GIGeoscientific Instrumentation, Methods and Data SystemsGIGeosci. Instrum. Method. Data Syst.2193-0864Copernicus GmbHGöttingen, Germany10.5194/gi-4-161-2015A wing pod-based millimeter wavelength airborne cloud radarVivekanandanJ.vivek@ucar.eduhttps://orcid.org/0000-0002-0952-2169EllisS.TsaiP.LoewE.LeeW.-C.EmmettJ.DixonM.BurghartC.https://orcid.org/0000-0003-4159-3343RauenbuehlerS.Earth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USAJ. Vivekanandan (vivek@ucar.edu)17August20154216117623January201514April20153August20154August2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gi.copernicus.org/articles/4/161/2015/gi-4-161-2015.htmlThe full text article is available as a PDF file from https://gi.copernicus.org/articles/4/161/2015/gi-4-161-2015.pdf
This paper describes a novel, airborne pod-based millimeter (mm) wavelength
radar. Its frequency of operation is 94 GHz (3 mm wavelength). The radar
has been designed to fly on the NCAR Gulfstream V HIAPER aircraft; however,
it could be deployed on other similarly equipped aircraft. The pod-based
configuration occupies minimum cabin space and maximizes scan coverage. The
radar system is capable of collecting observations in a staring mode between
zenith and nadir or in a scanning mode. Standard pulse-pair estimates of
moments and raw time series of backscattered signals are recorded. The radar
system design and characteristics as well as techniques for calibrating
reflectivity and correcting Doppler velocity for aircraft attitude and motion
are described. The radar can alternatively be deployed in a ground-based
configuration, housed in the 20 ft shipping container it shares with the
High Spectral Resolution Lidar (HSRL). The radar was tested both on the
ground and in flight. Preliminary measurements of Doppler and polarization
measurements were collected and examples are presented.
Introduction
One attractive feature of millimeter (mm) wavelength radar systems is their
ability to detect micron-sized particles that constitute liquid and ice
clouds. Even though the upper limit of transmit power at millimeter
wavelength band is more than 25 dB lower than at centimeter bands (S, C and
X bands), the larger backscatter cross section of particle sizes smaller than
the wavelength (Rayleigh scattering) significantly improves the detection
limit (Lhermitte, 1987; Clothiaux et al., 1995). The radar cross section at W
band is 40 dB larger than at X band in the Rayleigh scattering regime.
Another advantage of mm wavelength radars for airborne deployments is that
the radar antenna is much smaller in size than the cm wavelength antenna
for a specified angular beam resolution and the overall radar size is more
compact. Therefore it is easier to achieve finer beam
resolution < 1∘ and range resolution on the order of tens of
meters. Lower sidelobes and larger signal-to-clutter ratio at mm wavelength
band radars significantly enhance their detection capability, in particular
close to the radar (Kropfli and Kelly, 1996). However, mm wavelength radar
signals are more susceptible to attenuation. The amount of attenuation is
proportional to the intensity of the precipitation and gaseous absorption
(Ellis and Vivekanandan, 2010, 2011). As a result, they are not suitable for
observing even moderate precipitation.
Due to the above-described advantages of compact size, higher spatial
resolution, and enhanced sensitivity, scanning or vertically pointing
ground-based mm wavelength radars are used to study stratocumulus (Valli et
al., 1998; Kollias and Albrecht, 2000), fair-weather cumulus (Kollias et al.,
2001, 2002, 2007; Kollias and Albrecht, 2000) and fog properties
(Hamazu et al., 2003). Airborne mm wavelength radars have been used for
atmospheric remote sensing since the early 1990s (Pazmany et al., 1994; Horie
et al., 2000; Hanesiak et al., 2010; Wolde and Vali, 2001a, b). Airborne
cloud radar systems such as the University of Wyoming King Air Cloud
Radar (WCR) and the NASA ER-2 Cloud Radar System (CRS) can observe clouds in
remote regions and over the oceans (Li et al., 2004; WCR, 2012).
The scientific requirements of mm wavelength radar are mainly driven by
climate and cloud process studies. Millimeter wavelength radar with
dual-Doppler and dual-polarization capability is highly desirable for the
concurrent estimation of dynamical and microphysical properties of clouds and
precipitation. A polarization Doppler radar with dual wavelength and dual
beams is capable of retrieving microphysical properties and two-dimensional
winds.
In 2005, a survey of the cloud radar user community was conducted in order to
assess needs and help guide the design of a mm wavelength radar on the
National Science Foundation (NSF) Gulfstream V (GV) High-Performance
Instrumented Airborne Platform for Environmental Research (HIAPER) aircraft
(Laursen et al., 2006), named HIAPER Cloud Radar (HCR). Results of the survey
indicated a common preference for narrow-beam W-band radar with polarimetric
and single Doppler capabilities with -40 dBZ sensitivity at 1 km range
for airborne remote sensing of clouds. Additional desired capabilities
included a second wavelength and/or dual-Doppler winds. Modern radar
technology offers various options: dual-beam, dual-polarization and
dual-wavelength. Even though a basic Doppler radar system with a sensitivity
of -30 dBZ is capable of satisfying many common scientific needs, the
above-mentioned additional options significantly extend the measurement
capabilities and further reduce any uncertainty in radar-based retrievals of
cloud properties (Brown et al., 1995). Since the primary hardware
requirements of the proposed HCR were that it be deployable on an airborne
platform, use minimal cabin space and maximize scan coverage, a pod-based
configuration was adopted. However, the size of the wing pod limited the fore
and aft scanning capability, making airborne dual-Doppler retrievals
impossible.
In order to complement the existing capabilities in the NSF Lower Atmospheric
Observing Facility, the final configuration of the HCR will have Doppler,
polarimetric and dual-wavelength capabilities with a beam scanning option.
