and Houze, 2000; Rapp et al., 2013). In
contrast, airborne (Stevens et al., 2005; Wood et al., 2011; Moyer and
Young, 1994; Vali et al., 1998; Paluch and Lenschow, 1991; Sharon et al.,
2006) and ship-based (Yuter et al., 2000; Comstock et al., 2005; Feingold
et al., 2010) sensors can resolve the spatial and temporal variability of the
cloud and precipitation field, but field campaigns deploying such sensors
are often expensive to conduct and limited in temporal duration (Stevens
et al., 2003; Bretherton et al., 2004; Rauber et al., 2007). Island-based
observatories, such as the U.S. Department of Energy (DOE) Atmospheric
Radiation Measurement (ARM) Eastern North Atlantic observatory (ENA,
Mather et al., 2016; Kollias et al., 2016) and the Barbados Cloud
Observatory (BCO, Lamer et al., 2015; Stevens et al., 2016), operating
profiling and scanning remote sensors can provide long-term statistics of
marine light precipitation.
Beyond detecting rain, quantifying the spectrum from drizzle to rain is especially challenging since at small rates droplets
are mostly spherical and as such do not generate the typical
polarimetric signals required of common precipitation rate retrievals (e.g.,
Villarini and Krajewski, 2010; Gorgucci et al., 2000). As an alternative
to polarimetric signatures, a combination of sensors is typically required
to retrieve precipitation rate (R). Combinations of radar reflectivity (Z) and
in situ measurements have led to the development of Z–R relationships
(Wood, 2005; Comstock et al., 2004; VanZanten et al., 2005; Vali et al.,
1998); however, these tend not to be universally applicable since they are
based on assumptions about the drizzle particle size distribution, which may
vary with factors such as aerosol loading and liquid water path. Moreover,
relying on surface disdrometer measurements to characterize warm
precipitation may be especially unsuitable at the ENA where (i) a large
fraction of the precipitation does not reach the surface (Yang et al.,
2018), (ii) precipitation reaching the ground typically does so with an
intensity below the detection limit of most optical-based disdrometers
(∼10-2 mm h-1) and (iii) evaporation is an active
process such that water drop size distribution information retrieved at one
height may not be appropriate to represent the entire atmospheric column.
Alternatively, a method combining radar reflectivity and lidar backscatter
measurements has been proposed to retrieve R with fewer assumptions about the
drizzle particle size distribution (Intrieri et al., 1993; O'Connor
et al., 2005). Because of the current rarity of scanning lidar observations,
this technique has only been used to retrieve R in the column and cannot be
used to address concerns present in recent studies suggesting that
scanning systems are essential to map domain properties (Oue et al.,
2016).
Here we propose exploiting the availability of colocated
vertically pointing radar and lidar, as well as scanning radar systems, to
characterize marine precipitation rate variability over a domain of 40–60 km
around the ENA observatory. The eastern North Atlantic region, with its
abundance of marine boundary layer precipitating clouds, is an ideal
location for such study (Rémillard and Tselioudis, 2015; Wood,
2012). Observations from the Ka-band ARM zenith radar (KAZR2) and
zenith-pointing ceilometer lidar are combined to estimate adaptive (both in
time and height) Z–R relationships, which we then use to estimate precipitation
rate across the domain observed by the X-band scanning ARM precipitation
radar (XSAPR2). Domain-averaged and time-averaged precipitation rate estimates
obtained from zenith-pointing and scanning observations are compared to
document the complementarity and applicability of each sensor in documenting
precipitation rate from warm boundary layer clouds.
Specification of ARM ENA zenith and scanning second-generation radar systems.
KAZR2 KaSACR2 XSAPR2 Frequency (MHz)34 860 35 290 9500 Peak power (kW)2.2 2.2 300 Maximum duty cycle (%)5.0 5.0 0.1 Pulse compression capabilityYes and activated Yes but not activated No Pulse length200 ns4 µs0.4 µs 0.66 µs Sensitivity single pulse (dBZ)-32.5 (at 1 km)-44 (at 1 km)-15 (at 20 km) -21 (at 20 km) Pulse (dBZ)(at 1 km)(at 1 km)(at 20 km) (at 20 km) Dead zone (m)72737400 100 Unambiguous range (km)18 40 Over 100 Gate spacing (m)30 30 100 Antenna size (m)1.82 1.82 5.0 3 dB beam width (∘)0.3 0.3 0.45 Scan rate (∘ s-1)– 3 6 Scan strategyZenith PPI scan VCP scan Elevation angle (∘)90 0.5 0 to 5 every 0.5 Azimuthal sector (∘)– 360 160 Scan time2 s 2 min 5 min Scan intervalContinuous 15 min Continuous Transmit polarizationH Alternating H and V Simultaneous H and V Received polarizationH and V H and V H and V Amplifier typeKlystron (EIK) Klystron (EIK) Magnetron Signal processingFFT Pulse-pairFFTPulse-pairFFTDoppler spectraYes NoYesNoYesSecond-trip echo removal techniqueChallenging Frequency hoppingChallengingNoncoherent power technique Velocity dealiasingChallenging Staggered pulseChallengingContinuous techniquerepetition timeEastern North Atlantic observatory
In October 2013, the ARM program established a permanent observatory in the
eastern North Atlantic on the island of Graciosa (∼60 km2 area;
39.1∘ N, 28.0∘ W). The site, located in the Azores
archipelago, straddles the boundary between the subtropics and the
midlatitudes and as such is subject to a wide range of different
meteorological conditions, including periods of relatively undisturbed
trade wind flow, midlatitude cyclonic systems and associated fronts, and
periods of extensive low-level cloudiness (Rémillard and Tselioudis,
2015). The observatory hosts an extensive instrument suite, including three
second-generation radar systems: the Ka-band ARM zenith radar (KAZR2), the
dual-frequency Ka-band and W-band scanning ARM cloud radar (SACR2) and the X-band scanning ARM precipitation radar (XSAPR2), the specifications of which are listed
in Table 1. A short description of the radar systems is provided here with
emphasis on changes in configuration from the first to the second
generation.
KAZR2
KAZR2 operates at 34.8 GHz (λ=8.6 mm) and is an upgraded
version of the KAZR that replaced the ARM millimeter cloud radar (MMCR,
Kollias et al., 2016). KAZR2 uses an extended interaction Klystron
(EIK) amplifier with a 2.2 kW peak power and 5 % duty cycle. Its dual-receiver configuration allows for the simultaneous transmission of two
pulses: (i) a long (4 µs) pulse with frequency modulation (pulse
compression) for higher sensitivity (∼-44 dBZ at 1 km not
considering signal integration gain) at ranges from 737 m to
18 km from the radar and (ii) a short pulse (200 ns) with a sensitivity of (∼-32.5 dBZ at 1 km not considering signal integration gain) at ranges from 72 m to 18 km from the radar. KAZR2 has a narrow (0.3∘) 3 dB antenna
bandwidth and is nominally operated with a range resolution of 30 m and a
temporal resolution of 2 s and is set to record the full radar Doppler
spectrum with 256 or 512 fast Fourier transform (FFT) points. KAZR2 transmits a horizontal pulse and
receives both horizontal and vertical polarization such that the only
polarimetric information it can measure is the linear depolarization ratio.
SACR2
Ka-band scanning ARM cloud radar (KaSACR2) is a fully polarimetric radar that operates at 35.3 GHz (λ=8.5 mm) and is an upgraded version of the single polarization KaSACR
described in Kollias et al. (2014a, b). The KaSACR2 also uses an EIK
amplifier with a 2.2 kW peak power and has a 5 % duty cycle and a 3 dB
antenna beamwidth of 0.3∘. Currently, it is operated with a short
pulse, although it could be operated with a longer pulse with pulse
compression for increased sensitivity. Owing to its narrow beam width,
KaSACR2 must scan rather slowly (3–6∘ s-1) to collect
observations with a sensitivity of ∼-15 dBZ at 20 km (not
considering signal integration gain). The KaSACR2 conducts a cloud sampling
strategy that includes different modes (Kollias et al., 2014a, b). Here,
because of our interest in mapping precipitation structure and rate over a large
horizontal domain, we use observations collected in plan position
indicator (PPI) configuration that are only available at 0.5∘ elevation
angle over a 160∘ wide azimuth sector. The KaSACR2 conducts a PPI
scan every 15 min and takes 2 min to collect each PPI. The KaSACR2 employs
frequency hopping and staggered pulse repetition time techniques to mitigate
artifacts due to second-trip echoes and velocity aliasing. This, however,
comes at the expense of preventing the collection of the full Doppler
spectrum.
