NASA Conference Publication 10105, Part 1

DOT/FAA/RD-92 / 19-I

Airborne

Wind Shear

Detection and

Warning Systems

ORIGINAL P.,_GE.

COLOR PHOTOGRAPH

Fourth Combined Manufacturers'

and Technologists' Conference

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Compiled by

Dan D. Vicroy

and Rohmd L. Bowle_

NASA Langley Research Center

Hampton, Virginia

Roberl H. Pass,nan

Federal Aviation Administration

Washington, D.C.

Proceedings of a conference sponso_vd by Ihc

Nalional Aeronautics and Space Adminislralion

and the Federal Avialion Admmislralion

and held in Williamsburg, Virginia

April 14-16, 1992

SEPTEMBER 1992

fil/k_A

Nahonal Aeronauhcs and

Space Adm_nlslral_on

Langley Research Cenler

Hampton, Virginia 23665-5225

FOREWORD

The Fourth Combined Manufacturers' and Technologists' Conference was hosted jointly by

NASA Langley Research Center (LaRC) and the Federal Aviation Administration (FAA) in

Wflliamslm_, Virginia on April 14-16, 1992. The meeting was co-chaired by Dr. Roland Bowles

of LaRC and Robert Passman of the FAA. Dan Vicroy of LaRC served as the Technical Program

Chairperson and Carol Lighmer of the Bionetics Corporation was the Adminislrative Chairperson.

The purpose of the meeting was to transfer significant ongoing results of the NASA/FAA

joint Airborne Wind Shear Program to the technical industry and to pose problems of current

conoern to the combined group. It also provided a forum for manufacturers to review forwardlook technology concepts and for technologists to gain an understanding of the problems

encountered by the manufacturers during the development of airborne equipment and the FAA

certification requirements.

The present document has been compiled to record the essence of the technology updates

and discussions which followed each. Updates are represented here through the unedited

duplication of the vugraphs, which were generously provided by the respective speakers. When

time was available questions were taken form the floor; if time was not available questions were

requested in writing. The questions and answers are included at the end of each presentation. A

general question and answer session was conducted at the end of each day and is included at the

end of report along with closing remarks.
ii
TABLE OF CONTENTS

Part I

FOREWORD ..................................................................................................................................... i

TABLE OF CONTENTS ................................................................................................................... iii

WELCOMING ADDRESS ................................................................................................................

Jack Howell, FAA Technical Center

SESSION L -- NASA Flight Tests

Program Overview / 1991 Flight Test Objectives ........................................................................... 7

Dr. Roland Bowles, NASA Langley Research Center

Flight Test Operations .................................................................................................................. 23

Mike Lewis. NASA Langley Research Center

NASA Wind Shear Flight Test In Situ Results .............................................................................. 45

Rosa Oseguera, NASA Langley Research Center

Air/Ground Wind Shear Information Integration - Flight Test Results .......................................... 59

David Hinton, NASA Langley Research Center

Doppler Radar Results .................................................................................................................. 115

E. Bracalente, NASA Langley Research Center

Flight Test of an Infrared Wind Shear Detector ............................................................................. 141

Dr. Burnell McKissick, NASA Langley Research Center

SESSION II... Hazard Characterization

Wind Shear Hazard Determination ............................................................................................... 195

Mike Lewis, NASA Langley Research Center

Three-Dimensional Numerical Simulation of the 20 June 1991, Orlando Microburst .................... 213

Dr. Fred Proctor, NASA Langley Research Center

A "Numerical Field Experiment" Approach for Determining Probabilities

of Micmburst Intensity ................................................................................................................. 245

Dr. Kelvin Droegemeier, University of OIdahoma

Terry ZweOfel, Honeywell

SESSION IlL -- Reactive System Technology

An Approach to Evaluating Reactive Airborne Wind Shear Systems ............................................. 287

Joe Gibson, Martin Marietta

Panel Discussion ........................................................................................................................... 303

Kirk Baker, Federal Aviation Administration

Dr. Roland Bowles, NASA Langley Research Center

Joe Gibson, Martin Marietta

Howard Giover, Suadstrand

Dou& Ormiston, Boeing

Rosa Oseguera, NASA Langley Research Center

Dr. Paul Robinson, Lockheed Engineering & Sciences

Terry Zweifel, Honeywell

o..

PRECEDING P/iGE E_LAI'_K NOT RLMED m
SESSION IV. -- Airborne Doppler Radar / Industry

RDR-4D Doppler Weather Radar With Forward Looking Wind Shear Detection Capability ......... 317

Steve Grasley. Allied-Signal Aerospace

Airborne Doppler Radar Research at Rockwell International ........................................................ 357

Roy Robertson. Rockwell International

Acquisition and Use of Orlando, Florida and Continental Airbus Radar Flight Test Data ............. 373

Mike Eide, Westinghouse Electric

Bruce Mathews, Westinghouse Electric

SESSION V. -- Doppler Related Research

Vertical Wind Estimation from Horizontal Wind Measurements ................................................... 391

Dan Vicroy, NASA Langley Research Center

Microbm'st Characteristics Determined from 1988-91 TDWR Tcstbed Measurements .................. 417

Paul Biron, MIT Lincoln Laboratory

Mark lsaminger, MIT Lincoln Laboratory

Algorithms for Airborne Doppler Radar Wind Shear Detection .................................................... 457

Jeff Gillberg, lloneywell

Mitch Pockrand|, iloneywell

Peter Symosek, Iloneyweli

Earl Benser, lloneywell

SESSION Vi. -- Airborne I_ppler Radar / NASA

NASA Experimental Airborne Doppler Radar and Real Time Processor

for Wind Shear Detection ............................................................................................................ 477

P. Schaffnero NASA Langley Research Center

Dr. M. Richar_L_, Georgia Institute of Technology

W. Jones. NASA Langley Research Center

L. Crittenden, Research Triangle Institute

Ground Clutter Measurements Using the NASA Airborne Doppler Radar:

Description of Clutter at the Denver and Philadelphia Airports .................................................... 499

S. ilarrah, NASA Langley Research Center

Dr. V. Delnore, Lockheed Lockheed Engineering & Sciences

M. Goodrich, Lockheed Lockheed Engineering & Sciences

C. Von llagel, NASA Langley Research Center

Spectrum Characteristics of Denver and Philadelphia Ground Clutter and the Problem

of Distinguishing Wind Shear Targets from Moving Clutter ........................................................ 517

A. Mackenzie. NASA Langley Research Center

Comparison of Simulated and Actual Wind Shear Radar Data Products ........................................ 541

Dr. C. Britt, Research Triangle Institute

L. Crittenden, Research Triangle Institute

NASA Airborne Radar Wind Shear Detection Algorithm and the Detection

of Wet Microbursts in the Vicinity of Orlando, Florida ................................................................ 561

Dr. C. Britt, Research Triangle Institute

E. Bracalente, NASA Langley Research Center

Signal Processing for Airborne Doppler Radar Dete_ction of Hazardous Wind Shear

as Applied to NASA 1991 Radar Flight Experiment Data ............................................................ 587

Dr. E. Baxa, Cler_¢on University

iv
Part 2"

