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WEATHER AND FORECASTING
VOLUME 27
Applications of a Velocity Dealiasing Scheme to Data from the China
New Generation Weather Radar System (CINRAD)
GUANGXIN HE AND GANG LI
Center of Data Assimilation for Research and Application, Nanjing University of Information Science
and Technology, Nanjing, China
XIAOLEI ZOU
Center of Data Assimilation for Research and Application, Nanjing University of Information Science
and Technology, Nanjing, China, and Department of Earth, Ocean and Atmospheric Science,
The Florida State University, Tallahassee, Florida
PETER SAWIN RAY
Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, Florida
(Manuscript received 6 May 2011, in final form 30 August 2011)
ABSTRACT
An improved velocity dealiasing algorithm is developed as an extension of the Next Generation Weather
Radar (NEXRAD) dealiasing algorithm. The algorithm described in this paper is evaluated on selected China
Next Generation Weather Radar (CINRAD) S-band radar radial velocity observations. This algorithm includes four modules for removing weak signals and determining the starting radial as a prelude to identifying
and correcting aliased velocities. The proposed dealiasing algorithm was tested on 14 different weather
systems, composed of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and
stable for dealiasing S-band CINRAD radial velocity measurements. The performance levels for the typhoon
and heavy rain cases are slightly better than for squall-line cases.
1. Introduction
Radar observations play an increasingly important role
in numerical weather prediction (NWP) where real-time
forecasts of actual storms, initialized by current data, are
within reach. A radar network of 158 Doppler radars in
China, called the China Next Generation Weather Radar
(CINRAD) network, is one source that could provide the
needed observations of precipitation, wind, and hail in
near–real time. These data will soon be assimilated into
the Global and Regional Assimilation and Prediction
System (GRAPES), which is a 3D variational data assimilation system developed in China. It is anticipated
that radar data assimilation at the convective scale has the
potential to improve the prediction of hazardous weather
in China and elsewhere.
Corresponding author address: Dr. X. Zou, Dept. of Earth,
Ocean and Atmospheric Science, The Florida State University,
Tallahassee, FL 32306-4520.
E-mail: [email protected]
DOI: 10.1175/WAF-D-11-00054.1
Ó 2012 American Meteorological Society
The integration of radar data into real-time NWP
products requires substantial automation, adequate data
accuracy, and robust quality control (QC) procedures.
One challenge with radar data is correcting velocity
aliasing. The unambiguous velocity interval derived
from the complex time sample for a given pulse repetition frequency (PRF) and transmitted radar wavelength is given by [2Vmax , Vmax ] (Ray and Ziegler
1977), where
Vmax 5 (PRF)l/4
(1)
is the maximum observed velocity, called the Nyquist
velocity. Any frequency shift exceeding PRF/2 will be
aliased. The true velocity (VT ) is related to the observed
velocity (VO ) as follows:
VT 5 VO 6 2n 3 Vmax , n 5 0, 1, 2, ,
where 2Vmax is the Nyquist cointerval.
(2)
FEBRUARY 2012
HE ET AL.
Velocity aliasing in the Doppler radar radial velocity
measurements is a challenge for radar data assimilation.
An effective velocity dealiasing scheme must be applied
to recover the true signals (VT ) from the raw measurements (VO ) before the data are assimilated into GRAPES
or any other system incorporating radar data into a forecast model.
Software or hardware approaches can be used to solve
the velocity aliasing problem. Based on the expected
structure of the velocity data, such as spatial continuity,
dealiasing is accomplished here using a software algorithm.
The hardware approach adopts the staggered-PRT
(Doviak and Zrnić 1993) method, and can appreciably
enlarge the maximum unambiguous interval. Pulsed radars send out a succession of pulses at a scheduled pulse
repetition time (PRT). The reciprocal of that is the PRF
or the number of pulses per second. Currently, the
staggered-PRT technique is most often used with shortwavelength weather radars (because of their much
smaller Nyquist cointerval) to reduced the dealiasing
burden. However, the staggered-PRT method has the
disadvantage that measurements acquired at slightly
different times or locations are combined, which can lead
to representativeness errors (Haase and Landelius 2004)
and that spectral estimation is made difficult because
of the nonuniform sampling. Even with the staggeredPRT method, velocity aliasing can still occur when the
wind velocity is very large. Therefore, it is necessary to develop a robust and computationally efficient dealiasing
algorithm.
