Transcript DoD Radar Data Quality Control for NRL

3.13
RADAR DATA QUALITY CONTROL
FOR THE NAVAL RESEARCH
LABORATORY NOWCAST SYSTEM
PAUL R. HARASTI
UCAR Visiting Scientist at the Naval Research Laboratory, Monterey, CA 93943-5502, USA.
([email protected])
DAVID J. SMALLEY AND MARK WEBER
MIT Lincoln Laboratory, Lexington, MA, USA
CATHY J. KESSINGER
National Center for Atmospheric Research, Boulder, CO, USA
QIN XU
National Severe Storms Laboratory, Norman, OK, USA.
TED L. TSUI, JOHN COOK AND QINGYUN ZHAO
Naval Research Laboratory, Monterey, CA, USA
Outline
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Radar data quality control (QC) problems
NRL radar data flowchart
MIT LL Data Quality Assurance (DQA)
NCAR Radar Echo Classifier (REC)
NSSL Radar Data QC
Principal Component Analysis (PCA) QC
Current NRL QC approach
Summary
Future Work
References
1. Radar Data Quality Problems
 Noisy fields (e.g., due to small Nyquist velocity)
 Irregular variations due to scan mode switches
 Aliased data or unsuccessfully de-aliased data
 Contamination by migrating birds
 Ground clutter observed during normal propagation (NP)
 Ground clutter observed during anomalous propagation (AP)
 Clutter caused by moving ground and airborne vehicles
 Sea Clutter
 Constant power function (CPF) artifacts (calibration patterns,
hardware malfunctions, and sun strobes)
 Clear Air/Insects (if reflectivity is assumed from precipitation)
2. NRL Radar Data Flowchart
Raw Radar Data
{ Reflectivity (Z) , Radial Velocity (V) and Spectrum Width (S)
from WSR-88D (NOAA) and Supplemental Weather Radar (SWR at US Navy shore sites)
and future TPS-76 (transportable on US Marine Corp vans), SPS-48E (on US Navy ships), and possibly SPY-1(on US Navy ships)
Thresholding to Remove Noise and Nearby Clutter - Both Z and V Datum Removed If :
- S > 10 m/s
- Signal-to-Noise ratio < 10 db (if available )
- Range < 5 km for radar tilts less than 4 degrees (for land-based radars only)
NRL-Developed Clutter Removal and De-Aliasing Algorithms
- Clutter assumed where | V| < 1.5 m/s around each VAD circle if their number
exceed the theoretical limit based on a uniform wind assumption. Corresponding Z data rejected as well.
- Gate-by-gate V de-aliasing using Bargen and Brown (1980) method with an Environmental Wind Table Reference
Additional or Alternative Quality Control (Under Consideration)
- MIT/LL Data Quality Assurance, NSSL Radar Data QC algorithm,
NCAR Radar Echo Classifier, Principal Component Analysis method
Radar Product Generator for NOWCAST
- low-tilt Z and V
- echo tops
- NCAR Storm Tracker
- 2D composite Z - hourly precipitation
- 3D Multiple-radar Winds
- VAD winds
- MIT/LL Storm Tracker - 3D Radar Data Mosaic
COAMPS-OS® Data Assimilation
- 3.5DVAR winds and thermodynamic retrieval
- ADAS 3D Cloud Analysis System
COAMPS ® and COAMPS-OS ® are registered trademarks of the Naval Research Laboratory (see Geiszler et al. (2004))
3. MIT LL DQA Algorithm – Overview
(Smalley and Bennett 2001;2002 and Smalley et al. 2003)
• Originally developed for the FAA for use
with NEXRAD reflectivity data only, but
currently being adapted for use with US
military radar reflectivity, radial velocity,
and spectral width
• Uses radar reflectivity, radial velocity and
spectrum width to identify and remove CPF
artifacts and AP clutter
3.1 MIT LL DQA Algorithm - Concept
Applied in Two Sequential Stages:
(1) CPF Artifact detector
(2) AP detector, if minimal CPF artifacts detected
CPF Detector:
 Constant power signal (reflectivity  radius2)
over a sufficiently continuous portion of a single
radial
 Mixed CPF artifacts + weather in any single
radial cannot be removed
3.1 MIT LL DQA Algorithm - Concept
AP Detector:
 3-tiered approach:
(1) Identify gates with high reflectivity coincident with
very small radial velocity and spectrum width for each
single radial
(2) The detected AP gate is allowed to bloom radially to
adjacent gates if those gates are sufficiently close to but
not quite within the bounds of the basic test
(3) Scatter filter is applied over the entire tilt of data; i.e.,
the sufficiency of AP neighbors from (1) and (2) is
assessed and like-status assigned to the target central
gate within the filter
3.2 MIT LL DQA Algorithm: CPF Artifact