XSAPR2
The XSAPR2 operates at 9.5 GHz (λ=3.2 cm). It is an upgraded
version of the XSAPR, as it operates with an improved digital receiver and a
larger antenna (5 m), which results in an exceptionally narrow 3 dB antenna
beamwidth of 0.45∘. The requirement for the XSAPR2 to have a
narrow antenna beamwidth emerged from two main needs: (i) to reduce the
impact of sea clutter at low elevations and (ii) to maintain high angular
resolution over a 60 km radius in order to resolve small-scale oceanic
precipitating clouds. XSAPR2 uses a high-power Magnetron with a 300 kW peak
power and a maximum duty cycle of 0.1 %. Under nominal operational
conditions, the XSAPR2 transmits a 60 m long pulse and scans at a relatively
slow rate (6∘ s-1) to collect observations with a sensitivity
of ∼-21 dBZ at 20 km (not considering integration gain). The
XSAPR2 volume coverage pattern (VCP) scan strategy consists of a series of
PPI scans every 0.5∘ elevation between the angles of 0
and 5∘. Because of considerable beam blockage in the southerly
direction, a 160∘ azimuth sector coverage is achieved. The VCP scan
(i.e., the entire set of PPI scans) is completed within 5 min and
subsequently repeated. Horizontal and vertical polarization are possible for
both transmit and receive states, meaning XSAPR2 collects a full suite of
polarimetric variables while in scanning mode.
Radar observations post-processing
Radar observations require considerable post-processing for the removal of
non-meteorological targets before they can be scientifically interpreted or
used to retrieve geophysical quantities such as precipitation rate. Radar
data post-processing is described in Sect. 3.1 and cross-comparison
between different systems for calibration is described in Sect. 3.2. Note
that the KAZR2 data used for analysis are from “enakazrgeC1.a1” files,
KaSACR2 data are from “enakasacrppivhC1.a1” files and the XSAPR2 from the
“enaxsaprsecD1.00 files”. All data files were obtained from the ARM
archive (https://www.archive.arm.gov/discovery/, last access: 1 September 2019).
Removal of non-meteorological targets
First, signal processing artifacts (e.g., second-trip echoes) and echoes of
non-meteorological origin (e.g., biological echoes, sea clutter and
ground clutter) are identified and removed.
The KaSACR2 system operates in fully polarimetric mode and uses staggered
pulse repetition time and frequency hopping to automatically remove second-trip echoes, perform velocity dealiasing and increase the number of
independent samples (Pazmany et al., 2013). The XSAPR2 systems
operates using a magnetron system that is coherent upon reception (i.e.,
transmitted pulse phase is random). For the XSAPR2, the removal of second-trip echoes is done using normalized coherent power (NCP), which is the
coherency of the received pulse with respect to the last transmitted pulse.
For atmospheric echoes within the maximum unambiguous range, NCP is high since
the radar receiver is phase-locked on the phase of the last transmitted
pulse. Outside of the maximum unambiguous range, NCP is low since the radar
receiver has already phase-locked on the phase of another transmitted pulse.
Here, an NCP threshold of 0.3 is used to identify echoes originating from
outside the maximum unambiguous range (i.e., second-trip echoes).
Biological targets, such as insects and birds often contaminate radar
observations, especially over land (e.g., Luke et al., 2008). Their
occurrence varies with atmospheric condition, time of the year and time of
the day (Alku et al., 2015). KAZR2 observations at the ENA seem
minimally impacted by biological echoes. Furthermore, the fact that the bulk
of the KaSACR2 and XSAPR2 observations are collected over open ocean and
that Graciosa is a small island suggests that biological targets should not
be a concern at this particular location.
On the other hand, low-elevation-angle observations are susceptible to
sea-clutter contamination. Research on radar sea-clutter characterization
and remediation has been ongoing for over 20 years (e.g., Horst et al.,
1978; Gregers-Hansen and Mital, 2009; Nathanson et al., 1991). Observational
and modeling studies suggest that factors such as oceanic wave properties
(related to local wind speed and direction), swell and air density streams
can affect sea-clutter occurrence. Radar characteristics such as wavelength,
wave polarization, beam width and grazing angle are also known to affect
sea-clutter characteristics and amounts and our ability to isolate atmospheric
returns from sea clutter. Here, observations collected over a range of wind
conditions during nearly 100 h of clear sky conditions are used to
examine how sea-clutter characteristics vary with radar wavelength, beam
width and beam elevation angle.
For significant echoes, (1) radar reflectivity, (2) correlation coefficient (ρHV) and (3) relative frequency of
occurrence of clutter as observed by the (a) XSAPR2 at 0.5∘
elevation, (b) XSAPR2 at 1∘ elevation and (c) KaSACR at
0.5∘ elevation. (d) Clutter characteristics estimated using 93 h of clear sky observations.
First, the distribution of sea-clutter reflectivities as measured by the
XSAPR2 and KaSACR2 at elevation 0.5∘ are compared to document the
antenna beam width effect (Fig. 1d). The KaSACR2 (0.3∘ 3 dB
antenna beam width) sea-clutter reflectivity distribution is narrower with a
peak at -21 dBZ and a majority of echoes below -15 dBZ (Fig. 1d black line),
while the XSAPR2 (0.45∘ 3 dB antenna beam width) sea-clutter
reflectivity distribution is wider, peaks at -18 dBZ and covers a range from
-40 to +10 dBZ (Fig. 1d red line). This can be explained by the XSAPR2
wider antenna beam width, which results in a larger fraction of the radiated
energy to hit ocean waves, causing higher ocean clutter return power.
Similar to beam width, elevation angle affects how much sea is in the radar
field of view and the spatial extent of observed sea clutter. Figure 1d
shows that, at 1.0∘ elevation, XSAPR2 sea-clutter reflectivity
peaks at a lower reflectivity of -25 dBZ (blue line) and Fig. 1b3 shows
that in this configuration it frequently (> 25 % of the time)
detects clutter only over a domain of 10 km radius around the site which is
much less than it detects when collecting observations at a 0.5∘
elevation (significant clutter in a 20 km radius around the site in Fig. 1a3).
Now that we have characterized sea-clutter intensity and frequency of
occurrence using clear sky observations, we next evaluate its impact on the
detection of meteorological targets using observations containing a mixture of
hydrometeor and sea clutter. To isolate hydrometeors from clutter, we
exploit the correlation coefficient ρHV, which we know is affected
by the relative occurrence of signal to clutter; ρHV is typically
close to 1 for liquid-phase hydrometeors and lower for non-meteorological
targets. Looking at KaSACR2 reflectivity and ρHV confirms that at
Ka-band wavelength the signal-to-clutter ratio is high and hydrometeor
contributions dominate both radar reflectivity and correlation coefficient
measurements (Fig. 1c1 and c2, respectively). The enhanced
KaSACR2 signal-to-clutter ratio is attributed to two effects: (i) its narrow
beamwidth, which causes a smaller fraction of the transmitter energy to hit
the sea surface, and (ii) its shorter wavelength, which creates a larger
distinction between hydrometeor scattering (Rayleigh
scattering ∼1/λ4) and sea-clutter (scattering ∼1/λ). Using KaSACR2 observations as
a guide to locate cloud and precipitation location (Fig. 1c1), it is
apparent that it is not possible to distinguish atmospheric signals from
sea clutter in XSAPR2 radar reflectivity observation collected at
0.5∘ (Fig. 1a1).
Several techniques that use both time domain and frequency domain filtering
methods have been proposed to discriminate between sea clutter and
meteorological targets in precipitation radar observations (e.g., Torres
and Zrnic, 1999; Siggia and Passarelli, 2004; Nguyen et al., 2008; Alku et
al., 2015). Ryzhkov et al. (2002) present an echo classification
technique based on fuzzy logic and a multiparameter dataset including radar
reflectivity, mean Doppler velocity, spectrum width, differential
reflectivity, differential phase, linear depolarization ratio and
cross-correlation (ρHV). In the current study, given the radar's
narrow beam width and short wavelength, an approach solely based on ρHV is used to filter sea clutter. Since cross-correlation between
horizontal and vertical cross-polar received powers is largest for spherical
hydrometeors, we label observations with ρHV larger than a
certain threshold as atmospheric returns and the rest as sea clutter. The
analysis of a large sample of ρHV observations during clear and
cloudy sky conditions indicates that the use of a threshold of 0.9 for
KaSACR2, and an average (over five range gates and five azimuthal measurements)
threshold of 0.55 for the XSAPR2 can be used to isolate
hydrometeor-dominated from clutter-dominated observations. The proposed
ρHV technique successfully isolates atmospheric returns at the
same location for both the X band at 1.0∘ elevation and the
reference Ka band at 0.5∘ elevation (Fig. 1b2 and c2,
respectively, shown in pink). However, it only identifies a fraction of the
atmospheric returns in the X band at 0.5∘ elevation observations.