a. Literature review
Since the 1970s, there have been many dealiasing algorithms proposed. Ray and Ziegler (1977) put forward
the first dealiasing algorithm: a one-dimensional scheme
using only single radial velocity information to remove
aliased velocities in that radial. They proposed that all
the velocity gates along the same radial should be normally distributed, thus identifying the outlying gates that
need to be dealiased. This technique is sensitive to noise
and multiple folds [when n is greater than 1 in Eq. (2)]
and is effective only when minor aliasing occurs. Bargen
and Brown (1980) developed another one-dimensional
dealiasing scheme. They assumed that the first gate in
each radial was correct. Using the averages of previously
dealiased gates in the radial as a reference, they dealiased
the gates through minimizing the difference between the
previous gates in the radial and the next velocity. For
aliased gates that cannot be correctly dealiased, this scheme
permits user intervention to manually intervene. Thus,
their scheme is not feasible for very large datasets or
real-time applications. Merritt (1984) introduced a twodimensional dealiasing algorithm that allows successive
219
testing for both the radial and azimuthal directions. First,
all the data in each elevation angle were separated into
different regions. The data in each region have the same
Nyquist interval. Then, the shear along the boundary of
adjacent regions was minimized by determining the proper
aliasing interval for each region. Finally, a wind model was
used to determine the proper Nyquist interval for areas
that are spatially isolated with respect to the major echo
regions. Bergen and Albers (1988) enhanced Merritt’s algorithm by adding a noise filter and using a sounding instead of the wind model. This improved the dealiasing of
isolated echoes. Eilts and Smith (1990) developed a new
two-dimensional algorithm. They used a vertical wind profile from a sounding taken at nearly the same time that
the radar data were collected. Previously dealiased gates in
the same and previous radials (or velocity azimuth display,
VAD) wind profile information were used as reference
data to dealias each elevation angle, radial by radial. This
algorithm is effective when the Nyquist velocity is between
20 and 35 m s21. Each gate was processed with a radial
continuity check by comparing it with a reference velocity,
which is (the closest of five preceding gates) in the same
radial. If the difference between the dealiased velocity and
the reference is less than a threshold (e.g., 15 m s21), then
the dealiased velocity was retained and the algorithm
proceeds with the next gate in the same radial. If the initial
radial continuity check failed or there is no point within
five gates, then an average of nine neighboring gates (four
in the same radial and five in the adjacent preceding radial)
was used as the new reference for comparison. If the dealiased velocity fell within the threshold of the average,
then it is dealiased, and the algorithm proceeded to the
next set of gates. If the dealiased result is not within this
domain, then it is deleted. Or if there are no valid points
to compute the average, the algorithm looks back within
5 km toward the radar in the same radial and forward
2.5 km in the previous radial to find the first valid gate for
reference. The sounding or VAD wind information will
place the velocity in question into the proper aliasing interval when no points are found for comparison. Two types
of error checks were incorporated into the algorithm
during dealiasing. If five consecutive points are removed
by the algorithm, these points are replaced by initially
comparing the first point removed with the preceding velocity when dealiased by a larger threshold (1.5 times the
previous threshold). If the difference between two azimuthally adjacent velocities was larger than 1.2 times the
Nyquist velocity and all the azimuth pairs within 2.5 km
met this criterion, an azimuthal shear check was used.
These data were reexamined by a least squares minimizing technique until no errors exist. Finally, a gate-to-gate
check was made if there were any unrealistic discontinuities (e.g., large than 1.7 times the Nyquist interval). If an
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WEATHER AND FORECASTING
unrealistic jump exists, the previous radial was used instead of the current radial of data for dealiasing the velocities in subsequent radials. The error checks can stop
the propagation of errors caused by the improper dealiasing. Jing and Wiener (1993) assumed that the average
radial velocity of the local wind observed by radar is
less than the Nyquist velocity. They dealiased a twodimensionally connected region based on the assumption of a smooth velocity field, using the principle of
minimizing shear with respect to the environmental
wind field or VAD wind profile. Based on the twodimensional dealiasing algorithm, James and Houze
(2001) added information from different elevation angles and volume scans. This technique first eliminates the
low signal-to-noise ratio (SNR) data that may be caused
by ground clutter or second trip echo; a Bergen and
Albers (1988) filter is used to remove isolated data. Because high-elevation angles suffer less from ground clutter, dealiasing can first be performed at high-elevation
angles and then extended to lower-elevation angles. Highelevation velocities are considered to be a reference when
dealiasing the lower-elevation data. This new algorithm
was then applied to an extensive C-band radar velocity
dataset that incorporated 4300 different elevation angles.
Ninety-three percent of the data were correctly dealiased.
Gong et al. (2003) produced a three-step dealiasing algorithm to conduct quality control on the radial velocity
data. The VAD is the data collected on the surface of a
cone that is at a constant elevation angle. All the data are
at the same elevation but vary with azimuth and range.
