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Description  |
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FIELD OF THE INVENTION
This invention relates to computer generated images and, in particular, to
a system that creates a visual image of a multidimensional space to
present a filtered image of various three dimensional phenomena and
features that are contained within the multidimensional space as viewed
from any predefined locus within the space.
PROBLEM
It is a problem in complex computer controlled systems that deal with real
world phenomena to present a representation of the phenomena in a manner
that is both informative to the user and in a simple presentation format.
Computer generated graphics are ubiquitous and are typically used to
present an accurate representation of an object, a phenomena, a
multidimensional space and interactions therebetween. Computer generated
graphics are also used extensively in simulation systems to present an
image of a real world situation or a hypothetical situation to a user for
training, analysis or other purposes. Computer generated graphics have
become extremely sophisticated and can represent extremely complex and
fanciful situations in a manner that is virtually lifelike. The
application of computer graphics spans many technologies and applications.
One area in which computer graphics has yet to make a significant impact is
the area of real time display of complex real world phenomena. Some
elementary work has taken place in this area but systems of great
flexibility and adaptability that can handle extremely complex phenomena
are presently unavailable. This is because the volume of data that must be
processed to present an accurate display represents a significant
processing task and when coupled with a requirement to provide a display
in real time, exceeds the processing capability of present processors. It
is therefore a problem to visually display a complex multidimensional and
real time phenomena in a large multidimensional space in a simple manner
that maps the derived reality to a predefined user's viewpoint.
SOLUTION
The above described problems are solved and a technical advance achieved in
the field by the virtual reality image generation system of the present
invention. This apparatus takes a multidimensional space that contains
real world objects and phenomena, be they static or dynamic in nature, and
enables a user to define a point and/or a path through this
multidimensional space. The apparatus then displays the view to the user
that would be seen from the point and/or path through the multidimensional
space. This view is filtered through user definable characteristics that
refine the real world phenomena and objects to a perspective that is of
interest to the user. This filtered view presents the user with a virtual
view of the reality contained within this multidimensional space, which
virtual reality presents data to the user of only objects, views and
phenomena that are of particular interest to the user. This apparatus
highlights, emphasizes, deletes, and reorients the reality contained
within the multidimensional space to present an image to the user of only
what the user needs to see to accomplish a stated task. The selective
presentation of information in real time of real world phenomena enables
the user to process the reduced data set contained in the image presented
by this apparatus to perform a designated task in a manner that heretofore
was impossible. In addition, the phenomena that is displayed is stored and
processed in an efficient manner. The phenomena is reduced to a compact
data representation to simplify the processing task and data
communications.
The preferred embodiment described herein is that of an airport operations
system wherein an airport is located in a predetermined location in a
multidimensional space and is surrounded by various three dimensional
topological surface features. The three dimensional air space surrounding
the airport is typically managed by air traffic controllers to route
aircraft in the vicinity of the airport into arrival and departure
patterns that avoid the topological features, various weather conditions
around the airport, and other aircraft that share the airspace with a
particular flight. This problem is extremely complex in nature in that the
multidimensional space around the airport contains fixed objects such as
the airport and its surrounding topological features as well as dynamic
phenomena such as meteorological events that are beyond the control of the
air traffic controllers as well as dynamic phenomena, such as the
aircraft, that can be indirectly controlled by the air traffic
controllers. The dynamic phenomena vary in time and space and the movement
of the aircraft within this multidimensional space must be managed in real
time in response to real time and sometimes sudden changes in the
meteorological phenomena as well as the position of other aircraft.
