In flight doppler weather radar wind shear detection system

An airborne doppler radar wind shear detection system has a volumetric scanning pattern for providing atmospheric measurement data for individual resolution cells that are formed into a 3-D grid of atmospheric data samples. Volumetric feature extraction modules identify and group resolution cells having particular features into air masses of interest. A spatial feature association and filtering module combines the air masses of interest into a 3-D representation of atmospheric conditions and filters out ground clutter. A contextual matching and temporal tracking module compares the 3-D representation to known wind shear models and compares successive 3-D representations to one another to aid in identifying hazardous wind shear conditions in the aircraft flight path.

BACKGROUND OF THE INVENTION 
The present invention relates generally to weather radar systems and 
particularly to airborne doppler radar systems used to predict potentially 
dangerous wind shear conditions in the flight path of the aircraft. 
Microburst wind shear is one of the most serious hazards of aviation, and 
is particularly dangerous to heavily loaded jet transports flying low and 
slow on final approach or on takeoff before a comfortable margin of 
airspeed has been established. Pilots and engineers have learned to expect 
and deal with the nodal changes in horizontal winds as an aircraft 
descends. A microburst is different however and re,ires a very different 
response. 
Typically a microburst is of very short duration and covers a small space 
geographically; the core of a microburst may be as small as 1 nautical 
mile or less in diameter. It appears suddenly, develops its dangerous 
potential and dissipates in a matter of minutes. 
Early attempts to protect against this phenomenon included the development 
of a low-level wind shear alerting system. This alerting system consists 
of wind direction and velocity sensors around the perimeter of the 
airport., plus a central sensor. Data from the sensors are computed and 
displayed in the control tower and controllers then advise the pilots 
concerned. The information derived is seldom specific enough to justify 
aborting the takeoff or landing and the microbursts can sometimes pass 
undetected between the sensors or occur outside the sensor network. 
Other efforts have resulted in wind shear warning systems aboard the 
aircraft. Presently systems are available which look at the inertial 
instrumentation and flight performance information aboard the aircraft and 
provide a warning when the accelerations experienced could be conducive to 
wind shear. These systems are called "reactive" systems because they 
detect hazardous flight conditions as the aircraft enters the wind shear 
condition. The problem is that reactive wind shear detection may not 
provide the pilot with enough time to successfully escape the dangerous 
condition. 
A serious problem faced by airborne radar wind shear warning systems that 
are viewing the airspace in the aircraft glide path is how to deal with 
ground clutter. When ground based radar wind shear warning systems are 
used the ground clutter problem may be dealt with more easily since the 
ground clutter is essentially stationary. For example, with ground based 
radar a ground clutter map may be made periodically and then subtracted 
from the radar information. However, with airborne radar the ground 
clutter is seen in the primary antenna beam and is constantly changing. 
SUMMARY OF THE INVENTION 
The present invention solves these and other needs by providing an airborne 
wind shear detection system that provides advance warning of a hazardous 
wind shear condition. In accordance with the present invention an airborne 
wind shear detection doppler radar is provided which transmits successive 
beams into the airspace in front of an aircraft, receives reflected 
signals and analyzes the reflected signals for a hazardous wind shear 
condition. The radar utilizes a volumetric scan pattern which provides a 
three-dimensional (3-D) grid of atmospheric data samples, each 
characterized by a range, elevation and azimuth. Volumetric feature 
extraction processing identifies potential wind shear features within the 
3-D grid. Spatial feature association processing groups data samples 
having the potential wind shear features into air masses of interest by 
grouping from upper elevation features downward. Filtering means removes 
noise and clutter from the air masses of interest and provides a 3-D 
representation of the air masses of interest. Contextual feature matching 
and temporal tracking module compares the current 3-D representation of 
air masses of interest to known 3-D wind shear models and to previous 3-D 
representations of the same air masses of interest, and provide an output 
signal when a hazard threshold is exceeded.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
The characteristics of a microburst as seen from a Doppler radar are 
described in FIG. 1. The left-hand side of FIG. 1 shows the major 
characteristics of microburst winds, and it can be seen that they are 
characterized by converging winds and vertical winds in the upper portion 
of the structure, rotation and slight divergence at mid-structure, and 
diverging winds in the lower portion of the structure. The overall 
diameter of a microburst is 4 km or less. The Doppler velocities that will 
be observed by the radar for convergence, rotation, and divergence are 
shown at the right side of FIG. 1 as observed velocity signatures. 
A Doppler radar/clutter/microburst simulation capability for the volumetric 
scan approach has been developed by modifying software and models provided 
in User Guide for an Airborne Wind Shear Doppler Radar Simulation (AWDRS) 
program, Report #DOT/FAA/DS-90/7, June 1990. This simulation capability 
has provided us radar data samples for testing the concepts in the 
invention. In addition, we have developed our own software simulation of 
the wind shear detection system described herein. 
SIMULATION RESULTS 
Using the Doppler radar microburst signature simulation, sample data sets 
were generated to investigate signature detectability versus range and 
power, 3-D microburst signature characterization, and performance of the 
volumetric microburst detection algorithms. The radar transmit frequency 
for all of the data simulated was 9.3 GHz, and signatures from a wet 
microburst (maximum reflectivity .about.60 dBZ) model and a dry microburst 
model (maximum reflectivity .about.30 dBZ) were generated. 
3-D MICROBURST SIGNATURE CHARACTERIZATION 
Using a transmit power and range to microburst that assured good signature 
quality, the signature characteristics of a wet and dry microburst were 
investigated. The results of this investigation show that the velocity 
signatures of the microbursts are clearly divergent for the lowest 
elevation scans (3 deg above the aircraft glide slope) and transform into 
a convergent signature within the next 10 deg of elevation. This seems to 
be due to the turbulent "rolling" of the wind outflow. The remainder of 
the upper scans (23, 33 and 43 deg above glide slope) always exhibit the 
signature of an approaching wind, sometimes accompanied by a receding wind 
signature at a slightly closer range (indicative of converging winds). The 
relatively strong approaching wind signature is probably due to 
convergence of the microburst winds amplified by the vertical wind 
component observable by the Doppler radar with an elevated scan angle. In 
turn, the receding wind signature will be attenuated by the vertical 
microburst wind component at elevated scan angles. Rotation signatures 
were not observed in this investigation due to the axis-symmetric nature 
of the microburst models used. Presently available complete microburst 
model renderings are under investigation to corroborate the robustness of 
observed rotational features in recognizing microburst wind shear. FIG. 4 
show an artist's rendering of a wet microburst signature at three 
elevation "slices." The actual Doppler color images could not be used here 
due to publishing constraints. The dark colored areas are receding 
relative to the Doppler radar, and the light colored portions are 
approaching. Aircraft motion has been subtracted from the signatures. 