There, additional filtering, beyond the scope of this study, would be
required to suppress the remaining sea clutter and recover the missing
atmospheric returns (see Moisseev and Chandrasekar, 2009 and Unal, 2009, who
propose advanced techniques). Given this, XSAPR2 cross validation and
precipitation rate maps will be estimated using observations collected at
1.0∘ elevation since it offers the best compromise between
proximity to the surface and minimum sea-clutter contamination.
(a) Ka-band zenith radar (KAZR2) calibration offset to be
removed from the KAZR2 radar reflectivity in order to match Parsivel
disdrometer radar reflectivity estimates. (b) Ceilometer lidar calibration
factor to be multiplied to observed backscatter to match theoretical liquid
cloud lidar ratios. (c) Frequency of occurrence of observed backscatter
during clear sky conditions, solid black line is interpreted as the mean
aerosol backscatter signal, observations smaller than this threshold at each
height are eliminated from the drizzle analysis.
Radar calibration
Calibrated reflectivity observations are necessary to perform quantitative
precipitation rate retrievals. Following Kollias et al. (2019), KAZR2 calibration is performed using colocated surface-based
Parsivel laser disdrometer equivalent radar reflectivity estimates during
light precipitation events as well as CloudSat observations collected over a
small radius around the site. We estimate that, during the period of
interest (10 January to 1 April 2018), KAZR2 radar reflectivity measurements
are off by about +3 dB, which we proceeded to correct for. The detailed
time series of KAZR2 calibration offset is presented in Fig. 2a.
Comparison of total (Fig. 3a) and range-resolved (Fig. 3b) histograms of
radar reflectivity measured by KAZR2 (pre-calibration) and KaSACR2 at zenith
confirm that during the analysis period the KaSACR2 matched KAZR2. For this
reason, KaSACR2 radar reflectivity measurements were also adjusted by the
calibration constant depicted in Fig. 2a. Note how this comparison between
the KAZR2 and KaSACR2 was performed between 1.5 to 5 km to avoid any
differences in the reported radar reflectivities due to differences in how
they detect ground and sea clutter.
For the period when KAZR2 and KaSACR2 are matched in time and
range: (a) difference in radar reflectivity reported by both sensors over the
ranges between 1.5 and 5.0 km and the (b) difference in radar reflectivity reported
by both sensors as a function of range.
For the conditions that occurred on 4 March 2018 around
09:15 UTC as observed by (a) XSAPR2 radar reflectivity at 1∘ elevation
and (c) GPM-DPR Ku-band radar reflectivity at 1 km height. For the entire
geometry-matching dataset with 1516 points used for the calibration: (b) scatter, mean (orange) and standard deviation (dashed lines) of the
difference between the GPM-DPR Ku band and XSAPR2 radar reflectivity
measurements as a function of height and (d) a scatterplot comparing the XSAPR2
and GPM-DPR Ku-band reflectivity measurements above the GPM surface echo
height of 1.5 km. Also plotted is the 1-to-1 relationship (dashed line) and
the best linear fit to the observations (solid orange line).
Calibrating the XSAPR2 radar reflectivity measurements is more challenging
since it does not perform profiling observations and as such it cannot be
benchmarked against disdrometer and KAZR2 observations. Performing a
physical subsystem calibration remains the best way to calibrate the XSAPR2
system. Prior to the ACE-ENA field campaign (June 2017) the ARM engineering
team performed such a procedure, which is expected to bring the calibration
of the XSAPR2 system used in this study to within 1 dB. Here, in an effort
to develop alternative calibration and cross-validation methods, we also compare
the XSAPR2 radar observations to observations collected by the Global Precipitation Mission (GPM)
Ku-band dual-frequency precipitation radar (DPR). Comparison is limited to periods when the satellite track crosses within a 245 km radius of the
XSAPR2 radar site. It is not expected that both sets of observations will
perfectly match because of the different footprints, path lengths and
surface returns of both radar systems but this comparison should at least provide
some insight in the event that the difference between both sensors is larger
than several dB. For the comparison, the ground-based XSAPR2 reflectivity
measurements are smoothed and interpolated to the satellite sampling volume:
the azimuth-range measurements are smoothed using the 0.71∘ 3 dB
beamwidth antenna weighting function of the GPM DPR (5 km footprint).
Nearest neighbor is then used to match the satellite measurements in the
horizontal plane, while linear interpolation is used to match them in the
vertical plane (Warren et al., 2018). Matched XSAPR2 radar reflectivity
measurements are compared to GPM-DPR corrected reflectivity measurements
(GPM product version V06A, Iguchi et al., 2010). Considering
differences in radar sensitivity, radar reflectivity measurements with
returns smaller than 14 dBZ are not considered during the comparison
procedure (Toyoshima et al., 2015), and only periods when both radar
coincidently detect significant precipitation are used to perform
calibration. For the analysis period, a total of three GPM overpasses with
significant precipitation were observed for a total number of 1516 data
points for the comparison.
An example of concurrent XSAPR2 and GPM-DPR radar reflectivity observations
are shown in Fig. 4a and c, respectively. The example shows that both radar
detected several shallow precipitation cells with cloud top heights between
3 and 4 km (Fig. 4b). Beyond agreeing in the location of these precipitation
echoes, both radar systems (XSAPR2 and GPM-DPR) are found to agree on their
reflectivity intensity. To confirm their agreement, we estimated the contour of
frequency by altitude diagram (CFAD) of the differences in radar
reflectivities between the matched XSAPR2 and GPM-DPR for all 1516 available
observations (Fig. 4b). Above the height at which GPM-DPR is known to suffer
from surface echo contamination (i.e., 1.5 km), the comparison between
XSAPR2 reflectivities and GPM-DPR reflectivities shows no noticeable
difference (i.e., no bias). A scatter plot between the matched GPM-DPR and
XSAPR2 radar reflectivity for heights above 1.5 km confirms the overall lack
of bias beyond the expected 1 dB between the two radar at all reflectivities
(Fig. 4d, in which the orange line depicts the best fit to the data, the
dashed line represents a perfect match between the datasets and the grey
shading indicates the data density). As mentioned above, scatter is expected
because of the differences in configuration of both radar systems. The cloud
types present in the cases available could further enhance the impact of the
radar system differences, since the shallow clouds observed during the three
overpasses are of similar or even smaller size compared to the GPM-DPR
footprint. Small clouds could lead to nonuniform beam filling issues and as
such also lead to the GPM-DPR underestimating the reflectivity of these cloud systems, which could partially explain the seemingly “high” bias of the XSAPR2 in
Fig. 4d. Knowing that the ARM engineering team had calibrated the XSAPR2
just before the observations used here were collected and because this
comparison with the GPM-DPR showed no bias larger than several dB, we
conclude that, for the observation period between 10 January to 1 April 2018,
the XSAPR2 was reasonably well calibrated and does not require any radar
reflectivity adjustments.
Retrieval of popcorn convection precipitation rate on
2 February 2018 using (a) KAZR2 (zenith between 09:30 to 10:30 UTC) and (c) KaSACR2
(1∘ elevation PPI at 10:00 UTC). Retrieval of squall line
precipitation rate on 2 March 2018 using (b) KAZR2 (zenith between 07:30 to 08:30 UTC) and (d) KaSACR2 (1∘ elevation PPI at 08:00 UTC). Also indicated
are the location of cloud bases (black dots in panels a–b) and the general
wind direction (arrows in panels c–d). Note that KAZR2 is located at 0 km north–south and east–west.