No known system even remotely approaches providing the air traffic
controllers, the pilots or other potential users with a reasonable
distillation of air of the data contained with the multidimensional space
around an airport. Existing airport operations include a significant
amount of data acquisition instrumentation to provide the air traffic
controllers as well as the pilots of the aircraft with data relating to
weather, air traffic and spatial relationships of the aircraft with
respect to the airport and the ground level. The problem with this
apparatus is that all of the data acquisition instrumentation is
configured into individual units, each adapted to present one set of
narrowly defined relevant information to the user with little attempt to
integrate the plurality of systems into a universal instrument that can be
adapted to controllably provide an image of the multidimensional space to
the various users, with each image being presented to a user in terms of
their specific need for information. This is especially important since
the air traffic controller has a significantly different need for
information than the pilot of the aircraft. The data output by these
diverse systems varies greatly in both format and content and is not
easily integrated into a single system that can represent the
multidimensional space and its contents.
The apparatus of the present invention obtains data from a multitude of
data acquisition sources and controllably melds this information into a
database that represents all the information of interest relating to this
multidimensional space. Graphic processing apparatus responds to user
input to define a predetermined point or path (interactively or on a
predefined basis) through the multidimensional space as well as certain
visualization characteristics for each individual user. The graphic
processing apparatus thence, in real time, presents the user with a
customized view of the multidimensional space in a visual form by deleting
information that is extraneous or confusing and presenting only the data
that is of significant relevance to the particular user as defined by the
filter. In an airport operation environment, low level wind shear alert
systems (LLWAS) use ground-based sensors to generate data indicative of
the presence and locus of meteorological phenomena such as wind shear and
gust fronts in the vicinity of the airport. In addition, terminal doppler
weather radar (TDWR) may also be present at the airport to identify the
presence and locus of meteorological phenomena in the region surrounding
the airport to enable the air traffic controllers to guide the aircraft
around undesirable meteorological phenomena such as thunderstorms.
Additional data is available in the form of LANDSAT data indicative of
topological surface features surrounding the airport. This system can also
use other digital image data such as aviation charts, road maps, night
light imaging, etc. Air traffic control radar is also available to
indicate the presence and locus of aircraft within the space around the
airport for air traffic control purposes. Collectively, these systems
provide data representative of the immutable characteristics of the
multidimensional space as well as the dynamic phenomena contained in the
air space, including meteorological events and aircraft operations. It is
not uncommon for airport operations to take place in a zero visibility
mode wherein the pilot's ability to obtain a visual image of air space in
front of the aircraft is impaired to the point where the pilot is flying
blind. Further, some aviation weather hazards are not detectable by the
naked eye in clear air conditions, e.g., dry microbursts or turbulent
regions. The pilot must rely on the air traffic controllers and radar
contained within the aircraft to ensure that the pilot does not fly the
aircraft on a collision course with a solid object, such as another
aircraft or the topological features surrounding the airport.
The virtual reality imaging system of the present invention converts the
data obtained from the multitude of systems into compact data
representations of the phenomena of interest to the user. These compact
data representations from the various data collection systems can be
merged and the information contained therein simply distilled into a
visualization of the flight path presently in front of the aircraft. This
apparatus can delete extraneous information, such as clouds, fog, etc. and
illustrate to the pilot and/or the air traffic controller only phenomena
that would be of significant interest to the pilot, such as dangerous
meteorological phenomena and other aircraft, to present the pilot with a
clear image of hazards within the multidimensional space to permit the
pilot to chart a course through these hazards without the pilot being able
to see these dangers with the naked eye.
The specific example noted above is simply one of many applications of this
concept which operates to filter vast amounts of data typically found in a
visual imaging situation to present a "clearer image" to the user as
defined by the specific needs of the user. The user therefore sees only
what they need to see and can complete tasks that heretofore were
impossible due to the visual overload encountered in many situations, such
as flying an aircraft through fog or clouds or not being able to identify
a wind shear event in a meteorological phenomena of significant extent and
complexity. An additional capability of this system is the prediction of
future states of the dynamic phenomena. Data is collected by the multitude
of data acquisition systems over a plurality of sampling intervals and can
be extrapolated through trend analyses or through model simulations on the
data available to illustrate the state of the dynamic phenomena in future
sampling intervals. This capability enables the air traffic control
supervisor to model the weather activity around the airport to provide
information to plan airport operations for the immediate future.