The wind shear detection system of the present invention utilizes a full 
3-D volumetric scan pattern. 30 as illustrated in FIG. 2 to interrogate 
the region ahead of aircraft 26 by means of signals emitted and received 
by antenna 27. Scanning radar transceiver 28 includes an antenna driver 25 
and is preferably designed to scan vertically in elevation and then step 
through the desired azimuth coverage as this scanning pattern seems most 
efficient for the detection of wind shear obstacles in front of aircraft 
26. Scanning radar transceiver 28 provides both in-phase (I) and 
Quadrature (Q) data. Base data processor 32 performs processing required 
in order to analyze the Doppler radar returns to determine the presence of 
wind shear. Three "base" data products of returned power, mean velocity 
and spectral width must first be calculated from the raw radar samples. 
There are several known ways of calculating these products varying in 
complexity. The technique selected for the present invention was pulse 
pair processing, a technique that is relatively low in computational 
complexity and is widely used in pulsed Doppler weather radar signal 
processing. Base data filter unit 34 corrects for receiver noise 
characteristics 31 and filters the returned power, mean velocity and 
spectral width data. 
Volumetric scanning pattern 30 of the present invention results in a grid 
of resolution cells 35 with each cell having a unique range, elevation and 
azimuth. 
Volumetric feature extraction 40 requires Divergence/Convergence module 50, 
Rotation module 60, Reflectivity Core module 70 and Spectral Width Cluster 
module 80. 
After the three base products are calculated for each cell in volumetric 
scan, individual characteristic features of the microburst are searched 
for and spatially grouped together. Once the individual signature 
features, i.e., divergence, convergence, rotation, high reflectivity, and 
high spectral width are located, they are spatially associated using the 
azimuthal-slice centroid of each feature. This association forms a 3-D 
representation of the radar observations, which can then be matched with 
the known microburst: characteristics as illustrated in FIG. 1. The 
observed 3-D representations are tracked from scan to scan and tested for 
consistency to provide robust detection. The velocity signatures that are 
potentially observable in a microburst are divergence, convergence, and 
rotation as illustrated in FIG. 1. The potential shear conditions that 
exist are along each radial scan, i.e., divergence or convergence or along 
each azimuth scan, i.e., clockwise or counterclockwise rotation. The 
grouping algorithms operate on the differential velocity information 
obtained by subtracting the mean velocity of each cell from the previous 
cell. This subtraction is done along the radial scan line for divergence 
and convergence and is done across azimuth at a constant range for 
rotations. For a doppler radar base data processing approach that provides 
differential radial velocities directly, the subtraction step for 
computing the differential is only performed for locating rotations. The 
beginning and the end of a trend group .are identified by a consistent 
change of the differential velocity sign. The change of the differential 
velocity sign is along the radial for divergence and convergence and along 
the azimuth for rotations. The consistency of each sign change is tested 
with an operator that eliminates spurious velocity differentials. The 
shear value (.DELTA.V/.DELTA.D) and momentum (.DELTA.V.times..DELTA.D) of 
each trend group are calculated and are used to eliminate identified trend 
groups that are weak, i.e., low shear value and/or large shear momentum. 
Once the initial trend groups are found, they are grouped with other 
radial or azimuth trend groups using a proximity operator. Thus the 
grouping process of the present invention produces 3-D trend groups that 
identify and spatially associate consistent divergence, convergence, or 
rotations in the volumetric scan. 
The additional radar system hardware required to implement the invention 
described herein is minimal. A basic doppler weather radar must already 
process the raw radar data to determine reflectivity, velocity and 
spectral width to generate standard displays of information. This is the 
bulk of the required computational power of the system, since typically 
many pulsed samples are averaged to determine these quantities. The 
conventional airborne weather radar functions are shown at 29. The 
additional computational power required by this invention is minimal 
compared to the determination of the basic doppler radar output data. It 
is envisioned that the algorithms shown in block diagram form at 94 be 
hosted in an additional one or two digital signal processing (DSP) chips 
and associated memory within the airborne radar system (based on typical 
doppler weather radar system parameters and scan rates, and state of the 
art DSP chip technology). This volumetric scan pattern should be easily 
accommodated by most commercially available airborne weather radar sets, 
since antennae are required to have system controlled "tilt management". 
Tilt management requires positioning motors to move the antenna in the 
elevation direction. Modification to the standard control mechanism to 
scan the antenna in an elevation azimuth step scan or similar pattern 
should be straightforward. A more detailed description of the invention is 
provided in the following. 
In the more detailed descriptions that follow, references are made to 
certain portions of the volumetric scan pattern 30 employed by the radar 
to gather the grid of resolution cells 35. Several terms are used 
repeatedly to describe certain concepts; they are defined below. 
Radial 
One-dimensional portion of resolution cell grid covering all ranges for 
which radar data are measured at a given azimuth and elevation antenna 
position. A radial line segment is a portion of one complete radial. 
Cross-Azimuth Arc 
One-dimensional portion of resolution cell grid covering all azimuth angles 
for which radar data are measured at a given range and antenna elevation 
angle. A cross azimuth arc segment is a portion of one complete cross 
azimuth arc. 
Azimuth Slice 
Two-dimensional portion of resolution cell grid covering all ranges and all 
elevation angles for which radar data are measured at a given antenna 
azimuth angle. 
Elevation Plane 
Two-dimensional portion of resolution cell grid covering all ranges and all 
azimuth angles for which radar data are measured at a given antenna 
elevation angle. 
Air Mass 
The grouping algorithms operate to associate regions of similar feature 
value that meet certain threshold conditions. The resulting groups of the 
measured radar data are referred to as "air masses" of interest, meaning a 
region within the volumetric scan pattern that warrants further 
feature/pattern processing to determine the hazard to the aircraft. May 
also be referred to as "mass" or "mass of interest". 
Look 
A single complete volumetric scan may be referred to as a "look" or "full 
scan look". 
As features are extracted and grouped from the measured radar data, the 
degree or extent that the algorithms have performed grouping is referred 
to as "one-dimensional," "two-dimensional" and "three dimensional" or 
"volumetric." Each grouping module ultimately results in 3-D or volumetric 
groups, meaning that feature association have been determined for all 
azimuths, elevations and ranges in the volumetric scan. Intermediate or 
partial groupings are referred to as one-dimensional or two-dimensional, 
depending on grouping process performed. For example, groupings performed 
along a radial or cross-azimuth arc are referred to as one-dimensional and 
groupings performed across an azimuth slice or elevation plane are 
referred to as two-dimensional. 
Data From the Volumetric Scanning Pattern 
The basic input to modules 50, 60, 70 and 80 described below is the output 
of the base data processor 32 and the base data filtering 34 previously 
described (page 6). The input for each module (50, 60, 70 or 80) will be 
either the mean velocity, reflectivity or spectral width values from 
modules 32 and 34. In addition, range, antenna position relative to the 
airframe and airframe attitude shall accompany each data sample to 
establish a mapping of data samples from the volumetric scan pattern 30 to 
the physical airspace ahead of the aircraft. 