In their removal techniques, Intrieri et al. (1993) and later O'Connor et al. (2005) proposed constraining water drop size distribution using lidar backscatter
(related to water drop cross section) and radar Doppler spectral width
(related to the width of the water drop size distribution). This radar–lidar
technique can be used to estimate precipitation rate at all levels in the
subcloud layer when colocated radar and ceilometer observations are
available. We apply this technique to the vertically pointing ceilometer
lidar and KAZR2 pair operating at the ENA. The O'Connor et al. (2005)
technique requires ceilometer backscatter to be calibrated and remapped to
the radar spatiotemporal resolution (here 2 s × 30 m). Ceilometer
backscatter is calibrated following a variation of the O'Connor et al. (2004) technique by scaling observed path-integrated backscatter in thick
stratocumulus to match theoretical cloud lidar ratio values. Satisfactory
conditions for ceilometer backscatter calibration are identified as the
first (in time) 20 min periods each day with a standard deviation of lidar
ratio smaller than 1.5. The observed backscatter during the “satisfactory
20 min period” are input to Hogan (2006)'s multi-scattered model to
determine a daily backscatter calibration factor. For days where
satisfactory conditions are not observed, a climatological calibration
factor of 1.35 is used to calibrate the observed backscatter. For the
current analysis period, the ceilometer backscatter calibration constant was
estimated to vary by around 1.35±0.08 (Fig. 2b). Calibrated ceilometer
backscatter is subsequently mapped on the KAZR2 time–height grid using a
nearest neighbor approach.
This radar–lidar technique generates time–height maps of precipitation rate
from 200 m above ground level to 90 m below cloud base height that are
filtered for aerosol contamination. We use the clear-sky – according to
KAZR2 – calibrated lidar backscatter signals as a reference for aerosol
behavior. Lidar-calibrated backscatter values below the mean clear-sky
calibrated backscatter value at each height, depicted as the black vertical
line in Fig. 2c, are systematically removed from the analysis to leave only
drizzle signals. In addition to aerosol-contaminated returns, unphysical
values with a median diameter smaller than 10 µm or equal to or larger than
1000 µm are also removed from our analysis.
Two 1 h examples of cloud location (black dots) and precipitation rate
estimated using this technique are shown in Fig. 5a and b. Because of
evaporation, the most intense precipitation rates are observed near cloud
base height and a significant fraction of the precipitation does not reach
the surface and falls as virga.
XSAPR2
As previously mentioned, the estimation of the precipitation rate for the
XSAPR2 (i) cannot depend on the use of polarimetric observations because of
the absence of polarimetric signature from spherical drizzle drops and (ii) cannot depend on the use of disdrometer-based estimates of the relationship
between the radar reflectivity (Z) and the precipitation rate (R) because
observations collected at the surface may not be representative of other
levels in the subcloud layer, especially at the ENA where evaporation is an
active process.
Time series of the α(a) and β(b)
coefficients used to estimate precipitation rate 90 m below cloud base
height for a 30 d period that overlaps with the second phase of the
ACE-ENA field campaign. For the same time period, distribution of the
α(c) and β(d) coefficients with height, along with their
median (solid line) and 25th and 75th percentile values (dashed
line). Precipitation rate distributions retrieved using the O'Connor et
al. (2005) technique (red) and estimated using the adaptive coefficients (f,
black) or the fixed coefficients proposed by Comstock et al. (2004) (e, green). Comstock et al. (2004)
coefficients and coefficients determined from disdrometer observations are
both presented in panels (a) and (b) using dashed green lines and dashed orange lines,
respectively.
To accommodate changes in drizzle drop size distribution with height, which
could be associated, for example, with changes in aerosol loading or evaporation,
we propose constructing adaptive (both with time and height) Z–R relationships
in the form Z=αRβ from precipitation rates retrieved through
the KAZR–ceilometer technique (see Sect. 4.1). Every 30 min, independently
for every level in the subcloud layer, retrieved zenith precipitation rates
(R in mm h-1) and calibrated KAZR2 reflectivity (Z in mm6 m-3)
reported during a 12 h window around that time are related through the
following relationship:
log10Z=log10α+β⋅log10R.
The prefactor α and exponent β are estimated using a total
least-squares-regression technique only considering R between 10-3.5 and
100.5 mm h-1 and only if at least 350 precipitation
detections are available. When too few observations are available, average
(for the period of the current study) α and β are used. A 12 h
time window was determined to be the best compromise between data density
and the least change in water drop size distribution characteristics.
To evaluate the adaptive Z–R, we apply three different precipitation retrieval
techniques to KAZR2 reflectivity observations: we compare precipitation rate
statistics retrieved following the O'Connor et al. (2005) technique
(ideal technique, red), to those estimated using Z–R relationships constructed
using fixed (approach proposed by Comstock et al., 2004, green) or
adaptive (approach proposed here, black) coefficients (presented in Fig. 6e
and f respectively). Figure 6f shows that the proposed adaptive Z–R
relationships can reproduce the precipitation rate statistics obtained using
the ideal O'Connor et al. (2005) technique. The same cannot be said from
using traditional fixed Z–R relationships such as that proposed by
Comstock et al. (2004), which tends to create an underestimation of
precipitation intensity (Fig. 6e).
Figure 6a and b, respectively, present time series of α and β
near cloud base (i.e., 90 m below cloud base height) for a 30 d
period that overlaps with the second phase of the ACE-ENA field campaign.
Again, for comparison we illustrate our adaptive coefficients (black), the
Comstock et al. (2004) constant coefficients (dashed green) and
coefficients estimated from surface-based Parsivel laser disdrometer
measurements (dashed orange). The gradual increase in both the adaptive
α and β coefficients over time is consistent with reports of
observed conditions indicating a transition from shallow precipitation at
the end of January to deep frontal precipitation at the end of February.
CFADs of α and β (Fig. 6c and d, respectively) show how the
adaptive α additionally has a tendency to increase with distance
from cloud base (from top to bottom), which is consistent with the
evaporation of small drops that leads to an increase in mean drop size and
has been previously reported by Comstock et al. (2004) and discussed in
VanZanten et al. (2005).
(a) PPI scan geometry and (b) theoretical sensitivity of the XSAPR2
(blue) and KaSACR2 (black), along with the KaSACR2 “effective” sensitivity,
considering it is affected by gas attenuation (green).
Example of observations and retrievals of the conditions
on 13 February 2018 at 00:10 UTC. Shown for the KaSACR2 when performing
0.5∘ elevation PPI are (a) the radar reflectivity field, corrected for
gaseous attenuation and neglecting liquid water attenuation, and (b) the corresponding
precipitation rate retrieved using adaptive Z–R relationships; (c) the radar
reflectivity field, corrected for both gas and liquid water attenuation and
(d) corresponding precipitation rate; and (e) the difference between (a) and (c), showing
the range-accumulated radar reflectivity, liquid water attenuation correction,
and (f) the corresponding precipitation rate bias. The bottom panels (g) and
(h) show simultaneously collected XSAPR2 1.0∘ PPI observations
for reference.
Figure 5c and d show how, by applying the adaptive Z–R, XSAPR2 reflectivity
observations collected at 1∘ elevation can be converted to
precipitation rate. Note how the adaptive Z–R relationships were directly
applied to clutter-filtered calibrated XSAPR2 radar reflectivity
measurements since we estimate that, for the majority of the conditions
occurring at the ENA observatory, both two-way gas attenuation and liquid
attenuation at the X band are negligible. According to Rosenkranz (1998), at
X-band frequency, gas attenuation generally amounts to 0.03 dB km-1,
which is much smaller than even the radar calibration uncertainty.
Similarly, Matrosov et al. (2005) discusses how, for rain rates of 2 mm h-1, liquid attenuation roughly amounts to 0.015 dB km-1, which
over the depth of the shallow systems producing this type of precipitation
cumulates to liquid attenuation less than 1 dB, again within the radar
calibration uncertainty. We do, however, acknowledge that, for deep convective
systems, liquid attenuation correction would be granted, but since this type
of precipitating system was not being frequently observed at the ENA
observatory, we did not apply any liquid attenuation correction to the
XSAPR2 measurements.
KaSACR2
Before quantitatively estimating precipitation rate from KaSACR radar
reflectivity measurements, we also consider how its wavelength responds to
the presence of atmospheric gases. The Rosenkranz (1998) propagation model
suggests that, for the conditions observed at the ENA, two-way gas
attenuation of Ka-band signals can amount to 0.25 dB km-1. Although
this may seem small and can be insignificant when collecting observations of
boundary layer clouds in profiling mode, in scan mode attenuation of
Ka-band reflectivity by atmospheric gas can amount to 10 dB at 40 km range
(Fig. 7b difference between the black and green curve) and as such should
not be neglected. Also note that in addition to the gaseous attenuation,
Ka-band radar suffer from considerable liquid water attenuation. According
to Matrosov (2005), the relationship between one-way liquid attenuation
a (dB km-1) and precipitation rate R (mm h-1) is very robust (a=0.28R). His findings were verified using Mie scattering calculations on all
particle size distributions observed by the ENA Parsivel laser disdrometer.
