the casa integrated project 1 networked radar system
TRANSCRIPT
The CASA Integrated Project 1 Networked Radar System
FRANCESC JUNYENT AND V. CHANDRASEKAR
Colorado State University, Fort Collins, Colorado
D. MCLAUGHLIN AND E. INSANIC
University of Massachusetts—Amherst, Amherst, Massachusetts
N. BHARADWAJ
Colorado State University, Fort Collins, Colorado
(Manuscript received 13 February 2009, in final form 29 July 2009)
ABSTRACT
This paper describes the Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1
(IP1) weather radar network, the first distributed collaborative adaptive sensing test bed of the Engineering
Research Center for Collaborative Adaptive Sensing of the Atmosphere. The radar network and radar node
hardware and software architectures are described, as well as the different interfaces between the integrated
subsystems. The system’s operation and radar node control and weather data flow are explained. The key
features of the radar nodes are presented, as well as examples of different data products.
1. Introduction
Operational weather radars have traditionally been
physically large instruments, transmitting high peak
power, single-polarization S- and C-band microwave
signals. These long-range, stand-alone units operate in-
dividually and almost independently of the weather echo
conditions, continuously scanning over large volumes of
the troposphere.
When operating over large coverage domains, the
broadening of the radar beam at farther ranges results
in spatial resolution degradation, whereas fixed-mode
operation and scanning patterns limit the temporal reso-
lution at which the atmospheric phenomena are sampled.
In addition, the earth’s curvature and terrain-induced
blockage (NRC 2002) can reduce coverage at low alti-
tudes, preventing the observation of low-altitude phe-
nomena such as tornadoes and limiting the accuracy of
precipitation estimates near the ground. In the large
coverage area of such radar systems, a variety of weather
phenomena can coexist at any given time, each with
a varying degree of interest to the different user com-
munities (such as weather forecasters, emergency man-
agers, researchers, etc.). To maximize the radar utility
(understood as the system’s capability to best fulfill each
particular user’s expectations), the radar should be able
to adapt and operate according to the current needs of
the users.
Using agile short-range weather radars with over-
lapping coverage domains that are controlled by an
automated entity that continuously optimizes the radar
network utility is one way to solve the range related is-
sues and maximize the value of the radar observations
(McLaughlin 2001; Chandrasekar and Jayasumana 2001).
With this vision, the National Science Foundation (NSF)
established the Engineering Research Center for Col-
laborative Adapting Sensing of the atmosphere (CASA).
CASA is a consortium of four universities [Colorado State
University, University of Massachusetts (lead university),
University of Oklahoma, and University of Puerto Rico
at Mayaguez] and a partnership with industry and gov-
ernment laboratories. The common objective of CASA is
to change the weather sensing paradigm through distrib-
uted collaborative adaptive sensing (DCAS; McLaughlin
et al. 2005), improving the coverage of the lowest por-
tion of the atmosphere through coordinated scanning of
Corresponding author address: Francesc Junyent, Colorado State
University, 1373 Campus Delivery, Fort Collins, CO 80523-1373.
E-mail: [email protected]
JANUARY 2010 J U N Y E N T E T A L . 61
DOI: 10.1175/2009JTECHA1296.1
� 2010 American Meteorological SocietyUnauthenticated | Downloaded 05/05/22 05:54 PM UTC
low-power, short-range networked radars. In Junyent
and Chandrasekar (2009), a framework to study the
coverage characteristics of such radar networks was de-
veloped. The first DCAS demonstration test bed was
deployed in Oklahoma in early 2006: a network of four
low-power, short-range, dual-polarization Doppler radar
units, which will refer to as CASA Integrated Project 1
(IP1). The IP1 radar network is developed with the
system goal of mapping severe weather events in the
lowest 3 km of the troposphere with high spatial and
temporal resolution. Each radar node is developed to
accomplish this system goal through the coordinated
interaction with other radars in the network via a real-
time, closed-loop software control system.
This paper describes the IP1 radar network and radar
node features and operational capabilities. Special em-
phasis is placed on the system aspects that enable coor-
dinated radar operation and on other features that provide
substantial improvements over existing approaches. In
particular, the IP1 radar network is able to sample the
atmosphere with high spatiotemporal resolution, espe-
cially at low altitudes. The dual-polarization capabilities
of the network and simultaneous multiple-radar obser-
vations of weather phenomena enable the retrieval of
enhanced data products, including attenuation-corrected
reflectivity, dual-polarization parameters, and vector wind
fields. In addition, the modular radar control, data pro-
cessing, and communications software architecture allows
variations in the network topology, centralized and/or
distributed network control, and weather information
extraction, making the extension of the network easy
through the addition of potentially heterogeneous radar
nodes.
The paper is organized as follows: section 2 describes
the IP1 radar network architecture and its layout; sec-
tion 3 describes the architecture of a single IP1 radar.
Section 4 describes the radar operation and key features.
Section 5 shows sample data collected from the IP1 ra-
dar network.
2. IP1 radar network infrastructure
The IP1 radar network is deployed in southeastern
Oklahoma, as shown in Fig. 1. The radar nodes are in-
stalled along interstate 44, southwest of Oklahoma City,
Oklahoma, and are located within the coverage area of
the KFDR and KTLX Weather Surveillance Radar-1988
Doppler (WSR-88D) radars. The IP1 radar network im-
plementation consists of four polarimetric weather radar
nodes, designated with the Federal Communications
Commission (FCC) identifiers KSAO, KRSP, KCYR,
and KLWE, and a cluster of computers known as System
Operation and Control Center (SOCC) running a suite
of network control algorithms known as Meteorological
Command and Control (MCC). The SOCC is located
in the National Weather Center building in Norman,
Oklahoma. In Fig. 1, the IP1 radar network layout is il-
lustrated, with the four radar nodes located in the towns
of Chickasha (KSAO), Rush Springs (KRSP), Cyril
(KCYR), and Lawton (KLWE), Oklahoma, and each
radar node approximately 30 km away from the next
unit. Radio links provide Internet connectivity to the
radar node sites with a bandwidth of 4 Mbps. Table 1
contains the FCC identifier and coordinates of each radar
site, as reported by the global positioning system (GPS)
receiver unit with which each radar node is equipped.