SESSION VIL - Airborne LIDAR Technology

Two Micron Laser Development for Atmospheric Remote Sensing

at NASA Langley Research Center .............................................................................................. 617

Philip Brockman, NASA Langley Research Center

NASA/LMSC Instrument Design & Fabrication ........................................................................... 633

Dr. Russei Targ, Lockheed Missiles and Space

NASA/LMSC Cohexcnt LIDAR Airborne Shear Sensor:

System Capabilities and Flight Test Plans .................................................................................... 659

Dr. Paul Robinson, Lockheed Engineering & Sciences

Solid-State Coherent Laser Radar Wind Shear Measuring Systems ............................................... 673

R. Milton Hgffaker, Coherent Technologies

SESSION Vlll. -- Passive Infrared Technology

Development of the Advance Warning Airborne System (AWAS) ................................................ 687

Pat Adamson, Turbulence Prediction Syste_v

A Millimeter-Wave Radiometer for Detecting Microbursls ............................................................ 781

Dr. Robert McMUlan, Georgia Tech Research Institute

Colorado Slate University Research ............................................................................................... 805

Dr. Pete Sinclair, Colorado State University

SESSION iX. -- Terminal Doppler Weather Radar

The Orlando TDWR Testbed and Airborne Wind Shear Data Comparison Results ........................ 811

Dr. Steve Campbell, MH" Lincoln Laboratory

Anthony Berke, MIT Lincoln Laboratory

Michael Matthews, MIT Lincoln Laboratory

TDWR 1991 Program Review ....................................................................................................... 847

Kin Elmore, National Center for Atmospheric Research

SESSION X. -- Flight Management Research

Experimental Evaluation of Candidate Graphical Microburst Alert Displays ................................. 879

Craig Wanke, Massachu.vetts Institute of Technology

Dr. R. John l lansman, Massachu.vetts Institute of Technology

Wind Shear Related Research at Princeton University ................................................................... 905

Dr. Robert Stengel, Princeton University

SESSION Xl. -- Regulation, Certification and System Standards

Systems Issues in Airborne Doppler Radar/LIDAR Certification ................................................... 959

Dr. James Evans, MIT Lincoln Laboratory

FAA Regulatory / System Standards / Certification Status ............................................................. 971

Frank Rock, Federal Aviation Administration

Results of ln-sexvice Evaluation of Wind Shear Systems ............................................................... 977

Todd Murr, Northwest Airlines

* Published under separate cover.
In-service Evaluation of Wind Shear Systems ............................................................................... 997

Capt. Sam Shirck, Continental Airlines

Advanced Technology Wind Shear Prediction System Evaluation ................................................. 1007

Capt. GregGering. American Airlines

GENERAL QUESTIONS AND ANSWERS ..................................................................................... 1017

APPENDIX - List of Attendees ......................................................................................................... 1029

vi
WELCOMING ADDRESS

Jack Howell

Deputy Director, FAA Technical Center

Good morning, Ladies and Gentlemen, and welcome to the Fourth Combined Manufacturers'

and Technologists' Airborne Wind Shear Review meeting. I am very happy to be here today; in

fact, I have reached a point in my life where I am very happy to be anywhere on the green side of

the grass. I am even more happy to have the opportunity to be the keynote speaker today to talk

about a topic that is very important to our aviation system. As the keynote speaker, I feel it is my

duty to welcome you to this event, to congratulate you for the next 3 days and for the near future,

and -- perhaps most important of all -- to stop speaking on time so that we can get on with the

conference.

The welcome is already complete. Secondly, I do want to congratulate you on your many

accomplishments. I have been observing the process that has provided us with Wind Shear

protection for a number of years, and I have found it to be a very impressive process -- one that

could well serve as a model for other undertakings. I view the process as being represented by an

equilateral triangle with industry, NASA, and FAA at each apex. Each side, then, represents a

particular relationship.

The FAA-NASA relationship is a long-term relationship that exists in the form of memoranda

of agreements between the two agencies. This relationship needs to be maintained and nurtured

to keep the team of experts working together on an advanced sensor program and to use the

synergy that emerges from that relationship to prompt congress to provide adequate funding for

this important project.

The FAA-industry relationship, with the exemption 5256 program in place, is a good example

of participation between the FAA lind industry to accomplish important safety goals involved in

the development of predictive Wind Shear systems while simultaneously being mindful of

technology, manufacturing, and implementation realities.

In the NASA-industry relationship NASA passes data and information back and forth while

maintaining confidentiality in these relationships. In the predictive Wind Shear systems, for

example, micro-burst models have been transferred to industry. To me, as a senior executive of

the federal sector, this represents Technology Transfer as mandated by our Congress.

At each apex I have also observed an internal review process which for the FAA includes

R&D, Air Traffic, Flight Standards, and certification personnel.

I also recognize that these working relationships did not come into existence nor achieve

success overnight. Instead, they were steadily built by several individuals who developed strong
personalworking relationships along with the business relationships.

As we evolve our managerial styles to involve concepts of Total Quality Management, I feel I

should ask this question: what are the measurables? The measurables are at least five.

I. FAR 121.358 which mandates implementation of Wind Shear devices and the 5256

exemption process that permits certain exceptions.

2. There arc ground-based and airborne units operating in the system; these represent

manifestations of Technology Transfer as I mentioned e_u'lier.

3. We are, I am told, about i year ahead of .schedule in the Flight Program.

4. And, most important of all, is the fact that we have not had a fatal accident where Wind

Shear was a contributing factor since 1985.

5. On the other hand, we do have lbur very t-amous saves at Denver in 1988.

So I am convinced your congralulations are deserved.

Now lets talk about the CUITent program. ! see in my review that by employing a 4-prong

attack aimed at:

1. Hazard characterization;

2. Detection and wanting;

3. Recovery flight techniques; and

4. Crew Training on those recovery Ilight techniques, we are well on the way to providing a

,solution to this Wind Shear prol)letn. This is important because just as each accident is the

result of a series or chain of events involving mechanical, procedural, and/or performance

failures so also can this same model be held h_r a save. Thus it is very likely that any one

element of our 4-prong ai_pl'oach may have already interrupted a chain of events that could

have led to a disaster.

! see also that we are:

1. Combining technologies; Ihese combinations may lead to site-specific ground-based

solutions; similarly these combinations may al_ lead to aircraft-specific procedures. But,

what we want to avoid is site-specific aircraft prt_.'cdures.

2. Next I see that we have a certain amount of competition. Competition that exists for the

purpose of optimization and not duplication is g_x_d. This competition exists between

airborne and ground-based systems and between predictive and reactive systems.

3. Lastly I see we are expanding the capabilities of the assc_ciated technologies for

applications to other problem areas. These areas include clear air turbulence; wake vortex
d_q_ction; mountain wave or rotor or other omgraphic flow studies; low visibility surface

operations; and the detection and the avoidance of volcanic ash clouds.