Since the elevation is not necessarily zero, the height of
the data also increases with range from the radar. The
wind profile from a VAD algorithm is not always correct
when aliasing exists. Therefore, they used the improved
VAD algorithm (Tabary et al. 2001) to select and precondition possibly ambiguous velocity gates. Then, using the
more accurate reference wind field computed from the
traditional VAD algorithm, all the gates of the preconditioned velocity field were dealiased as needed. Zhang and
Wang (2006) proposed a two-dimensional multipass dealiasing algorithm (2DMPDA), which is independent of external data sources. They minimized the velocity gradients
between unfolded velocities and adjacent folded velocities
in each scan. After multipass searching, the search area is
successively relaxed to incorporate more reliable velocities for the minimization of the gradient. This algorithm
was then assessed by dealiasing over 1000 volume scans
from Taiwan and from Weather Service Radar-1988
Doppler (WSR-88D) data. More than 99% of the velocity data were correctly dealiased. Witt (2007)
compared the dealiasing results between the current
Supplemental Product Generator (SPG) algorithm and
2DMPDA (Zhang and Wang 2006) on data from the
VOLUME 27
Terminal Doppler Weather Radar (TDWR) base
data for eight severe weather events including squall
lines, hailstorms, and supercells. Based on the scoring
methodology procedure (Brown and Wood 2005), the
2DMPDA performed slightly better than current SPG
algorithm. The 2DMPDA offers the potential for improved TDWR velocity data especially at the smaller
Nyquist intervals. However, despite the overall improvement of the 2DMPA, the SPG can show better performance in severe weather signatures, such as those from
mesocyclones. Witt (2007) also suggested that the velocity
values with reflectivity less than 25 dBZ should be treated
as noise, or errors may propagate into higher-reflectivity
regions. Removing noise before dealiasing will help improve the velocity dealiasing process. Witt et al. (2009)
proposed a new, improved algorithm named the two-dimensional velocity dealiasing algorithm (2DVDA) based
on the Jing and Wiener (1993) algorithm. They found that
the current Next Generation Weather Radar (NEXRAD)
algorithm can easily cause velocity dealiasing errors when
NEXRAD radars operate in volume coverage pattern 31
(VCP 31) and wind speeds or vertical wind shear are high.
The new algorithm consistently outperformed the current
NEXRAD algorithm for convective storm and hurricane
events. The performance improvement of the 2D-VDA
was substantially greater for the VCP-31 cases versus the
VCP-12/212 cases. Neither algorithm performs very well at
the lowest elevation angle, which potentially has more
aliased velocities and stronger horizontal wind shear regions. The new algorithm is planned to replace the current
NEXRAD algorithm after more extensive testing is done
in the future, particularly for the VCP-31 data. Lim and
Sun (2010) introduced a new dealiasing scheme that uses
the forecast field from a radar assimilation model as the
reference field. The radar velocity data were first interpolated from the original two-dimensional polar grid
onto a Cartesian coordinate system with a 1-km resolution. Then, the forecast field (4-km resolution), derived from a four-dimensional storm-scale radar
assimilation system (the Variational Doppler Radar
Analysis System, VDRAS; Sun and Crook 1997), was
employed to dealias the interpolated radar data. They
compared the differences in the dealiasing results when
using a blend of data from the Weather Research and
Forecasting (WRF) surface network and the VAD
profiles’ mesoscale background and VDRAS analysis as
references. They reported that the algorithm can reduce
dealiasing errors when VDRAS analysis is used as the
reference wind.
WSR-88D is also sometimes referred to as the NEXRAD
radar. The NEXRAD velocity dealiasing algorithm (VDA;
Eilts and Smith 1990) is fundamentally based on minimizing velocity gradients along a radial, and has proven to
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HE ET AL.
FIG. 1. The average of the absolute values of the measured velocities at all of the valid gates along each radial (solid curve) and
the total number of valid gates along each radial (dashed–dotted
curve). The dashed horizontal line is the average number of all the
valid gates and the solid horizontal line indicates the average
number of all the valid gates multiplied by 2/ 3.
be a robust and reliable algorithm, having been used for
a number of years for dealiasing radial velocity measurements and producing operationally useful products in
the United States. In this study, we apply the NEXRAD
algorithm to CINRAD data for different weather regimes
in China. Modifications to the NEXRAD algorithm are
proposed, and the results are compared with those from
the original scheme.
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FIG. 2. The dealiasing processes are carried out clockwise and
counterclockwise for 1808 in both directions of the initial reference
radial. Variables r and u indicate the radial direction and azimuth
direction, respectively.
reflectivity is less than 20 dBZ. Different radar systems
may require different thresholds depending on transmitted power, antenna gain, etc.
Step 2: Noisy velocity data near aliased areas that are
not completely removed in step 1 are further removed
in this step. First, the sum of the absolute velocities of
all the valid gates in each radial, V, is calculated.
Velocities smaller than a threshold value, a, are removed, where a is larger if V is larger. For examples,
b. Paper organization
We include the following sections: section 2 describes
the four steps in the modified NEXRAD algorithm in
detail; section 3 introduces a dealiasing software from
SOLO II (Oye et al. 1995), and uses the dealiasing result
from it as the ‘‘true value’’ needed for our assessment;
section 4 provides the dealiasing result of CINRAD velocity data utilizing the CINRAD improved dealiasing
algorithm (CIDA), and compares it with the dealiasing
result from the previous NEXRAD algorithm; section 5
provides a statistical analysis of dealiasing results of velocity data from CINRAD by the improved NEXRAD
algorithm under different weather conditions; and, finally, section 6 concludes with a brief summary.