As a first approximation, the IP1 radar network can be
seen as composed of two triangular network cells with
the radar nodes at a distance of 30 km from each other
and each radar node with a maximum range Rmax 5
40 km. In the networked radar model introduced by
Junyent and Chandrasekar (2009), this network cell type
can be described by the number of radars (N 5 3) and
the overlap factor (M 5 4/ffiffiffi
3p
), and it can be imagined as
a unit of a larger extended network deployment. Al-
though limited in size, having a network with two cells
(KLWE–KCYR–KRSP and KCYR–KRSP–KSAO) that
share two radars (KCYR and KRSP) and have multiple
FIG. 1. IP1 weather radar network layout from the bottom:
KLWE (Lawton), KRSP (Rush Springs), KCYR (Cyril), and
KSAO (Chickasha) radar coverage. Coverage circles are 40 km in
radius.
62 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
radar coverage has enough complexity to address the
generic problem of targeted coordinated scanning built
in the DCAS paradigm.
The radar node sites, as shown in Fig. 1, are composed
of a fenced area containing a short to medium height
tower, an air conditioning unit, and a connection to the
power grid. Other communications, data storage, and
computing equipment are housed inside a small cabin
inside or next to the fenced area. The tower height is
determined by different factors depending on the site,
including preexisting infrastructure. In the KLWE and
KRSP radar node sites, a 20-ft tower section is sufficient
to clear the surrounding trees, whereas in the KCYR
radar node site a 50-ft tower is needed for the dedicated
point-to-point communication radio links. The KSAO
radar node is deployed on a preexisting 50-ft tower
clearing the surrounding buildings. Connectivity to the
Internet at all radar node sites is provided by OneNet,
a CASA partner.
The radar nodes are connected via the Internet to the
centralized control site known as SOCC, a cluster of
computers and storage devices, which house the scan
rules and algorithms (Zink et al. 2005) that are respon-
sible for the network automated operation. The MCC
continuously ingests and stores data files received from
the radar nodes, detects the relevant weather features in
the individual and overlapping radar data, and creates
a list of tasks associated to the detected features. The
detected features and their associated tasks are then
used to generate optimized scan strategies that are fed
back into the radar nodes with a 60-s period. This real-
time, closed-loop, automated network operation system
is illustrated in Fig. 2.
The general radar node architecture is presented in
Fig. 3. It is divided into three different sections, according
to their physical location. The tower-top subsystems are
housed inside an air-conditioned radome, which provides
a weather-proofed, temperature-controlled environment
for the radar electronics. The radome is composed of
a half-sphere section on top of a cylindrical section and
is 8 ft in diameter and 8 ft in height.
The tower-top rotating subsystems are those located
above the azimuthal axis of motion and include the ra-
dar transmitter, receiver, data acquisition system, and
elevation motion actuator. The tower-top nonrotating
subsystems are housed in an open-frame rack on the
radome floor. They include a computer acting as the
radar motion controller; a gigabit Ethernet switch; an
Ethernet-controlled thermostat; two webcams; and an
Ethernet-controlled outlet strip, which is used to remotely
and independently power cycle the different subsystems.
At the tower base, there is a second gigabit Ethernet
switch connected via optic fiber to the one on the tower
top, a computer performing signal processing and com-
munication tasks referred to as the sensing node signal
processing computer (SNSPC) and a redundant array of
independent disks (RAID), which stores the obtained
radar data. A network router and Ethernet radio link
provide Internet connectivity.
The radar node is completely controllable from any
remote location with access to the Internet. This is driven
by the fact that the deployment towers have limited ac-
cessibility, as well as the fact that ultimately the network
operation could be controlled by algorithms distributed
over the network. Figure 4 shows a flow diagram of the
radar node signals and controls.
Based on a client–server architecture, the radar user,
which can be the MCC algorithms running at the SOCC
or any other authorized entity connecting to it, can send
high-level commands specifying radar parameters and
actions. Positioning controls specifying start and stop
angles, scan speeds, increment steps, axis of motion, and
time of execution can be issued. Arbitrary waveforms
FIG. 2. IP1 weather radar network architecture: the radar nodes
are connected via the Internet to a central control site, which au-
tomatically generates radar control commands based on detected
features in the incoming radar data stream.
TABLE 1. IP1 network radar nodes location.
Location FCC identifier Lat (8) Lon (8) Alt (m)
Chickasha KSAO 35.0312 297.9567 353.99
Rush Springs KRSP 34.8129 297.9313 414.84
Lawton KLWE 34.6238 298.2720 377.45
Cyril KCYR 34.8739 298.2514 445.30
JANUARY 2010 J U N Y E N T E T A L . 63
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
can be generated to control the transmitter and receiver
settings, and the number of range gates, decimation fac-
tor, and passband filter can be set on the data acquisition
system. In addition, the down-conversion frequencies in
both the analog receiver and the data acquisition sys-
tem’s digital receiver are also controllable. The high-
level commands sent by the user are broken down and
interpreted by the different subsystems to which they
are addressed, and the radar node state is reported back
as part of the radar’s weather data stream.
From a maintenance point of view, the IP1 radar
network has a three-tier approach. First, the whole sys-
tem is continuously monitored remotely, tracking that
the operating parameters are within expected ranges.