Now comes a piece of the presentation I did not warn you about -- a Challenge. If you are

sitting there pretty smug, complacent, or content to stay focused on things that make you

comfortable, I offer you the opportunity to review with me some of the macro events of the last

1.5 years. First of all, who would have ever guessed it would be possible to coalesce world

opinion against a single nation to the extent that we could have a war because that nation had

invaded another? Who could have ever forecast the disintegration of the USSR into the UFFR

(the union of fewer and fewer republics)? Isn't it sad to realize that our own children's quality of

life is in jeopardy because of the status of the national and global economies, and who would ever

guess that a state presidential primary could ever have "undecided" as the top vote-getter?

My point is that we are embroiled in a world of change, and we had all better be prepared to

lead, follow, or get out of the way. And so, in conclusion, I would like to leave you with two

challenges.

1. For those of you in the federal sector -- I challenge you to elevate your thinking to the

next level of management; focus your analytical skills on the economic realities of a shrinking

budget; learn how to leverage those precious R&D dollars and to take advantage of the DOD

capabilities and availability's; seek solutions to administrivia with the same vigor you use in

seeking solutions to technical problems. For, and you can mark my words, if you don't do it,

some unqualified person will.

2. And for those of you in the public sector -- I challenge you to respond to FAA's Acting

Deputy Administrator, Mr. Joe Del Balzo's challenge that he issued during the Awards

Luncheon at ATCA '91: don't be satisfied with just bringing problems to the FAA; instead, be

prepared to be part of the development of solutions just as you have done in the Wind Shear

Protection Program. Don't let Technology Transfer be a one-way process; instead, do some

reverse engineering and send back to us some certification suggestions so that we regulators

can do a better job of both regulating and stimulating the industry by using uniform criteria.

In summary, then, let me conclude by saying I welcome you to this review; I congratulate you

on what you have accomplished so far; I approve of the program that you have outlined for

yourselves, and I urge you to get ready for change. Lastly, I hope that you have a very good

conference and that you enjoy your stay in Williamsburg.
4
Session I. NASA Flight Tests

Session I. NASAFlight Tests N 9 3 - 1 9 5 9 1

Program Overview / 1991 Flight Test Objectives

Dr. Roland Bowles, NASA Langley Research Center
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Program Overview / 1991 Flight Test Objectives

Questions and Answers

Q: Bob Otto (Lockheed) - A potential recommendation for carrying out a sensor comparison

program would be to use all candidate sensors (TDWR, reactive, radar, IR, etc.) to determine

F-factor as a function of space and time for the same microburst event. A reference or ground

truth needs to be decided upon. It may be TDWR or reactive data properly processed. Then the

w,nsm's can be evaluated and compared. A parametric evaluation can be done for wet and dry,

different microburst spatial sizes and temporal duration, different microburst strengths and various

parangters of the event. Please comment on this recommendation and tell what the actual plan

wiU be.

A: Roland Bowles (NASA Langley) - The easy answer is that we don't write those plans, but

we certainly f¢¢1 sensitive to the need to help the FAA put together a technical rationale. That is

our job. We are not going to dictate how you are going to get your systems approved, but we are

going to be in the background along with the other programs operating out of the FAA like the

Lincoln and the NCAR program. Wherever we can get relevant and pertinent data to bare on the

subject we are going to get it.

Q: Bob Otto (Lockheed) - I have attended all of the wind shear conferences that you have had

and each time I come here I see a great deal of progress being made. I have this vision that at

some point we are going to be able to take all the data from all the different sensors for the same

of mieroburst events and compare all these different sensors and say this sensor works best in

this regime, this sensor works best for that regime. In other words, trying to accumulate enough

scientific data and try to make a v',did comparison among all these different sensors. I think

NASA is in a unique position to do that sort of thing. I am just asking if that is what you really

want to do or should do in order to satisfy the program objectives?

A: Roland Bowles (NASA Langley) - Our job was basically three fold: a) Define the relevant

technologies appropriate to airborne hazard detection and avoidance, b) Out of that admissible

list, decide through priority structure what we think the system requirement is going to end up to

be and realize in hardware and software those candidates and c) fly them off and compare against

suitable environments that we can call truth. I don't call TDWR ground truth. I call TDWR

another estimate of what is out there. But I'll tell you what I do think the truth is, I believe it is

the airplane. Newton as alive and well. That is why we are stressing a great deal using our In

Situ data. Brac showed some results that were extremely encouraging. The data that he showed

yesterday had the antenna looking down two degrees below the horizon. There were range gates

out there in the ground. In fi_ct, the strongest event we incurred was right over top of the

interstate in Orlando. So we had plenty of stuff around to reject or mess it up. When the radar

took a snapshot and made a prediction 8 kilometers out and subsequently the airplane flew

through that environment and you compare the results, we can even see the latency in the reactive

alert due to the gust rejection filtering and it is right on the money. That was based on a snapshot

30 to 40 seconds earlier. So that is one means by which you can judge the validity in the

prediction. We do not see anything coming off of the TDWR that is inconsistent with what we

arc sccing in the air, when we fly in the vicinity or an appropriate neighborhood of the event.

21
Now, we did fly through icons last year where there just wasn't anything in them. That is another

problem that I think Steve is going to address tomorrow. I think this is a good question. I look

at it as, what would b¢ the appropriate mix that industry would have to place on the FAA

doorstep bctwe, cn flight results, simulation results, and those tes| procedures that will bc outlined

in the Interim Standards document and or a TSO, if we cvcr get to one. I think it is the mix that

is important. But, it is going to cost the industry money. Knowing Kun and his people, I don't

think anyone is going to b¢ able to back in on this one. There is a lot of homework to bc done. I

think w¢ arc on the fight track. The NASA laser, built by UTAS and integrated by Lockheed, is

going to get a good ride. I guarantee you it will get a fair objective comparison. This year we

have refined the algorithms throughout the airplane and have bccn pulling all the data together on

a common basis of measurencnt and display. There are a lot of events that we threw away last

year that we will take this year. If things cooperate reasonably well we are going to have a good

smmnct and it will get a fair fide.

22
Session I. NASA Flight Tests

N93-19592

Flight Test Operations

Mike Lewis, NASA Langley Research Center

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Session I. NASA Flight Tests

N93- 1 9.5 9 ;?

NASA Wind Shear Flight Test In Situ Results

Rosa Oseguera, NASA Langley Research Center

45
NASA WINDSHEAR FLIGHT TEST

IN SITU RESULTS

Presented By:

Rosa M. Oseguera

NASA Langley Research Center

at

4th Combined Manufacturers' and Technologists'

Airborne Windshear Review Meeting

April 14-16, 1992

OUTLINE

• BACKGROUND

- OBJECTIVES

- DESIGN REQUIREMENTS

• IMPLEMENTATION

• RESULTS

- ALGORITHM TESTING

- MICROBURST FLIGHTS

• SUMMARY
OBJECTIVES

• PROVIDE MEASUREMENT STANDARD FOR

FORWARD-LOOK SENSOR EVALUATION

• DEMONSTRATE OPERATIONAL UTILITY

The main objectives in developing the NASA in situ windshear detection algorithm were

to provide a measurement standard for validation of forward-look sensors under

development, and to demonstrate the algorithm's ability to operate with a suitably low

nuisance alert rate. It was necessary to know exactly how the algorithm was

implemented and what parameters and filtering were used, in order to be able to fully

test its effectiveness and correlate in situ results with forward-look sensor data.