2. Algorithm description
The following new modifications and additions to the
NEXRAD algorithm were considered and proposed: 1)
noise removal, 2) selection of the first radial, 3) dealiasing,
and 4) an error check. Each step is outlined below.
a. Module 1: Noise removal (preconditioning of the
data)
This module consists of the following two steps:
Step 1: Velocity data are removed if the corresponding spectrum width is higher than 8 m s21 and the
FIG. 3. The r and u indicate the radial direction and azimuth
direction, respectively. (a) All the radials are marked with a flag of
0 before dealiasing. Black dots indicate the valid gates in each radial. (b) When dealiasing in the clockwise direction in step 1 of
module 3, the gates that have preceding neighbors as a reference
velocity are marked with black dots after dealiasing. The radial is
marked with a flag of 1 if all the valid gates in the current radial are
marked with black dots after dealiasing. Those gates without valid
preceding neighbors (missing gates) both in the current and previous radials will be segregated and marked with black triangles (in
radials l3 , l4 , and l5 ) in step 1. Then, the gates with black triangles
will be dealiased by the reference radial l6 in the counterclockwise
direction in step 2 of module 3.
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FIG. 4. Track of Typhoon Fungwong from 0000 UTC 24 Jul to 1800 UTC 31 Jul 2008. The
large black open circle indicates the radius of 34-kt winds and the smaller shaded circle indicates the maximum observation domain of the Wenzhou radar station.
if V 5 20 m s21, a 5 2:5 m s21; and if V 5 15 m s21,
a 5 1:5 m s21. It is acknowledged that noise removal will remove some ‘‘good’’ data. But retaining
bad data will cause errors to propagate into regions of
otherwise good data. It is a better strategy to lose
some (as little as possible) good data to avoid the
consequences of retaining noisy data.
b. Module 2: Selection of the first radial
Velocities are dealiased along a radial based on velocities of the previous radial (see module 3). If the velocities in the initial radial are not in the proper Nyquist
interval, then the dealiasing results in the following radials will also be wrong; if the initial gate in the initial
radial is ambiguous, then the discrepancy between the
dealiased results and the true velocity value of all data in
the sweep might be off by 62n 3 Vmax . It is therefore
important to choose an initial radial along which there is
no aliasing present. In the original NEXRAD dealiasing
algorithm, the sounding data from the neighboring height
level at nearly the same time was used as the reference
value to dealiase the initial gate in the initial radial.
However, the time resolution (12 h) and space resolution
(over 300 km) of sounding data are far more sparse than
the time (around 6 min) and space (0.25 km) resolutions
of radar data, which can lead to a false dealiasing result for
the initial gate (Zhang and Wang 2006). In addition, this
algorithm neglects the specific definition of the initial radial. An initial radial must be selected to start the process
of determining which velocities need to be unfolded or
dealiased. A starting angle (e.g., the azimuth angle of 08)
must be selected as the initial radial in each sweep. This
does not ensure that that there are no aliased velocities in
the initial radial. We propose a methodology that nearly
ensures that the initial radial does not contain velocities
that are aliased. Figure 1 illustrates how the first radial
is chosen. First, the average of the absolute values of the
measured velocities at all the valid gates along each radial
is calculated (solid curve in Fig. 1). The total number of
the valid gates along each radial (dashed–dotted curve)
and the average number ny of the valid gates of all the
radials (dashed horizontal line) are recorded meanwhile.
Then, the initial radial is chosen among radials whose
total number of the valid gates is larger than (2/3) 3 ny
(solid horizontal line). There are two bottoms of the
symmetric ‘‘V’’ shaped curve that are separated by
nearly 1808 in the red curve in Fig. 1. A radial with the
minimum average velocity and more valid gates was
chosen to represent the initial radial (in the left-hand
bottom of the solid curve). Since NEXRAD and many
other algorithms depend on previous ‘‘unfolded’’ radials in addition to radial shear, it is important that the
initial radial is devoid of aliases velocities. If there is
only one azimuth for which this is satisfied based upon
this model, then implementation of module 3 is mandated.
c. Module 3: Dealiasing
Starting from the radial next to the first radial chosen
in module 2, the NEXRAD algorithm by Eilts and Smith
(1990) is implemented. The original NEXRAD algorithm
performs contiguous radial-by-radial dealiasing in a clockwise direction. Each pass goes through 3608. In the new
algorithm, dealiasing starts from the radials on both sides
of the initial radial in two passes: one in the clockwise
direction and the other in the counterclockwise direction.