The current software implementation (described below) of the invention 
concepts assumes that the radar transceiver will scan through elevation 
before scanning through azimuth (i.e., the sensor will scan vertically as 
it steps through a horizontal scan). The concepts of invention are 
straightforwardly adaptable to work with different sequencing of the 
volumetric scan pattern. 
Divergences/Convergences 
Module 50 Output 
Module 50 produces at 58 a list of volumetric (3-D) air masses with similar 
wind velocity gradients along the range distance dimension.. These 
gradients represent divergent and convergent air flows. The data 
associated with each member (a single air mass of interest) of the list 
includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Shear Value and Shear Momentum found within 
the air mass. 
A sublist of radial components of this air mass. For each radial line 
segment which makes up the volumetric air mass, we track the following 
information: 
Minimum and Maximum range of this radial segment. 
Change in air velocity over this segment. 
Shear Value of this segment. 
Shear Momentum of this segment. 
Hazard F-Factor for this segment. 
Elevation and Azimuth of this segment. 
The hazard F-factor is described by R. L. Bowles in "Wind shear Detection, 
Warning, and Flight Guidance," NASA CP 10004: DOT/FAA/PS-87/2 October 
1987. This factor relates doppler velocity measurements to aircraft 
dynamics to form a quantified hazard factor of impending wind shear. 
Module 50 Input 
Module 50 has as input the mean velocity data samples from the volumetric 
scanning pattern as previously described. 
Submodules 52, 54 and 56 
Air masses of interest are found by going through three major steps. First, 
for each radial of doppler velocities received (one velocity per each 
range bin in the radial), we compute the differential velocities between 
each range bin, then look for segments on the radial of similar velocity 
differentials (i.e. regions of changing velocity). Second, the segments 
found on each radial are compared to segments found in similar positions 
on the radial above it. Through these comparisons, we form a collection of 
2-D air masses with similar rates of changing velocity. This process is 
repeated for all radials in a single azimuth slice of the scan (i.e. a 
single scan through elevations). Third, the 2-D air masses of one azimuth 
slice, are compared with the 2-D air masses of adjacent azimuth slices. 
These comparisons form the final collection of 3-D air masses which are 
the output of this module. 
Find Radial Trends (1-D Grouping) 
Submodule 52 Output 
Submodule 52 creates a list of segments of interest on each radial. The 
information currently included for each segment in the list includes: 
Minimum and Maximum range of this radial segment. 
Change in air velocity over this segment. 
Shear Value of this segment. 
Shear Momentum of this segment. 
Hazard F-Factor for this segment. 
Elevation and Azimuth of this segment. 
Submodule 52 Input 
The unit of input to submodule 52 is a set of doppler velocities along a 
single radar radial. There is one velocity value for each range bin along 
the radial. The differences between the velocities in adjacent range bins 
are computed at the start of the submodule before looking for segments of 
interest along the radial. 
The following operational parameters are also input to this submodule to 
fine tune its behavior: 
Shear Value and Shear Momentum Threshold--Only consider segments with shear 
and momentum values meeting a dual-parameter critereon based on a logical 
combination (i.e., and/or) of Shear and Shear Momentum Thresholds. 
Bin Count Threshold--Only consider segments containing at least this many 
Range Bins. 
Test Count Threshold--M of N test criteria to help filter spurious: data 
within a potential segment. 
Minimum Range--Only consider segments at or beyond this range distance. 
Submodule 52 Process 
Starting at the Far End of the radial, inward to the Minimum Range: 
Find the start (far end) of a segment of interest (a change in velocity 
trend) This is done by looking for a change of sign in the velocity 
differentials while checking the values of each range bin from the Far 
End. An M of N check is performed to be sure that Range Bin suspected of 
being the start of a group is not just a noisy value compared to 
neighboring Range Bins. 
Find the end (near end) of a segment of interest. 
Compute Shear Value for this segment: (Delta Velocity/Delta Range Distance) 
Compute Shear Momentum for this segment: (Delta Velocity * Delta Range 
Distance) 
Add this segment to the list of 1-D segments of interest if each of the 
following thresholds is met: Bin Count Threshold, and logical combination 
of Shear Value Threshold and Shear Momentum Threshold. 
Look for the start of the next segment of interest. 
Group Through Elevation (2-D Grouping) Submodule 54 Output 
Submodule 54 creates a list of 2-D air masses of interest within a given 
azimuth slice. The data structure of this 2-D air mass list is identical 
to the 3-D air mass list described below as the output of the final 
submodule. Each member of this list will contain information of each set 
of radial segments that have been determined to belong to the same 2-D 
feature in the air mass. 
Submodule 54 Input 
Submodule 54 takes as input the list created by the Find Radial Trends (1-D 
Grouping) submodule described above. This submodule will operate on a 
single azimuth slice of radials at once. 
The following operational parameters are also input to submodule 54 to fine 
tune its behavior: 
Adjacent Elevation Threshold--A segment of interest must be within a 
certain number of degrees in the elevation direction (the adjacent 
elevation threshold) of a segment on another radial in order to be 
logically considered part of the same 2-D mass. 
Adjacent Range Threshold--Segments on separate radials within the Adjacent 
Elevation Threshold must also be within similar range distances from the 
sensor. Segments may be considered part of the same mass if they are 
within this linear range distance of each other. 
Submodule 54 Process 
The highest elevation radial is the base for this grouping process. 
Each segment of interest on this radial will become the top edge of a 2-D 
mass of interest. 
Working downward from the second highest elevation radial to the lowest 
elevation radial: For each radial: 
For each 1-D segment of interest on this radial: 
Check the 1-D segments on the radials above this radial to find a higher 
segment which has the same characteristics (velocity differential trend) 
as this segment. Consider the Adjacent Elevation Threshold and Adjacent 
Range Threshold to make this decision. 
If a higher segment meeting the thresholds is found, then this segment is 
added to the 2-D mass of the higher segment, otherwise this segment will 
become the top edge of a new 2-D mass of interest. 
Group Across Azimuth (3-D Grouping) Submodule 56 Output 
Submodule 56 produces a list of volumetric (3-D) air masses with similar 
wind velocity gradients. The data associated with each member (a single 
air mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Shear Value and Shear Momentum found within 
the air mass. 
A sublist of radial components of this air mass. For each radial line 
segment (i.e. a portion of air mass between two range distances at a 
single elevation angle and a single azimuth angle from the aircraft's 
radar sensor) which makes up the volumetric air mass, we track the 
following information: 
Minimum and Maximum range of this radial segment. 
Change in air velocity over this segment. 
Shear Value of this segment. 
Shear Momentum of this segment. 
Hazard F-Factor for this segment. 
Elevation and Azimuth of this segment. 
Submodule 56 Input 
Submodule 56 takes as input the list created by the Group Through Elevation 
(2-D Grouping) submodule 54 described above. This submodule logically 
operates on a full scan "look" of data at once. 