Figure 8a and b illustrate an example of observations collected by
the KaSACR at 0.5 elevation on 13 February 2018. In this example, liquid
contributed anywhere from 2 to 10 dB in total attenuation at the Ka-band frequency over
the 40 km observation domain (Fig. 8e). If left uncorrected, liquid
attenuation can lead to errors in precipitation rate estimates up to 3 mm h-1 in this example (Fig. 8f). Figure 8g and h also show
reflectivity and precipitation rate for the XSAPR2 which, as discussed in
the previous section, only suffers from negligible attenuation. With the
caveat that we are comparing rain rates retrieved at slightly different
slanted elevations, comparing rain rates retrieved from the XSAPR2
observations (Fig. 8h) and from the KaSACR2 observations corrected for both
gas and liquid attenuation (Fig. 8d) also highlights the fact that even
after all corrections are performed, the KaSACR2 “realized” sensitivity does
not allow it to detect some of the precipitation the more sensitive XSAPR2
can detect. The range-dependent sensitivity of both sensors can be
contrasted in Fig. 7b.
Radar systems complementarity
As discussed in Sect. 2, the KAZR2, KaSACR2 and XSAPR2 radar sample
light precipitation using very different transmission and sampling
strategies. In this section we highlight some of the advantages and
tradeoffs of using each radar system to characterize different aspects of
light precipitation variability.
First, this is done by contrasting the two scanning radar XSAPR2 and KaSACR2. Although the
Ka-band SACR2 experiences less sea clutter than the X-band SAPR2, it has a coarser temporal resolution; the KaSACR2 only currently performs one PPI scan at
0.5∘ every 15 min because it also performs other scan strategies for cloud sampling. In
addition, based on their technical specifications (Table 1), the XSAPR2
single pulse radar sensitivity is approximately 10 dB higher than that of
the KaSACR2 (Fig. 7b blue and black line, respectively). Finally, the Ka-band
SACR2 also suffer from significantly more attenuation from atmospheric gases
(Fig. 7b green line) and liquid water, which even if corrected for still
decreases its “realized” sensitivity. For all these reasons, we conclude
that the XSAPR2 is more suitable for characterizing light precipitation
variability over large domains.
For a 36 h period (00:00 UTC, 2 February, to 12:00 UTC,
3 February), hourly probability density functions (pdfs) of precipitation
rate are presented, estimated from (a) XSARP2 when performing a 1∘ elevation PPI
scan, (b) KAZR2 200 m from the surface, and (c) KAZR2 90 m below cloud base
height.
Second, to contrast the XSAPR2 and KAZR2, we compare, over the course of 36 h between 00:00 UTC, 2 February, and 12:00 UTC, 3 February, hourly
precipitation rate variability in the forms of frequency of occurrence in
different precipitation rate bins (pdfs). Figure 9a shows estimates from the
scanning XSAPR2 collecting observation in PPI mode covering a domain between
2.5 and 40 km at 1∘ elevation thus transecting heights between
∼100 and 750 m (also refer to Fig. 7a to visualize the
XSAPR2 sampling geometry). Figure 9b and c, respectively, show estimates from
the vertically pointing KAZR2 200 m above the surface and 90 m below cloud
base, which was around 850 m.
From Fig. 9b and c, it is evident that KAZR2, with its high sensitivity, is
especially well suited to document light precipitation and drizzle falling
at a rate as low as 10-4 mm h-1. KAZR2 observations show a
reduction in the number of precipitation events and in precipitation
intensity from cloud base (Fig. 9c) towards the surface (Fig. 9b). This
supports the previous hypothesis that at the ENA a large fraction of the light
precipitation falls in the form of virga (Ahlgrimm and Forbes, 2014;
Yang et al., 2018). Under these circumstances, where the character of
precipitation changes dramatically with height and its intensity is very low
(below 10-3 mm h-1), scanning radar observation at a fixed
elevation may become inadequate to characterize surface precipitation over a
large domain owing to the Earth's curvature effects. Figure 7a illustrates the
height above the surface of a 1∘ elevation scan with distance away
from the radar; at a distance of 10–20 km the radar beam is already 250 m
above the surface while at a distance of 20–30 km this same radar beam is
now 500 m from the surface. This nonuniformity of the radar beam height
with distance makes scanning cloud radar observations at one elevation angle
more adequate to document the character of vertically uniform precipitation.
The rapid sampling rate of the KAZR2 also allows it to describe the vertical
structure of precipitation variability at a high temporal scale (as short
as 2 s).
On the other hand, one drawback of vertically pointing KAZR2 observations is
that they are limited to sampling only those precipitation events advected
overhead. It is not uncommon to temporally average vertically pointing
observation to create a proxy for domain-averaged statistics; however, as
depicted in Fig. 5, it may be difficult to address the domain
representativeness of 1 h of vertically pointing precipitation rate
estimates. It can also be challenging to interpret the mesoscale
organization of the precipitation field using vertically pointing
observations alone. Scanning systems such as the XSAPR2 can help fill this
gap. Figure 5c and d show XSAPR2 1∘ elevation PPI scans collected
at 10:00 and 08:00 UTC, respectively, which corresponds to the center time of
the KAZR2 time–height observations presented in Fig. 5a and b. XSAPR2 can
observe the structure and scales of popcorn precipitation and squall line
precipitation over a domain of roughly 2500 km2. In its current
configuration, the XSAPR2 system can be used to document the horizontal
structure and temporal variability of light-to-moderate precipitation on
scales of ∼5 min. Referring back to Fig. 9a, looking at the hourly
precipitation rate pdfs it is evident that by covering a larger domain
XSAPR2 is able to observe a larger number of near surface sporadic
precipitation events such as that observed on 3 February around 00:00 and of
isolated deep convective events responsible for more intense precipitation
(R >3 mm h-1) such as that observed on 3 February around 08:00.
Gridded domain precipitation rate estimation
One way for scanning radar systems to overcome some of the limitations of their
scanning strategy is to develop horizontal, two-dimensional, gridded maps of
the radar observables and other quantities (i.e., precipitation rate) using
measurements collected at different elevation angles (i.e., construct
constant altitude plan position indicator, CAPPI, maps). Here, gridded
XSAPR2 CAPPI maps are constructed as follows: we perform the polar to Cartesian
transformation for each individual reflectivity measurement using a standard
atmosphere radio propagation model that considers the height of the beam
above the Earth's surface and the distance between the radar and the
projection of the beam along the Earth's surface (Doviak and Zrnic, 1993).
Using these Cartesian coordinates each PPI is mapped on a 100 m horizontal
grid in which each grid point is populated using a triangulation technique
(i.e., the nearest three observations are linearly interpolated to populate
the grid cell). Then, every 100 m in the horizontal, a grid point at
constant altitude is populated by (i) a measured value if falling on an
elevation where observations were collected or (ii) a weighted
average of the gridded data from the three closest PPI. The weight being the
inverse horizontal distance from the grid location. The aforementioned
adaptive Z–R relationships are then applied to the Cartesian grid reflectivity
observations to produce precipitation rate CAPPI. Note that producing an
unbiased assessment of precipitation rate over the domain covered by the
scanning radar would require the application of a uniform sensitivity
threshold over the entire domain. The need for such a threshold creates a
tradeoff between documenting a large domain and documenting weak
precipitation events. As quantified in Fig. 7b, at a distance of 40 km the
XSAPR2 is only capable of detecting precipitation events of an intensity larger
than 10-2.8 mm h-1 and any desire to document weaker
precipitation rate events would further limit domain size.
Probability density function of average (over different
time windows) precipitation rate as estimated the XSAPR2 and by the KAZR2
(red), both at 500 m above the surface in 100.5 mm h-1 bins. The
XSAPR2 precipitation rates 500 m above the surface being from gridded CAPPI
constructed using a collection of PPI scans and limited to the domain
between 2.5 and 40 km around the location of the KAZR2. Over each box is the
correlation coefficient (R) between the XSAPR2 and the KAZR2 average
precipitation rates.
Domain-averaged precipitation rate – when do temporal and horizontal
precipitation variability converge?