Second, a local field technician from OneNet is available
on a need basis to work in remote coordination with the
IP1 network radar engineer (not at site) in general site
maintenance (e.g., Internet access, buildings, electric util-
ities, air conditioning, etc.), minor corrective actions, and
debugging efforts. Third, should a radar failure happen, it
can typically be diagnosed through the above-mentioned
remote monitoring tools, allowing the IP1 radar engi-
neer to arrive at the site with the necessary spare parts to
resolve the issue. This approach allows minimizing trips
to the radar sites and reducing the amount of time spent
in the field diagnosing problems. Scheduled radar hard-
ware preventive maintenance is mainly inspection and
testing of mechanical, electrical, and radio frequency
(RF) subsystems (roughly once every 3 months) and
magnetron replacement (about once a year) and sub-
sequent transmitter calibration, whereas unscheduled
maintenance has to do with failed computer parts, mi-
crowave components, cabling, and interconnections, as
well as issues arising from Internet network access and/
or main power outages. Although the IP1 radar network
is an evolving research platform undergoing changes
and modifications in its configuration and operation
throughout its deployment, this maintenance procedure
together with stocked spare parts has ensured radar
availability upward of 98%.
3. IP1 radar architecture
a. IP1 radar hardware
The IP1 radar, shown in Fig. 5, integrates transmitter,
receiver, and data acquisition subsystems in a single
assembly mounted directly behind the antenna. The
transmitter employs a magnetron that has some limited
agility on duty cycle and supported waveforms. The
transmitter delivers a peak power of 25 kW at a maxi-
mum duty cycle of 0.1%. The maximum pulse length is
FIG. 3. Radar node architecture: the tower-top rotating assembly
contains the radar antenna, transceiver, data acquisition system,
and elevation actuator, all mounted on a frame on top of the azi-
muth positioner. On the radome floor, the nonrotating subsystems
include a gigabit Ethernet switch, Ethernet-controlled outlet strip,
GPS amplifier, and position controller computer. A gigabit
Ethernet optic fiber links the tower top with the data processing
server on the ground. The site is connected to the Internet through
a radio link.
FIG. 4. Radar node control and weather data flow. User-issued
high-level commands are broken in scanning, transmitter, receiver,
and data acquisition settings. The radar node state and configura-
tion is reported back to the user as part of the radar’s weather data
stream.
64 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
1 ms, which yields a maximum average power of 25 W at
the maximum pulse repetition frequency (PRF) of 1 kHz.
That would set the system’s unambiguous Doppler ve-
locity to 67.5 m s21. This is clearly insufficient for severe
weather applications, where velocities in excess of 625
m s21 are often encountered. To increase the system’s
unambiguous Doppler velocity and maintain the ability to
efficiently both suppress ground clutter echoes and per-
form spectral processing on the received weather echoes,
blocks of pulses at two higher PRFs (typically 1.6 and
2.4 kHz) are transmitted. To do so, the transmitter peak
power is reduced from its maximum value, which allows
an increase in duty cycle and PRF. Although the adopted
dual-PRF waveform solution is more demanding on the
system hardware, it provides better performance than
a comparable staggered PRT waveform solution in terms
of combined unambiguous Doppler velocity, second-trip
echo suppression, and ground clutter echo filtering, which
are capabilities of core importance in an X-band, short-
range, low-scanning radar. In addition to the dual-PRF
operation at 1.6 and 2.4 kHz, the system admits opera-
tion at other dual-PRF values and at single PRF. A
detailed description of the waveforms’ design in the IP1
system can be found in Bharadwaj and Chandrasekar
(2005, 2006).
The antenna is a center-fed reflector supporting dual-
polarization, mounted on an agile azimuth pedestal ca-
pable of very high scan rates up to 1208 s21. The antenna
motion in elevation is limited to 358, because the system
is intended to scan at low elevations. A linear actuator
is employed, which can achieve a maximum speed of
308 s21. During radar operation, the measurement scans
are taken at a speed of 218 s21, with an antenna repo-
sitioning speed of 608 s21 between measurement scans.
These scan rates enable this mechanically steered sys-
tem to emulate aspects of the beam steering that would
be more typical of electronically scanned antennas.
The dual-channel analog receiver is composed of
a connectorized front end followed by a mixed-signal
surface-mount circuit board that integrates two down-
converting stages and supports advanced features such
as digital frequency and phase control and phase-matched
automatic gain control (AGC). It is a relatively wide-
band receiver (60-MHz bandwidth approximately) when
compared with the radar signal nominal bandwidth of
1.5 MHz. This is justified by the need to accommodate
both the output frequency variability of the particular
magnetron used and the tolerance in nominal output
frequency specification when a used magnetron is re-
placed by a new one. A remotely programmable direct
digital synthesizer (DDS), together with an automatic
frequency control (AFC) software loop, allows the system
to track the transmitted frequency and to control digi-
tally the analog receiver’s final stage down-conversion
frequency. The DDS also allows the two (H and V) re-
ceiving channels phase difference to be set to any arbi-
trary number.
The inclusion of a noise source and dedicated signal
paths enable receiver gain and transmitter power cali-
bration, as well as accurate sampling of the transmitted
pulse, used to determine its amplitude, phase, and fre-
quency. The calibration signal path meets the antenna
path at a high-isolation, low-loss RF switch placed in
front of the low noise amplifier (LNA). When the mag-
netron fires, the switch selects the sampled signal from
the transmit pulse and then switches over to the antenna
path, creating a single signal containing both the trans-
mit pulse sample and the weather echo.