DESIGN REQUIREMENTS

• MINIMIZE AIRCRAFT-INDUCED HAZARD INDEX

DUE TO:

-CONFIGURATION CHANGES

-THRUST EXCURSIONS

- MANEUVERING FLIGHT

-TURNS IN STEADY WIND

MINIMIZE NON-HAZARDOUS ATMOSPHERE-INDUCED

HAZARD INDEX

- TUNE TO APPROPRIATE SCALE OF MOTION

- GUST REJECTION / TIME-TO-ALERT TRADE-OFFS

- LOW NUISANCE ALERT RATE

• EMPLOY CURRENTLY AVAILABLE STANDARD

SHIP-SET SENSORS

The major design requirements are 1) minimize effects of aircraft-induced motions,

such as those shown in the first bullet item, and 2) minimize the effects of nonhazardous atmospheric motions, which is done using gust-rejection filters. The second

item shows the major issues addressed in development of the filters, such as tuning the

filters to the larger-scale motions associated with windshear, choosing an acceptable

trade-off between improving the gust-rejection characteristics and decreasing the

latency in the system, and maintaining a low nuisance alert rate; 3)implementing the

system using currently available, standard sensors, to make the implementation

feasible on any inertially-equipped airplane.

4?
WIND SHEAR HAZARD INDEX

THEORY

T-D

- POTENTIAL CLIMB ANGLE Yp - mg

- HAZARD INDEX F __

" e a w h

g Va

F

• IN $1TU IMPLEMENTATION

F = { _i " _e"- va Wh }-'v aThe method for quantifying the windshear hazard is by computing the windshear

hazard index (F-factor), which is shown as it relates to an airplane's potential climb

angle and ratio of thrust-minus-drag to weight. The definition of F-factor (second

equation) is shown as a function of the wind vector dot product with a unit vector in the

direction of the airspeed vector, vertical wind component, and true airspeed. The

bottom equation shows the general full 3-dimensional implementation of an in situ

algorithm, with F computed from aircraft-measured parameters such as inertial velocity

rate and airspeed rate, rather than wind measurements. The NASA implementation was

realized in full 3-D form, to not degrade its performance in any flight regime.

CONCEPTUAL REPRESENTATION OF NASA IN SITU

F-FACTOR ALGORITHM

INERTIAL

AND AIR

DATA

SENSORS

"1/

WLGO.rr.b_6,=I---T" I

F..ocEss,.c _ _ (_-"F

This shows the how the in situ algorithm is implemented on NASA's B-737-100, where

the Algorithm Processing represents the first part of the in situ algorithm, which

produces the three terms shown. These terms are then filtered (shown as G(s) boxes)

to give horizontal, vertical, and total in situ F-factor.

48
PUSHOVER/PULL-Up

(PtJ_ DATA, 23 MAY 91 )

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The in situ algorithm was flight-tested locally, with maneuvers intended to induce

significant changes in specific state variables to ensure the algorithm's ability to reject

aircraft maneuvering effects. This figure shows a pushover/pullup maneuver, where the

airplane was pitched up and down in a porpoising type of motion to induce high normal

acceleration changes. Ideally, F-factor (bottom plot) should be close to zero, with

allowances for acceptable levels of turbulence and signal noise, and well below the

FAA-established alert threshold level of F=0.105. As shown, there was no adverse

effect of the pitching motion on the in situ F-factor measurement.

pUSHOVER/PULL-UP

(PtJGHT DATA, 23 MAY 91)

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The top figure shows the range of measured normal acceleration, which equals 1.0 g in

level, unaccelerated flight. This maneuver induced an increase of 0.6 g and decrease

of 0.4 g trom the nominal value. True airspeed and groundspeed (bottom plot) are close

in value, indicated there was no significant wind.

49
ACCELERATION/DECELERATION

(IqJIQHT DATA, oe MAY S)l)

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The effect of changing longitudinal acceleration was tested by executing abrupt

acseleratlons and decelerations, where the maximum rate of change was sustained

over at least GOknots change in airspeed, The effect of this motion did not appear to

Pause any adverse effect on the F-factor (bottom plot) computed by the algorithm,

TURNING FLIGHT

(FLIGHT DATA, 23 MAY 91)

o I -I....[\ !I+ i \_ +_

+-+o ; + ]\+1 ,_ _P 40

< : l i "T :V. -I .eo + + ! + ,

n, 20 40 80 80 100 120 140 160 180

0.10

n- 0.05

_0.00

U.

' .o.o5

-0.10

I ' t !

I I L i_ I

L

i i i I

+'.... _ +i i i i

0 20 40 60 00 100 120 140 1110 1110

TIME, seconds

The effectiveness of the 3-D implementation was tested by executing turns at high bank

angles. The top figure shows a number of partial turns, at high bank angles and

through abrupt changes in direction.

8O
Ttmm_ IN STEADY WIND

(PLJQHT DATA, 2 OCT 90)

-20 1 IP , ')' ' i -I I

0.10

O.OS 0.00

Ur.O.OS

-0.10

0

i t I ,

50 100 150 200 250 300

TIME, seconds

A final manuevering test was turning in a steady wind condition. The top plot shows the

bank angle for the two turns executed in a steady wind of greater than 60 knots. The

first was through a 360 o heading change at 20 ° bank, the second through 180 o

heading at 45 o bank. F-factor (bottom plot) shows no adverse effect of this manuever.

500

Z

0

- 450

u 4 O0

,350

TURNING IN STEADY WIND

(FLIGIIT DATA, 2 (1CT OO)

.._._ • , .... , .... , .... _.'._, ....

\ /

\ I

- _ "

\ I

t

\ / 't "

--true ilrlpeed

-- llroundJpoed

• ,.I,,,*| .... l,,,*l=,,,l ....

0 tO0 1)00 300 400 500 600

100

.?: o

-50

q

< ==-I00

-150

"+''l''''l .... I''''I''''I''''

,* *1 .... I .... I.,, L| ,,,'I'''

0 100 200 300 400 500 600

TIME, seconds

These plots show the effect of the turns in steady wind on the airplane's velocity. The

top plot shows true airspeed was constant, while groundspeed varied throughout the

turns. The bottom plot shows the along-track wind measured by the airplane, varying by

150 knots over 20 seconds (t=250 to 270 sec), and indicates the algorithm's ability to

reject the change of longitudinal wind, rather than measure it as a shear.

51
LANDING APPROACH

THROUGH MICROBURST

(SIMULATOR DATA)

(_ 0.2 _=iild,der ive /

i , I •

o., I i'

o.o __-_y I_ !_ -o., -I, I I_,_, .I

I I ,I>--

0 10 20 30 40 50 60 70 80 90 100110 120130140

TIME, seco.ds

After having shown in flight that the algorithm rejected aircraft maneuvering effects, it

was necessary to show that it could also detect a windshear, which was done in

simulation, as shown. The in situ F-factor shows some lag and attenuation of the peak,

which is primarily due to the effect of the gust-rejection filters, and was expected. Windderived F-factor is an instantaneous F-factor computed directly from the known winds.