BRIEF DESCRIPTION OF THE DRAWING
FIG. 1 illustrates in block diagram form the overall architecture of the
apparatus of the present invention;
FIG. 2-4 illustrate in flow diagram form the operation of the various
segments of the improved weather alert system;
FIG. 5 illustrates in block diagram form the overall architecture of the
improved weather alert system;
FIG. 6 illustrates a plot of a typical airport configuration, including
LLWAS and TDWR installations and typical weather conditions;
FIG. 7-12 illustrate an example of converting the compact data
representation of a phenomena to a three-dimensional object
representation;
FIG. 13-17 illustrate typical visual images produced by this apparatus;
FIG. 18 illustrates additional detail of the renderer; and
FIG. 19 illustrates in flow diagram form the operation of the presentation
subsystem.
DETAILED DESCRIPTION.
FIG. 1 illustrates in block diagram form the overall architecture of the
virtual reality imaging system 10 of the present invention. Within the
virtual reality imaging system 10, a data acquisition subsystem 1
functions to collect and produce the real time data that is representative
of the multidimensional space and the features and phenomena extant
therein. Graphics subsystem 2 functions to utilize the real time data that
is produced by the data acquisition subsystem 1 to produce the visual
displays required by the plurality of users. To accomplish this, a shared
database 3 is used into which the real time data is written by the data
acquisition subsystem 1 and accessed by the various processing elements of
graphics subsystem 2. A user data input device 5 is provided to enable a
user or a plurality of users to enter data into the graphics subsystem 2
indicative of the particular information that each of the plurality of
users desires to have displayed on the corresponding display device 11.
In operation, the data acquisition subsystem 1 comprises a plurality of
data acquisition apparatus 21-2n, each of which produces data
representative of measurements performed on the phenomena or features that
are located in the multidimensional space. These data acquisition
apparatus 21-2n can process the real time measurement data into compact
data representations of the phenomena and features, which compact data
representations are transmitted to graphics subsystem 2 for processing
into the visual images. The graphics subsystem 2 converts the compact data
representations produced by the plurality of data acquisition apparatus
21-2n into visualizations as defined by each of the users of the virtual
reality imaging system 100. This visualization is produced by performing a
database transversal to present the data in a form and format of interest
to each of the users.
Aviation Weather Display System
A typical application of this apparatus is an aviation weather display
system whose data acquisition subsystems make use of a plurality of
aviation weather instrumentation that are used in and about an airport
installation. The aviation weather instrumentation may include ground
based sensors such as radar, lighting detection networks, and wind sensors
as well as airborne sensors, such as sounding balloons or aircraft based
sensors. Each of the aviation weather instrumentation produces raw data
indicative of real time meteorological phenomena, topological features and
aircraft operations in the multidimensional space, which real time data is
processed by the data acquisition subsystem 1 to produce compact
representations of the real time data. These data processing steps often
include filtering, feature extraction, and correlation/integration of more
than one data stream. Furthermore, this processed data may be used as
input to physically based models, which attempt to predict the evolving
phenomena based on the stored measurements.
From the compact data representations, the graphics subsystem 2 generates
generalized graphical representations of the phenomena and features. This
involves the creation of an object or objects which exist in a virtual
multidimensional space. In an aviation weather display application, this
virtual reality imaging system 10 must operate in real time since
significantly delayed data affects the validity and functionality of the
system as a whole. The visualization presented to the user typically
includes frame of reference information such as terrain, overlaid with
identifiable features in the form of highways, range rings or icons
representing municipalities or airports. Furthermore, the terrain surface
can be colored by texture mapping it with an image such as a LANDSAT image
or a digital map. This system can also use other digital image data such
as aviation charts, road maps, night light imaging, etc. In order to
integrate the plurality of data streams that are produced in a data
acquisition subsystem 1, the graphics subsystem 2 must perform numerous
operations such as database culling, relative level of detail
determination and rendering to create user recognizable images from the
raw data or compact data representations that are stored in database 3.