The receiver output is fed into a high-speed, reconfig-
urable, data acquisition and processing system developed
and manufactured by Dynamic Sensing Technologies for
this particular application. The data acquisition system
is a stand-alone unit based on field programmable gate
array (FPGA) technology. The data acquisition system
performs sampling, digital down-conversion, and filter-
ing and generates the low-level radar hardware control
signals. This allows the synchronization of the sampling,
transmitter triggering, and receiver control signals, which
permits subrange gate processing. Figure 6 shows a block
diagram of the transmitter, receiver, and data acquisition
system assembly.
FIG. 5. IP1 radar node: the radar transmitter and receiver (alu-
minum box) and data acquisition (black coated box) are mounted
behind the antenna, the whole assembly being moved in elevation
by a linear actuator. The open-frame rack on the radome floor
contains the position controller computer, a gigabit Ethernet
switch, and an outlet strip. The signal lines from the rack are made
available to the transceiver through the azimuth positioner’s slip
ring assembly.
JANUARY 2010 J U N Y E N T E T A L . 65
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
The data acquisition system is a single board unit in-
tegrating a high-speed (100 MSps), dual-channel, 14-bit
analog to digital (A/D) front end, a data processing core
built on a high-performance FPGA, a real-time data
management and transport core, and a microcontroller-
based embedded Linux system. The data acquisition
system control and data output interfaces are Ethernet
based. In its current configuration, the data processing
core implements a real-time digital in-phase (I) and quad-
rature (Q) demodulation, followed by multistage band
limiting and decimation. It employs a programmable nu-
merically controlled oscillator (NCO) to down-convert
the incoming intermediate frequency (IF) signals, which
are dynamically tuned by the AFC software loop.
A miniature GPS receiver is integrated in the data
acquisition system housing. The main purpose of the
GPS receiver is to accurately time stamp the received
weather echoes, as well as to provide a common but
independent reference clock for all the IP1 radar nodes.
This common clock allows the synchronization of pro-
cesses at different radar nodes without relying on a net-
work connection, and can be used to lock the transmitter
and data acquisition triggering between different radars.
Independently of the GPS-derived time used to tag the
radar data, the radar node computers use the network
time protocol (NTP) to maintain their time. The nomi-
nal characteristics of the IP1 radar nodes are summa-
rized in Table 2.
b. IP1 radar software
The IP1 radar hardware–software interface and ar-
chitecture is presented in Fig. 7. A first layer, labeled as
hardware, contains the different radar subsystems on the
tower top. The software layer contains the software
processes interfacing to the hardware layer, also running
on the tower top, and all other client processes in-
terfacing to the previous set either pulling radar data or
pushing commands to control the radar operation. The
local–remote software interface is realized through trans-
mission control protocol (TCP) and user datagram pro-
tocol (UDP) sockets, which allows the remote software
to be anywhere with Ethernet connectivity. In the cur-
rent configuration, most of the remote clients are run-
ning a computer at the tower base.
The data acquisition control server, residing in the
data acquisition board, accepts commands to configure
the data acquisition operation such as loading and un-
loading configurations for the FPGAs, enabling and dis-
abling the data acquisition and demodulation process,
FIG. 6. IP1 radar node transmitter and receiver system block and functional diagram: items in dashed arrows pointing to blocks indicate
programmable operation parameters.
66 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
setting the NCO frequency, and setting the number of
range gates. The data acquisition control server also
accepts commands to configure the signals that control
the radar transceiver state, such as (i) transmit pulse
length and triggering waveform, (ii) high voltage mod-
ulator enable and disable, (iii) receiver gain settings,
(iv) transmit pulse sampling, and (v) internal noise source
enabling. The GPS data server, also in the data acquisi-
tion board, gathers time and location information from
an onboard miniature GPS receiver.
The demodulated radar data packets and GPS data
packets are sent over UDP sockets through the slip rings
in the azimuth positioner to the time series data server.
The time series data server gathers (i) the demodulated
radar data packets, (ii) the GPS data packets, (iii) the
current antenna position data packets from the position
control server, and (iv) the current down-conversion fre-
quency values and estimated transmitted frequency data
packets from the automatic frequency control server.
Together, these yield a digital signal data structure con-
taining a radar state header followed by the demodulated
complex echo voltages at horizontal and vertical polar-
izations. The digital signal data server allows straight-
forward dissemination of the digitized time series data
using TCP sockets.
A number of different clients can connect to the cur-
rent radar node and network configuration. The digital
signal view client creates a real-time, A-scope-type dis-
play used mainly for monitoring and debugging pur-
poses. The digital signal save client allows sending the
digital signal data to a high-capacity RAID, from which
the data can be retrieved for offline postprocessing and
algorithm testing. The weather Doppler spectral mo-
ments are currently being calculated locally at the radar
node: The spectral moment data server connects to the
digital signal data stream from the digital signal data
server; after running algorithms for spectral moment
calculation, ambiguity mitigation, clutter filtering, and
signal attenuation correction (Liu and Bringi 2006), the
output is streamed to the network common data form
(NetCDF) file server, where it is packaged into a NetCDF
file. These NetCDF radar measurement files are sent via
the Internet to the system operation control center
(SOCC), where the suite of meteorological feature ex-
traction algorithms, together with user policy–driven
scan optimization, are employed to automatically con-
figure the radar network’s operation. In addition to this
mode of operation, a separate position control client
allows for remote control of the radar scanning.
The modular radar control, data processing, and com-
munications architecture admit variations in the network
topology, effectively allowing any combination of mul-
tilayer centralized and/or distributed network control
and weather information extraction. This flexibility ac-
commodates the extension of the network through the
simple addition of further radar nodes. Also, it makes
the hardware transparent to the network, making it pos-
sible to compose a system consisting of heterogeneous
sensors.