I

MICROBURST PENETRATION

FLIGHT DATA, 20 JUN 91

52

0.15.. , . , . , . , • , - , • , - , • , • , • , - .

! o-o'F _i \ j

°°_t /k _ _

15.. , • , - , • , .'r'. , • , • , • , • , - , •

10 _ --.long-track p_ ._j_ .j

o___, I ..-"f - .,.,-,...

-20 I , I- , • , • , • , , I , I • ' . a . i . i .

0 10 20 30 40 50 60 70 80 90 100 110 120

TIME, seconds

Data shown is for a microburst

penetration during the 1991 NASA

windshear flights; this particular

case was catalogued as event

#142, during which in situ F-factor

approached the alert threshold of

F=0.105, and showed good

correlation with the observed

change in along-track windspeed

(bottom).
MICROBURST PENETRATION

FLIGHT DATA, 17 JUN 91

0.20 ,.

0.15

0.10

0.08 0.00

| -0.05

-0.10 i.. , . , . I . J . , , I . I., ,, . , .

0 I0 20 80 40 50 O0 ?0 80 90 1001 0

3o l

i _0 ,---along-track/ __,MI_.._._:,.___

-I!0 . J . I . I . I . I . J . m . i . j . i , I

0 I0 20 80 40 50 O0 ?0 80 00 tOOl 10

TIME, leconde

I II i I iHi

MICROBURST PENETRATION

FLIGHT DATA, 17 JUN 91

Data shown is for microburst

penetration, event #143, with a

peak in situ F of 0.167. Along-track

and vertical wind time histories

show characteristics of passing near

the core of a microburst.

0.10 - , • = - n • , - , • , • , - , • , • , - = •

0.05

0.00

-0.05

E

-0.10

0 10 20 30 40 50 60 70 80 90 100110120

151., ,.i.,., , , , ,., ,

i" t --along-track

10 - - - vertical

5

-5

-10

0 10 20 30 40 50 60 70 80 90 100 110 120

TIME, seconds

Data shown is for microburst

penetration, event #97. In this case,

in situ F peaked at about 0.05,

though along-track wind shows a

general headwind-to-tailwind trend.

The time scale of this event shows

that the in situ algorithm is tuned to

windshear that is hazardous to the

airplane's climb performance,

whereas this event was over a

longer time scale (or distance), and

as such was not a hazard to the

airplane. The smaller-scale

fluctuations in along-track wind

(period of about 10 sec) are evident

in the in situ F-factor plot (between

t=75sec and end of run).

53
MICROBURST PENETRATION

FLIGHT DATA, 20 JUN 91

0.15

" I " I " I " l " I " i " I " ! ' g " I " | "

0 numerl©, 3-ucond av_Kags

_.. _ln sltu (llrplsne dots)'J (_

O. 10 I" - - -flltm'edwind derivative _",_

o.o I oo

o.oo /

-0.05

-0.10

0 10 20 30 40 50 60 70 80 90 100 110 120

15 • I " z " • " a • a "' l • I " I • a • l " I •

.,o

5

0

-5

-10

-15

-20 - ' ,

0 10 20 30 40 50 60 70 80 90 100 110 120

TIME, seconds

III ii i

To demonstrate how in situ F and

wind-derived F correlate in-flight, F

was computed from aircraftmeasured along-track winds, and

differentiated with a gust-rejection

filter identical to the one used in the

in situ algorithm. This is plotted

along with the horizontal portion of

in situ F-factor (top plot), and an

unfiltered numerical differentiation of

averaged along-track wind (wind

data was averaged over 3-seconds,

then differentiated). All three curves

show very similar characteristics,

indicating that F-factor from the

in situ algorithm is nearly equivalent

to the along-track wind derivative.

Data shown is from event #142.

MICROBURST PENETRATION

FLIGHT DATA, 20 JUN 91

o.15 " ' " ' " ' " ' " ' 6. ' 6 _,u,,;,,=_s',,,c;vg;' "

£ --in sltu (airplane dais)

O.10 :.._1_ - - -filtered wind derivative

tYI

0.05 o o /f Io .._

1-0.05o.oo-_ ' O0 0_ V

010 ' ' " ' " ' " ' • ' " ' ' ' ' ' " ' " '^" '

" ' 0 10 20 30 40 50 60 70 80 90 100110 0

30a. , . , . , • , • , • , • , 'J , . , • , • ,

20

15

I ;F,

-10 I , ..........

0 10 20 30 40 50 60 70 80 90 100 110 120

TIME, seconds

Same analysis method as previous

case, for event #143.

54
MICROBURST PENETRATION

FLIGHT DATA. 17 JUN 91

0.15 • , - , • , • . • . • i • . - , • , • . • . -

O.10 I_ln altu (alrpllme data) 1

i - - .filtered wind derivative O ,(_ O

0.05 O , O ,

0.00 _

-0.05

-0.I0

0 10 20 30 40 50 60 70 80 90 100110 120

Same analysis method as for

previous case, for event #97.

15 " • " ! " I " l " I " I ' I " I " I " I " I "

o

-10 ......... ' " ' " ' ..... ' " ' "

10 20 30 40 50 60 70 80 90 100 110120

TIME,=econd=

ALGORITHMPERFORMANCETO DATE

*OVER 100 FUGHT HOURS COMPLETED

,APPROXIMATELY 320 TAKE-OFFS AND LANDINGS

eNO NUISANCE ALERTS GENERATED

*ALERTS GENERATED DURING MICROBURST

PENETRATIONS CONRRMED BY GROUND RADAR

=IN SITU HAZARD INDEX CONRRMED BY WIND

MEASUREMENTS

The in situ algorithm's performance is summarized as shown. The algorithm has

operated on NASA's B737 for over 100 flight hours, included over 320 take-otis and

landings. No nuisance alerts were generated during low-level flight (below 1400'AGL),

which included flight in convective weather, gust fronts, and aggressive maneuvering;

alerts were generated during microburst penetrations, and confirmed by an

independent measurement (ground radar); analysis of in situ hazard index

measurement showed that it compared well with hazard index from measured alongtrackwind.

55
 UMMARY

OBJECTIVES MET

• VAUOATED IN SITU ALGORITHM AS MEASUREMENT

STANDARD FOR FORWARD-LOOK SENSOR EVALUATION

• DEMONSTRATED OPERATIONAL UTILITY

DESIGN REQUIREMENTS MET

• MINIMIZED AIRCRAFT MANEUVERJNDUCED ERRORS

IN HAZARD INDEX

• MINIMIZED EFFECTS OF TURBULENCE AND

NON-HAZARDOUS ATMOSPHERIC MO11ONS

• STANDARD SENSOR IMPLEMENTATION

Results can be summarized by re-stating objectives and design requirements, which

were satisfied as originally set forth.

I

56
NASA Wind Shear Flight Test In Situ Results

Questions and Answers

Q: Pete Sinclair (Colorado State University) - I think you might want to be a little cautious

about estimating the total F-factor from just the long track winds. Our flight measurements

indicate that the vertical term can be as large or larger than the horizontal component and that can

throw the F-factor to values above 0.15. Yours looks like that is suppressed quite a bit in the

traces you have shown us.