When the radar is not deployed on the airborne platform, it can be operated
in a ground-based configuration. The HCR shares a single 20 ft shipping
container with the NCAR High Spectral Resolution Lidar (HSRL) for operations
on the ground, providing collocated measurements. This provides the impetus
for combined radar–lidar studies and retrievals of more accurate
micro-physical parameters, namely, mean particle size and precipitation
amount (Donovan and van Lammeren, 2001).
A phased approach is used to build the HCR. Phase A consists of a pod-mounted
W-band Doppler radar with co- and cross-polarization measurements,
i.e., reflectivity (Z) and linear depolarization ratio (LDR). Pulse
compression and differential reflectivity (ZDR) capability are
planned for phase B, and a second wavelength, solid state, matched beam
Ka-band radar system is planned for phase C. In order to include
these extensions the phase A system is designed to accommodate the phase B
and C requirements to the extent possible. Phase A of the radar was
ground-tested and flight-tested in spring 2013, fall 2013 and fall 2014.
View of an NCAR pod mounted below the surface of the right wing.
The goal of this paper is a system description of phase A of HCR including
specifications, hardware and performance characteristics with example
measurements. To this end, Sect. 2 describes the design concept of a
pod-based radar system. Detailed radar block diagram and subsystem
descriptions are presented in Sect. 3. Technical specifications of the HCR,
expected sensitivity of the radar, spatial resolution of measurements, and
measurement errors in velocity and reflectivity are described in Sect. 4. The
calibration procedure for reflectivity and the correction of Doppler velocity
for aircraft platform motion are shown in Sect. 5. Examples of HCR
measurements are shown in Sect. 6. Section 7 presents a summary.
Design concept
The HCR is mounted in the underwing pod. The HIAPER underwing pod is attached
to the mid-hardpoint of the right wing as depicted in Fig. 1. It is 20′′
(inches; 0.5 m) in diameter and 160′′ (4.1 m) in length. The total weight
of the pod including instrumentation cannot exceed 800 lbs (360 kg). The
HIAPER instruments located in wing pods must conform to the basic
infrastructure of the wing pod. Power available onboard GV is limited and the
number and type of connections in the wing pod are also pre-determined.
Therefore the HIAPER instruments installed in the wing pod have to be
designed taking into consideration the above-mentioned parameters. This
necessitates that nearly the entire radar system be located within the pod;
the radar control and data display/archive computer, however, are mounted in
a 19′′ aircraft rack in the main cabin for communication and controls to
the radar system. The overall weight of the pod, including the radar, is
514 lbs (234 kg).
A pod-based system on a high-altitude platform imposes engineering design
challenges in terms of the radar's size, weight, and ability to handle
pressure and temperature extremes. The National Center for Atmospheric
Research Earth Observing Laboratory (NCAR EOL) performed the mechanical
design and fabrication. They also generated the necessary drawings and
documentation for FAA certification of the structure; FAA certification of
all instrumentation is required for operation on the GV.
Figure 2 shows the computer-aided design of the HCR system layout interior to
the pod. The layout shows the placements of an inertial navigation system
(C-MIGITS-III), a rotating reflector, a 12′′ lens antenna and a pressure
vessel. The choice of the antenna is explained in Sect. 3.2. The pod is not
environmentally controlled. In order to ensure a stable radar operation under
conditions of temperature and pressure from sea level to 45 000 ft m.s.l. (mean sea
level), all radar electronics including the high voltage transmitter are
housed in the pressure vessel. This pressure vessel is 60′′ in length and
15′′ in diameter and is pressurized with dry nitrogen. To further mitigate
potential arcing in the externally located waveguide and antenna feed, the
antenna and associated waveguide are also pressurized through the pressure
vessel. The 12′′ lens antenna illuminates a rotatable reflector plate that
allows the coverage of a 220∘ sector including zenith and nadir
directions in the plane normal to the fuselage.
Radar system description
This section provides brief descriptions of the various radar sub-systems of
the HCR including the transmitter and transceiver, antenna and radome, and
the data system.
Transmitter and transceiver
A block diagram of the radar transceiver is shown in Fig. 3. The HCR uses a
conduction-cooled extended interaction klystron amplifier (EIKA) to amplify
the signal for transmission. The EIKA is the similar to the one used in the
CloudSat radar (Stephens et al., 2002). The EIKA, its modulator and the
entire receiver electronics are housed in the pressure vessel. Since the
modulator is rated for a maximum operating altitude 7000 ft m.s.l., the
pressure vessel must be maintained between 15 and 16 PSIA by a supplemental
pressurization system. Both the EIKA and modulator are designed to operate at
a 5 % duty cycle. The 5 % duty cycle will allow for pulse compression
planned in phase B.
The HCR system transmits a single-frequency pulse with a programmable pulse
width that can range from 256 to 1024 ns (38.4 to 153.6 m range resolution)
at a 10 kHz PRF (pulse repetition frequency). The receiver bandwidth is adjusted to match the bandwidth of the transmit pulse.
A dual-channel receiver is used for measuring co- and cross-polarization
signals. Each channel utilizes a two-stage super-heterodyne receiver. The
first stage intermediate frequency is 156.25 MHz and the second stage is
1406.25 MHz. A 10 MHz GPS STALO (stable local oscillator) is used as the
system coherent source as well as the GPS location and time reference. All
oscillators in the transceiver are phase locked to 125 MHz, which is
ultimately referenced to the 10 MHz GPS STALO.
Side view of the 20′′ HIAPER wing pod showing the layout of the
radar electronics. The front of the pod is on the left-hand side of the
figure. The reflector plate is positioned such that the beam clears the
leading edge of the wing when pointing toward zenith.