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FIG. 5. Radar radial velocity at a 1.58-elevation angle from Wenzhou station at 1045 UTC 29 Jul 2008: (a) raw
velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved
NEXRAD algorithm. The yellow circles in (a) indicate areas of aliased velocity.
Each pass goes through 1808 (Fig. 2). This strategy restricts any potential error propagation from extending
beyond 1808 from the initial radial (Zhang and Wang
2006). The core concept of the original NEXRAD algorithm is the comparison between the current gate and
radial and the proximal gate(s) in the preceding correctly
dealiased radial. Dealiasing was performed through
minimizing the difference between the current gate and
the proximal gate(s). When there are no valid reference
gates within 5 km in the current radial and 2.5 km away
from the radar in the previous radial, sounding data or
VAD wind information are used to dealiase the current
gate. But, the resolution of the sounding data cannot
represent small-scale wind shear information well. While
VAD winds have similar spatial and time resolutions as
the radial velocity field, the wind profile from the VAD
algorithm is not always correct when aliasing exits (Gong
et al. 2003). To reduce the dependence on sounding or
VAD wind fields, the new algorithm will dealiase the gates
that have preceding neighbors as the reference. Those
gates without valid preceding neighbors in the current and
previous radials will be dealiased in two steps. The
following describes these two steps of the clockwise
dealiasing shown in Fig. 2; the counterclockwise part can
be dealiased in the same manner. First, all of the radials are
marked with a flag of ‘‘0’’ and all of the valid gates in each
radial are marked with a black dot (Fig. 3a). Then, the
process continues as follows.
1) Starting from the radial next to the reference radial,
the dealiased gates with valid preceding neighbors
will be still marked with a black dot after dealiasing
(Fig. 3b). Those gates without valid preceding neighbors (missing gates) both in the current and previous
radials will be segregated and marked with black
triangles. When dealiasing in the current radial is
completed, if all gates are properly dealiased, then
the whole radial is labeled with flag of ‘‘l.’’ Otherwise, the flag 0 for the current radial is kept if there
are still some velocities with black triangles.
2) If the current radial is marked with flag of 1 or 0, the
process of unfolding will proceed to the next radial.
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FIG. 6. Radar radial velocity at a 3.38 elevation angle from Xiamen station at 1023 UTC 8 Aug 2008: (a) raw
velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved
NEXRAD algorithm. The yellow circle in (d) indicates a problem area after dealiasing.
However if the current radial is labeled with a flag of
1 (good), and the previous radial is marked with a flag
of 0 (unfolding needed), the current radial labeled
with a flag of 1 will be a new reference radial and the
radial(s) labeled with a flag of 0 are reexamined for
dealiasing with the new reference radial (e.g., radial
L6 in Fig. 3b). The algorithm will go back (counterclockwise) to dealias the isolated gates indicated by
the black triangles in the radials with the flag of 0, until
it reaches a radial with the flag of 1 (e.g., radial L2 in
Fig. 3b). Then modules 2 and 3 are repeated, continuing dealiasing the radial next to radial L6 in a clockwise
direction until all the radials are finished dealiasing in
the 1808 semicircle.
field (Rossa et al. 2005). All of these errors can lead to the
failure of the algorithm. When dealiasing errors are made,
they tend to propagate in the azimuthal or radial directions. Thus, an error check is incorporated into the
new algorithm. Before replacing the observed value with
the dealiased velocity, the dealiased result is compared
with the average velocity of all the valid gates in the
previous four radials and seven gates nearest to the radar. If the difference between the dealiased result and
the average value is less than Nyquist velocity, then the
dealiased result is correct. Otherwise, module 3 is repeated or the observed value is just kept.
3. Algorithm applications
d. Module 4: Error check
Some residual errors are likely after implementing
module 1, because of electronic stability, signal processing
accuracy, and other sources that create errors in the data
CIDA, as outlined above, is applied to 35 different
weather systems, including 5 typhoon cases, 3 squall line
cases, and 27 heavy rain cases as observed by the CINRAD
S-band radar system. The CINRAD S-band data have
a range gate spacing of 250 m and an azimuth spacing of
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FIG. 7. Radar radial velocity at a 3.38 elevation angle from Wenzhou station at 10:45 UTC 29 Jul 2008: (a) raw velocity, (b)
reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved NEXRAD algorithm.
nearly 18. Several preset scanning modes of operations
are referred to as VCPs. In VCP-21 mode, each radar
volume contains nine elevation scans: 0.58, 1.58, 2.48, 3.48,
4.38, 6.08, 9.98, 14.68, and 19.58. The Nyquist velocity is
about 26 m s21 at low-elevation angles (,9.98) and about
32 m s21 for high-elevation angles ($9.98). Several case
examples of the dealiasing results using CIDA are shown
in following sections. The NEXRAD algorithm is illustrated for comparison.
a. The ‘‘true’’ dealiasing result serving
as reference
The National Center for Atmospheric Research
(NCAR) SOLO II package is a widget-driven, interactive program that displays sweeps of radar data
and enables the user to manually edit (i.e., unfold or
dealias) the velocity data. The SOLO II assisted,
manually edited, and subjectively generated velocity fields
will be used as the true velocity field for developing and
testing an automatic dealiasing algorithm for CINRAD
data.
b. Dealiasing points near range folding and
missing gates
The dealiasing results from both the existing NEXRAD
and CIDA algorithms work very well when the radial velocity field is continuous. But when aliased gates are in
the midst of range folding, missing gates, and noise, the
dealiasing algorithms often fail.