Example from KMLB WSR-88D
Sun strobe
Reflectivity
Before QC
Reflectivity
After QC
3.2 MIT LL DQA Algorithm: AP Clutter
Example from KAMA WSR-88D
AP Clutter
Reflectivity
Before QC
Reflectivity
After QC
4. NCAR REC Algorithm – Overview
(Kessinger et al. 2003)
• Current algorithm tailored for NEXRAD data,
and used on the WSR-88D Open Radar
Product Generator system to improve radarderived rainfall estimates and other products
used by forecasters
• Developed and “truthed” using WSR-88D and
NCAR S-Pol radar data
• Uses reflectivity, radial velocity and spectrum
width in fuzzy logic detection algorithms to
make echo-type classifications
• Four separate algorithms to detect AP Clutter,
Precipitation, Insect-Clear-Air, and Sea Clutter
4.1 NCAR REC Algorithm - Concept
INPUT
Z
V
W
FEATURE
GENERATION
FUZZY LOGIC ENGINE
Mean
Median
STDev
Texture
Spin
Sign
w
w
w
w
Apply
membership
functions
Apply
weights
Swf
Sw
Compute
Interest
Field
T
Final
Product
General schematic of the algorithms within the radar echo classifier. The
steps of the process include: ingesting the base data for reflectivity (Z),
radial velocity (V), and spectrum width (W), generation of features that are
derived from the base data fields, use of a fuzzy logic engine to determine
the initial interest output, application of the appropriate threshold (T), and
the final output product for the type of radar echo being considered .
4.1 NCAR REC Algorithm - Concept
Clutter
a)
Precipitation plus clear air
b)
Corresponding Membership
Functions for
a) Texture of the Reflectivity (TDBZ)
1
0
0
45
1000
b) Texture of the Reflectivity (TDBZ)
1
c)
d)
0
0
30
1000
c) Median Radial Velocity (MDVE – similar to mean)
1
0
-50
Example histogram plots of two of the feature fields used by REC. The fraction of range
gates within clutter (left column) and within precipitation plus clear air return from insects
(right column) are shown for a) and b) texture of the reflectivity (TDBZ; dBZ2), and c) and d)
the mean velocity field (MVE; m s-1). Data were derived from one scan of the Dodge City,
KS, WSR-88D at 0.5 degree elevation and the truth field. The corresponding membership
functions for (a)-(c) are shown to the right (example for (c) is the median, not mean).
-2.3
0
2.3
50
a)
c)
b)
d)
S-Pol data from the IHOP field experiment on 16 June 2002 at 0000 UTC. Fields shown
include: a) reflectivity (dBZ), b) radial velocity (m s-1) with values near zero shaded
cyan, c) thresholded APDA (green) and d) thresholded PDA (gold). The red arrow in d)
denotes a region of clear air return that is incorrectly classified as precipitation. The 0.0degree elevation angle is shown. Range rings are at 30 km intervals.
4.2 NCAR
REC
Algorithm Example
5. NSSL Radar Data QC – Overview
(Gong et al. 2003, Liu et al. 2003;2005, Zhang et al. 2005)
Two-part package designed for NEXRAD:
1. Gate-by-gate de-aliasing

Three-step algorithm of Gong et al. (2003)
2. Tilt-by-tilt QC
 Uses feature fields/QC parameters derived from reflectivity
and radial velocity, similar to NCAR REC. Thresholds for
these fields are determined based on accumulated statistics
(probability distribution functions) and classified for the
different WSR-88D Volume Coverage Patterns (scan types)
 Noise is detected using thresholds of statistics parameters
 Fuzzy logic of statistics parameters used to detect AP clutter
from both stationary clutter and moving vehicles
 Employs Bayes Conditional Probability Theorem to
determine the two probabilities that either the radar data is, or
is not, contaminated by birds
5.1 NSSL Radar Data QC:
3-Step De-aliasing – Concept by Example: WSR-88D KTLX
Aliased Radial Velocity
Reference check
Dealiased Radial Velocity
Step 1: Modified
(gradient) VAD
winds, derived from
aliased data, used as
reference for first dealiasing sweep
Step 2: Traditional VAD
winds, derived after step 1,
used as new reference;
remaining data jump points
confined to small areas
Step 3: Reference check area in Step
2 used for a continuity check from
all directions around flagged areas to
de-alias data within these areas
5.2 NSSL Radar Data QC:
Noise and Bird Removal- Concept
Example of some of the NSSL QC Parameters:
(1) Percentage of along-beam sign changes of radial velocities (SN).