Block diagram of polarimetric HCR transceiver. The blue arrow
couples the transmit signal for monitoring the transmitted power. The red
arrow shows the signal path for monitoring receiver gain using a known noise
source.
At the front-end, a network of ferrite switches is used to alternate the
transmit polarization and also achieve isolation between transmit and receive
paths. Three ferrite switches at the output of EIKA direct the transmit power
between horizontal (H) and vertical (V) polarizations to provide
alternating polarization capability. Three cascaded ferrite switches in front
of the LNAs provide a minimum of 75 dB isolation to the receivers during the
transmission.
A portion of the transmit signal is coupled through a 40 dB waveguide
coupler, attenuated, and monitored by a crystal detector as shown in Fig. 3.
A noise source is switched into the receiver's path periodically to monitor
fluctuations in the receiver gain during operations (red path in Fig. 3). Due
to the space limitation inside the pressure vessel, the noise source cannot
be switched into both channels to monitor both receiver gains.
The phase B system will use pulse compression to increase the sensitivity of
the system. To achieve low range-time sidelobes with a pulse compression
system, the spectrum of the transmitted pulse is shaped. All of the receiver
components for the phase A system are designed for a greater than 20 MHz
passband to accommodate a pulse compression scheme. The sensitivity of the
system is optimized to the pulse length used by digitally filtering the
received signal to the desired signal bandwidth after digitization.
Antenna and radome
The dimensions of the pod limited the performance and capabilities of the HCR
in two ways. First, the antenna aperture of the HCR is restricted to 12′′.
This aperture constraint is necessary to avoid beam blockage by the pod's
internal support structure as well as to enable zenith viewing through the
use of a rotating reflector. This limits both the sensitivity and spatial
resolution, as the gain of an antenna is directly proportional to its
aperture, while its beamwidth is inversely proportional to its aperture.
Part of the pod's nose cone is constructed with millimeter wavelength radome
material. The design of this nose cone radome satisfies the following key
requirements: structural robustness for aviation requirements, low loss at W
and Ka bands, and electrical conductivity for preventing damages
due to lightning strikes. One-way loss through the radome is expected to be
∼ 0.3 dB for W band and ∼ 0.2 dB for Ka band. The
radome will also distort the relative phases of the incident wavefront due to
its curvature.
The relative merits of two types of suitable antenna were considered in
designing the HCR: (a) a parabolic reflector, including prime focus,
Cassegrain and offset Gregorian, and (b) lens antennas. Planar arrays and
their variants were not considered due to their lack of fast switching
polarization capability. Lens antennas at millimeter wavelength frequencies
give superior performance in terms of peak sidelobe level and cross-pol
isolation over all parabolic types, with the possible exception of offset
Gregorian. However, the complexity and required alignment precision of offset
Gregorian antennas makes them less attractive in a pod based application
where vibration and wide temperature variations exist. Although lens antennas
are generally heavier and have larger focal length to diameter ratios than
parabolic antennas, there is sufficient space and weight handling capability
in the pod to accommodate the lens antenna. The co- and cross-pol antenna
patterns are shown in Fig. 4. The first sidelobe is ∼-20 dB and
cross polarization lower than 38 dB at the bore sight.
E-plane co- and cross-pol radiation patterns of HCR's lens antenna.
A 45∘ slanted rotating reflector plate in front of the antenna is used
to steer the beam to meet angular coverage requirements. The reflector plate
is constructed of aircraft grade aluminum, machined to a flatness
of 0.005′′ (0.000127 m) root mean square (rms). The small rms value helps
to maintain polarization purity of transmit and receive signals. Precise
positioning of the plate relative to the antenna is required to avoid beam
distortion and spillover. A brushless DC motor coupled with an optical shaft
encoder is used to rotate the plate. A similar but smaller motor is used to
adjust the tilt of the reflector. The motors are programed to rotate from
-10∘ from zenith (toward the fuselage) to 210∘ (30∘
from nadir) perpendicular to along-track direction and from -6 to
+6∘ in the tilt axis. The tilt axis adjustment compensates for the
aircraft pitch. Both rotation and tilt are controlled in real time to
compensate for platform motion to minimize the bias of aircraft motion into
the velocities. A CMIGITS-III inertial reference unit mounted in the nose
cone, just 12′′ forward of the reflector, provides the necessary spatial
reference and minimizes the moment arm. A consequence of using a reflector
plate is that the polarization of the transmitted waveform changes as the
reflector plate rotates. A rotational transformation will be used to recover
the intrinsic polarization state of the received signal (Vivekanandan et al.,
1990).
HCR specifications.
ParameterSpecificationTransmit frequency94.4 GHz, W-bandAntenna diameter0.30 m, lens antennaAntenna gain46.21 dBBeamwidth0.68∘Peak power1.6 kWPulse width0.256–1.024 µsPRF10 kHzSystem noise power-104 dBmNoise figure9.4 dBFirst and second IF156.25 and 1406.25 MHzSensitivity (0 dB SNR,-34.6 dBZ at 1 kmsingle pulse pair, 0.512 µspulse width)Sensitivity (0.1 s-43.3 dBZ at 1 kmaveraging, 0.512 µspulse width)Minimum linear-27 dBdepolarization ratioUnambiguous velocity±7.75 m s-1Along-track resolution20 m at 250 m range200 m at 15 km rangeData system
HCR places some unique requirements on data system characteristics such as
size, weight and environmental factors. Radar control, preliminary signal
processing and data display and archiving are handled by a computer located
on a 19′′ rack mounted in the aircraft cabin, while radar timing, real-time
data acquisition and housekeeping are handled by a data system located in the
pod. The housekeeping structure consists of system status, GPS time, antenna
angle, and aircraft attitude. The cabin and pod data systems are linked by a
fiber optic, gigabit network connection. These connections relay radar
control commands from the cabin as well as digital time series data,
housekeeping and status from the pod.