1) SYNOPTIC OVERVIEW
Tropical Cyclone Fungwong formed in the northwest
Pacific Ocean at 0600 UTC 25 July 2008 and moved in
the northwest direction at 25–30 km h21. It intensified
slowly and was upgraded to severe typhoon status at
1800 UTC 27 July. Fungwong made its first landfall at
around 0000 UTC 28 July near the border of Hualien
County in Taiwan with a maximum wind speed of 45
m s21. The tropical storm made its second landfall over
mainland China at 1400 UTC, 60 km south of Fuzhou.
Fujian Province was hard hit, receiving wind gusts of up
to 155 km h21. Figure 4 shows the path of Typhoon
Fungwong from 0000 UTC 24 July to 1800 UTC 31 July
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FIG. 8. The vector wind fields for the (a) NCEP FNL results at 900 hPa at 1200 UTC 3 Jun 2009. (b) Wind fields
observed by all the automatic weather stations near Shangqiu radar station at 1340 UTC 3 Jun 2009. The surface station
data have been interpolated onto a Cartesian grid. The Shangqiu radar station is located at the center of the domains.
2008. The small circle with a radius of 230 km indicates
the maximum range of the radial velocity sampling at the
Wenzhou, China, radar station. The large circle is the area
of near-gale-force winds at 1100 UTC 29 July 2008.
2) DEALIASING RESULTS
Figure 5a shows the raw radial velocity at the elevation angle of 1.58 at 1045 UTC 29 July 2008 for Typhoon
Fungwong, as observed by the Wenzhou radar station.
Two areas of velocity aliasing are indicated by yellow
circles. A significant area of range folding and missing
data is evident in the radial velocity fields. The truth (i.e.,
dealiased by SOLO II and then manually edited) is presented in Fig. 5b. Dealiased results using the NEXRAD
algorithm without the proposed modifications are shown
in Fig. 5c. Some aliased gates near the area of the range
folding and missing gates caused an area of incorrect
dealiasing to the south of the radar. In contrast, the desired result is obtained if all the modules outlined in
section 3 are implemented (Fig. 5d). Module 3 in section
3 improved the dealiasing results for this case.
c. Dealiasing near noise gates
Radar data include noise, which can arise from clutter,
second-trip echoes, birds, weak signals, and sidelobe echoes. If these errors are not removed from the data, they
will cause incorrect dealiasing results.
1) SYNOPTIC OVERVIEW
Typhoon Morakot (international designation: 0908)
was the deadliest typhoon to impact Taiwan in recorded
history. It formed early on 2 August 2009 as a tropical
depression and developed into a typhoon at 1230 UTC
5 August. It made its first landfall near the border of
Hualien County at 1545 UTC 7 August and its second
landfall in Fujian Province on mainland China at 1030 UTC
9 August. The pressure was 970 hPa and the maximum
wind was 33 m s21 when it made landfall in mainland
China.
2) DEALIASING RESULTS
Figure 6a shows the raw radial velocity at the elevation
angle of 3.38 at 1023 UTC 8 August 2008 for Typhoon
Morakot as observed from the Xiamen, China, station.
There are many noisy gates near the radar. The aliased
velocities adjacent to the noise caused significant errors in
the NEXRAD algorithm, as shown to the northeast of the
radar in Fig. 6c. Figure 6d is the result of dealiasing by the
CIDA algorithm. The noise to the northeast of the radar
center was almost removed after module 1 as outlined in
section 3. The remaining noise northwest of the radar
station leads to some dealiasing errors as shown in the
yellow circle in Fig. 6d. But the dealiasing results were
much better than those from the old NEXRAD algorithm
when comparing to the reference velocity field in Fig. 6b.
Figure 7 shows the raw radial velocity with an elevation angle of 3.38 at 1045 UTC 29 July 2008 as observed
by the Wenzhou radar station. As a result of noise located
southeast and southwest of the radar, the old NEXRAD
scheme incorrectly modified two areas in Fig. 7c. In
constant, the CIDA algorithm dealiased the velocity field
correctly.
FEBRUARY 2012
227
HE ET AL.