(2) Along-beam standard deviation of radial velocities (STD).
(3) Percentage of along-beam perturbation radial-velocity sign
changes (VSC).
(4) Mean reflectivity (MRF).
(5) Valid radial-velocity data coverage (VDC).
 Large SN (> 15%) and/or STD (> 3 m s-1) in (1)-(2) indicate
noisy data fields (Liu et al. 2003)
 Large SN, MRF and VDC in (3)-(5) indicate a high
probability of contamination by migration birds (Zhang et al.
2005)
5.2 Migrating Bird Contamination
Example: Reflectivity Mosaic
Most of the circular-shaped WSR-88D echoes shown above are migrating birds (some indicated
by yellow arrows). Birds can be a very wide-spread problem, depending on the time of day and
year (see http://www.npwrc.usgs.gov/resource/othrdata/migratio/migratio.htm)
5.2 NSSL Radar Data QC:
Estimated PDFs for Birds
(a)
0.20
(b)
p(x1|A)
0.20
p(x1|B)
p(x2|B)
0.15
0.15
PDF
PDF
p(x2|A)
0.10
0.05
0.10
0.05
0.00
0.00
-2
0
2
4
6
8
10
12
14
MRF (dBZ)
10 15 20 25 30 35 40 45 50 55 60 65 70
VDC (%)
(c)
0.25
p(x3|A)
Conditional Probabilities
p(x3|B)
PDF
0.20
A: not contamined by birds
0.15
0.10
B: contaminated by birds
0.05
0.00
20 22 24 26 28 30 32 34 36 38 40 42
VSC (%)
x1=MRF; x2=VDC; x3=VSC
5.2 Flowchart
of NSSL Real-time Migrating
Bird Identification
Raw data
5.3 NSSL QC Verification:
Calculate QC parameters
Migrating Bird QC Statistics
Night?
no
Verification
Parameter Type
Multi-parameter
(combined MRF, VDC,
and VSC) Statistic
Hit Rate
94.6%
False Alarm Rate
37.2%
yes
Bayes identification and calculate
posterior probability
P(B |xi) >0.5
no
yes
Bird echo
Next QC step
6. PCA QC – Concept
(Harasti 2000, Harasti and List 2005)
(1) Create the data matrix D whose
Dij element is the radial velocity
datum at the ith range gate and jth
azimuth position (S2 mode).
(2) Dr is D centered along the range
coordinate.
6.1 PCA QC – Concept
by Example:
•Hurricane Bret (1999)
Above: WSR-88D Reflectivity in units of dBZ
from Corpus Christi, TX (KCRP) with position
of Brownsville, TX (KBRO) also shown. Right
panel: Radial velocity from KCRP (top) and
KBRO (bottom).
6.1 PCA QC – Concept by Example
• PCA Synthesis of Doppler Velocity
 For each PPI, make
the following
approximation for some
signal-to-noise
eigenvector cut-off
value k:
Dr  PkETk
 P and E are matrices
containing the principal
components and
eigenvectors,
respectively, of the
covariance matrix of Dr.
PCA variance summary of the first PPI. There are up to 14 PPIs
within the WSR-88D radar volume scans from KBRO and KCRP
 k is determined by
the “broken-stick model”
approach (Jolliffe 1986);
e.g., 2 < k < 10 for Bret.
6.1 PCA QC – Concept by Example
•Procedure:
Use the PCA Synthesis approximation of each VAD circle to edit outliers using a 2
standard deviation (SD) threshold – remove corresponding reflectivity data as well if
the radar moments are not split on different PPIs.