The following operational parameter is also input to submodule 56 to fine 
tune its behavior: 
Adjacent Azimuth Threshold--A 2-D mass of interest in a single azimuth 
slice must be within a certain number of degrees in the azimuth direction 
(the adjacent azimuth threshold) of a 2-D mass of a nearby slice in order 
to be logically considered part of the same 3-D volumetric mass. 
Submodule 56 Process 
This algorithm uses either the right-most azimuth slice or the left-most 
azimuth slice as the base for the grouping process. This allows for 
bidirectional radar scans. Each 2-D mass of interest in the base azimuth 
slice will become the right/left edge of a 3-D air mass in the final 
output list of this module. 
Working across from the second azimuth slice (next to the base azimuth 
slice) to the end azimuth slice at the opposite end of the scan: For each 
azimuth slice: 
For each 2-D mass of interest in this azimuth slice: 
Determine a "bounding ]box" and "centroid" for this mass in this slice. 
Look through nearby (within Adjacent Azimuth Threshold) previous azimuth 
slices find a neighboring 2-D mass with the same characteristics (velocity 
differential trend) as this mass. 
If a previous segment meeting these requirements is found, then this mass 
is added to the 3-D mass of the previous segment, otherwise this mass will 
become the right/left edge of a new 3-D mass of interest. 
Rotations 
Module 60 Output 
Module 60 produces at 68 a list of volumetric (3-D) air masses with similar 
wind velocity gradients along the cross-azimuth dimension. These gradients 
represent rotational airflows to the doppler radar (since the radar senses 
radial velocities only). The data associated with each member (a single 
air mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Rotation Value and Rotation Momentum found 
within the air mass. 
A sublist of cross-azimuth arc components of this air mass. For each 
cross-azimuth arc segment which makes up the volumetric air mass, we track 
the following information: 
Left and Right Azimuth of this cross-azimuth arc segment. 
Change in air velocity over this segment. 
Rotation Value of this segment. 
Rotation Momentum of this segment. 
Hazard Factor for this segment. (The intent of the "Hazard Factor" for 
rotations is to mimic the "F-Factor" used in divergences/convergences 
(previously discussed) for the case of the rotating wind field.) 
Range and Elevation of this segment. 
Module 60 Input 
Module 60 has as input the mean velocity data samples from the volumetric 
scanning pattern as previously described. 
Submodules 62, 64 and 66 
Air masses of interest are found by going through three major steps. First, 
for each cross-azimuth arc in the full scan, we compute the differential 
velocities between adjacent range bins on the arc, then look for segments 
of similar velocity differentials (i.e., cross-azimuth regions of changing 
velocity). Second, the segments found on each arc are compared to segments 
at similar azimuth angles, at the same elevation angle, and at nearby 
range distances from the sensor. Through these comparisons we form a 
collection of 2-D air masses with similar rates of changing velocity in 
the cross-azimuth direction. This process is repeated for all 
cross-azimuth arcs in a single elevation plane. Third, the 2-D air masses 
of one elevation plane are compared with the 2-D air masses on elevation 
planes above them. These comparisons form the final collection of 3-D air 
masses which are the output of this module. 
Find Cross-Azimuth Trends (1-D Grouping) 
Submodule 62 Output 
Submodule 62 creates a list of segments of interest on each cross-azimuth 
arc. The information currently included for each segment in the list 
includes: 
Left and Right Azimuth of this cross-azimuth arc segment. 
Change in air velocity over this segment. 
Rotation Value of this segment. 
Rotation Momentum of this segment. 
Hazard Factor for this segment. 
Range and Elevation of this segment. 
Submodule 62 Input 
Submodule 62 considers a single cross-azimuth arc as its basic unit of 
input. There is one velocity value for the range bin at each discrete 
azimuth angle along the arc. The differences between the velocities in 
adjacent range bins are computed at the start of the submodule before 
looking for segments of interest along the arc. 
The following operational parameters are also input to this submodule 62 to 
fine tune its behavior: 
Rotation Shear Value and Rotation Momentum Threshold--Only consider 
segments with rotation shear and rotation momentum values meeting a dual 
parameter critereon based on a logical combination (i.e., and/or) of 
rotation shear and rotation momentum thresholds. 
Azimuth Distance Threshold--Only consider segments with measured length 
greater than this linear distance threshold (measured between the two ends 
of the segment). 
Test Count Threshold--M of N test criteria to help filter spurious data 
within a potential segment. 
Minimum Range--Only consider arcs at or beyond this range distance. 
Submodule 62 Process 
Starting at one end of the arc, across to the opposite end: 
Find the start of a segment of interest (a change in velocity trend) This 
is done by looking for a change of sign in the velocity differentials 
while checking the values of each sample across the arc. An M of N check 
is performed to be sure that the sample suspected of being the start of a 
group is not just a noisy value compared to neighboring samples. 
Find the end of a segment of interest. 
Compute Rotation Shear Value for this segment: (Delta Velocity/Delta 
Distance) 
Compute Rotation Momentum for this segment: (Delta Velocity , Delta 
Distance) 
Add this segment to the list of 1-D segments of interest if the following 
thresholds are met: Azimuth Distance Threshold, and a logical combination 
of the Rotation Shear Value Threshold and Rotation Momentum Threshold. 
Look for the start of the next segment of interest. 
Group Through Range (2-D Grouping) 
Submodule 64 Output 
Submodule 64 creates a list of 2-D air masses of interest within a given 
elevation plane. The data structure of this 2-D air mass list is identical 
to the 3-D air mass list described below as the output of final submodule 
66. Each member of this list will contain information of each set of 
cross-azimuth arc segments that have been determined to belong to the same 
2-D feature in the air mass. 
Submodule 64 Input 
Submodule 64 takes as input the list created by the Find Cross-Azimuth 
Trends (1-D Grouping) submodule 62 described above. Submodule 64 will 
operate on a single elevation plane of radials at once. 
The following operational parameters are also input to submodule 64 to fine 
tune its behavior: 
Adjacent Range Threshold--A segment of interest must be within this range 
distance of a segment on another radial in order to be logically 
considered part of the same 2-D mass. 
Adjacent Azimuth Threshold--Segments on separate radials within the 
Adjacent Range Threshold must also be within similar azimuth distances 
from the sensor. Segments may be considered part of the same mass if they 
are within this linear distance of each other. 
Submodule 64 Process 
The most distant (farthest range bin) cross-azimuth arc is the base for 
this grouping process. 
Each segment of interest on this arc will become the far edge of a 2-D mass 
of interest. 
Working inward from the second farthest range arc radial to the Minimum 
Range arc: For each arc: 
For each 1-D segment of interest on this arc: 
Check the 1-D segments on the arcs beyond this arc to find a segment which 
has the same characteristics (velocity differential trend) as this 
segment. Consider the Adjacent Range Threshold and Adjacent Azimuth 
Threshold to make this decision. 
