Honeywell has developed algorithms for the detection of wind shear/microburst using airborne Doppler radar. The Honeywell algorithms use three dimensional pattern recognition techniques and the selection of an associated scanning pattern forward of the aircraft. This 'volumetric scan' approach acquires reflectivity, velocity, and spectral width from a three dimensional volume as opposed to the conventional use of a two dimensional azimuthal slice of data at a fixed elevation. The algorithm approach is based on detection and classification of velocity patterns which are indicative of microburst phenomenon while minimizing the false alarms due to ground clutter return. Simulation studies of microburst phenomenon and x-band radar interaction with the microburst have been performed and results of that study are presented. Algorithm performance indetection of both 'wet' and 'dry' microbursts is presented

Benser, Earl T.

Gillberg, Jeff

Pockrandt, Mitch

Symosek, Peter

English

NASA Technical Reports Server

Session V. Doppler Related Research
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Algorithms for Airborne Doppler Radar Wind Shear Detection
Jeff Gillberg, Honeywell
Mitch Pockrandt, Honeywell
Peter Symosek, Honeywell
Earl Benser, Honeywell
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https://ntrs.nasa.gov/search.jsp?R=19930010417 2020-03-17T08:11:59+00:00Z
ALGORITHMS FOR AIRBORNE
DOPPLER RADAR
WIND SHEAR DETECTION
Jeff Gillberg
Mitch Pockrandt
Peter Symosek
Earl T. Benser
Honeywell Inc.
Systems and Research Center
3660 Technology Drive
Minneapolis, Minnesota 55418
ABSTRACT
Honeywell has developed algorithms for the detection of wind
shear/microburst using airborne Doppler radar. The Honeywell algorithms
use three dimensional pattern recognition techniques and the selection of
an associated scanning pattern forward of the aircraft. This "volumetric
scan" approach acquires reflectivity, velocity and spectral width from a
three dimensional volume as opposed to the conventional use of a two
dimensional azimuthal slice of data at a fixed elevation. The algorithm
approach is based on detection and classification of velocity patterns
which are indicative of microburst phenomenon while minimizing the
false alarms due to ground clutter return. Simulation studies of microburst
phenomenon and X-band radar interaction with the microburst ha,, e been
performed and results of that study are presented. Algorithm performance
in detection of both "wet" and "dry" microbursts is presented.
SLIDE 1
Title Slide
SLIDE2
The development of algorithms for detection of wind shear/microburst using airborne
Doppler radar is a part of a larger Honeywell effort for the development of an Enhanced
Situation Awareness System (ESAS). This multifunction system will increase pilot
situation awareness through provision of landing guidance in adverse weather, detection
of severe weather, detection of severe weather, detection of microburst/wind shear,
detection of wake vortices and clear air turbulence. This integrated system seeks to
provide the above functionality with minimal impact to the aircraft and minimal
requirements for additional hardware.
SLIDE 3
The Honeywell remote windshear detection research has concentrated on development of
algorithms which are not based on any particular sensor technology. The algorithms
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require measurements of air mass velocities that could be provided by laser doppler
velocimeters, other types of laser radar as well as Doppler weather radar. The algorithm
approach is based on detection and classification of velocity patterns which are indicative
of microburst phenomenon while minimizing the false alarms due to ground clutter
return. These algorithms have been developed and tested using the NASA-Langley
Airborne Windshear Doppler Radar Simulation (AWDRS) and modeling of X-band
Doppler radar characteristics.
SLOE4
The core of the Honeywell approach is the use of three dimensional pattern recognition
techniques and the selection of an associated scanning pattern forward of the aircraft.
This "volumetric scan" approach acquires reflectivity, velocity and spectral width from a
three dimensional Volume as opposed to the conventional use of a two dimensional
azimuthal slice of data at a fixed elevation. For each volume element (voxel) in azimuth-
elevation-range space the radar provides measurements of reflected power, mean velocity
and spectral width. Receiver noise characteristics are measured and used to enhance the
quality of the received data. Four separate types of features are then identified including
regions of positive and negative divergence, regions of high reflectivity, regions of
rotation and regions with similar spectral width. These regions are then collectively
assessed by a three dimensional association algorithm which identifies three dimensional
features which are associated clusters of the four basic feature types listed above. These
three dimensional features are then compared with known attributes of microburst
phenomenon and temporally tracked to identify those three dimensional features which
are indications of microbursts.
SLIDE 5
These algorithms have been developed and tested using the NASA-Langley Airborne
Windshear Doppler Radar Simulation (AWDRS) and modeling of X-band Doppler radar
characteristics. We have modified the software for operation on our Sun workstations. It
has also been modified to provide data from a simulated volumetric scan pattern and
provides simulated output characteristic of that expected from nominal X-band Doppler
radar using a Honeywell 12-inch flat plate antenna which produces an 8 ° beamwidth. We
have been using the axisymmetric models developed in the past and are now upgrading
the software to include the newer three dimensional models recently released.
SLOE6
The Honeywell approach was developed to minimize probability of false alarms at an
acceptable detection capability. This is accomplished by the use of full three dimensional
correlation approaches which reject those features which are characteristic of ground
clutter and are not supported by measurements throughout the three dimensional scanning
volume. Temporal tracking is used to assure the rejection of transient phenomenon.
Additional information is also used to reduce false alarm rate including thresholding
using the F-Factor hazard level, assessment of spectral width characteristics and
comparison of the physical size of the hazard with known phenomenological
understanding and aircraft upset requirements.
SLIDE 7
The volumetric velocity and hazard region data for a "wet" microburst (core reflectivity
of 60 dBz) event is shown for three elevation scans at 0 °, 20 ° and 40 ° elevations. This