Velocity accuracy as a function of independent samples and
signal-to-noise ratio. Spectrum width is assumed 1 m s-1, PRF is
10 kHz and transmit frequency is W band.
The received signal is digitized at the rate of 125 MHz. Both in-phase and
quadrature data are archived in HCR. Standard moment products such as
reflectivity, Doppler velocity and spectrum width are provided in CfRadial
format (URL: https://www.eol.ucar.edu/content/standard-data-formats).
The moment products are processed and displayed in real time on the archiving
computer in the cabin. Data rates for the phase A system are
∼ 70 MB s-1. This is handled by a single gigabit Ethernet
connection coupled to a 24 terabyte redundant array of disks (RAID). Data are
simultaneously written to the RAID and two removable external USB3 drives.
The USB3 drives offer quick access to data after completion of the research
flight.
All HCR data are archived on the NCAR High Performance Storage System (HPSS),
a state-of-the-art data center storage facility. Data are available in a
CfRadial format and EOL provides basic tools to access those data. EOL
supports basic software for display of radar data, editing of radar fields,
and derivation of several value-added products.
Performance characteristics of the HCR
An overview of the performance characteristics achieved by the design of HCR
described in Sect. 3 is presented here. For ease of reference some of the
important system parameters and performance characteristics under typical
operations for HCR are listed in Table 1.
Standard error in reflectivity measurements. For a specified number
of samples, standard error in reflectivity becomes lower as the Doppler
spectrum becomes broader. For greater than 1000 samples, standard error in
reflectivity is > 0.5 dB.
Measurement accuracy of mean velocity and reflectivity
The measurement accuracy of Doppler radial velocity and reflectivity is a
function of time-to-independence (TD), PRF, and signal-to-noise
ratio (SNR) (Doviak and Zrnic, 1993). Time-to-independence determines the
interval between two radar measurements that are statistically independent.
It is a function of transmit frequency and spectrum width (Bringi and
Chandrasekar, 2001). In the case of W band, TD is smaller than
the corresponding value of cm wavelength radars. As a result, more
independent samples are collected at W band for a specified dwell time and
Doppler spectrum width as compared to larger wavelengths such as S, C, and X
bands.
Figure 5 shows the number of independent samples required as a function of
signal-to-noise ratio (SNR) for various accuracy values of measured mean
radial velocity at W band. For 100 independent samples, an SNR of 3.5 dB is
required for estimating radial velocity within 0.2 m s-1 accuracy.
Since TD at W band is smaller than at cm band, more accurate
estimates of mean velocity can be achieved in a shorter dwell time interval.
Therefore, the HCR offers finer spatial resolution and more accurate radial
velocity than a cm wavelength band airborne radar.
The standard error in HCR reflectivity as a function of Doppler spectrum
width and number of samples is shown in Fig. 6 (Bringi and Chandrasekar,
2001). Since the number of independent samples increase as spectrum width
increases, standard error reduces for a specified number of samples at higher
spectral widths. As the PRF of HCR is 10 kHz, averaging 1000 samples or a
0.1 s time average reduces the standard error of reflectivity to less than
0.5 dB.
Sensitivity and spatial resolution
For the technical specifications listed in Table 1, Fig. 7 shows the
sensitivity of the HCR as a function of range for a single pulse-pair
measurement. The curves shown in the figure take into account radome
attenuation and receiver filter loss. The sensitivity of the HCR can be
enhanced by increasing the transmit pulse width and by performing noise
subtraction and temporal averaging of the received sample power. Since the
transmit pulse width can be varied between 0.256 and 1.024 µs, the
sensitivity of the radar can be enhanced by a transmitting a longer pulse at
the expense of range resolution. Reducing the range resolution by increasing
the pulse length by a factor of 2 improves sensitivity by 6 dB as shown in
Fig. 7. For a Doppler spectrum width of 0.2 m s-1, TD is
0.0018 s and averaging over 0.1 s corresponds to 55 independent samples.
Averaging over 0.1 s and noise subtraction improves the minimum reflectivity
by 8.7 dB. In summary, HCR sensitivity is -37.3 dBZ at 1 km for a
0.256 µs transmit pulse width when the received signal is corrected
for noise and is averaged over 0.1 s.
Pulse-pair estimates are averaged over a time interval to reduce fluctuations
in the Doppler moment estimates, namely, reflectivity, mean velocity and
spectrum width. The along-track resolution is a function of aircraft speed
and the dwell time of the beam. The footprint of the HCR beam is 3 m at the
range of 250 m and it increases to 180 m at 15 km range. Since the
aircraft traverses 20 m during 0.1 s or 1000 sample averaging, the
footprint of the HCR beam increases from 20 to 200 m as the range increases
from 250 m to 15 km.
Sensitivity of the HCR as a function of range is shown for two
different transmit pulse widths and also for single pulse and averaging over
1000 samples.
Since the PRF is 10 kHz, around 100 statistically independent samples can be
obtained from clouds with Doppler spectrum width > 0.4 m s-1. It
should be noted that the number of independent samples is determined by the
dwell time of the beam, which is primarily determined by aircraft speed and
the Doppler spectrum width of the cloud.
Data quality assuranceRadar system calibration
Obtaining an accurate system calibration is essential for HCR's scientific
missions. Calibration schemes are broadly divided into two categories:
internal calibration and external calibration. Internal calibration methods
include measurements using a noise source, the calibrated test signal source.
External calibration methods include measurements of a corner reflector cross
section, backscatter measurements from light precipitation and reflection
from the ocean surface.