Data Acquisition Subsystem Architecture
FIG. 1 illustrates the major subcomponents of a typical data acquisition
apparatus 21. In a typical configuration, a plurality of sensors 201 are
used to make measurements during a sampling interval of predetermined
duration and repetition frequency, of one or more characteristics of a
particular phenomena or feature within the multidimensional space. The
output signals from the plurality of sensors 201 are received by data
filtering and feature extraction element 202 which functions to filter the
data received from the plurality of sensors 201 to remove ambient noise or
unwanted signal components therefrom. The data filtering, feature
extraction element 202 also functions to convert the raw data received
from the plurality of sensors 201 into a definition of the particular
phenomena or feature that is being monitored by this particular data
acquisition apparatus 21. An example of such a capability is the use of an
improved low level wind shear detection apparatus which converts the wind
magnitude measurements from a plurality of ground based sensors into data
representative of wind shear events within the multidimensional space. To
accomplish this, the raw data obtained from the sensors 201 must be
converted into a form to extract the wind shear events from the plurality
of wind measurements taken throughout the multidimensional space. The
resultant information is used by compact data representation apparatus 204
to produce a set of data indicative of the extracted feature in a
convenient memory efficient manner. This can be in the form of gridded
data sets, feature extent and location data as well as other possible
representations. Furthermore, the data acquisition apparatus can include a
predictive element 203 which uses the data obtained from data filtering,
feature extraction apparatus 202 to extrapolate into one or more
predetermined future sampling intervals to identify a future temporal
state of the feature or phenomena that is being measured. The data output
by the predictive element 203 is also forwarded to compact data
representation element 204 for inclusion in the data set that is produced
therein. The resultant compact data representations are transmitted to the
graphics subsystem 2.
It is obvious that if the feature being monitored is temporally and
spatially static, the data that is produced is invariant and need not be
updated during successive sampling intervals. However, most phenomena that
are monitored in this environment tend to be temporally and in many cases
spatially varying and the operation of the data acquisition apparatus 1 is
on a time sampled basis, with a set of data being produced at the end of
each sampling interval. The plurality of data acquisition elements 21-2n
preferably operate in a time coordinated manner to produce synchronized
sets of data sets in the database 3 so that graphics subsystem 2 can
produce temporally coordinated views of the phenomena and features located
in the multidimensional space on a once per sampling interval basis or
over a plurality of sampling intervals, dependent on the amount of data
that must be processed. In a real time environment, the plurality of data
acquisition apparatus 21-2n function to collect tremendous amounts of data
and reduce the data to manageable amounts for use by the graphics
subsystem 2.
The improved low-level wind shear alert system, illustrated in block
diagram form in FIG. 5, provides an improved method of identifying the
presence and locus of wind shear in a predefined area. This low-level wind
shear alert system enhances the operational effectiveness of the existing
LLWAS system by mapping the two-dimensional wind velocity, measured at a
number of locations, to a geographical indication of wind shear events.
This resultant geographical indication is displayed in color-graphic form
to the air traffic control personnel and can also be transmitted via a
telemetry link to aircraft in the vicinity of the airport for display
therein. In addition, gust fronts are tracked and their progress through
the predefined area displayed to the users.
This low-level wind shear alert system can also integrate data and
processed information received from a plurality of sources, such as
anemometers and Doppler radar systems, to produce low-level wind shear
alerts of significantly improved accuracy over those of prior systems. In
particular, the apparatus of the improved low-level wind shear alert
system makes use of the data and processed information produced by the
existing Low-Level Wind Shear Alert System (LLWAS) as well as that
produced by the Terminal Doppler Weather Radar (TDWR) to precisely
identify the locus and magnitude of low-level wind shear events within a
predetermined area. This is accomplished by the use of a novel integration
system that utilizes the data and processed information received from
these two systems (LLWAS & TDWR) in such a way that the limitations of the
two stand-alone systems are ameliorated. This integration scheme, while
addressing these limitations, simultaneously maintains the strengths of
the two stand-alone systems. This technique then provides the best
possible wind shear hazard alert information. Furthermore, this
integration methodology addresses the operator interaction problem
discussed above. The integration is fully automated, requires no
meteorological interpretation by the users and produces the required
graphical and alphanumeric information in an unambiguous format. Lastly,
this integration technique is implemented fully without any major software
modifications nor without any hardware modifications to the existing
stand-alone systems.