4. IP1 radar operation and important features
The transmitter, receiver, and data acquisition system
functionality is illustrated in Fig. 6, which also shows the
software-controlled operation parameters of each sub-
system. Once the transmitter waveform is set and the
data acquisition process and high-voltage modulator are
enabled, the magnetron’s trigger signal is released. A
switch at the receiver’s front end allows sampling of
the transmitted pulse, as illustrated in Fig. 8; a sample of
the high-power transmitted pulse is coupled through
a dedicated high-isolation path, preventing the distorted
leakage through the receiver protection limiter from
contaminating the transmitted pulse sample signal. This
TABLE 2. IP1 radar node design characteristics.
Transmitter
Type Magnetron
Center frequency 9410 6 30 MHz
Peak power output 8.0 kW (per channel)
Avg power output 12 W (per channel)
Pulse width 660–1000 ns
Polarization Dual linear, H and V
Max duty cycle 0.16%
Antenna and pedestal
Type (diameter) Dual-polarized parabolic
reflector (1.2 m)
3-dB beamwidth 1.808
Gain 38.0 dB
Azimuth scan rate up to 2408 s21
Elevation scan rate up to 308 s21
Acceleration up to 1208 s22
Receiver
Type Parallel, dual-channel,
linear output I/Q
Dynamic range
(bandwidth 5 1.5 MHz)
103 dB
Noise figure 5.5 dB
Data acquisition system
Sampling rate 100 MSps
Dynamic range
(bandwidth 5 1.5 MHz)
108 dB
Data transfer rate 88.3 Mbps
Decimation factor Adjustable
Video bandwidth Adjustable
JANUARY 2010 J U N Y E N T E T A L . 67
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
preserves the transmitted pulse sample integrity, because
it is used to estimate the transmitted power, frequency,
and starting phase.
The resulting signal is down-converted from X band to
a (DDS) programmable IF frequency, and the IF signal is
then sampled by the data acquisition system’s A/D front
end. Prior to digitally down-converting the digitized IF
signal, its actual carrier frequency must be obtained; a
magnetron is a cavity-based, noncoherent signal source,
and therefore its output frequency will change depend-
ing on the particular operation settings, whereas each
transmitted pulse will have a random start phase. These
effects are estimated and used to correct the received
echo signals to ensure the right operation of the radar.
The estimated frequency value is used to set the NCO
down-conversion frequency.
a. Automatic frequency control
A magnetron’s frequency stability is dependent on
factors such as operating temperature, pulsing duty cycle,
and modulator voltage stability. Figure 9 shows mea-
surement results of the IP1 magnetron output frequency
dependence on temperature and duty cycle, illustrating
some clear trends. Figure 9a shows both the duty cycle
influence on the magnetron’s output frequency at a given
temperature and the effect of temperature change on the
output frequency at a given duty cycle. The magnetron
operating duty cycle offsets the output frequency from its
nominal value by a fixed amount, which is found to be
about 2550 kHz per 0.025% duty cycle increment. The
output frequency decreases linearly as the magnetron
operating temperature increases, showing a mean slope
of 2166 kHz C21. Figure 9b shows the effect of chang-
ing between alternating PRFs (relevant to dual-PRF
operation of the radar). A frequency excursion around
200 kHz is observed when alternating bursts of pulses at
1.6- and 2.4-kHz PRF, in which the output frequency
decreases when the PRF is increased. This agrees with
the trend observed in Fig. 9a, because a PRF increase
creates a duty cycle increase, which finally translates in
a lower output frequency. To keep the receiver tuned to
the changing transmitted frequency, a digital AFC al-
gorithm is implemented. The IP1 radar node AFC al-
gorithm is a real-time software loop that uses a digitized
sample of the transmitted pulse to estimate the magne-
tron’s output frequency and sets the receiver DDS and
data acquisition digital receiver NCO to the appropriate
down-conversion frequencies. This fully digital solution
provides nearly arbitrary tuning resolution and a very
fast settling time. The estimated AFC frequency and
FIG. 7. IP1 radar node software architecture: broad lines are for radar transmitted and received weather echo signals.
68 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
employed NCO and DDS frequencies are reported in the
radar data stream.
The final digital receiver bandwidth, which is pro-
grammable, is set to a value slightly wider than that of
the transmitted pulse bandwidth, allowing for some tol-
erance in the frequency estimation. The frequency esti-
mation algorithm is based on the following: assuming
that the transmitted waveform is described by
s(t) 5 A cos(2p fot 1 u), t 2 [0, Dt], (1)
the NCO is set to the frequency fNCO,
fNCO
51
N �N
i51f
i, (2)
where fi is the frequency that maximizes the expression
fi
maxðDt
0
w(t)s(t)e j2pfit dt
�
�
�
�
�
�
�
�
2" #
, (3)
where w(t) is the windowing function
w(t) 51
21� cos 2p
t
Dt
� �h i
, (4)
Dt is the transmitted pulse duration, and N is the number
of transmitted pulses averaged. The frequency search is
initialized by doing a fast Fourier transform and select-
ing the highest power coefficient. The frequency is fur-
ther refined by computing Eq. (3) at lower and higher
frequency values and at a frequency interval slightly
larger than half of the previous frequency resolution
interval. The frequency value that maximizes Eq. (3)
is then selected, and the frequency search is narrowed
by iterating the process. The AFC estimation is done
continuously, but the actual NCO down-conversion
frequency is changed only if the estimated frequency
value departs more than a given tolerance interval from
its current value, as illustrated in Fig. 10. The down-
conversion frequency tolerance factor is set so that the
radar signal always falls inside the final digital receiver
bandwidth. The down-conversion residual frequency error
FIG. 8. Transmitted pulse sampling: a sample of the transmitted pulse is coupled through
a dedicated signal loop into the receiver (gray arrow) prior to switching over to the antenna port
(black arrow).