The addition of the XSAPR2 at the ENA observatory offers new insights into
precipitation variability and organization over a domain of 40–60 km radius
around the size. However, the XSAPR2 data record is not as long as the KAZR2
data record, which now spans 5 years at the ENA, totaling up to 7.5 years
if we consider the Cloud, Aerosols and Precipitation in the Marine Boundary
Layer (CAP-MBL) campaign that took place at the site from April 2009 to
January 2011 (Wood et al., 2015). Because of their longer data record,
profiling radar observations have the potential to inform us about decadal
precipitation variability both temporal and structural. However, with
vertically pointing observations, it is near impossible to disentangle
temporal evolution from horizontal structure. Classical approaches rely on
the Taylor hypothesis of frozen turbulence to convert elapsed time to horizontal
dimension using the horizontal wind speed responsible for advecting cloud
and precipitation overhead. While widely used, little research has been
conducted to determine the validity and limitations of this assumption (see
Oue et al., 2016 for a discussion on cloud fraction). In this section
we seek to determine how long one needs to observe precipitation
advected overhead to gather statistical precipitation information equivalent
to that of a 40 km radius domain.
Over the 3-month period between 10 January and 1 April 2018, the domain
representativeness of KAZR2 precipitation rate estimates is evaluated using
XSAPR2 observations collected over a domain of 40 km radius around the site.
Although any height could be used, we perform this comparison at the
specific height of 500 m. While KAZR2 precipitation retrievals can be
directly extracted at 500 m, those from XSAPR2 must be extracted from
gridded CAPPI fields that are constructed following the details provided in
Sect. 6 using a collection of PPI scans. To remove any bias caused by
variations in minimum performance of both sensors, a minimum precipitation
rate threshold of 10-2.8 mm h-1 is applied to both sensors
reflecting the detectability of the XSAPR2 over the selected domain.
Statistics for both sensors are estimated using different set averaging time
intervals (30 min, 1 h, 3 h, 12 h and 24 h), which allows us to monitor the
temporal variability of domain-averaged precipitation rate. For XSAPR2, using
a sliding window, we average all 5 min PPI observations collected during the
chosen time interval. For KAZR2, we center the time window on the XSAPR2
estimates and average all 2 s observations collected during the chosen time
interval.
Figure 10 shows the precipitation rate pdfs estimated from the XSAPR2 (blue)
and KAZR2 (red) for varying averaging time intervals. Focusing on features
such as the width, the minimum, maximum, and modes of the precipitation rate
statistical distribution, results indicate that neither 30 min nor 1h
averaging of KAZR2 precipitation rate estimates can be used to replicate the
precipitation rate statistics corresponding to those of the domain averaged over
30 min (Fig. 10, left column). Averaging of 3 h of KAZR2 data improves
the representativeness of the domain-averaged rain rate variabilities on scales
of 1 to 3 h (third row, second and third columns). Convergence
between XSAPR2 and KAZR2 time-averaged precipitation rate estimates is
seemingly best when considering the variability of domain-averaged
precipitation rate over 12 h (correlation coefficient R=0.25) or longer
timescales. The 12 h average domain-averaged precipitation rate pdf from XSAPR2
and 12 h average precipitation rate pdf from KAZR2 are similar in both
magnitude and mode location.
Although these results are estimated with few observational cases (3-month
period), they clearly suggest that XSAPR2 observations are necessary to
characterize short-term (< 1 h) domain-averaged precipitation rate
characteristics. They also suggest that longer-term (12 h) domain-averaged
precipitation rate characteristics can be estimated by averaging either
XSAPR2 or KAZR2 observations using time windows of similar lengths.
Summary and conclusions
The ARM ENA observatory is the first island-based climate research facility
equipped with colocated radar and lidar capable of sampling light oceanic
precipitation. Here we presented the characteristics and first light
observations from three state-of-the-art second-generation radar systems:
the Ka-band zenith radar (KAZR2), the Ka-band scanning ARM cloud radar
(KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2).
One of the initial concerns of operating scanning cloud and precipitation
radar over the ocean is the impact of sea clutter, especially at
low elevation angles. Nearly 100 h of clear sky observations
were used to characterize the properties of sea clutter in KaSACR2 and
XSAPR2 observations. Analysis of clear and cloudy-sky periods and
intercomparison of the meteorological and non-meteorological echoes of the
KaSACR2 made it possible to design a relatively simple filtering technique
to isolate precipitation echoes in XSAPR2 observations. In short, a
threshold on normalized coherent power (< 0.3) and on average (5×5
window) cross-correlation (< 0.55) can mitigate second-trip echoes
and sea-clutter echoes. Everything considered, we find that XSAPR2
observations collected at 1∘ elevation, albeit suffering from more
clutter contamination than KaSACR2, offer the best compromise between
clutter contamination and proximity to the surface.
Measurement calibration is also essential to quantitative precipitation rate
retrieval. We applied the Kollias et al. (2019) technique
to calibrate the KAZR2 radar reflectivity measurements using Parsivel
disdrometer and CloudSat observations. Because they were found to match, the
same offset is applied to the KaSACR2 observations. To confirm the recent
calibration performed by the ARM engineering team and to explore alternative
calibration methods, the XSAPR2 reflectivity measurements were statistically
compared to GPM Ku-band radar observations collected around the ENA site.
The analysis indicated no noticeable offset; thus, no calibration offset was
applied to the XSAPR2. These techniques could be used in the future as a
supplement to the ARM radar engineering group efforts to characterize the
ENA radar's reflectivity measurements.
We capitalized on the availability of closely collected (in both time and
physical distance) KAZR2, ceilometer lidar and XSAPR2 measurements to
estimate precipitation rate. Precipitation rates retrieved using the
O'Connor et al. (2005) radar–lidar technique have the advantage of being
estimated with fewer assumptions on the drizzle drop size distribution and
can accommodate changes in aerosol loading, liquid water path and
evaporation. Unfortunately, due to a lack of scanning lidar observations, we
cannot apply this technique to scanning radar observations. Instead, we
showed how relating the retrieved precipitation rates in the column to radar
reflectivity can be used to estimate adaptive (in both time and height)
parameters that related observed radar reflectivity (Z) to precipitation rate
(R) in the form Z=αRβ. These adaptive parameters can then
be applied to retrieve precipitation rate over the domain covered by
scanning cloud radar systems. We report these adaptive parameters for the period
between 10 January and 1 April 2018, which includes the second phase of the
ACE-ENA campaign. These adaptive parameters were shown to capture changes in
drop size distribution with height as well as temporal changes in the cloud
field.
Throughout this work, comparison of precipitation rate statistics estimated
by all three sensors highlighted the following:
Because of strong signal attenuation by gases and liquid at the Ka band, X-band
radar systems are more suited for precipitation mapping, especially over large
domains.
When the character of precipitation varies rapidly with height, for instance
owing to an active evaporation process, zenith-pointing radar systems are more
suited for precipitation characterization.
However, zenith-pointing observations collected over periods shorter than
12 h should not be considered representative of a domain, especially one as
large as 2500 km2 (i.e., ∼40 km radius half circle).
When it comes to capturing the general shape of the precipitation rate
distribution, 12 h of zenith-pointing radar observations can be averaged
to represent the 12 h variability of a ∼40 km radius
half circle domain.
Shorter-term domain precipitation rate variability can only be captured by
scanning precipitation radar systems, in particular those operating at
weakly attenuating frequencies and with high sensitivity, such as the XSAPR2.
Scanning sensors such as the XSAPR2 are also better suited to documenting
sporadic and horizontal homogeneous precipitation including precipitation
presenting mesoscale organization.
In a nutshell, the considerable differences in precipitation rate statistics
estimated by the XSAPR2 and KAZR2 challenge our ability to objectively
estimate precipitation rate statistics over a domain for applications such
as the evaluation of high temporal resolution model output. Factors such as
instrument sensitivity, sampling resolution, sampling height and domain size
should always be considered when comparing model output to observations. One
way to consider these factors could be to convert model output rain rates to
observable rain rate through the use of forward simulators, which can use
drop size and atmospheric conditions information to reproduce the
attenuation affecting radar signals. Several forward simulators further take
into consideration the dependency of radar sensitivity with range, which
dictates the minimum detectable rain rate at various distances within a
domain (e.g., Tatarevic et al., 2015; Lamer et al., 2018).
Data availability
Ground-based data were obtained from the Atmospheric radiation measurement (ARM) user
facility, a U.S. Department of Energy (DOE) Office of Science user facility managed by the Office
of Biological and Environmental Research. Spaceborne data were obtained from the National
Aeronautics and Space Administration. Column rain rate retrievals were made available by the
authors as an ARM PI product.