The TDWR apparatus uses a 5 cm. C-band Doppler radar system to measure
radial winds when atmospheric scatterers are present. This system
processes the radar return signals to create a field of radially oriented
line segments indicative of the radial velocity data received from the
radar. The TDWR apparatus bounds isolated sets of segments that are above
a predetermined threshold to define an area which would contain a
specific, potential low-level wind shear event. The bounding is such that
it incorporates the smallest area which includes all of the line segments
above the predetermined threshold. A predefined geometric shape is used to
produce this bounding and the characteristics of this geometric shape are
adapted in order to encompass all of the required data points in the
minimal area.
The apparatus of the improved low-level wind shear alert system is divided
into two independent sections: detection of wind shear with loss
situations (microbursts, etc.) and detection of wind shear with gain
situations (gust fronts, etc.). The TDWR system outputs wind shear with
loss data in the form of microburst shapes. The enhanced low-level wind
shear alert system generates equivalent LLWAS microburst shapes using the
triangle and edge divergence values produced by the existing LLWAS
apparatus. The LLWAS microburst shapes are validated by using auxiliary
information from LLWAS and TDWR to eliminate marginal and false-detection
LLWAS microburst shapes. The resultant two sets of microburst shapes are
then considered for alarm generation purposes. The wind shear with gain
portion of this system simply divides the coverage area into two regions,
with TDWR producing wind shear with gain runway alarms for wind shear
events that occur outside of the LLWAS sensor while the LLWAS runway
oriented gain alarms are produced for wind shear events that occur inside
of the LLWAS sensor network.
This integration architecture enables the concurrent use of a plurality of
sensorbased systems to provide the wind shear detection function, with
increased accuracy. Both ground-based and aircraft-based sensor systems
can be used to provide wind data for this apparatus. The mapping of
diverse forms of input data into a common data structure (predefined
geometric shapes) avoids the necessity of modifying existing sensor
systems and simplifies the production of information displays for the
user. The use of a common information display apparatus and format renders
the combination of systems transparent to the user.
Improved Low-Level Wind Shear Detection System
Adverse weather conditions, especially those affecting-airport operation,
are a significant safety concern for airline operators. Low level wind
shear is of significant interest because it has caused a number of major
air carrier accidents. Wind shear is a change in wind speed and/or
direction between and two points in the atmosphere. It is generally not a
serious hazard for aircraft en route between airports at normal cruising
altitudes but strong, sudden low-level wind shear in the terminal area can
be deadly for an aircraft on approach or departure from an airport. The
most hazardous form of wind shear is the microburst, an outflow of air
from a small scale but powerful downward gush of cold, heavy air that can
occur beneath or from the storm or rain shower or even in rain free air
under a harmless looking cumulus cloud. As this downdraft reaches the
earth's surface, its spreads out horizontally like a stream of water
sprayed straight down on a concrete driveway from a garden hose. An
aircraft that flies through a microburst at low altitude first encounters
a strong headwind, then a downdraft, and finally a tailwind that produces
a sharp reduction in air speed and a sudden loss of lift. This loss of
lift can cause an airplane to stall and crash when flying at a low speed,
such as when approaching an airport runway for landing or departing on
takeoff. It is therefore desirable to provide pilots with a runway
specific alert when a fifteen knot or greater headwind loss or gain
situation is detected in the region where the aircraft are below one
thousand feet above ground level and within three nautical miles of the
runway ends.