Histograms of rain rates, raindrop size distributions and
computed W-band reflectivities: (a) rain rate, (b) concentration,
(c) median volume diameter, and (d) reflectivity. Mean value of reflectivity is 19 dBZ
and standard deviation is less than dB for rain rates between 5 and 15 mm h-1.
Internal calibration
Figure 3 shows two internal calibration paths of HCR. The transmit signal is
coupled from its path and monitored on a pulse-by-pulse basis by a W-band
detector (path colored blue). A known noise source is injected into the
receiver path to monitor receiver gain (path colored red). The noise source
is switched on to track differential changes in the receiver gain during
operation as the receiver gain fluctuates with ambient temperature. The
advantage of noise source is its stable performance of better than 0.004 dB
as the ambient temperature varies over 30∘. The stable noise source
reference allows robust monitoring of the receiver calibration. Due to the
limited space in the pressure vessel, the noise source calibration method is
configured only for the vertical polarimetric channel. The gain of the
horizontal polarimetric channel can be estimated from the physical
temperature of the low noise amplifier in conjunction with the known gain of
the calibrated vertical polarimetric channel.
Radar measurements in stratiform rain. The HCR was on the ground and
the beam was pointing at 30∘ elevation. The B-scan display shows time
versus range for the following: (a) reflectivity,
(b) velocity and (c) LDR. A histogram of reflectivity at
200 m is shown in (d).
External calibration using measurements from light rain
Since the reflectivity remains similar for a wide range of rain rates,
observations from light rain can be used for verifying the W-band
reflectivity (Hogan et al., 2003). At W band, attenuation and Mie scattering
dominate scattering from particle sizes > 0.3 mm. The combination of Mie
scattering and attenuation effects causes W-band reflectivity measurements at
near ranges to saturate (Hogan et al., 2003). Electromagnetic scattering and
wave propagation models can be used for quantifying reflectivities of light
rain (Vivekanandan et al., 1991). The computation of reflectivity requires
the specification of raindrop size distribution parameters. Raindrop size
distribution parameters are varied over their natural variations (Ulbrich,
1983). Figure 8a–c show histograms of computed rain rate, median volume
diameters and number concentration parameters for rain rates between 5 and
20 mm h-1. For these parameters of raindrop size distributions,
reflectivity values were calculated using scattering amplitudes (Vivekanandan
et al., 1991). Particle shapes were assumed spherical since differential
reflectivity for rain at W band is less than 0.5 dB. The modeled
reflectivity values at 200 m distance from the radar are shown in Fig. 8d.
The mean value of the modeled reflectivity is 19.0 dBZ and standard
deviation is 0.5 dB.
The calibration procedure using the measurements from light rain is presented
only for a ground-based configuration of the HCR. Its radome is protected
from rain and condensation by a canopy shelter. The HCR transmitted 256 ns
pulse width and the receiver oversampled the signal for obtaining 19.2 m
range resolution. The far range for the HCR is 57 m. The HCR collects data
in the 75 m range onwards.
Figure 9a–c shows ground-based HCR reflectivity, radial velocity and linear
depolarization measurements in rain rates between 5 and 10 mm h-1 for
fixed beam pointing as a function of time. The measurements were taken on
8 September 2014 around 21:40 UTC. The radar beam was fixed at 30∘
elevation from the horizontal. The reflectivity structure shows frozen
precipitation at the top layer, aggregation in the mid-layer and light rain
below 400 m a.g.l. Increased radial velocity and a strong gradient in LDR
structure shows the melting level is around 0.5 km a.g.l. A canopy was used
for keeping the radome dry during the rain event. A wet radome attenuates
radar signals and will introduce a large uncertainty in reflectivity
measurements (Hogan et al., 2003).
A histogram of reflectivity at the 250 m range is shown in Fig. 9d. The mean
value of measured reflectivity is 17.5 dBZ. This value is 1.5 dB lower than
the expected value shown in Fig. 8d. Comparison between measured and modeled
reflectivity values indicates the measured HCR reflectivity should be
increased by 1.5 dB. This result will be verified independently using
measurements from a corner reflector and water surface in the future.
Correction for platform motion
In order to obtain sufficient precision and accuracy of Doppler velocity
measurements for the types of studies for which the HCR is designed, it is
necessary to correct for the platform motion (Lee et al., 1994; Testud et
al., 1995; Durden et al., 1999; Bosart et al., 2002; Haimov and Rodi, 2013).
Global Position Satellite/Inertial Navigation System (GPS/INS) measurements
in the HCR pod are made in order to facilitate correction for the platform
motion. The strategy for the HCR is 2-fold. First, during flight the pointing
angle of the reflector (i.e., radar beam) is actively corrected for changes
in the pod attitude using the real-time navigation data. There is a small
time lag in the real-time pointing angle correction, which may result in
small errors in the reflector pointing angle. Second, the final data set is
generated with the aircraft motion corrected in post-analysis removing any
time lag between the navigation data and the radar data. This section
describes the navigation data, the real-time pointing angle correction of the
reflector and the final aircraft attitude correction procedure.
Navigation measurements
The C-MIGITS III GPS/INS system, manufactured by Systron Donner Inertial, is
used for platform attitude and motion measurements. The dimensions are
approximately 81 × 90 × 145.5 mm, the weight is 1.1 kg
and the data rate is 100 Hz. Its compact size makes the C-MIGITS III a good
choice because it fits easily in the HCR pod. One advantage of having the
GPS/INS system in the pod over using the aircraft system is that the moment
arm between the GPS/INS measurement and the reflector is small, significantly
simplifying the navigation correction computations. Another advantage is that
the C-MIGITS III directly measures the motion of the pod including wing and
pod flexing that are not measured by the aircraft navigation system. Using
navigation measurements from the fuselage would result in the independent
motions from wing and pod flexing being unknown and uncorrectable errors in
Doppler velocity.