A: Rosa Oseguera (NASA Langley) - Maybe there is a little bit of a misunderstanding. The

overall F-factor that we were showing; the first one I showed, is a total F-factor. We are

including the vertical term in there. The last slides that I showed where strictly for comparison

purposes with the along-track winds. In those slides I was just using the horizontal portion of the

F-factor to compare with. That is really all that we are computing from along-track winds. For

the purpose of comparing with the forward-look sensors and for providing the alert, the total

F-factor was used and that included the vertical term. In fact, that was shown on the block

diagram. I just did not clearly point it out. The third term that was computed there was the

vertical part of the F-factor.

Q: Pete Sinclair (Cohwado Slate University) - How do you measure the vertical component?

A: Rosa Oseguera (NASA l,angley) - it is computed I¥om the difference between inertial

flight-path angle and airmass flight-path angle, and groundspeed and airspeed. Roland did you

want to expand on that'?

Roland Bowles (NASA i,angley) - The whole point is that we want to reject certain scales of

motion. This measurement is the difference between tile ainnass and the inertial flight-path

angles. This was a fourteen knot peak downdraft in that microburst. When you look at the

airplane performance loss the In Situ system peaked out at about fourteen hundred feet per

minute, which is about fourteen knots. In other words, that was the measurement of that

microburst.

Pete Sinclair (Colorado Slale University) - What I am saying Roland, is that your system may

not be seeing all of the vertical ICl'nl'?

Roland Bowles (NASA l,angley) - We don't want it to see all of the vertical term. We don't

want small scale turbulence to trip tile system. Wc are not making a wind measurement, we are

making a total energy change measurement on the airplane. That is what is hazardous to the

airplane.

Pete Sinclair (Colorado Slate Universily) - But that vertical term is part of the total hazard to

the airplane.

Roland Bowles (NASA Langley) - Sure, at the right scale. This was a small scale microburst.

The vertical channel there shows you how the vertical term is estimated. Notice, we are not

making wind measurements and processing winds. We are pulling from the backbone sensors on

57
anairplane,theaccelerometersandair-datasystem.We are not making a wind measurement and

then processing the winds. You do not see winds anywhere in there. That's the key.

Q: Jim Evans (MIT) - There is a different issue which I think one has to be concerned about

and that is the altitudes at which this testing was done. We know that some microburst have big

thick outflows and some of them have much stronger outflows near the surface. We will be

showing examples of that later in the conference. One of the questions that comes up is most of

this testing was done at the minimum altitude of 1,000 feet, and yet in the context of the guidance

we had for TDWR/LLWAS users group, that is the altitude at which people start to get

concerned about Wind Shear. One of the questions that would come up is whether the agreement

would be as good if you flew down at lower altitudes were we see much more evidence of strong

pitching moments. If you look at the Dallas/Fort Worth crash traces for example, you see very

strong eddies and things that were definitely effecting the plane at low altitude. So one of the

questions I think you would have to ask is, to what extent can you extrapolate the measurements

here, at about 1,000 feet altitude, down to much lower altitudes?

A: Roland Bowles (NASA Langley) - That is a good question. The evidence shows that the

total energy change to the airplane stays about the same, because the vertical wind component

diminishes as a function of altitude were as the horizontal gradient may peak at about 80 to 100

meters, but the overall performance loss is about the same; at normal approach speeds. We were

making measurements at the point at which we were testing our sensors. We are not trying to

characterize the relative threat level, we were making the in Situ measurement to use as a

standard of goodness to compare to the predictions made by the remote sensors.

Dan Vieroy (NASA Langley) - The other point I would like to make is that the F-factor is a

performance measurement and in reference to your comment about the pitching moment, that is

more of a handling qualities problem and the F-factor is ,lot going to reflect that at all.

Roland Bowles (NASA Langley) - Again, it is a scale of motion you are trying to identify.

Pat Adamson (Turbulence Prediction Systems) - This is just a point of clarification. The

F-factor that you guys are talking abot, t is slightly larger because of the airspeed you were flying.

The airspeed plays a big factor in the magnitude of F.

58
Session I. NASA Flight Tests N 9 3 ][ _ o-__ _

Air/Ground Wind Shear infonnation Integration - Flight Test Results

David Hinton, NASA Langley Research Center

59

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61
Air/Ground Wind Shear Information Integration

Flight Test Results

David A. Hinton

ABSTRACT

An element of the NASA/FAA wind shear program is the

integration of ground-based microburst information on the

flight deck, to support airborne wind shear alerting and

microburst avoidance. NASA conducted a wind shear flight

test program in the summer of 1991 during which airborne

processing of Terminal Doppler Weather Radar (TDWR) data was

used to derive microburst alerts. High level microburst

products were extracted from TDWR, transmitted to a NASA

Boeing 737 in flight via data link, and processed to estimate

the wind shear hazard level (F-factor) that would be

experienced by the aircraft in the core of each microburst.

The microburst location and F-factor were used to derive a

situation display and alerts. The situation display was

successfully used to maneuver the aircraft for microburst

penetrations, during which in situ "truth" measurements were

made. A total of 19 penetrations were made of TDWR-reported

microburst locations, resulting in 18 airborne microburst

alerts from the TDWR data and two microburst alerts from the

airborne in situ measurements. The primary factors affecting

alerting performance were spatial offset of the flight path

from the region of strongest shear, differences in TDWR

measurement altitude and airplane penetration altitude, and

variations in microburst outflow profiles. Predicted and

measured F-factors agreed well in penetrations near

microburst cores. Although improvements in airborne and

ground processing of the TDWR measurements would be required

to support an airborne executive-level alerting protocol, the

feasibility of airborne utilization of TDWR data link data

has been demonstrated.

62
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63
Research Goal

Under the terms of the Integrated Wind Shear Program, NASA,

the FAA and industry have jointly developed solutions to the

wind shear hazard to commercial transports. The NASA efforts

are concentrated in airborne aspects such as hazard

characterization, aircraft performance impact, advanced

in sltu and forward-look sensor technology, and flight deck

integration. The FAA efforts have been concentrated in

ground side aspects such as crew training (ref. 1) and

ground-based detection systems such as low-level wind shear

alerting systems and Terminal Doppler Weather Radar (TDWR).

The TDWR system has proven its capability to detect the

microburst phenomenology in tests and operational

demonstrations, but experiences suggest (ref. 2) that the

information is not reaching flight crews in a timely matter

or in a form that is compatible with existing and planned

onboard wind shear detection systems.

In 1990 a Memorandum of Agreement between NASA and the FAA

was implemented with a major program element to "Demonstrate

the practicality and utility of real-time assimilation and

synthesis of ground-derived wind shear data to support

executive level cockpit warning and crew-centered information

display." The goal can be divided into subgoals of

identifying ground-based information products required on the

flight deck to derive a crew-centered hazard index and

rapidly transmitting this data to the flight deck.