Author contributions
KL coordinated the project, performed the intercomparisons between the
precipitation rates produced by the three radar systems and produced the final
manuscript draft. PK supervised ZZ and BPT as they, respectively, analyzed the KAZR2 and both the KaSACR2 and
XSAPR2 observations. Analysis steps included performing data
post-processing, calibration and precipitation rate retrievals. BPT also produced the CAPPI part of this work. BI and NB provided a wealth of information about the radar
system characteristics as well as guidance on radar data calibration. All
coauthors read the manuscript draft and contributed comments.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the
two anonymous reviewers and Gianfranco Vulpiani for their involvement in the review process.
Financial support
Katia Lamer's contributions were supported by subcontract 300324 of the
Pennsylvania State University and the Brookhaven National Laboratory in
support of the U.S. Department of Energy (DOE) ARM-Atmospheric Science
Research (ASR) Radar Science group. Bernat Puigdomènech Treserras'
contributions were supported through a subcontract with the Brookhaven
National Laboratory in support of the ARM-ASR Radar Science group. Zeen Zhu's
contributions were supported by the U.S. DOE ASR ENA Site Science award. Bradley Isom and Nitin Bharadwaj's contributions were supported by Pacific Northwest
National Laboratory. Pavlos Kollias' contributions were supported by the U.S. DOE
under contract DE-SC0012704.
Review statement
This paper was edited by Gianfranco Vulpiani and reviewed by two anonymous referees.
References
Adler, R. F., Wang, J.-J., Gu, G., and Huffman, G. J.: A ten-year tropical
rainfall climatology based on a composite of TRMM products, J.
Meteorol. Soc. Jpn., 87, 281–293, 2009.
Ahlgrimm, M. and Forbes, R.: Improving the representation of low clouds and
drizzle in the ECMWF model based on ARM observations from the Azores,
Month. Weather Rev., 142, 668–685, 2014.
Alku, L., Moisseev, D., Aittomäki, T., and Chandrasekar, V.:
Identification and suppression of nonmeteorological echoes using spectral
polarimetric processing, IEEE T. Geosci. Remote,
53, 3628–3638, 2015.
Bretherton, C. S., Uttal, T., Fairall, C. W., Yuter, S. E., Weller, R. A.,
Baumgardner, D., Comstock, K., Wood, R., and Raga, G. B.: The EPIC 2001
stratocumulus study, B. Am. Meteorol. Soc., 85,
967–978, 2004.
Comstock, K. K., Wood, R., Yuter, S. E., and Bretherton, C. S.: Reflectivity
and rain rate in and below drizzling stratocumulus, Q. J.
Roy. Meteor. Soc., 130, 2891–2918, 2004.
Comstock, K. K., Bretherton, C. S., and Yuter, S. E.: Mesoscale variability
and drizzle in southeast Pacific stratocumulus, J. Atmos.
Sci., 62, 3792–3807, 2005.
Doviak, R. and Zrnic, D.: Doppler Radar and, Academic Press,
562 pp., 1993.Ellis, T. D., L'Ecuyer, T., Haynes, J. M., and Stephens, G. L.: How often
does it rain over the global oceans? The perspective from CloudSat,
Geophys. Res. Lett., 36, 10.1029/2008GL036728, 2009.
Feingold, G., Koren, I., Wang, H., Xue, H., and Brewer, W. A.:
Precipitation-generated oscillations in open cellular cloud fields, Nature,
466, 849–852, 2010.
Gorgucci, E., Scarchilli, G., and Chandrasekar, V.: Sensitivity of
multiparameter radar rainfall algorithms, J. Geophys. Res.-Atmos., 105, 2215–2223, 2000.
Gregers-Hansen, V. and Mital, R.: An empirical sea clutter model for low
grazing angles, Radar Conference, 2009 IEEE, 1–5, 2009.
Hogan, R. J.: Fast approximate calculation of multiply scattered lidar
returns, Appl. Optics, 45, 5984–5992, 2006.
Horst, M., Dyer, F., and Tuley, M.: Radar sea clutter model, Antennas and
Propagation, 6–10, 1978.
Iguchi, T., Seto, S., Meneghini, R., Yoshida, N., Awaka, J., and Kubota, T.:
GPM/DPR level-2 algorithm theoretical basis document, NASA Goddard Space
Flight Center, Greenbelt, MD, USA, Tech. Rep, 2010.
Intrieri, J. M., Stephens, G. L., Eberhard, W. L., and Uttal, T.: A method
for determining cirrus cloud particle sizes using lidar and radar
backscatter technique, J. Appl. Meteorol., 32, 1074–1082, 1993.
Kollias, P., Bharadwaj, N., Widener, K., Jo, I., and Johnson, K.: Scanning
ARM cloud radars. Part I: Operational sampling strategies, J.
Atmos. Ocean. Tech., 31, 569–582, 2014a.
Kollias, P., Jo, I., Borque, P., Tatarevic, A., Lamer, K., Bharadwaj, N.,
Widener, K., Johnson, K., and Clothiaux, E. E.: Scanning ARM cloud radars.
Part II: Data quality control and processing, J. Atmos. Ocean.
Tech., 31, 583–598, 2014b.
Kollias, P., Clothiaux, E. E., Ackerman, T. P., Albrecht, B. A., Widener, K.
B., Moran, K. P., Luke, E. P., Johnson, K. L., Bharadwaj, N., and Mead, J.
B.: Development and applications of ARM millimeter-wavelength cloud radars,
Meteor. Mon., 57, 17.11–17.19, 2016.Kollias, P., Puigdomènech Treserras, B., and Protat, A.: Calibration of the 2007–2017 record of ARM Cloud Radar Observations using CloudSat, Atmos. Meas. Tech. Discuss., 10.5194/amt-2019-34, in review, 2019.
Lamer, K., Kollias, P., and Nuijens, L.: Observations of the variability of
shallow trade wind cumulus cloudiness and mass flux, J. Geophys.
Res.-Atmos., 120, 6161–6178, 2015.Lamer, K., Fridlind, A. M., Ackerman, A. S., Kollias, P., Clothiaux, E. E., and Kelley, M.: (GO)2-SIM: a GCM-oriented ground-observation forward-simulator framework for objective evaluation of cloud and precipitation phase, Geosci. Model Dev., 11, 4195–4214, 10.5194/gmd-11-4195-2018, 2018.
Luke, E. P., Kollias, P., Johnson, K. L., and Clothiaux, E. E.: A technique
for the automatic detection of insect clutter in cloud radar returns,
J. Atmos. Ocean. Tech., 25, 1498–1513, 2008.
Mather, J., Turner, D., and Ackerman, T.: Scientific maturation of the ARM
Program, Meteor. Mon., 57, 4.1–4.19, 2016.
Matrosov, S. Y.: Attenuation-based estimates of rainfall rates aloft with
vertically pointing Ka-band radars, J. Atmos. Ocean. Tech., 22, 43–54, 2005.
Matrosov, S. Y., Kingsmill, D. E., Martner, B. E., and Ralph, F. M.: The
utility of X-band polarimetric
radar for quantitative estimates of rainfall parameters, J.
Hydrometeorol., 6, 248–262,
2005Miller, M. A. and Yuter, S. E.: Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements, Atmos. Meas. Tech., 6, 1–13, 10.5194/amt-6-1-2013, 2013.
Moisseev, D. N. and Chandrasekar, V.: Polarimetric spectral filter for
adaptive clutter and noise suppression, J. Atmos. Ocean. Tech., 26, 215–228, 2009.
Moyer, K. A. and Young, G. S.: Observations of mesoscale cellular
convection from the marine stratocumulus phase of “FIRE”, Bound.-Lay.
Meteorol., 71, 109–133, 1994.
Nathanson, F. E., Reilly, J. P., and Cohen, M. N.: Radar design
principles-Signal processing and the Environment, NASA STI/Recon Technical
Report A, 91, 1991.
Nguyen, C. M., Moisseev, D. N., and Chandrasekar, V.: A parametric time
domain method for spectral moment estimation and clutter mitigation for
weather radars, J. Atmos. Ocean. Tech., 25, 83–92,
2008.
O'Connor, E. J., Illingworth, A. J., and Hogan, R. J.: A technique for
autocalibration of cloud lidar, J. Atmos. Ocean. Tech., 21, 777–786, 2004.
O'Connor, E. J., Hogan, R. J., and Illingworth, A. J.: Retrieving
stratocumulus drizzle parameters using Doppler radar and lidar, J.
Appl. Meteorol., 44, 14–27, 2005.Oue, M., Kollias, P., North, K. W., Tatarevic, A., Endo, S., Vogelmann, A.