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data is simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse
repetition frequency of 6kHz and a pulse width of l_tsec. As is shown there is a large
region at near ground level where significant hazard is identified. Note the extension of
velocity and hazard features above in the 20 ° and 40 ° slices.
SLmE8
The reflectivity, Doppler velocity, intermediate data and final identified hazard region
data for a "wet" microburst (core reflectivity of 60 dBz) event is shown. This data is
simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse repetition
frequency of 6kHz and a pulse width of ll.tsec. As is shown there is a large region at near
ground level where significant hazard is identified. Note the rejection of spurious hazard
regions surrounding the actual hazard region.
SLIDE 9
The volumetric velocity and hazard region data for a "dry" microburst (core reflectivity
of 25 dBz) event is shown for three elevation scans at 0 °, 20 ° and 40 ° elevations. This
data is simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse
repetition frequency of 6kHz and a pulse width of ll.tsec. As is shown there is a large
amounts of clutter at near ground level. Note the extension of hazard features above in
the 20 ° and 40 ° slices which eliminates most of the ground clutter as potential
microbursts.
SLIDE 10
The reflectivity, Doppler velocity, intermediate data and final identified hazard region
data for a "dry" microburst (core reflectivity of 25 dBz) event is shown. This data is
simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse repetition
frequency of 6kHz and a pulse width of l_tsec. As is shown there is a large amounts of
clutter at near ground level. Note the rejection of the significant level spurious hazard
regions surrounding the actual hazard region
SLIDE 11
Honeywell is continuing the development of algorithms for detection of wind
shear/microburst using airborne Doppler radar as well as developing data fusion
approaches to utilize data provided from remote sensors as well as in-situ sensors for an
overall integrated wind shear detection system. We anticipate testing of the algorithms
using flight test data in 1992 and continued development of optimal guidance approaches
exploiting data from remote sensors and in-situ sensors.
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Algorithms for Airborne Doppler Radar Wind Shear Detection
Questions and Answers
Q: Kim Eimore (NCAR) - What did you use as a basis for using spectrum width estimates as an
indicator of a hazard? Also, simply as a caveat, we found that in the Denver environment the use
of rotation and convergence aloft does not seem to work very well as an estimation of whether
the storm is actively producing a microburst or will produce one. It does work in the wet kind of
environments in the South East, but it doesn't seem to work very well for us. Jim Wilson and Rita
Roberts spent quite some time on that and finally threw up their hands in despair. How do you
plan to address that, if this is going to be a primary constituent of the hazard determination.
Secondly, what did you use as the basis to utilize spectrum width as a hazard estimation?
A: Earl Benser (Honeywell) - Right now there is a minimum and maximum spectral width
threshold set that are used. We also look for areas that are of common spectral width for the
volumetric feature recognition. So those are the two approaches that we have been using in terms
of spectral width thresholding for detection. In terms of the correlation of all the volumetric
features simultaneously, we do not necessarily demand that they are all simultaneously present,
but we use the lack of all features as a part of the clutter rejection approach.
Q: Kim Eimore (NCAR) - I am curious as to where you got your information to form a
hypothesis about spectral width associations.
A: Earl Benser (Honeywell) - I guess I am not necessarily familiar with those details of the
activity.
Kim Elmore (NCAR) - Again, I wound up spending a lot of time chasing spectral width on
Denver storms, and we found that it was next to useless. It was extremely viewing angle
dependent. It may well be that you have done some sort of correlation with your beam width and
what kind of spectral width you can expect from a meteorological aspect. That may have some
utility. But, the work we did with our radars showed that it was not necessarily a good indicator.
Earl Benser (Honeywell) - As I understand it, and again I am not necessarily familiar with the
details of that particular part of the algorithm, there are spectrum widths that are consistent with
the type of phenomenon we are looking at. Things that have very little spectral width tend to be
point targets as opposed to diswibuted targets that have relatively moderate spectral widths. The
large spectral widths, as I understand it, are somewhat noisy. Anyway, the basic point is that
there is activity going on in that area. I am not really familiar with the details.
Q: Paul Robinson (Lockheed) - From your slides it looks like you could estimate the hazard at
zero degrees relative to the horizon, but you had the airplane flight path that looked like it was
three degrees down. Does that not mean that you are looking at a hazard that the airplane may
not encounter and perhaps underestimating that hazard?
A: Earl Benser (Honeywell) - The analysis that was presented today showed results where we
looked at zero up to 40 degrees in 10 degree slices. Our activity right now is looking at specific
scan pattern issues with respect to overall scan rate, scan range, and scan resolution.
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Q: Paul Robinson (Lockheed) - Is there some reason why the program may not work looking
three degrees down?
A: Earl Benser (Honeywell) - We do not have any reason to believe that it should not work.
We have not completed the testing. Zero degrees was the initial cot_leted activity.
Q: Dan Vicroy (NASA Langley) - Are you estimating a vertical winds? The fact that you are
doing multipleverticalscansallowsyou todo some prettyinterestingthingsinthe vertical
domain.
A: Earl Benser (Honeywell) - We have not gotten that far in our efforts. To date we have been
looking at merely the in-plane velocity information, for detection of areas with consistent shear
numbers for feature identification. We have not gotten to the point to either map out the vertical
velocity structure within the events or to develop an F-factor estimate based on that initial data.
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Session VI. Airborne Doppler Radar / NASA
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Algorithms for airborne Doppler radar wind shear detection

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