Real-time pointing stabilization
When the HCR beam is in staring mode, the reflector can be adaptively
corrected for platform motion in real time using the C-MIGITS III output.
This physical stabilization of the beam is important when staring at nadir or
zenith to minimize the errors from unknown horizontal winds at heights
different than the flight level. As an example of these errors, imagine the
HCR beam was in nadir pointing mode in order to make measurements in the
vertical, but due to platform motion was off by 2∘ from the vertical.
If at some range the beam encounters a horizontal wind of 30 m s-1,
the projection of this wind velocity into the beam will be up to about
1 m s-1, a large error in vertical radial velocity measurements. Since
this wind is typically unknown, these errors cannot be corrected reliably.
The reflector can be corrected fore and aft up to 6∘ in each
direction. Since the reflector can scan in the cross-track direction, the
pointing correction is adequate except in steep banking turns. The reflector
pointing position is updated at a rate of 20 Hz. This means that there is a
0.05 s latency in the reflector position correction in real time.
Furthermore, there may be a small offset between the requested pointing angle
and what is actually achieved. This offset is typically less than
0.1∘. Therefore, final correction of the platform motion must be
performed using the navigation data and the measured position of the HCR
reflector.
Final navigation correction
Many investigators have developed methods to correct airborne Doppler radar
velocity measurements for platform motion. This requires both accurate
platform motion measurements and accurate beam pointing (Heymsfield, 1989). Lee et al. (1994)
derived a comprehensive radar coordinate transformation and expression for
measured Doppler velocity including components from platform motion and
particle motion (wind plus fall speed). The formulation of Lee et al. (1994)
was general enough to be used on different radar platforms and it assumed
accurate pointing angles. Haimov and Rodi (2013) propose a set of flight
maneuvers to use for calibrating the pointing angle measurements. They show
that the pointing angle calibration is consistent for a deployment such that
the procedure does not need to be repeated for each flight, only after each
radar installation on the aircraft. Once the pointing angle is calibrated,
the measured Doppler velocity can easily be corrected with geometric
calculations.
One minute of corrected Vr data within ground echo
measured during the test flight of 3 October 2014.
For the HCR, the approach of Haimov and Rodi (2013) is first utilized for
calibrating the HCR pointing angle and then the platform motion correction
is accomplished as defined in Lee et al. (1994) using the Earth relative
coordinates (their Eq. 15).
After correcting for the platform motion, the ground echoes should have a
radial velocity of 0 m s-1, so the residual Vr represents the
errors due to pointing angle offsets combined with errors in vertical motion
of the platform measured by the C-MIGITS III and the measurement variance of Vr.
The pointing angle calibration and platform motion correction procedure were
tested during the fall of 2013. The calibration flight legs to determine the
pointing calibration were flown on 17 September 2013 and the correction to
the pointing angle applied for the entire project. The measured
Vr of ground echoes on subsequent days was analyzed. For example,
at nadir pointing the mean and standard deviation of the corrected
Vr in ground echo over a 10 min period on 3 October were found
to be 0.001 and 0.086 m s-1, respectively. An example of 1 min of
corrected Vr within the ground echo is shown in Fig. 10.
Even though the nominal data rate for the HCR data is 10 Hz, the 100 Hz
navigation data are used to correct for platform motion. This is to resolve
and correct as much high-frequency platform motion due to turbulence as
possible. Thus, the HCR moments are computed at 100 Hz using the full
resolution navigation data, and then the data are averaged to the more
practical 10 Hz nominal time resolution, or any other user-specified
temporal resolution. Since HCR nominally runs with a pulse repetition
frequency of 10 000 Hz, there are 100 samples to compute the moments at
100 Hz, which is sufficient for platform motion correction. This strategy
reduces the radial errors in SW and Vr that occur if the platform
accelerates at a timescale shorter than the resolution of the navigation
data.
Measurement examples
The radar is capable of estimating winds and microphysics to a range of
15 km range with 19.2 m gate spacing. The HCR can be operated in scanning
and staring modes for detecting cloud boundaries, cloud liquid and ice and
also for estimating radial winds. Along-track resolution is nominally about
60 m. There is a real-time, onboard display of the HCR measurements. Its
capability to serve as a surveillance radar is very limited, as attenuation,
and Mie scattering at the W-band frequency would limit maximum detectable
reflectivity to about 30 dBZ. The dynamic range of HCR reflectivity is
between -40 and 30 dBZ. Reflectivity can be estimated within ±0.5 dB
accuracy. In weakly and non-precipitating conditions, ice and liquid water
content amounts can be estimated from reflectivity measurements.
The HCR will participate in its first field deployment in the summer of 2015.
The project is named CSET (Cloud System Evolution in the Trades) and aims to
study the characteristics and evolution of stratocumulus clouds over the
eastern Pacific Ocean. In preparation for the inaugural HCR project, data
have been collected during two test flight campaigns as well as in the
ground-based configuration. The motivation for the data collection was
primarily engineering tests of calibration, reliability, and platform motion
correction. Also being developed are real-time displays onboard the aircraft
and analysis tools for post-processing display of the HCR moment and Doppler
spectral data. During research flights the HCR operator will be able to
monitor system performance and real-time data display. Summary images will be
automatically generated at user-specified time intervals and can be sent to a
land-based operations center via the GV communications system. Therefore
scientists on the ground and in the aircraft can coordinate (via internet
chat) during the mission.
Even though there was no particular phenomenon being studied during HCR
testing, several interesting data sets have been collected in different
weather conditions. These data illustrate the capability and measurement
potential of the HCR. Examples of these data sets are shown in this section.