64
65
Ground Rules

Ground rules were established for the conduct of this

program. The key ground rule was that neither the existing

TDWR system nor the current division of responsibilities and

roles between air traffic control and pilots would be

altered. The TDWR system was to remain unchanged because

years of testing have demonstrated its microburst detection

capability, the system design was essentially frozen for

production, and even minor changes would be prohibitively

expensive. Rather than change the system, those high level

products produced by TDWR that are required for airborne

processing were to be identified and provided to an aircraft

via data link. The emphasis was to provide an executive

level warning (requiring immediate corrective or compensatory

action by the crew). Such a warning requires a very low

nuisance alarm rate, on the order of 1 nuisance per 250 hours

of system operation. A nuisance is defined as an alert

received when system alert threshold conditions exist but do

not produce a hazard to the aircraft.

The air/ground roles of the proposed system are tailored to

reflect current ATC/pilot roles. The TDWR is to classify

events as a microburst and provide location and microburst

parameters to the airborne system. The airborne component

will quantify the threat, compare to a threshold, and

annunciate. The concept is analogous to other ground systems

providing meteorological data such as runway visual range,

wind, and ceiling. The decision to continue is made on the

aircraft based on required minima and operating procedures.
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System Architecture

The baseline TDWR system consists of a radar, a ground

processor to identify regions of divergence and classify them

as microbursts, a geographic situation display to depict

microburst locations relative to runways and approach paths

to the ATC tower supervisor, and an alphanumeric ribbon

display for presenting wind shear and microburst information

to the local controller for voice transmission to pilots. A

typical message from the local controller is "Microburst

alert, threshold wind 140 at 5, expect a 50 knot loss two

mile final." The additions to the TDWR system required to

support the NASA alerting concept are a cockpit server

software package to extract the necessary TDWR data for

transmission over a data link, the data link

recelver/transmitter, airborne algorithms to compute the wind

shear hazard from TDWR supplied data, and annunciation and

display. Only air to ground data link is required to provide

airborne alerting. The intent of the down link is to provide

the ATC system with information that a wind shear alert has

been generated by the airborne system. No changes to the

existing TDWR system are required to support this concept.
Em0

6_
Operational Concept

The current TDWR operational concept is to detect microbursts

by examining radar-observed wind velocity information for

regions of divergence. When the radar detects a divergence

of greater than 15 meters per second over a distance of at

least 1 kilometer, a shape algorithm draws a microburst icon

around the divergence region. "Wind shear" icons are drawn

around divergence regions of at least 7.5 meters per second.

The mlcroburst is then quantified for ATC and pilots by the

divergence value. The actual hazard to the aircraft depends

heavily, though, on the scale length of the divergence, i.e.,

the change of wind per unit distance, or shear (ref. 3).

Existing airborne wind shear systems as well as those under

development derive an F-factor hazard index (ref. 3) that is

based on wind change per unit distance and down draft. To

provide airborne executive level alerting from TDWR

information, an estimate must be made of the wind shear in

the microburst and the down draft component. The information

required for this estimate are readily available from the

TDWR system. Since (at a readily available level) the TDWR

produces a single velocity and distance number for each

microburst, insufficient data are available to estimate the

shear along arbitrary paths through the event. The airborne

F-factor estimate tested in this study describes the threat

only in the core of the event. The core F-factor estimate is

then combined on the aircraft with microburst location

information to determine if an alert should be given.
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71
Least-Square Estimate of Linear Shear

The wind shear within a microburst can be estimated from the

wind change and scale length information provided by the TDWR

and an assumed wind profile. The TDWR information describes

the endpoints of the peak-to-peak winds and the assumed wind

profile is used to derive information about the wind field

between the peaks. The horizontal wind profile of the

analytical Oseguera/Bowles microburst model described in

reference 4 was used to estimate the least-squares shear

value over a distance D about the core of the microburst.

Since aircraft performance degradation from wind shear

requires shear lengths on the order of 1 kilometer, or

greater, a value of 1 kilometer for D was used in the

experiment.

The mIcroburst F-factor can be estimated from the shear value

just determined and an estimate of the down draft in the

event. Mass continuity considerations are used to estimate

down draft over the same interval as the shear. The

resulting equations, as originally derived by Bowles (ref. 3)

are shown on the adjacent figure. The information required

from the TDWR to estimate F-factor is the wind change (AU),

the scale length of the wind change (aR) and the altitude of

the radar beam in the microburst core.

Each microburst icon is composed of numerous divergence

segments, each one degree apart in radar azimuth. Each

divergence segment has its own wind change and length. In

this experiment the AU and aR sent to the aircraft was

determined as follows. If 5 or fewer segments define an icon

then send the maximum AU value. If this test fails then if

20 or fewer segments define an icon send the second largest

AU value. If more than 20 segments define an icon then send

the 90th percentile segment _U value. In practice, nearly

all icons consisted of less than 20 segments and either the

largest or next largest divergence value was normally sent.

The AR value was determined by examining the shear value of

each segment in the icon and choosing the 85th percentile

shear value. A aR value was then determined that would

produce this 85th percentile shear when divided into the

transmitted AU value. As an example, one icon penetrated in

the 1991 flight tests (event 143) was defined by 4 segments

having AU values of 17.1, 18.9, 22.6, and 20.2 meters/second

and &R values of 3140, 3460, 4500, and 4210 meters,

respectively. The corresponding shear values were 5.45,

5.46, 5.02, and 4.80 meters/second/kilometer. Since four

segments defined the icon the largest aU value (22.6) was

transmitted. The 85th percentile shear value was the second

largest (5.45) which produced a transmitted AR value of 4150

meters (rounded to the nearest I0 meters).

72

Alert Criteria

The TDWRdata link provided the required data to estimate the

mlcroburst core F-factor and to depict the TDWR-derived

mlcroburst icons on a cockpit moving map display. In order

to issue an executive-level alert, a microburst icon must

exist on the projected instantaneous trajectory of the

aircraft (defined by the centerline of the track-up moving

map display), the range to the icon must be less than 1.5

nautical miles, and the core F-factor estimate must be at

least 0.105. Note that in a classical microburst wind field

the strongest wind gradient and F-factor exists in the core

of the event, where the winds are weakest, while very weak

wind gradients and F-factors exist in the vicinity of peak

wind outflow. Since the TDWR-produced microburst shapes tend

to enclose the peak-to-peak wind field, it is logical to

assume that the shapes will overestimate the region of strong

shear. Since insufficient data was available to determine

which region within the icon contained the strongest shear,

an alert was generated when any part of an icon intersected

the flight path. The alert threshold is consistent with

thresholds specified in FAA TSO-CII7 for the certification of

reactive wind shear devices and the 1.5 mile range is

consistent with proposed crew procedures and the supporting

alerting strategies.

74
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75
Flight Test Procedure

The TDWR data link concept was tested during NASA combined

sensor flight tests conducted at Orlando, Florida and Denver,

Colorado in June and July of 1991. The tests provided the

opportunity to measure microburst winds with an array of

remote sensors (TDWR, airborne radar, and infrared) and

correlate those remote measurements with aircraft in situ

wind shear measurements taken during microburst penetration.