JANUARY 2010 J U N Y E N T E T A L . 69
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
introduces an extra phase offset, which is naturally
canceled in all the relative phase measurements, such as
Doppler velocity. This ensures operational stability and
prevents excessive retuning resulting from estimation
errors.
b. Doppler spectral moment estimation
Parallel to the AFC process, the digitized data are
down-converted, decimated, and filtered. Then, the trans-
mitted pulse phase is measured and subtracted from the
received echo signal. This standard technique, known as
coherent on receive, enables phase-based measurements
such as Doppler velocity from noncoherent source echoes.
The transmitted pulse start timing is set so that, after
decimation, the pulse peak sample is preserved. This
ensures the highest SNR at this sample, from which the
transmitted pulse phase is obtained.
After coherent-on-receive correction of the digital
signal, the moments of the Doppler spectrum are com-
puted. A detailed description of the employed algo-
rithms can be found in Bharadwaj and Chandrasekar
(2005, 2006). From an operational standpoint, the im-
plementation of the spectral moment calculation algo-
rithms admits real-time variations of the transmitted
waveforms (from single to dual PRF and different PRF
frequencies and integration times), allowing switching
between operation modes without interruption in the
data flow or manual intervention. Similarly, some signal
processing blocks such as the ground clutter filter can be
turned on and off via software (using the spectral mo-
ment control client) without interfering with the normal
operation of the radar, which allows, for example, in-
terleaving of ground clutter scans needed for refractivity
measurements with the normal operation of the radar.
The metadata necessary to track the state of the radar
node during operation are included in the data stream.
For the Doppler spectrum moment calculation algo-
rithms to work properly, the changes in the transmitted
waveform (going from single to dual PRF, separating
first and second PRF blocks in dual PRF, changing
number of pulses and PRF frequency values, etc.) have
to be detected. This is accomplished by reading the re-
ceiver’s reference clock–derived time stamp with which
every data ray is tagged. This time stamp allows the
Doppler spectrum moment calculation algorithms to
estimate intrapulse periodicities with a resolution of
10 ns, permitting the separation and processing of pulse
blocks according to their estimated PRF. In addition to
that, to provide a ‘‘wall’’ clock common to all radar
nodes, a miniature GPS receiver is employed at each
FIG. 9. IP1 radar node magnetron output frequency drift mea-
surements: (a) a laboratory measurement of the magnetron output
frequency as a function of anode temperature. Top line (squares)
is 0.050% duty cycle, middle (diamonds) is 0.075%, and bottom
(triangles) is 0.100%. (b) The transmitter frequency drift vs PRF is
illustrated. The higher output frequencies correspond to a burst of
pulses at 1.6-kHz PRF, whereas the lower-frequency values cor-
respond to a burst of pulses at 2.4-kHz PRF.
FIG. 10. AFC-estimated transmitted frequency (solid line) and
down-converting frequency (DDS and NCO; dashed line) for
a dual-PRF operation example.
70 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
radar node. The data acquisition system’s waveform gen-
eration software can accept interruptions coming from
the GPS clock, to which the radar node transmitter,
receiver, and data acquisition control waveforms can be
locked. In addition to the reference clock–derived time
stamp (unique to each radar node), a GPS-based time
stamp (common to all radar nodes in the network) is
added to the radar data stream.
Once the data moments are computed, the appropri-
ate radar constant has to be added to the estimated
signal power to obtain a reflectivity value. To accom-
modate variations in the magnetron’s output frequency,
the analog receiver’s bandwidth is maintained several
times wider than that of the transmitted pulse. There-
fore, there is a certain ripple and slope associated to the
‘‘broadband’’ analog receiver’s gain, which will depend
on the particular frequency at which it is evaluated. To
take this effect into account, a tool in the IP1 radar
nodes allows a broadband calibration of the receiver to
be remotely obtained. A known signal from an onboard
noise source is injected in the receiver, and the output is
recorded while the DDS and NCO are swept in fre-
quency. Because the particular DDS and NCO values
employed in down-converting the radar data are avail-
able in the data stream, the appropriate receiver cali-
bration values corresponding to the current frequency
settings are dynamically selected and employed in the
radar constant determination.
c. Receiver and transmitter calibration
To maintain the accuracy of the radar constant and
received signal power values, the IP1 radar nodes com-
bine techniques, such as using a noise source and feed-
ing a sample of the transmitted pulse through the
receiver with the capability of setting the receiver down-
conversion frequencies, to obtain a joint broadband re-
ceiver and transmitter calibration. In particular, the
following procedure is established: first, the analog plus
digital receiver gain G and noise figure NF are estab-
lished for the entire receiver bandwidth using the noise
source embedded in the receiver. Once the receiver gain
is characterized, the transmitted power is obtained by
tracking the power measured through the receiver.
Figure 11 illustrates the signal paths in the combined
transmitter–receiver calibration.
The noise source injects a signal of known excess
noise ratio ENR (dB) into the receiver through the
calibration path with loss L4 (dB), creating a receiver
input signal Photin (dBm). The corresponding receiver
output signal Photout (dBm) is recorded for all DDS and
NCO down-conversion frequencies in the receiver
bandwidth. This procedure is repeated turning the
noise source off and recording the ambient thermal
noise at the receiver output Pambout (dBm). The noise
factor NF (dB), referenced at the receiver calibration
port (receiver switch input in Fig. 11), is obtained from
FIG. 11. IP1 radar node combined transmitter–receiver calibration paths.