If a more distant segment meeting the thresholds is found, then this 
segment is added to the 2-D mass of the further segment, otherwise this 
segment will become the far edge of a new 2-D mass of interest. 
Group Across Elevation (3-D Grouping) 
Submodule 66 Output 
Submodule 66 produces a list of volumetric (3-D) air masses with similar 
wind velocity gradients (representing rotations). The data associated with 
each member (a single air mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Rotation Value and Rotation Momentum found 
within the air mass. 
A sublist of cross-azimuth arc components of this air mass. For each 
cross-azimuth arc segment which makes up the volumetric air mass, we track 
the following information: 
Left and Right Azimuth of this cross-azimuth arc segment. 
Change in air velocity over this segment. 
Rotation Value of this segment. 
Rotation Momentum of this segment. 
Hazard Factor for this segment. 
Range and Elevation of this segment. 
Submodule 66 Input 
Submodule 66 takes as input the list created by the Group Through Range 
(2-D Grouping) submodule described above. 
The following operational parameter is also input to submodule 66 to fine 
tune its behavior: 
Adjacent Elevation Threshold--A 2-D mass of interest in a single elevation 
plane must be within a certain number of degrees in the elevation 
direction (the adjacent elevation threshold) of a 2-D mass of an overhead 
plane in order to be logically considered part of the same 3-D volumetric 
mass. 
Submodule 66 Process 
This algorithm uses the highest elevation plane as the base for the 
grouping process. Each 2-D mass of interest in the base elevation plane 
becomes the top edge of a 3-D air mass in the final output list of this 
module. 
Working down from the second highest elevation plane (next to the top 
elevation plane) to the lowest elevation plane at the bottom of the scan: 
For each elevation plane: 
For each 2-D mass of interest in this elevation plane: 
Determine a "bounding box" and "centroid" for this mass in this plane. 
Look through nearby (within Adjacent Elevation Threshold) higher elevation 
planes to find a neighboring 2-D mass with the same characteristics 
(velocity differential trend) as this mass. 
If a higher segment meeting these requirements is found, then this mass is 
added to the 3-D mass of the previous segment, otherwise this mass will 
become the top edge of a new 3-D mass of interest. 
Reflectivity 
Module 70 Output 
Module 70 produces at 78 a list of volumetric (3-D) air masses with similar 
radar reflectivity values. The data associated with each member (a single 
air mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Reflectivity found within the air mass. 
A sublist of radial components of this air mass. For each radial line 
segment which makes up the volumetric air mass, we track the following 
information: 
Minimum and Maximum range of this radial segment. 
Minimum, Maximum, and Average Reflectivity of this segment. 
Elevation and Azimuth of this segment. 
Module 70 Input 
Module 70 has as input the radar reflectivity data samples from the 
volumetric scanning pattern as previously described. 
Submodules 72, 74 and 76 
Air masses of interest are found by going through three major steps. First, 
we look for segments on the radial of similar radar reflectivities. 
Second, the segments found on each radial are compared to segments found 
in similar positions on the radial above it. Through these comparisons, we 
form a collection of 2-D air masses with similar reflectivities. This 
process is repeated for all radials in a single azimuth slice of the scan 
(i.e. a single scan through elevations). Third, the 2-D air masses of one 
azimuth slice are compared with the 2-D air masses of adjacent azimuth 
slices. These comparisons form the final collection of 3-D air masses 
which are the output of this module. 
Radial Range Bin Grouping (1-D Grouping) 
Submodule 72 Output 
Submodule 72 creates a list of segments of interest on each radial. The 
information currently included for each segment in the list includes: 
Minimum and Maximum range of this radial segment. 
Minimum, Maximum, and Average Reflectivity of this segment. 
Elevation and Azimuth of this segment. 
Submodule 72 Input 
The unit of input to submodule 72 is a set of radar reflectivities along a 
single radar radial. There is one reflectivity value for each range bin 
along the radial. 
The following operational parameters are also input to submodule 72 to fine 
tune its behavior: 
Reflectivity Threshold--Only consider segments with a Reflectivity greater 
than this. 
Bin Count Threshold--Only consider segments containing at least this many 
Range Bins. 
Test Count Threshold--M of N test criteria to help filter spurious data 
within a potential segment. 
Break Count Threshold--The number of Range Bins below the Reflectivity 
Threshold needed to determine a break between groups (hence, used to find 
the end of a group.) 
Minimum Range--Only consider segments at or beyond this range distance. 
Submodule 72 Process 
Starting at the Far End of the radial, inward to the Minimum Range: 
Find the start (far end) of a segment of interest (a reflectivity over 
threshold) An M of N sliding window check is performed to be sure that a 
Range Bin suspected of being the start of a group is not just a noisy 
value compared to neighboring Range Bins. 
Find the end (near end) of a segment of interest (use Break Count Threshold 
to confirm). 
Add this segment to the list of 1-D segments of interest if each of the 
following thresholds is met: Bin Count Threshold, Reflectivity Threshold. 
Look for the start of the next segment of interest. 
Group Through Elevation (2-D Grouping) 
Submodule 74 Output 
Submodule 74 creates a list of 2-D air masses of interest within a given 
azimuth slice. The data structure of this 2-D air mass list is identical 
to the 3-D air mass list described below as the output of the final 
submodule. Each member of this list will contain information of each set 
of radial segments that have been determined to belong to the same 2-D 
feature in the air mass. 
Submodel 74 Input 
Submodule 74 takes as input the list created by the Radial Range Bin 
Grouping (1-D Grouping) submodule described above. This submodule will 
operate on a single azimuth slice of radials at once. 
The following operational parameters are also input to this submodule to 
fine tune its behavior: 
Adjacent Elevation Threshold--A segment of interest must be within a 
certain number of degrees in the elevation direction (adjacent elevation 
threshold) of a segment on another radial in order to be logically 
considered part of the same 2-D mass. 
Adjacent Range Threshold--Segments on separate radials within the Adjacent 
Elevation Threshold must also be within similar range distances from the 
sensor. Segments may be considered part of the same mass if they are 
within this linear range distance of each other. 
Submodule 74 Process 
The highest elevation radial is the base for this grouping process. 
Each segment of interest on this radial will become the top edge of a 2-D 
mass of interest. 
Working downward from the second highest elevation radial to the lowest 
elevation radial: 
For each radial: 
For each 1-D segment of interest on this radial: 
Check the 1-D segments on the radials above this radial to find a higher 
segment which has similar reflectivity characteristics as this segment. 
Consider the Adjacent Elevation Threshold and Adjacent Range Threshold to 
make this decision. 
If a higher segment meeting the thresholds is found, then this segment is 
added to the 2-D mass of the higher segment, otherwise this segment will 
become the top edge of a new 2-D mass of interest. 
Group Across Azimuth (3-D Grouping) 
Submodule 76 Output 
Submodule 76 produces a list of volumetric (3-D) air masses with similar 
reflectivity values. The data associated with each member (a single air 
mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Reflectivity found within the air mass. 