KBRO Results: Difference in R2 (Square of the Multiple Correlation Coefficient) of VAD Model Fit
between using PCA-edits and not using PCA-edits for all VAD Circles at all ranges within the 0.5 PPI
Scan. These results show that the PCA Synthesis removes a significant amount of clutter and noise at close
ranges.
6.1 Radial velocity
(vertical axis, ms-1)
versus azimuth angle
examples of PCA QC
edited ground clutter
and noise shown as
unfilled red squares.
High- order (up to ~10
wavenumbers),
truncated Fourier
Series Fit shown as
blue line. Retained
data points shown by
blue-filled red squares.
These results are from
the study by Harasti
and List (2001) who
required these Fourier
coefficients for their
hurricane wind
retrieval method.
KBRO
VAD Circle Radius
(km)
21 20
31 34
55 50
KCRP
7. Current NRL QC Approach
7.1 NRL Ground Clutter Removal Algorithm - Concept
Uniform Wind Assumption :
V
  r  cos    
VH
typical  
Vr = Radial Velocity
VH = Horizontal Wind Speed
3 kt
 0.15 for clutter
20 kt
 = Horizontal Wind Direction
 =Azimuth Angle of Radar Beam
For all VAD circles whose fraction of Vr < 3 kt exceeds the fraction expected,
reject all Vr and corresponding Z values where Vr < 3 kt
  may be optionally adjusted for different VH conditions at each altitude as
indicated by COAMPS-OS® SkewT data.


1

cos





2


Fraction Expected 

2
COAMPS ® and COAMPS_OS ® are registered trademarks of the Naval Research Laboratory
7.2 NRL Radial Velocity De-aliasing Algorithm –
Concept
 The Bargen and Brown (1980) technique,
Algorithm B, is applied gate-by-gate. A reference
wind is required at the first gate.
 Reference wind at each altitude provided by the
Gradient Velocity Azimuth Display method
(GVAD - Gao et al. (2004) )
 If GVAD winds are not available due to lack of
data then SkewT winds from COAMPS-OS® are
utilized instead.
COAMPS ® and COAMPS_OS ® are registered trademarks of the Naval Research Laboratory
7.3 NRL-QC Example: SPY-1/TEP data onboard a US
Navy ship near Jacksonville, FL
Reflectivity Before QC
Reflectivity After QC
7.3 NRL-QC Example: Hurricane Charley (2004),
WSR-88D, Key West, FL
Raw Radial Velocity
De-aliased Radial Velocity
utilizing the GVAD reference
winds only.
7.3 NRL-QC Example: SWR, Point Loma, CA
AP ground clutter and
sea clutter
Second Trip Echo from
distant mountains
Reflectivity Before QC
Reflectivity After QC
7.3 NRL-QC Example: SWR, Fallon, NV
Radial Velocity Before QC
Radial Velocity After QC
Note: Badly aliased radial velocity due
to low, 13.3 m/s Nyquist velocity of the
SWR.
Note: All radial velocities were successfully dealiased.
This was the first demonstration of the new NRL
dealiasing software that combines both GVAD winds
and COAMPS-OS® SkewT winds to form a dualreference Environmental Wind Table for Dealiasing
via the Bargen and Brown (1980) technique. Unlike
VAD winds, GVAD Winds are not susceptible to
radial velocity aliasing.
COAMPS ® and COAMPS_OS ® are registered
trademarks of the Naval Research Laboratory
7.3 NRL-QC Example: SWR, Fallon, NV
Ground Clutter
Reflectivity Before QC
Reflectivity After QC
7.4 NRL – NOWCAST Example: The US Naval Air Station, Fallon-Area (red and blue polygon regions) showing
real-time surface observations, satellite data, and NRL QC radar data (composite reflectivity and VAD winds from KNFL
(Fallon, NV, SWR), KRGX (Reno, NV, WSR-88D) and KLRX (Elko,NV, WSR-88D).
8. Summary
The target classifications of each of the aforementioned radar
data QC algorithms are summarized in the following table:
Method
Ground
Clutter
MIT/LL
DQA
X
NCAR
REC
X
NSSL
QC
X
PCA
QC
X
NRL
QC
X
Precipitation
Insects Clear Air
Sea
Clutter
Noise
Birds
CPF
Artifacts
X
X
X
X
X
X
X
X
X
X
9. Future Work
 Prepare
all the aforementioned radar data quality
control algorithms for use with both WSR-88D and US
Military Radar data received at NRL
Test all of the radar data quality control algorithms on a
series of case studies and accumulate performance
statistics according to each algorithm’s target
classifications
 Determine the optimal combination, in a layered
sequence, of the QC algorithms (either complete, partial
or no components) that optimizes the quality of the radar
data for NOWCAST and COAMPS-OS®.