FIG. 9. Radar radial velocity at a 4.38 elevation angle from Shangqiu station at 1339 UTC 3 Jun 2009 (a) raw
velocity, (b) reference velocity, (c) dealiased by the NEXRAD algorithm, and (d) dealiased by the improved
NEXRAD algorithm.
d. Dealiasing wind shear velocity field
When strong wind shear exists in the velocity field,
such as is often found with squall lines, tornadoes, and
other severe convective weather, the NEXRAD algorithm
can easily fail. It is a challenge for any algorithm.
1) SYNOPTIC OVERVIEW
A squall line containing a line of severe thunderstorms
developed in Henan Province of China between 0746 and
1600 UTC 3 June 2009. The 140-km-long squall line
moved toward the southeast with winds of 50–60 km h21.
The squall line developed its maximum strength when it
passed through Shangqiu, around 1300 UTC. A maximum
wind speed of 29 m s21 was observed in Yongcheng.
Twenty-seven people died and the economic losses were
estimated to be $2.212 billion in U.S. dollars. Figure 8a
shows the vector wind field at the 900-hPa level from the
NECP Final Analyses (FNL) data at 1200 UTC 3 June
2009. There was a wind confluence in Fig. 8a extending to
the west of the radar station. Figure 8b is the surface wind
field extrapolated onto a Cartesian grid at 1340 UTC
3 June 2009. The radar station is located at the center of
Figs. 8a,b.
2) DEALIASING RESULTS
The dealiasing results for the squall-line case are shown
in Fig. 9. The missing velocities to the north in Fig. 9b
occur because the spectrum widths for these velocities
TABLE 1. The total tilts and total valid gate numbers of each type
of weather condition radar data in the 14 test cases. All test cases
are classified into 3 different types of weather conditions: typhoon,
squall line, and heavy rain.
Weather condition
No. of tilts
Typhoon
Squall line
Heavy rain
All
45
27
54
126
No. of valid gates
4
2
4
12
372
978
831
182
068
588
822
478
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WEATHER AND FORECASTING
VOLUME 27
TABLE 2. Dealiasing results from all 14 of the test cases, which are classified into three different types of weather conditions by different
algorithms. Acronyms used here are NEXRAD1, the current NEXRAD algorithm; NEXRAD2, the NEXRAD algorithm; CIDA, the
CINRAD improved dealiasing algorithm; A, the total number of aliased gates in each case; B, the total number of correctly dealiased gates
in each case; C, the total number of incorrectly dealiased gates in each case; and D, the total number of missed dealiased gates in each case.
Typhoon
A
NEXRAD1
NEXRAD2
CIDA
NEXRAD1
NEXRAD2
CIDA
B
229 971
A
27 282
Squall line
C
219 814
14 586
229 098
4538
228 382
2871
Heavy rain
B
26 961
27 169
26 814
C
192
18
102
exceeded the noise threshold of 8 m s21. The original
NEXRAD scheme works well except in a small region
highlighted by the yellow circle in Fig. 9c. Aliased velocities are not completely corrected near the outer edge
of the scan (i.e., larger radius). The best results, again,
are obtained after implementing all the modifications
proposed in section 3.
5. Statistical results
The CIDA for velocity dealiasing has been applied to
CINRAD S-band VCP-21 data for 35 different weather
systems, including 5 typhoon cases, 3 squall-line cases
and 27 heavy rain cases. There were some very difficult
dealiasing problems because of noise (e.g., ground clutter), range folding, missing gates, and strong azimuthal
and radial shears. Out of the total 35 cases, there are 14
cases (5 typhoon, 3 squall line, and 6 heavy rain) for which
aliased radial velocities are found to be present. One
volume with aliased velocities was selected for dealiasing
from each case separately. Table 1 shows the total tilts
and total valid gates number of each type of weather
condition radar data. Table 2 presents the details of different dealiasing results among the current NEXRAD,
new NEXRAD (Witt et al. 2009), and CIDA algorithms
the for each type of weather condition cases. The statistical results in Table 3 and Fig. 10 are calculated by the
following formulas:
D
A
B
10 157
873
1589
57 590
55 939
56 703
56 564
C
D
965
1968
783
1651
887
1026
C
15 743
6524
3756
D
12 129
1873
3083
All
D
321
113
468
A
314 843
B
302 714
312 970
311 760
probability of detection (POD) 5 B/A,
false alarm rate (FAR) 5 C/A, and
(3)
critical success index (CSI) 5 B/(B 1 C 1 D),
where A is the total number of aliased gates in each case,
B is the total number of correctly dealiased gates in each
case, C is the total number of incorrectly dealiased gates
in each case, and D is the total number of missed dealiased
gates in each case. It is seen that the dealiasing results of
both the CIDA and new NEXRAD algorithms are better
than the current NEXRAD algorithm, which has the
lowest POD and CSI, as well as the highest FAR, for all
14 cases, when CINRAD radar operates in VCP-21. The
POD of the new NEXRAD algorithm (99.41%) is higher
than those of the CIDA algorithm (99.02%) and the
current NEXRAD algorithm (96.15%), which means
that the new NEXRAD algorithm can identify and correctly dealias more aliased data. But the FAR of the new
NEXRAD algorithm (2.07%) is also higher than the
improved algorithm (1.19%). And the CSI of the new
NEXRAD algorithm (97.39%) is slightly lower than that
for CIDA (97.85%), but still higher than for the current
NEXRAD algorithm. Also, both the CIDA and new
NEXRAD algorithms improve the dealiasing results
when dealing with different weather conditions. The
dealiased results for the typhoon and heavy rain cases
TABLE 3. Statistical dealiasing results (%) from all 14 of the test cases classified into three different types of weather conditions by
different algorithms.