B-scan plots of Ze and Vr collected on
23 February 2013 from the HCR looking nadir.
Airborne dataWinter precipitation with convective features
Figure 11 shows B-scan plots of Ze and Vr (corrected for
platform motion) collected by HCR on the GV looking nadir on 23 February 2013
at 02:11 UTC during an upslope snow event along the Front Range of Colorado.
Shown is 1 min of data, and the vertical axis indicates altitude above mean
sea level (a.m.s.l.) and the horizontal axis shows the horizontal distance
the GV flew during the minute. The ground echo can be seen as the high
reflectivity (black) with 0 m s-1Vr at about
1.7 km a.m.s.l. As per the standard convention, receding wind from radar
has positive sign and approaching wind toward radar has negative sign. The
00:00 UTC Denver sounding from the National Weather Service (not shown)
indicated the surface temperature was only slightly above 0 ∘C and
decreased rapidly with height to about -35 ∘C at 7 km a.m.s.l.
Two distinct cloud layers were detected by HCR at this time. Small-scale
(< 1 km) convective features are seen at the top of the echo layer
between roughly 6 and 7 km a.m.s.l. The wind at this level was
northwesterly at about 25 kts according to the sounding and the plane was
flying towards the southwest. The base of the cloud undulates up and down
along the horizontal at a scale that is larger than the convective features
on top. This layer shows distinct regions of receding, or downward (positive
Vr) and approaching, or upward (negative Vr) velocity
measurements. The largest positive Vr is over 1 m s-1 and
the largest negative Vr is about 0.8 m s-1. The measured
Vr is a combination of the fall speed of the particles and the
air motion, so the upward Vr measurements indicate vertical air
motion and the pattern of alternating upward and downward motion is
indicative of waves. The lower layer shown in Fig. 12 is observed to be about
2 km thick and is a result of easterly upslope flow evident on the Denver
sounding. Many fine-scale features are seen in the Ze and
Vr fields in the lower level.
Stratiform rain and drizzle
Figure 12 shows B-scan displays of Ze, Vr (platform
motion corrected), and also corrected SW. The data were collected by HCR
pointing nadir from the GV on 11 October 2013 at 19:26 UTC. Again, 1 min of
data is shown and the horizontal axis designates the distance the aircraft
travelled in that time. The weather is a stratiform rain and drizzle case.
The echo in the low levels is about 3 km thick. The melting layer is
apparent at just below 2 km a.m.s.l. and is indicted by an increase in the
Ze, Vr and SW. The increase in Ze is due to the
increase in the refractive index of liquid over snow and ice crystals. As the
ice melts the density increases and thus so does the fall speed, resulting in
the increase in Vr. The increase in SW below the melting layer is
due, at least partially, to the fact that liquid drops exhibit a larger range
of fall speeds and larger differences in terminal fall speed for different
sizes. Additional turbulence in the boundary layer will also contribute to
the increase in measured SW. There is also a small cirrus cloud at about
9 km m.s.l. detected with measured Ze values between about -30 and
-39 dBZ. Interestingly the measured Vr varies in the cirrus
from roughly 0 m s-1 to almost 1 m s-1.
B-scan plots of Ze, Vr and spectrum width collected
on 11 October 2013 from the HCR looking nadir.
Ground-based data
The HCR was also deployed in its ground-based configuration with coincident
high spectral resolution lidar (HSRL) (Eloranta, 2005) data looking
vertically in November of 2013 in Boulder Colorado. Figure 13 shows B-scan
displays of HCR Ze and Vr on 22 November 2013 from
04:00 to 05:00 UTC during a snow event. Light snow was falling at the
surface and the Denver sounding (not shown) indicated the temperature was
below freezing for the entire layer and the surface temperature was about
-10 ∘C. The Ze values are low and do not exceed about
-10 dBZ, consistent with the light snow observed at the surface. The
Vr values are all negative (approaching), consistent with the
downward motion of ice crystals falling.
Coincident ground-based HCR and HSRL measurements of liquid
and ice clouds. (a) W-band reflectivity, (b) W-band radial velocity,
(c) lidar backscatter and (d) lidar circular depolarization ratio. Data were
collected on 22 November 2013.
HCR spectrum collected in a mixed-phase cloud. The radar was
ground-based and pointed vertically.
The HSRL data in Fig. 13 shows a layer of high backscatter and low circular
depolarization, indicating there is a layer containing super-cooled liquid
drops at the top of the observed cloud. Using the HSRL data alone it is
difficult to determine if the layer containing super-cooled liquid is mixed
phase or not – i.e., does the layer also contain ice crystals. Since the
liquid cloud droplets dominate the lidar measurements, the backscatter signal
from any ice crystals is masked in this layer. The apparent fall streaks that
can be seen coming from the bottom of the liquid layer are consistent with
the presence of ice crystals falling out of the liquid layer (Shupe et al.,
2008). Examining the HCR Vr data in Fig. 14b, it can be seen that
the Vr values in the HSRL-indicated liquid layer are negative,
ranging from -0.5 to -1.0 m s-1. This is also consistent with the
presence of ice crystals falling out of the layer, assuming the vertical air
velocity is close to 0 m s-1. The definitive evidence is the HCR
spectral data presented in Fig. 14, which shows the received power as a function
of Vr within a given range gate. The spectra are obtained by
taking the fast Fourier transform of the time-series in-phase and quadrature
(I and Q) data to determine the return power (after accounting for the
radar constant) as a function of Doppler velocity. The spectrum in Fig. 14
was selected from a radar range gate within the liquid layer that had a
measured Vr of about -0.5 m s-1. The spectrum is bimodal
with a peak close to 0 m s-1 and a peak close to -0.9 m s-1.