COAMPS ® and COAMPS_OS ® are registered trademarks of the Naval Research Laboratory
10. References
Bargen D. W., and R. C. Brown, 1980: Interactive radar velocity unfolding. Preprints, 19th
Conference on Radar Meteorology, Miami Beach, FL, Amer. Meteor. Soc.,278-285.
Gao, J., K. K. Droegemeier, J. Gong, and Q. Xu. 2004: A method for retrieving mean horizontal
wind profiles from single-Doppler radar observations contaminated by aliasing. Mon. Wea.
Rev., 132, 1399–1409.
Geiszler, D., J. Kent, J. Strahl, J. Cook, G. Love, L. Phegley, J. Schmidt, Q. Zhao, F. Franco, L.
Frost, M. Frost, D. Grant, S. Lowder, D. Martinez, and L. N. McDermid, 2004: The Navy’s
on-scene weather prediction system, COAMPS-OS®. Preprints, 20th International
Conference on Interactive Information and Processing Systems for Meteorology (IIPS),
Oceanography, and Hydrology, Seattle, WA, Amer. Meteor. Soc., P19.1.
Gong, J., L. Wang, and Q. Xu, 2003: A three-step dealiasing method for Doppler velocity data
quality control. J. Atmos. & Oceanic Technology, 20, 1738-1748.
Harasti, P. R., 2000: Hurricane properties by principal component analysis of Doppler radar
data. Ph.D. dissertation, Department of Physics, University of Toronto, 162 pp.
10. References
Harasti, P. R., and R. List, 2001: The hurricane-customized extension of the VAD (HEVAD)
method: Wind field estimation in the planetary boundary layer of hurricanes. Preprints, 30th
Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 463-465.
Harasti, P. R., and R. List, 2005: Principal component analysis of Doppler radar data. Part I:
Geometric connections between eigenvectors and the core region of atmospheric vortices.
J. Atmos. Sci. (in press).
Jolliffe, I. T., 1986: Principal Component Analysis. Springer-Verlag New York Inc., 271 pp.
Kessinger, C., S. Ellis, and J. Van Andel, 2003: The radar echo classifier: A fuzzy logic algorithm
for the WSR-88D. Preprints-CD, 3rd Conference on Artificial Applications to the
Environmental Science,, Long Beach, CA, Amer. Meteor. Soc.
Liu, S., P. Zhang, L. Wang, J. Gong, and Q. Xu, 2003: Problems and solutions in real-time
Doppler wind retrievals. Preprints, 31th Conference on Radar Meteorology, 6–12 August
2003, Seattle, Washington, Amer. Meteor. Soc., 308-309.
10. References
Liu, S., Q. Xu, and P. Zhang, 2005: Quality control of Doppler velocities contaminated by
migrating birds. Part II: Bayes identification and probability tests. Submitted to J.
Atmos. Oceanic Technol. (in press).
Smalley, D.J., and B.J. Bennett, 2001: Recommended improvements to the Open RPG APEdit algorithm. MIT Lincoln Laboratory Wx Project Memorandum No. 43PM Wx0081, November 2001. MIT Lincoln Laboratory, Lexington, MA. 37 pp.
Smalley, D.J., and B.J. Bennett, 2002: Using ORPG to enhance NEXRAD products to
support FAA Critical Systems. Preprints, 10th Aviation, Range, and Aerospace
Meteorology Conference, Portland, OR, Amer. Meteor. Soc., 3.6.
Smalley, D.J., B.J. Bennett and M. L. Pawlak, 2003: New products for the NEXRAD ORPG
to support FAA Critical Systems. Preprints, 19th International Interactive Processing
Systems Conference, Long Beach, CA, Amer. Meteor. Soc.,14.12.
Zhang, P., S. Liu, and Q. Xu, 2005: Quality control of Doppler velocities contaminated by
migrating birds. Part I: Feature extraction and quality control parameters. J. Atmos.
Oceanic Technol. (in press).