JANUARY 2010 J U N Y E N T E T A L . 71
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
the two noise measurements using the Y factor method
(Pozar 1999) as
NF 5 ENR� L4� 10 log
10(10(Phot
out�Pambout )/10 � 1), (5)
and the combined analog and digital gain of the receiver,
referenced at the antenna port, is then obtained as
G 5 Pambout � Pamb
in �NF� L2, (6)
where L2 (dB) is the loss between the receiver and an-
tenna ports and Pambout (dBm) is the ambient thermal
noise power at the receiver input, obtained as
Pambin 5 10 log
10(kTB) 1 30. (7)
Substituting all equations, the analog plus digital re-
ceiver gain referenced at the antenna port is
G 5 Pambout � 10 log
10(kTB)� 30� ENR 1 L
4
1 10 log10
(10(Photout�Pamb
out )/10 � 1)� L2.
(8)
It must be noted that the calibration procedure does
not take into account the return loss at the receiver’s
input switch or any gain difference between the switch
receive and calibration paths, as the switch is assumed
to be well matched and balanced. Once the receiver is
characterized, the transmitter calibration is performed.
An initial measurement of the transmitted pulse power
at the antenna port P calant is obtained with a power meter,
together with the corresponding power measurement
by the AFC algorithm at the data acquisition output
P caldaq. Looking at Fig. 11, the two measurements are
related as follows:
FIG. 12. IP1 radar network scan task example.
FIG. 13. IP1 radar network-adaptive scan pattern (after scan task shown in Fig. 12).
72 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
P calant 1 L
1� L
35 Pcal
daq 1 Gcal 1 C, (9)
where L1 (dB) is the loss between the transmitter and
antenna ports, L3 (dB) is the loss between the trans-
mitter and transmitted pulse sample duplexer port, Gcal
is the receiver gain [as obtained in (8)] for the particular
frequency at which the calibration is made, and C (dB)
is a constant that accounts for the signal processing
gain difference between the transmitted pulse AFC pro-
cessing and regular echo processing and the signal path
loss between the duplexer and the receiver calibration
port.
When the radar is in operation, the same relation
applies to the transmitted power at the antenna ports:
Ptxant 1 L
1� L
35 Ptx
daq 1 Gtx 1 C, (10)
FIG. 14. KRSP radar node adaptive scan data collected while executing the scan pattern shown in
Fig. 13. All different elevation sector scans and RHI scans are executed during a system heartbeat
(60-s interval between network scan tasks). Data were collected during routine network operation.
JANUARY 2010 J U N Y E N T E T A L . 73
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
where the superscript tx refers to the real-time trans-
mitter frequency during operation. Subtracting (9) from
(10), it can be shown that the current transmitted power
at the antenna port Ptxant can be tracked from the AFC
transmitted power measurement Ptxdaq taking the initial
calibration measurement as reference
Ptxant 5 Pcal
ant 1 (Ptxdaq � Pcal
daq) 1 (Gtx �Gcal). (11)
Based on the previous expressions, the IP1 radar nodes
obtain an updated transmitted power and radar constant
value for every spectral moment data ray.
d. Motion control and scanning
An important feature of the IP1 radar nodes is their
ability to perform arbitrary scans, which enables real-
time network adaptive coordinated scanning. In the IP1
network, the MCC algorithms running at the SOCC
periodically (every 60 s, referred to as the system
‘‘heartbeat’’) create optimal sets of scan tasks that are
fed back to the radar nodes, which continuously listen
and queue such commands. The scan tasks are inter-
preted at each radar node, where the antenna position-
ing control unit accepts scan commands specifying a
start point and an end point (containing both azimuth
and elevation coordinates), together with motion di-
rection and velocity. The radar antenna is moved from
its current position to the new scan starting point
through the shortest path and at its fastest operational
speed (set at 608 s21) and then moves to the scan end
point at the required velocity (typically 218 s21) while
collecting data. Using this capability as a building block,
any arbitrary scan pattern can be constructed through
the noninterrupted execution of a queued sequence of
piecewise linear scans, allowing the radar nodes to in-
terleave different operation modes.
This process is illustrated in Figs. 12, 13, and 14, which
show a typical operational scanning sequence. Figure 12
FIG. 15. IP1 network reflectivity composite data showing the passage of a cluster of rain cells.
Data were collected while executing the scan pattern shown in Fig. 13, during routine network
operation.
74 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
illustrates the MCC scan task output in response to
detected data features (colored polygons), where the
multiple transparent arcs and different angular widths
represent the different scans to be executed by each
radar. Figure 13 presents the different scans performed
at each node during that heartbeat in more detail. In
particular, one can see how KSAO is tasked to perform
full PPI scans at two different elevations followed by an
RHI, whereas KCYR, KLWE and KRSP are tasked to
perform a sector scan at the lowest elevation, followed
by a full PPI and more sector scans at the next eleva-
tions, and finalizing also with an RHI scan. The different
sector sizes and number of elevation scans per radar
node are intended to optimally cover the weather scene
following the MCC automated feature detection output
and optimization process (Zink et al. 2005). Figure 14
shows the KRSP reflectivity and Doppler velocity fields
corresponding to the scanning sequence in Fig. 13,
where a total of six different scans are obtained in the
60-s heartbeat period.
5. Data examples from IP1 radar network
During the last three years, the IP1 radar network
has collected an extensive dataset, from which data
samples are selected to illustrate the network opera-
tion. Figures 14 and 15, obtained on 24 May 2009,
show a linear cluster of rain cells crossing the network
domain. Figure 15 shows a snapshot of the event as
captured by the network, whereas Fig. 14 shows it as
seen by the KRSP radar node. The azimuthal sector
scans are centered around the areas of higher reflec-
tivity, with a full PPI scan at 28 providing a broader
view. The RHI scan shows the vertical wind shear and
high reflectivity core associated with the targeted rain
cell.
Figure 16, obtained on 14 June 2007, shows network
composite reflectivity data obtained before the passage
of a cluster of storm cells through the network domain.
The leading gust front can be clearly seen extending from
south of the KCYR radar node to KSAO’s north–east,
FIG. 16. IP1 network reflectivity composite data showing the gust front ahead of a storm en-
tering the network domain. Data were collected during routine network operation.