FIG. 6 illustrates a top view of a typical airport installation wherein the
airport is within the region indicated on the horizontal axis by the line
labeled L and a Terminal Doppler Weather Radar system 502 is located a
distance D from the periphery of the airport. Included within the bounds
of the airport are a plurality of Low Level Wind Shear Alert System
sensors 505. The sensors 505 are typically anemometers located two to four
kilometers apart and are used to produce a single plane, two dimensional
picture of the wind velocity within the region of the airport. The
Terminal Doppler Weather Radar 502, in contrast, consists of a one
dimensional (radial) beam which scans all runways (R1-R4) and flight paths
but can measure only a radial horizonal outflow component of wind. The
nominal TDWR scan strategy produces one surface elevation scan per minute
and scans aloft of the operational region to an altitude of at least
twenty thousand feet every two and a half minutes. This strategy is
intended to provide frequent updates of surface outflow while monitoring
for features aloft to indicate that a microburst is imminent. Microbursts
(M1-M8) are recognized primarily by surface outflow although they can be
anticipated to a certain extent by monitoring features and events in the
region above the airport location.
Thunderstorms typically produce a powerful downward gush of cold heavy air
which spreads out horizontally as it reaches the earth's surface. One
segment of this downflow spreads out away from TDWR radar while an
opposing segment spreads out towards the TDWR radar. It is generally
assumed that these outflows are symmetrical for the purpose of detecting
microburst wind shears. Because most microbursts do not have purely
symmetrical horizontal outflows, the TDWR system can have problems
detecting or estimating the true intensity of asymmetrical microburst
outflows. As can be seen from FIG. 6, the anemometers 505 of the Low Level
Wind-Shear Alert System are sited on both sides of airport runways R1-R4
but do not extend to the full three mile distance from the end of the
runway as is desirable. Therefore, the anemometers 505 can only detect
horizontal airflows that occur in their immediate vicinity (M2, M3, M5-M8)
even though there can be horizontal airflow outside the anemometer network
(M1, M4) that can impact airport operations but are outside of the range
of the limited number of anemometers 505 sited at an airport.
Improved Wind Shear Alert System Architecture
FIG. 5 illustrates in block diagram form the overall architecture of the
improved low-level wind shear alert system 100. This low-level wind shear
alert system 100 integrates the ground level wind data collected by one
set of stationary ground level sensor (anemometers) 505 with the higher
altitude wind data collected by a second sensor (Doppler radar) 502 in
order to accurately identify both the locus and magnitude of low-level
wind shear conditions within a predetermined area A. The two sets of data
inputs illustrated in this embodiment of the invention include the data
produced by existing data processing systems associated with the sensors
in order to preprocess the data prior to integration into the unified
precise output presented to the end user.
The sensor systems include the existing Low Level Wind Shear Alert System
(LLWAS) front end processing 101 which is an anemometer-based wind shear
alert system used to detect the presence and identify the locus of wind
shear events at or near ground level. The LLWAS system 101 generates data
indicative of the wind velocity (magnitude and direction) at each of a
plurality of fixed sites 505 located within a predefined area. The
collected wind velocity data is then preprocessed by the LLWAS system 101
to identify the locus and magnitude of wind shears at ground level by
identifying the divergence or convergence that occurs in the measured wind
velocity throughout the predefined area. Similarly, the second set of
sensors is the Terminal Doppler Weather Radar (TDWR) 502 which uses a
Doppler radar system to measure low-level wind shear activity in the
predefined area. The TDWR system 502 searches its radar scan for segments
of the radar beam of monotonically increasing radial velocity. These
regions and areas of radial convergence are identified as the locus of
wind shear events.
The integration system 103 that has been developed for the integration of
TDWR 502 and LLWAS 101 uses a product-level technique and is divided into
two independent sections: the detection of windshear-with-loss situations
(microbursts, etc.) and windshear-with-gain situations (gust fronts,
etc.).