A sublist of radial components of this air mass. For each radial line 
segment which makes up the volumetric air mass, we track the following 
information: 
Minimum and Maximum range of this radial segment. 
Minimum, Maximum, and Average Reflectivity of this segment. Elevation and 
Azimuth of this segment. 
Submodule 76 Input 
Submodule 76 takes as input the list created by the Group Through Elevation 
(2-D Grouping) submodule described above. Submodule 76 logically operates 
on a full scan "look" of data at once. 
The following operational parameter is also input to submodule 76 to fine 
tune its behavior: Adjacent Azimuth Threshold--A 2-D mass of interest in a 
single azimuth slice must be within a certain number of degrees in the 
azimuth direction (adjacent azimuth threshold) of a 2-D mass of a nearby 
slice in order to be logically considered part of the same 3-D volumetric 
mass. 
Submodule 76 Process 
This algorithm uses either the right-most azimuth slice or the left-most 
azimuth slice as the base for the grouping process. This allows for 
bidirectional radar scans. Each 2-D mass of interest in the base azimuth 
slice will become the right/left edge of a 3-D air mass in the final 
output list of this module. 
Working across from the second azimuth slice (next to the base azimuth 
slice) to the end azimuth slice at the opposite end of the scan: For each 
radial: 
For each 2-D mass of interest in this azimuth slice: 
Determine a "bounding box" and "centroid" for this mass in this slice. 
Look through nearby (within Adjacent Azimuth Threshold) previous azimuth 
slices find a neighboring 2-D mass with the same reflectivity 
characteristics as this mass. 
If a previous segment meeting these requirements is found, then this mass 
is added to the 3-D mass of the previous segment, otherwise this mass will 
become the right/left edge of a new 3-D mass of interest. 
Spectral Width 
Module 80 Output 
Module 80 produces at 88 a list of volumetric (3-D) air masses with similar 
radar spectral width values. The data associated with each member (a 
single air mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Spectral Width found within the air mass. 
A sublist of radial components of this air mass. For each radial line 
segment (i.e. a portion of air mass between two range distances at a 
single elevation angle and a single azimuth angle from the aircraft's 
radar sensor) which makes up the volumetric air mass, we track the 
following information: 
Minimum and Maximum range of this radial segment. 
Minimum, Maximum, and Average Spectral Width of this segment. 
Elevation and Azimuth of this segment. 
Module 80 Input 
Module 80 has as input the spectral width data samples from the volumetric 
scanning pattern as previously described. 
Submodules 82, 84 and 86 
Air masses of interest are found by going through three major steps. First, 
we look for segments on the radial of similar radar spectral widths. 
Second, the segments found on each radial are compared to segments found 
in similar positions on the radial above it. Through these comparisons, we 
form a collection of 2-D air masses with similar reflectivities. This 
process is repeated for all radials in a single azimuth slice of the scan 
(i.e. a single scan through elevations). Third, the 2-D air masses of one 
azimuth slice are compared with the 2-D air masses of adjacent azimuth 
slices. These comparisons form the final collection of 3-D air masses 
which are the output of this module. 
Radial Range Bin Grouping (1-D Grouping) 
Submodule 82 Output 
Submodule 82 creates a list of segments of interest on each radial. The 
information currently included for each segment in the list includes: 
Minimum and Maximum range of this radial segment. 
Minimum, Maximum, and Average Spectral Width of this segment. 
Elevation and Azimuth of this segment. 
Submodule 82 Input 
The unit of input to submodule 82 is a set of spectral width values along a 
single radar radial. There is one spectral width value for each range bin 
along the radial. 
The following operational parameters are also input to submodule 82 to fine 
tune its behavior: 
Spectral Width Threshold--Only consider segments with a Spectral Width 
greater than this. 
Bin Count Threshold--Only consider segments containing at least this many 
Range Bins. 
Test Count Threshold--M of N test criteria to help filter spurious data 
within a potential segment. 
Break Count Threshold--The number of Range Bins below the Spectral Width 
Threshold needed to determine a break between groups (hence, used to find 
the end of a group.) 
Minimum Range--Only consider segments at or beyond this range distance. 
Submodule 82 Process 
Starting at the Far End of the radial, inward to the Minimum Range: 
Find the start (far end) of a segment of interest (a spectral width over 
threshold) An M of N sliding window check is performed to be sure that a 
Range Bin suspected of being the start of a group is not just a noisy 
value compared to neighboring Range Bins. 
Find the end (near end) of a segment of interest (use Break Count Threshold 
to confirm). 
Add this segment to the list of 1-D segments of interest if each of the 
following thresholds is met: Bin Count Threshold, Spectral Width 
Threshold. 
Look for the start of the next segment of interest. 
Group Through Elevation (2-D Grouping) 
Submodule 84 Output 
Submodule 84 creates a list of 2-D air masses of interest within a given 
azimuth slice. The structure of this 2-D air mass list is identical to the 
3-D air mass list described below as the output of final submodule 86. 
Each member of this list will contain information of each set of radial 
segments that have been determined to belong to the same 2-D feature in 
the air mass. 
Submodule 84 Input 
Submodule 84 takes as input the list created by Radial Range Bin Grouping 
(1-D Grouping) submodule 82 described above. Submodule 84 will operate on 
a single azimuth slice of radials at once. 
The following operational parameters are also input to submodule 84 to fine 
tune its behavior: 
Adjacent Elevation Threshold--A segment of interest must be within a 
certain number of degrees in the elevation direction (adjacent elevation 
threshold) of a segment on another radial in order to be logically 
considered part of the same 2-D mass. 
Adjacent Range Threshold--Segments on separate radials within the Adjacent 
Elevation Threshold must also be within similar range distances from the 
sensor. Segments may be considered part of the same mass if they are 
within this linear range distance of each other. 
Submodule 84 Process 
The highest elevation radial is the base for this grouping process. 
Each segment of interest on this radial will become the top edge of a 
2-dimension mass of interest. 
Working downward from the second highest elevation radial to the lowest 
elevation radial: For each radial: 
For each 1-D segment of interest on this radial: 
Check the 1-D segments on the radials above this radial to find a higher 
segment which has the same spectral width characteristics as this segment. 
Consider the Adjacent Elevation Threshold and Adjacent Range Threshold to 
make this decision. 
If a higher segment meeting the thresholds is found, then this segment is 
added to the 2 dimension mass of the higher segment, otherwise this 
segment will become the top edge of a new 2-D mass of interest. 
Group Across Azimuth (3-D Grouping) 
Submodule 86 Output 
Submodule 86 produces a list of volumetric (3-D) air masses with similar 
spectral width values. The data associated with each member (a single air 
mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Spectral Width found within the air mass.. 
A sublist of radial components of this air mass. For each radial line 
segment (i.e. a portion of air mass between two range distances at a 
single elevation angle and a single azimuth angle from the aircraft's 
radar sensor) which makes up the volumetric air mass, we track the 
following information: 
Minimum and Maximum range of this radial segment. 