JANUARY 2010 J U N Y E N T E T A L . 75
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
while the storms to the north start entering the network
domain.
Figure 17, obtained on 6 October 2008, shows net-
work composite reflectivity data collected during the
passage of a storm cell. The cell developed a hook echo
at its northwest leading edge, falling under the cover-
age of both KRSP and KSAO. A zoomed-in image of
the hook echo region, containing dual-Doppler vector
winds overlaid on reflectivity, reveals a weak rotation at
the hook echo tip embedded in the general northwest
flow.
Figure 18, obtained on 10 February 2009, shows net-
work composite reflectivity data collected during the
passage of a train of storm cells, which developed a
number of hook echo features and eventually produced
tornadoes once outside of the network domain. One
such hook echo feature can be seen at the southwest
edge of the cell just north of the KCYR radar node. The
dual-Doppler vector winds in the zoomed hook echo
region shows an area of rotation at the hook echo tip.
All the data collected by the IP1 radar network are
routinely archived and used in developing a number of
both real-time and postprocessing research weather ra-
dar and meteorological data products, such as nowcast-
ing and forecasting analysis, rainfall estimation, wind
analysis, and weather feature detection.
6. Summary
This paper presented the IP1 radar system, designed
and developed specifically to operate in a networked
environment. To make a radar suitable for dense net-
worked deployment, a number of challenges must be
solved: the radars have to be small, easy to deploy
and maintain, and controllable from remote locations.
The need to keep the physical size of the radar node
small leads to a higher frequency of operation (X band),
which creates a new set of challenges: the radar’s range–
velocity ambiguity is worse when comparing to the usual
lower frequencies employed in operational weather radar
FIG. 17. IP1 network reflectivity data and KRSP–KSAO dual-Doppler wind zoom on a de-
veloping hook echo in the storm leading edge. Data were collected during routine network
operation.
76 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
systems. The effects created by this are mitigated
through hardware configurations that support advanced
waveforms. In addition, the system is able to effectively
filter ground clutter as it is intended for operation at low
beam altitude.
The design solutions adopted to mitigate the issues
arising from operation in a networked environment have
been presented, as well as the system implementation
and its operation. The radars operate in real time, under
remote automated control, and generate attenuation-
corrected data, with a high unambiguous velocity in-
terval and reduced second trip and clutter contamination,
making them fit to perform in a dense network. Sev-
eral data cases collected under such an operation are
presented.
Acknowledgments. This work was supported primar-
ily by the Engineering Research Centers Program of
the National Science Foundation under NSF Award
0313747. Any opinions, findings, and conclusions or rec-
ommendations expressed in this material are those of the
authors and do not necessarily reflect those of the
National Science Foundation.
The authors acknowledge Mr. Luko Krnan of Dy-
namic Sensing Technologies for his contributions towards
the integration of the data acquisition solution in the ra-
dar nodes and field work support.
REFERENCES
Bharadwaj, N., and V. Chandrasekar, 2005: Waveform design for
CASA X-band radars. Proc. 32nd Conf. on Radar Meteorology,
Albuquerque, NM, Amer. Meteor. Soc., P10R.13. [Available
online at http://ams.confex.com/ams/pdfpapers/96347.pdf.]
——, and ——, 2006: Waveform design considerations for CASA
radar network. Proc. Fourth European Conf. on Radar Mete-
orology and Hydrology (ERAD 2006), Barcelona, Spain,
Servei Meteorologic de Catalunya et al., 117–121.
Chandrasekar, V., and A. P. Jayasumana, 2001: Radar design and
management in a networked environment. Proc. ITCOMM,
Denver, CO, International Society for Optical Engineering,
142–147.
Junyent, F., and V. Chandrasekar, 2009: Theory and character-
ization of weather radar networks. J. Atmos. Oceanic Tech-
nol., 26, 474–491.
FIG. 18. IP1 network reflectivity data and KCYR–KSAO dual-Doppler wind zoom on
a developing hook echo in the storm-trailing edge. Data were collected during routine network
operation.
JANUARY 2010 J U N Y E N T E T A L . 77
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC
Liu, Y., and V. N. Bringi, 2006: Improved rain attenuation cor-
rection algorithms for radar reflectivity and differential re-
flectivity with adaptation to drop shape model variation. Proc.
26th Int. Geoscience and Remote Sensing Symp. (IGARSS
2006), Denver, CO, Institute of Electrical and Electronics
Engineers, 1910–1913.
McLaughlin, D. J., 2001: Presentation to National Research Coun-
cil: Weather Radar Technology beyond NEXRAD. National
Academies Press, 96 pp.
——, and Coauthors, 2005: Distributed collaborative adaptive
sensing (DCAS) for improved detection, understanding, and
predicting of atmospheric hazards. Proc. Ninth Symp. on In-
tegrated Observing and Assimilation Systems for the Atmo-
sphere, Oceans, and Land Surface (IOAS-AOLS), San Diego,
CA, Amer. Meteor. Soc., 11.3. [Available online at http://
ams.confex.com/ams/pdfpapers/87890.pdf.]
NRC, 2002: Weather Radar Technology beyond NEXRAD. Na-
tional Academy Press, 81 pp.
Pozar, D. M., 1999: Microwave Engineering. Wiley, 720 pp.
Zink, M., and Coauthors, 2005: Meteorological command and control:
An end-to-end architecture for a hazardous weather detection
sensor network. Proc. Workshop on End-to-End, Sense-and-
Respond Systems, Applications, and Services (EESR ’05), Seat-
tle, WA, USENIX Association and ACM SIGMOBILE, 37–42.
78 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27
Unauthenticated | Downloaded 05/05/22 05:54 PM UTC