The outputs from the Windshear-with-loss portion of the TDWR system 502 are
microburst shapes--which are used both as graphical information and to
generate the textual runway alerts. As an integration "add-on" to the
existing LLWAS system 101, an enhanced LLWAS section 102 was developed to
generate LLWAS microburst shapes. These shapes are computed using triangle
and edge divergence values obtained from the LLWAS system 101. Even though
the methods used to generate these shapes is quite different, these LLWAS
microburst shapes are identical--in both form and content--to the TDWR
microburst shapes. This allows for the same alert-generation logic to be
applied, and for the common graphical display 116 of microburst
detections.
The TDWR/LLWAS (windshear-with-loss) microburst integration 114 is
essentially the combined use of microburst shapes from each sub-system
112, 502. This combination, however, is not a spatial merging of the
shapes: each shape is considered as a separate entity. Furthermore, the
LLWAS microburst shapes have been passed through a validation process in
symmetry test 113. By this we mean that auxiliary information 703 from
both TDWR and LLWAS is utilized in an attempt to eliminate certain of the
"weaker" LLWAS microburst shapes--ones that could generate nuisance or
false alarms. The motivation and implementation for this procedure is
described below. However, an alternative to this process, the sensor data
from each of the sub-systems 112, 502 could be merged to produce a
composite set of shapes indicative of the merged data. This alternative
process is noted herein in the context of this system realization.
Once a set of microburst shapes are produced by the enhanced LLWAS
apparatus 102 and integration apparatus 103, these shapes are transmitted
to the Terminal Doppler Weather Radar system 502 which contains the runway
loss alert generation process. Similarly, the integration apparatus 103
receives LLWAS runway oriented gain data and TDWR gust from data in gust
front integration apparatus 115. The LLWAS runway-oriented-gain data
includes data front tracking system 119 which uses the LLWAS anemometer
wind vectors to detect, track, and graphically display gust-fronts within
the predetermined area. LLWAS runway-oriented-gain (ROG) is also used for
detection of generic wind shear with gain hazards within the LLWAS
network. This is not necessarily tied to a specific gust front detection.
Wind shear with gain situations can occur independently of gust
fronts--e.g. the leading edge of a microburst outflow, or larger-scale
(meteorological) frontal passage. The selected data is then transmitted to
the TDWR system 505 where a runway gain alert generation process produces
an alarm indicative of the presence of a wind shear with gain hazard.
Alarm arbitration process in TDWR system 502 selects the alarm produced by
either runway loss alert generation process or runway gain alert
generation process to present to TDWR displays 116. The existing displays
116 consist of the TDWR Geographic Situation Display (GSD) which
illustrates in graphical form the microburst shapes, gust fronts and
indicates which runways are in alert status. The TDWR and LLWAS Ribbon
Display Terminal (RDT) gives an alphanumeric message indicating alert
status, event type, location and magnitude for each operational runway.
It is obvious from the above description that the existing LLWAS 101 and
TDWR 502 systems are utilized as much as possible without modification to
minimize cost and impact on existing installations. It is also possible to
implement these features in other system configurations. Any other data
collection system can be similarly integrated with the existing TDWR
system 502 or the existing LLWAS system by the application of the
philosophy described above. For example, the addition of another Doppler
radar, or another anemometer network.
Shape Generation Philosophy.
The LLWAS microburst shape computations are based upon the detection of
divergence in the surface winds. These triangle and edge divergence
estimates are mapped onto a rectangular grid. Contiguous "clumps" of
above-threshold grid points are collected and then used to generate
microburst shapes. Compensating for the spatial under-sampling of the true
surface wind field inherent in the LLWAS data, a "symmetry hypothesis" is
used in generating the location, extent, and magnitude (loss estimate) for
these microburst shapes. This hypothesis is applied as if a symmetric
microburst were centered at each (above threshold) grid point. In general,
microburst outflows are not symmetric. However, the spatial superposition
of these symmetric "grid-point-microbursts" in a given clump does a very
good job of approximating a non-symmetric event.
While a given detected divergence may be real, the LLWAS data alone cannot
be used to determine whether it is truly associated with a microburst.