Minimum, Maximum, and Average Spectral Width of this segment. 
Elevation and Azimuth of this segment. 
Submodule 86 Input 
Submodule 86 takes as input the list created by the Group Through Elevation 
(2-D Grouping) submodule described above. Submodule 86 logically operates 
on a full scan "look" of data at once. 
The following operational parameter is also input to submodule 86 to fine 
tune its behavior: Adjacent Azimuth Threshold--A 2-D mass of interest in a 
single azimuth slice must be within a certain number of degrees in the 
azimuth direction (adjacent azimuth threshold) of a 2-D mass of a nearby 
slice in order to be logically considered part of the same 3-D volumetric 
mass. 
Submodule 86 Process 
This algorithm uses either the right-most azimuth slice or the left-most 
azimuth slice as the base for the grouping process. This allows for 
bidirectional radar scans. Each 2-D mass of interest in the base azimuth 
slice will become the right/left edge of a 3-D air mass in the final 
output list of this module. 
Working across from the second azimuth slice (next to the base azimuth 
slice) to the end azimuth slice at the opposite end of the scan: For each 
radial: 
For each 2-D mass of interest in this azimuth slice: 
Determine a "bounding box" and "centroid" for this mass in this slice 
Look through nearby (within Adjacent Azimuth Threshold) previous azimuth 
slices find a neighboring 2-D mass with the same spectral width 
characteristics as this mass. 
If a previous segment meeting these requirements is found, then this mass 
is added to the 3-D mass of the previous segment, otherwise this mass will 
become the right/left edge of a new 3-D mass of interest. 
3-D Spatial Feature Association and Filtering Module 90 
The purpose of module 90 is to combine the data outputs (for a single full 
scan look) of the previous four data grouping modules: 
Divergences/Convergences 50, Rotations 60, Reflectivity 70, and Spectral 
Width 80. Filtering criteria are also applied to the 3-D grouped air 
masses to remove the noise and clutter influences from the contents of the 
resultant 3-D air masses of interest list. 
Module 90 Output 
Module 90 outputs a list of 3-D air masses of interest which combines the 
data of the input lists, and filters out the air masses which do not 
contribute to (or even distract from) the detection of microbursts and 
wind shear events. 
Module 90 produces 3D observation representations a list of volumetric 
(3-D) air masses with similar characteristics which may indicate the 
presence of a microburst or wind shear threat. The data associated with 
each member (a single air mass of interest) of the list includes: 
An internal identifier. 
The extreme positions of each of the six edges of the air mass as measured 
from the aircraft. That is, the range in azimuth, elevation, and range 
covered by the mass of interest. 
Minimum, Maximum, and Average Shear Value and Shear Momentum found within 
the air mass. 
Minimum, Maximum, and Average Rotation Shear Value and Rotation Momentum 
within the air mass. 
Minimum, Maximum, and Average Reflectivity and Spectral Width Values within 
the air mass. 
A sublist of radial components of this air mass. For each radial line 
segment (i.e. a portion of air mass between two range distances at a 
single elevation angle and a single azimuth angle from the aircraft's 
radar sensor) which makes up the volumetric air mass,, we track the 
following information: 
Minimum and Maximum range of this radial segment. 
Change in air velocity over this segment. 
Shear Value of this segment. 
Shear Momentum of this segment. 
Hazard Factor for this segment. 
Minimum, Maximum, and Average Reflectivity for this segment. 
Minimum, Maximum, and Average Rotation Shear Value and Rotation Momentum 
for this segment. 
Minimum, Maximum, and Average Spectral Width for this segment. 
Elevation and Azimuth of this segment. 
Module 90 Input 
The major input to module 90 are the output lists of these modules: 
Divergence/Convergence module 50 
Reflectivity module 70 
Spectral Width module 80 
Rotations module 60 
See these modules' descriptions contained herein for details of the data in 
these lists. 
In addition, known 3-D microburst characteristics in the form of stored 
data models are input for aiding in the filtering process. 
The following operational parameters are also input to module 90 to fine 
tune its performance: 
Minimum Group Area - 3-D masses on a single elevation must be larger than 
this minimum area to be considered as a possible wind shear or microburst 
threat. (This helps eliminate ground clutter and noise. This parameter is 
determined by typical microburst size and the desired sensitivity of the 
warning system.) 
Module 90 Process 
The approach of the algorithm is: 
Establish knowledge of potential ground clutter regions within the grid of 
resolution cells (35) from the volumetric scanning pattern (30). 
Parameters from the volumetric scan pattern (azimuth, elevation and range 
sampling intervals) are used to establish this knowledge along with 
information relating to the antenna position to the airframe, airframe 
attitude, and antenna beam width. A "flat earth" model is used to 
determine intersection points of the resolution cell grid with the earth's 
surface. 
Strip out 3-D masses within the Divergences/Convergences list which are 
composed of segments on only a single radial. (These will usually be the 
result of ground clutter or noise.) 
Strip out 3-D masses within the Divergences/Convergences list which exist 
only on a single elevation plane (usually ground clutter) that have a 
surface area smaller than Minimum Group Area. 
Strip out 3-D masses within the Divergences/Convergences list that are 
located in potential ground clutter regions (from knowledge established as 
described above) and have no corroborating evidence in elevations where 
ground clutter is known not to be present. 
Merge Reflectivity values and Spectral Width values from corresponding 3-D 
air masses in the other input lists into the stripped list to create the 
output list of 3-D air masses of interest. 
Contextual Feature Matching and Temporal Tracking 
Module 100 has two principle functions. One, to track features output by 
the 3-D spatial feature association and filtering module over the course 
of multiple "looks". Hence the development of significant features of 
interest will be tracked as time progresses. This will allow us to spot 
the development of microbursts and give the ability to predict where a 
wind shear may exist within the next few minutes. (The leading edge of a 
microburst will descend from high altitudes toward the ground. Our data 
tracking allows us to spot this.) Significant features are tracked 
relative to the aircraft as the aircraft moves through previously scanned 
areas. Changes in the radar's perspective are compensated for on 
significant features to aid tracking of those features. 
The second function is to compare data configurations of significant 
features with stored data models of contextual features known to exist 
with microbursts and wind shear conditions computer memory 101 is provided 
to more model data. The spatial relationship between diverging, converging 
and rotating wind fields as well as high spectral width and high radar 
reflectivity regions comprises the basic microburst model. In addition, 
knowledge of the time history evolution of the model is incorporated in 
the algorithm. The model-feature comparison operation draws upon 
techniques recently and currently being developed in the computer science 
subfields of Knowledge Engineering, Machine Learning, and Contextual 
Pattern Matching. 
Module 100 Output 
The ultimate output of module 100 is a set of alerts and warnings to the 
pilot and flight deck crew of the aircraft. The minimum warning time is 
fifteen to thirty seconds before the aircraft may pass through a wind 
shear or microburst event. The form of the alert will be both audible and 
visual alerts and a radar screen display showing the position of hazard 
events relative to the aircraft and its course. 