Typhoon
NEXRAD1
NEXRAD2
CIDA
Squall line
Heavy rain
All
POD
FAR
CSI
POD
FAR
CSI
POD
FAR
CSI
POD
FAR
CSI
95.58
99.62
99.31
6.34
1.97
1.25
89.88
97.69
99.08
97.13
98.50
98.22
1.67
3.42
1.36
95.53
95.21
96.90
98.82
99.59
98.25
0.70
0.07
0.37
98.13
99.52
97.92
96.15
99.41
99.02
3.64
2.07
1.19
91.57
97.39
97.85
FEBRUARY 2012
HE ET AL.
229
6. Summary and conclusions
FIG. 10. Different statistical results for (a) POD, (b) FAR, and (c)
CSI among the current NEXRAD (light gray bars), new NEXRAD
(dark gray bars), and CINRAD improved dealiasing (hatched
bars) algorithms for different weather condition cases with aliased
data. Acronyms used are T, typhoon; SL, squall line; and HR 5
heavy rain.
are better than for the squall-line cases, partly because
there are quite large, meteorological shears present that
the algorithms still struggles to handle. The CIDA performs very well, with 99.02% of the aliased velocity
observations being successfully compared with the
SOLO II assisted and manually edited results (section
3). Only 2.17% of the dealiased velocities [(C 1 D)/A]
were either improperly dealiased (1.19%) or missed
being dealiased (0.98%) due to range folding or noises.
A quality flag is then added to these data for their future
applications in data assimilation.
A CINRAD improved dealiasing algorithm (CIDA)
is developed based on the NEXRAD (and prior) schemes.
The performance of the proposed algorithm is examined
for CINRAD S-band observations. This algorithm includes four modules: 1) removing weak signals, 2)
determining the starting radial, 3) identifying aliased
velocities, and 4) correcting aliasing in the observed
velocities.
Radar measurements include noisy data and can be
contaminated by clutter, second-trip echoes and sidelobe echoes. When gates are near noise, the noise can
cause incorrect dealiasing results. Therefore, it is necessary to remove all noise before beginning dealiasing.
This algorithm has a method that utilizes the range, echo
strength, and variance of surrounding data to identify
noise. It also incorporates an important improvement,
choosing an initial radial along which there is no or
minimal aliasing present. The velocities near the ‘‘zero’’
line are smaller and less likely to be aliased. So choosing
a reference radial near the zero line can reduce the error
rate of dealiasing. The new algorithm dealiases the gates
such that the data will be in the same Nyquist interval as
the preceding gates, which act as reference velocities.
The chosen gates are from a different location than the
NEXRAD algorithm. Gates without correctly dealiased
neighbors are marked with a flag. An attempt to dealias
these gates with flags is made by approaching them from
the other side. This scheme does not require an additional
sounding or a VAD wind field as is used in other dealiasing schemes. It can also improve the dealiasing result
when nearby gates have been range folded or are missing
gates. Before replacing the observed value with the
dealiased velocity, the dealiased result is compared with
the average velocity of all the valid gates in the previous
four radials and the seven gates nearest to the radar. This
error check can prevent the propagation of error caused
by any remaining noise and observation errors.
The proposed dealiasing algorithm was tested for 14
different weather scenarios consisting of typhoons, squall
lines, and heavy rains. The results show that the algorithm
is robust and stable for dealiasing S-band CINRAD radial VCP-21 velocity data. The proposed algorithm can
improve the dealiasing results when dealiasing near noisy
gates, range folding, missing gates, and velocity fields with
strong wind shear. The performance for typhoon and
heavy rain cases was slightly better than the results for
squall lines. The algorithm correctly dealiased more than
99% of the aliased radial velocity data when compared
to the manually edited results. Future investigations are
being conducted to improve the remaining 2.17% improperly dealiased velocities and velocities that missed
230
WEATHER AND FORECASTING
being dealiased, which often appear near the edge of
data-missing or range-folding areas.
Acknowledgments. This research is supported by the
Chinese Ministry of Science and Technology under 973
project ‘‘Assessment, Assimilation, Recompilation and
Applications of Fundamental and Thematic Climate
Data Records’’ (2010CB951600).
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