M., and Gustafson, W. I.: Estimation of cloud fraction profile in shallow
convection using a scanning cloud radar, Geophys. Res. Lett., 43,
10.1002/2016GL070776, 2016.
Paluch, I. and Lenschow, D.: Stratiform cloud formation in the marine
boundary layer, J. Atmos. Sci., 48, 2141–2158, 1991.
Pazmany, A. L., Mead, J. B., Bluestein, H. B., Snyder, J. C., and Houser, J.
B.: A mobile rapid-scanning X-band polarimetric (RaXPol) Doppler radar
system, J. Atmos. Ocean. Tech., 30, 1398–1413, 2013.Rapp, A. D., Lebsock, M., and L'Ecuyer, T.: Low cloud precipitation
climatology in the southeastern Pacific marine stratocumulus region using
CloudSat, Environ. Res. Lett., 8, 014027, 10.1088/1748-9326/8/1/014027, 2013.
Rauber, R. M., Stevens, B., Ochs III, H. T., Knight, C., Albrecht, B. A.,
Blyth, A., Fairall, C., Jensen, J., Lasher-Trapp, S., and Mayol-Bracero, O.:
Rain in shallow cumulus over the ocean: The RICO campaign, B.
Am. Meteor. Soc., 88, 1912–1928, 2007.
Rémillard, J. and Tselioudis, G.: Cloud regime variability over the
Azores and its application to climate model evaluation, J. Climate,
28, 9707–9720, 2015.
Rosenkranz, P. W.: Water vapor microwave continuum absorption: A comparison
of measurements and models, Radio Sci., 33, 919–928, 1998.
Ryzhkov, A., Zhang, P., Doviak, R., and Kessinger, C.: Discrimination
between weather and sea clutter using Doppler and dual-polarization weather
radars, Proc. 27th General Assembly of the International Union of Radio
Science, 3, 2002.
Savic-Jovcic, V. and Stevens, B.: The structure and mesoscale organization
of precipitating stratocumulus, J. Atmos. Sci., 65,
1587–1605, 2008.
Schumacher, C. and Houze Jr., R. A.: Comparison of radar data from the TRMM
satellite and Kwajalein oceanic validation site, J. Appl.
Meteorol., 39, 2151–2164, 2000.
Sharon, T. M., Albrecht, B. A., Jonsson, H. H., Minnis, P., Khaiyer, M. M.,
van Reken, T. M., Seinfeld, J., and Flagan, R.: Aerosol and cloud
microphysical characteristics of rifts and gradients in maritime
stratocumulus clouds, J. Atmos. Sci., 63, 983–997,
2006.
Siggia, A. and Passarelli, R.: Gaussian model adaptive processing (GMAP)
for improved ground clutter cancellation and moment calculation, Proc. ERAD,
421–424, 2004.
Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist,
B., Brenguier, J.-L., Bretherton, C., Burnet, F., and Campos, T.: Dynamics
and chemistry of marine stratocumulus – DYCOMS-II, B. Am.
Meteorol. Soc., 84, 579–594, 2003.
Stevens, B., Vali, G., Comstock, K. K., Wood, R., Van Zanten, M. C., Austin,
P. H., Bretherton, C. S., and Lenschow, D. H.: Pockets of open cells and
drizzle in marine stratocumulus, B. Am. Meteorol.
Soc., 86, 51–58, 2005.
Stevens, B., Farrell, D., Hirsch, L., Jansen, F., Nuijens, L., Serikov, I.,
Brügmann, B., Forde, M., Linne, H., and Lonitz, K.: The Barbados Cloud
Observatory: Anchoring investigations of clouds and circulation on the edge
of the ITCZ, B. Am. Meteorol. Soc., 97, 787–801,
2016.Tatarevic, A., Kollias, P., Oue, M., and Wang, D.: User's Guide CR-SIM
SOFTWARE v 3.0. McGill University
Clouds Research Group, Document, available at: http://radarscience.weebly.com/radar-simulators.html, last
access: 1 July 2019.
Torres, S. M. and Zrnic, D. S.: Ground clutter canceling with a regression
filter, J. Atmos. Ocean. Tech., 16, 1364–1372, 1999.
Toyoshima, K., Masunaga, H., and Furuzawa, F. A.: Early evaluation of Ku-and
Ka-band sensitivities for the global precipitation measurement (GPM)
dual-frequency precipitation radar (DPR), Sola, 11, 14–17, 2015.
Unal, C.: Spectral polarimetric radar clutter suppression to enhance
atmospheric echoes, J. Atmos. Ocean. Tech., 26,
1781–1797, 2009.
Vali, G., Kelly, R. D., French, J., Haimov, S., Leon, D., McIntosh, R. E.,
and Pazmany, A.: Finescale structure and microphysics of coastal stratus,
J. Atmos. Sci., 55, 3540–3564, 1998.
VanZanten, M., Stevens, B., Vali, G., and Lenschow, D.: Observations of
drizzle in nocturnal marine stratocumulus, J. Atmos.
Sci., 62, 88–106, 2005.
Villarini, G. and Krajewski, W. F.: Review of the different sources of
uncertainty in single polarization radar-based estimates of rainfall,
Surv. Geophys., 31, 107–129, 2010.
Wang, H. and Feingold, G.: Modeling mesoscale cellular structures and
drizzle in marine stratocumulus. Part I: Impact of drizzle on the formation
and evolution of open cells, J. Atmos. Sci., 66,
3237–3256, 2009.
Warren, R. A., Protat, A., Siems, S. T., Ramsay, H. A., Louf, V., Manton, M.
J., and Kane, T. A.: Calibrating ground-based radars against TRMM and GPM,
J. Atmos. Ocean. Tech., 35, 323–346, 2018.
Wood, R.: Drizzle in stratiform boundary layer clouds. Part II:
Microphysical aspects, J. Atmos. Sci., 62, 3034–3050,
2005.
Wood, R.: Stratocumulus clouds, Mon. Weather Rev., 140, 2373–2423,
2012.Wood, R. and Hartmann, D. L.: Spatial variability of liquid water path in
marine low cloud: The importance of mesoscale cellular convection, J.
Climate, 19, 1748–1764, 2006.
Wood, R., Bretherton, C. S., Leon, D., Clarke, A. D., Zuidema, P., Allen, G., and Coe, H.: An aircraft case study of the spatial transition from closed to open mesoscale cellular convection over the Southeast Pacific, Atmos. Chem. Phys., 11, 2341–2370, 10.5194/acp-11-2341-2011, 2011.Wood, R., Wyant, M., Bretherton, C. S., Rémillard, J., Kollias, P., Fletcher, J., Stemmler, J., de Szoeke, S., Yuter, S., Miller, M., Mechem, D., Tselioudis, G., Chiu, J. C., Mann, J. A., O’Connor, E. J., Hogan, R. J., Dong, X., Miller, M., Ghate, V., Jefferson, A., Min, Q., Minnis, P., Palikonda, R., Albrecht, B., Luke, E., Hannay, C., and Lin, Y.: Clouds, aerosols, and precipitation in the marine boundary layer: An arm mobile facility
deployment, B. Am. Meteorol.
Soc., 96.3, 419–440, 10.1175/BAMS-D-13-00180.1, 2015.Yamaguchi, T. and Feingold, G.: On the relationship between open cellular convective cloud patterns and the spatial distribution of precipitation, Atmos. Chem. Phys., 15, 1237–1251, 10.5194/acp-15-1237-2015, 2015.
Yang, F., Luke, E. P., Kollias, P., Kostinski, A. B., and Vogelmann, A. M.:
Scaling of drizzle virga depth with cloud thickness for marine stratocumulus
clouds, Geophys. Res. Lett., 45, 3746–3753, 2018.
Yuter, S. E., Serra, Y. L., and Houze Jr., R. A.: The 1997 Pan American
climate studies tropical eastern Pacific process study. Part II:
Stratocumulus region, B. Am. Meteorol. Soc., 81,
483–490, 2000.
Zhou, X., Heus, T., and Kollias, P.: Influences of drizzle on stratocumulus
cloudiness and organization, J. Geophys. Res.-Atmos.,
122, 6989–7003, 2017.
Zhou, X., Ackerman, A. S., Fridlind, A. M., and Kollias, P.: Simulation of
Mesoscale Cellular Convection in Marine Stratocumulus. Part I: Drizzling
Conditions, J. Atmos. Sci., 75, 257–274, 2018.