Module 100 Input 
The lists of 3-D air masses of interest from the 3-D Spatial Feature 
Association and Filtering module will be collected for each "look" or full 
radar scan. Multiple "looks" will be used for Temporal Tracking. 
The algorithm will also have stored data models of known wind shear 
contextual features to assist in confirming microburst and wind shear 
conditions. 
Module 100 Process 
The lists of 3-D air masses of interest along with the volumetric scan 
pattern sampling interval descriptions, relationship of antenna position 
to the airframe, and ancillary airframe attitude, position and motion 
information form the "world observation model" of the wind shear 
measurement system. This world observation model is the clearinghouse for 
all current information regarding system observations. It also serves as 
the event "tracking file", keeping track of air mass of interest motion 
relative to the airframe and changes in features observed within each air 
mass of interest. 
Based on the data contained in the world observation model described above 
and the stored a priori 3-D microburst data models (previously described), 
module 100 performs the following functions: 
Match to contextual models: The data in the world observation model are 
matched to known "a priori" 3-D microburst data models (contextual 
models). This matching process can be performed with one of many 
techniques used in pattern matching that produces a match confidence 
value. The particular technique selected for this application is an 
evidence accrual technique, since all microburst features may not 
necessarily be observable in all cases. Evidence accrual techniques such 
as the Dempster Schaffer technique can be formulated to accrue only 
available evidence to formulate a match confidence (or "degree" of match). 
Track position and feature propagation from look to look: The position and 
observed features and spatial orientation of observed features are tracked 
from look to look. This is also called temporal tracking, since potential 
microburst position and characteristics are tracked over time. Included in 
this tracking function is spatial location association based on airframe 
motion and scanning parameters, feature similarity comparisons to 
alleviate potential mismatches, and the tracking of multiple air masses of 
interest. 
Predicting state of microburst evolution: Based on tracked observed air 
mass features over time, a prediction of microburst characteristics in the 
next few minutes of time can be made. This operation is performed on the 
world observation model based on feature tracking over time and on known 
microburst evolution information. As an example, it has been observed that 
microbursts evolve from converging winds in the upper atmosphere, followed 
by a falling high reflectivity core of air. These features are observed 
and tracked by the tracking function. The prediction function predicts a 
hazardous divergence in the aircraft's flight path (lower elevations) if 
enough evidence of these features is present in the world observation 
model. 
Wind shear indication: When a region in the world observation model is of 
sufficient hazard to the aircraft and is observed with sufficient 
confidence level, cockpit alert indications will be generated. These 
indications will have at least two levels advisory and warning. The 
advisory alert is given when the wind shear is of no immediate danger to 
the aircraft (30 seconds--several minutes away) and the warning is given 
if more immediate danger is present (wind shear 15-30 seconds away). As 
previously stated the indicators will be both visual and aural cues, as 
well as position information on a cockpit radar display or similar device. 
Simulation display information is provided herein in FIGS. 5 and 6 for a 
simulated "wet" microburst example, and in FIGS. 7 arid 8 for a simulated 
"dry" microburst example. Each display shows one elevation plane of data 
from the volumetric scan. The elevation plane shown is the lowest: 
elevation scanned for these examples, 3 degrees above aircraft glide 
slope. All displays show azimuth in degrees along the top of the display, 
and range in meters along the right side of the display. 
FIG. 5a represents one elevation plane of data provided to the reflectivity 
module 70. 
FIG. 5b represents one elevation plane of data fed into the 
Divergence/Convergence module 50 and the Rotations module 60. This image 
divides into two obvious air flow zones. Area 111 has a positive air 
velocity (tailwind) and area 112 has a negative velocity (headwind). Area 
115 is primarily the result of radar sensor "side lobe echo" and clutter. 
Areas 113 and 114 are primarily the result of ground clutter. 
FIG. 6a represents one elevation plane of the data output of 
Divergence/Convergence module 50. Many areas of interest are shown in FIG. 
6a. 
The output of Divergence/Convergence module 50 is then fed into 3-D Spatial 
Feature Association and Filtering module 90 and the output of module 90 
produces the display of FIG. 6b. It can be seen that the many areas of 
interest of FIG. 6a are filtered down to two main areas of interest in 
FIG. 6b. The darker area is a zone of negative shear value and the larger 
lighter gray area is a large zone of positive shear value. 
FIG. 7a represents one elevation plane of raw radar reflectivity data for a 
simulated "dry" microburst which is fed into reflectivity module 70. 
FIG. 7b represents one elevation plane of raw radar velocity data for a 
simulated "dry" microburst that is fed into Divergence/Convergence 
Processor 50 and Rotations Processor 60. These are mostly blobs of 
negative velocity which turn out to be mostly ground clutter. There are a 
few small zones of positive velocity. 
FIG. 8a represents the data output of Divergence/Convergence module 50. 
The output of Divergence/Convergence module 50 is then fed into 3-D Spatial 
Feature Association and Filtering module 90 and the output of module 90 
produces the display of FIG. 8b. In the "dry" case, all but one of the 
areas of interest from the Divergence/Convergence module 50 output were 
filtered out. 
The one remaining blob 118 in FIG. 8b is the "dry" microburst, as 
corroborated by data at higher elevations scans. The rest of the areas of 
interest of 8a were clutter. While the blob in FIG. 8b may seem small, it 
represents a mass of air on the planes flight path that is approximately 
100 meters across, 150 meters long and 200 meters high and is at a 
distance of 3.9 KM in front of the plane. 
Now that the construction and operation of the present invention have been 
set forth, certain advantages can be set forth and appreciated. Another 
approach to airborne wind shear detection is to use high resolution 
spectral editing. In this approach the system attempts to identify clutter 
by looking at the doppler spectrum of the clutter. The high resolution 
spectral editing approach requires a much more sophisticated radar set 
with its attendant high cost. The present invention takes the approach of 
recognizing the microburst features and then editing the clutter based on 
the recognized features. The present invention can be implemented with 
less sophisticated and lower cost radar hardware than the doppler spectrum 
approach. In addition, because of the availability of upper atmospheric 
data provided by the volumetric scan 30 of the present invention, there is 
more information on which to predict a hazardous wind shear condition. The 
additional information available with the present invention will result in 
a system that will have a higher detection probability and fewer alarms. 
Further, with regard to detection performance, microbursts can be 
recognized with the present invention in there very early stages and well 
in advance of the aircraft. 
In accordance with the foregoing description, Applicants have developed an 
airborne wind shear detection system that builds on conventional airborne 
doppler weather radar systems. Although a specific embodiment of 
Applicants' invention is shown and described for illustration purposes, a 
number of variations and modifications will be apparent to those of 
ordinary skill in the relevant arts. It is not intended that coverage be 
limited to the disclosed embodiment, but only by the terms of the 
following claims.