The two peaks indicate two distinct populations of particles with different
fall speeds. The peak near 0 m s-1 is most likely due to liquid cloud
droplets that do not have an appreciable fall speed, while the peak near
-0.9 m s-1 is most likely caused by ice crystals that are forming in
the liquid layer growing quickly and falling out (Danne et al., 1999). In
order to obtain a smooth spectrum from a single gate, the finite impulse
response (FIR) filter of Hubbert and Bringi (1995) was used on the original
spectrum data. It was found that using the FIR filter was preferable to using
time or range averaging because the cloud was moving and evolving quickly.
The HCR and HSRL measurements are often complementary and the combination of
the two can provide additional insights into the cloud being studied. In the
ground-based configuration, the two instruments can share a single standard
20 ft shipping container for coincident operations. The GV can also deploy
the instruments simultaneously.
Ground-based vertically pointing W-band spectral data have been shown to be
useful for separating the air velocity from the particle fall velocity (Luke
et al., 2010; Luke and Kollias, 2013; Danne et al., 1999). In clouds, bimodal
spectra result from the presence of two different size modes, namely, small
cloud drops on the order of several microns and larger precipitating
particles of either ice or liquid drizzle. As seen in Fig. 14, the two
populations can result in two distinguishable peaks at different
Vr values. The cloud droplets are assumed to be moving with the
wind and therefore the cloud peak represents the air motion. The distinct
spectra can be analyzed independently, thus providing detailed microphysical
information that would not be obtainable without the spectral data.
Summary
The HCR is an airborne, mm wavelength, dual-polarization polarimetric,
Doppler radar that serves the atmospheric science community by providing
cloud remote sensing capabilities. Engineering and scientific aspects of the
pod-based HCR are described in the paper. The pod-based configuration
alleviates competition for cabin space onboard the GV (or other) aircraft and
offers flexible scan coverage. The challenges associated with a pod-based
system on a high-altitude platform are managed by housing the radar system in
a pressurized vessel within the pod. The compact pod-based configuration
minimized waveguide losses and offered expected sensitivity for detecting
clouds. Results presented in this paper regarding spatial and temporal
resolutions and sampling requirements for desired accuracy in radar
measurements can be used for designing an optimal data collection strategy
for a specified scientific mission.
The current system is a single-frequency polarimetric Doppler radar. The
transmit and receive systems are designed to accommodate implementation of
pulse compression to achieve finer range resolution. The layout of the pod
will accommodate a second radar at Ka band for dual-wavelength
measurements. Inclusion of dual-wavelength capability will significantly
enhance the accuracies of retrieved cloud microphysical quantities.
Internal and external calibration schemes are used for monitoring data
quality. For radar systems, internal calibration is important to track
changes in the receiver gains. The methodology presented in this paper for
calibrating reflectivity makes use of light rain as a calibration target and
it ensures standard error reflectivity measurements < 0.5 dB. The amount
of aircraft motion contribution to airborne Doppler measurements is
determined by aircraft velocity along the radar beam pointing direction. This
requires precise estimates of the antenna pointing angle and aircraft
attitude and velocity. The two-step procedure for correcting the influence of
platform motion on measured radial velocity ensures better than
0.1 m s-1.
Preliminary measurements show the radar is capable of estimating accurate
reflectivity and velocity observations for climate science and cloud process
studies that are dominated by cloud liquid and cloud ice particles. The HCR
will serve the atmospheric science research community by adding mm wavelength
remote sensing capabilities to the HIAPER aircraft. The HCR measurements in
conjunction with other HIAPER instrumentation have the potential to
significantly increase our understanding of cloud physics.
List of acronyms.
CRSCloud Radar SystemCfRadialClimate and forecasting radialdBZRadar reflectivity factorEIKAExtended interaction klystron amplifierEOLEarth Observing LaboratoryFAAFederal aviation AdministrationGVGulfstream VHCRHIAPER Cloud RadarHALOHigh-altitude long-range research aircraftHPSSHigh Performance Storage SystemHIAPERHigh-Performance Instrumented Airborne Platform for Environmental ResearchHSRLHigh Spectral Resolution LidarICPRIntegrated cross-polar ratioIFIntermediate frequencyLNALow noise amplifierLDRLinear depolarization ratiom.s.l.Mean sea levelNCARNational Center for Atmospheric ResearchNOAANational Oceanic and Atmospheric AdministrationNSFNational Science FoundationPRFPulse repetition frequencyQCQuality controlRAIDRedundant array of inexpensive disksRFRadio frequencyRHIRange height indicatorRxReceiverSNRSignal-to-noise ratioSoloSoftware for radar translation, visualization, editing and interpolationSTSRSimultaneously transmit and simultaneously receiveTDTime-to-independenceTxTransmitterVVerticalVrRadial velocityZeReflectivityλoTransmit wavelengthAcknowledgements
NCAR is primarily funded by the National Science Foundation. This material is
based upon work supported by the National Science Foundation under
cooperative grant numbers NSF0015 and MO904552. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the
author(s) and do not necessarily reflect the views of the National Science
Foundation.
The guidance and support of Al Cooper of NCAR was invaluable throughout the
development. The authors would like to thank Mike Strong for his technical
expertise and leadership in the project. We also thank James Ranson,
David Allen and Karl Schwenz of the NCAR Design and Fabrication Services for
fabrication and design of mechanical infrastructure. The authors also thank
Sam Haimov of the University of Wyoming for many helpful technical
discussions. The authors appreciate Charlie Martin's extensive software
development for the initial prototype. The authors appreciate the thoughtful
and helpful manuscript review by John Hubbert of NCAR. Edited by: M. Zribi
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