Therefore, the application of the symmetry hypothesis may not always be
valid. The problem is two-sided. If the symmetry hypothesis is always
used, it could generate false alarms in certain non-microburst situations.
For example, strong surface winds setting up in a persistent divergent
pattern. On the other hand, if the symmetry assumptions are never used,
wind shear warnings for valid microburst events could be delayed,
inaccurate, or even eliminated. The issue is then to determine whether a
given LLWAS-detected divergence is associated with a microburst and hence
determine whether the symmetry hypothesis should be applied.
The algorithm that was developed combined "features-aloft" information from
TDWR: three-dimensional reflectivity structures and microburst precursors,
(both projected down to the surface); and detected "strong" surface
divergence (microburst shapes) from both TDWR 502 and LLWAS 101. This
information is then synthesized, both spatially and temporally to create a
set of geometric discs. The intent of these discs is to indicate a region
of the atmosphere within and/or above the disc, (i.e. a cylinder), where
there is good likelihood of microburst activity. This "region" could be in
space: the detection of the surface outflow, or microburst features above
the surface (reflectivity and/or velocity signatures). It could also be in
time, that is, a microburst is either: going to occur, is in progress, or
has recently been present.
These discs are then examined for "closeness" to those LLWAS microburst
shapes that are to be validated. If this proximity criteria is met, the
LLWAS microburst shape is "validated" and passed onwards. That is, the use
of the symmetry hypothesis is assumed to be appropriate in this case, and
this LLWAS microburst shape is to be used for generating wind shear
warnings and to be displayed on the GSD. If the proximity test fails, the
LLWAS shape is discarded. However, in this latter circumstance, there
could be a valid wind shear hazard occurring that is not associated with a
microburst--or possibly a microburst that is not being correctly
identified in the symmetry disc calculations. To prevent this type of
missed detection, the LLWAS Runway-Oriented-Loss (ROL) information 703 is
then used as a fall-back to generate any appropriate wind shear warnings.
Enhanced LLWAS System-Preprocessing
The enhanced LLWAS system creates a grid point table for use in creating
microburst shapes. This process is illustrated in FIG. 3 and is activated
at system initialization. As a preprocessing step, a set of pointers are
generated which map triangle and edge microburst detection areas to an
analysis grid. During real-time operation, LLWAS triangle and edge
divergence values are then mapped onto the grid --applying a magnitude
value at each grid point. This set of grid point magnitudes are used with
the clumps produced by clump shape generation apparatus 111 to generate a
set of low level wind shear alert system microburst shapes. The "pointers"
for the mapping of triangle and edges to the grid is a
"first-time-through", preprocessing step. This is done this way since the
"pointer" information is solely a function of a given site's LLWAS
anemometer network geometry--which doesn't change.
The preprocessing, location specific table data generation is initiated at
step 1201 where the anemometer location values are retrieved from memory
and, at step 1202 the site adaptable parameters needed to modify the
calculations are also retrieved from memory. At step 1203, a grid is
created by computing the number of grid points in an x and y Cartesian
coordinate set of dimensions based on the number of input data points to
create a minimal size xy grid to perform the computations. At step 1204, a
set of grid pointers is produced to map the divergence estimates that are
above a threshold value with the particular points in the grid system
created at step 1203. This is to locate the center of a microburst that
would be causing an alarm. Since a number of grid points are above the
divergence element threshold value it is difficult to denote the location
where the microburst to be centered which would cause these elements to
create the alarm. Each sensor or network element is tested by placing a
mathematical microburst at each grid point and each one of the grid points
so tested that would cause the given network element to be an alarm status
is then associated with that particular network element. As a result, a
set of grid points associated with each Low Level Wind Shear Alert System
101 triangle and edge is produced to create the element grid point
pointers. In order to perform this calculation, a symmetrical microburst
model is used: a simplistic half sine wave model which is time invariant
and symmetric in both space and magnitude and is only a function of
amplitude and a maximum radius. Even though a real microburst may be
spatially asymmetrical, it can be approximated by a linear supe | | |