In addition to the TDWR research aspect, the TDWR system was

also used operationally to predict microbursts, maneuver the

aircraft for penetrations, and monitor flight safety criteria

such as storm reflectivity values. Both for flight safety

and for later data correlation, microburst penetrations were

conducted on a track either toward or away from the TDWR to

minimize the effects of any microburst asymmetry.

The flight tests were conducted in cooperation with the MIT

Lincoln Laboratory at Orlando and the National Center for

Atmospheric Research (NCAR) at Denver. Both Lincoln Lab and

NCAR developed cockpit server software to extract the

required parameters from the TDWR and format the data for

transmission to a NASA ground station via modem and dedicated

phone lines. Only low cost hardware was required to complete

the data link to the aircraft. The data was transmitted at

1200 baud over an MFJ Enterprises MFJ-1270B TNC packet radio

system. The data transmitted over the data link consisted of

the AU, &R, radar beam altitude, and coordinates of each

microburst icon, as well as overhead data such as the GMT

time of the beginning of the TDWR radar scan, number of icons

in the data link message, and checksum. Each data link

message required 14 bytes for overhead data plus 25 bytes per

microburst icon. This data was transmitted approximately

once every 60 seconds and the elapsed time between the

beginning of a TDWR radar antennae sweep and the receipt of

that data onboard the aircraft was on the order of 30

seconds. Onboard the aircraft the icons were displayed on a

moving map display and used to maneuver the aircraft for

microburst penetrations.

Moving Map Display

The TDWR icon information was presented on a moving map

display, along with supporting flight state parameters, and

recorded on video tape for later analysis. The supporting

data included the TDWRdata age (elapsed time since last data

link reception) and in situ F-factor in the upper right

corner; true airspeed, time, radar altitude and inertial wind

vector in the upper left corner; groundspeed and barometric

altitude below the ownship symbol; and magnetic track angle

above the track scale. Microburst alerts generated by the

onboard computation and criteria were displayed by the

message "TDWR ALERT" in red letters just below the track

scale. The wind change and F-factor of each icon were shown

numerically by labels that stepped from one icon to the next

at the rate of about one icon per second (to reduce display

clutter) and by color coding the icons. White was used to

draw icons with F less than 0.105, amber for icons between

0.105 and 0.15 F, and red for icons with F-factors at or

above 0.15. Also shown on the display were the limits of

TDWR coverage and a waypoint which could be transmitted from

the TDWR operator to accurately locate places of interest

such as gust front boundaries, microburst cores, or predicted

mlcroburst locations. This display is not intended to

represent a format that should be implemented for fleet

operational use. The display was intended as an aid to data

analysis as well as a tool for situational awareness during

research flights.

The accompanying display sketch was drawn from a video tape

of the approach to event 143 on June 20, 1991. Four

microburst icons are ahead of the airplane and a waypoint

transmitted by the TDWR operator is on the flight path at a

range of about 1.5 miles. The aircraft has a groundspeed of

237 knots and the radar altimeter value is 1061 feet. A TDWR

alert has been generated by onboard logic and is displayed.

The dotted line just beyond the nearest icon represents a 30

kilometer range ring from the TDWR site, which is behind the

aircraft.

78
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I
Results

The simple data link hardware proved very reliable at both

deployment locations both while on the ground preparing for

takeoff as well as while flying at low altitude 30 to 40

kilometers from the antennae site. The situation display

combined with voice information from the TDWR proved

invaluable for 15 to 30 minute projections of the weather

situation, positioning the aircraft to intercept microbursts

that were being predicted but not yet developed, maneuvering

with respect to active microbursts, and subsequent data

analysis. The situation display was used for maneuvering the

airplane to penetrate active microbursts and for assessing

the strength of those microbursts before penetration. The

voice link was used for other operational data such as

reflectlvity at the surface and aloft, short term microburst

predictions, and general weather trends.

During the two week deployment at Orlando the NASA aircraft

penetrated 19 weather events that were generating TDWR icons

at the time of penetration. Numerous other events were also

encountered such as gust fronts, rain shafts, and divergent

flows that had not yet strengthened to the point of

generating an icon or decaying microbursts that were no

longer producing icons. These other events are not included

in this analysis. During a three week deployment at Denver

the only observed microbursts were above flight safety

reflectivity limits or could not be reached. Hence all data

presented here is from the 19 icon penetrations in the

Orlando area.

The data is analyzed from two perspectives. The first issue

was the performance of the F-factor estimation algorithm.

The second issue was the overall alerting performance of the

TDWR system (TDWR, airborne processing, and alerting

criteria) during the flight tests.

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81
F-Factor Algorithm Performance

While the overall alerting performance analysis uses data

from all 19 icon penetrations, evaluation of the F-factor

estimation algorithm can be done only in those cases where

the airplane passed through the region described by the

estimator, which is in or very near the core of the

microburst. Early in the flight tests it became apparent

that the TDWR depicts microbursts with multiple icons,

typically three or four, to locate areas of larger and

smaller divergence (AU magnitude). All icons associated with

a microburst were treated equally by the airborne F-factor

estimator although not all icons contained a center of

divergence. Observation by TDWR operators, who could observe

flight path as well as radar reflectivity and doppler

velocity in real time, indicated that penetration of an icon

could miss the divergence core by a kilometer or more. Later

flights used the TDWR operator waypoint data link function to

help locate the desired cores of the microbursts.

To evaluate the F-factor algorithm a selection criteria was

established to determine which penetration data sets were

applicable. The selection was based on TDWR radar velocity

plots overlaid with aircraft trajectory. To include a

penetration in the F-factor data set two criteria must be

met; l) that the TDWR velocity plot show a well-defined

microburst outflow, and 2) that the flight path intersect the

core of this outflow. Only five of the 19 events satisfied

this criteria. Three of the five events were achieved during

multiple penetrations of a single microburst on the final day

of test flights, at growing, near peak, and decaying periods

of the event. Event numbers were assigned to each data block

of interest during the deployments. The five core

penetrations are events 81, 134, 142, 143, and 144.

For comparisons between the F-factor estimator and in situ

measurements, the TDWR radar scan taken closest to the time

of airplane penetration was chosen. The average error

between the TDWR F-factor estimator and in situ was only 0.02

F with the largest error being 0.04 F. The primary factors

affecting the estimation, to be discussed in more detail,

were differences between TDWR radar measurement altitude and

airplane altitude in the microburst, and errors in estimating

the one kilometer shear from TDWR peak-to-peak winds.

All TDWR radar reflectivity, velocity, and shear maps were

provided to NASA by the MIT Lincoln Laboratory.

82
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83
TDWR Plot of Missed Microburst Core and Microburst Core

Penetration

These two plots show examples of a microburst icon

penetration that did not encounter the core region described

by the F-factor estimation algorithm and a penetration

through a microburst core. The first event is not included

in the set of five core penetrations. The second plot shows

the airplane in the core of the penetration cataloged as

event 142. Note in the second plot that the flight path

passes through the doublet of highest doppler velocity

return.

84
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87

Speed and Altitude Effect on F-Factor

As the altitude of microburst penetration increases above the

altitude of maximum outflow, the horizontal wind change

decreases while the down draft increases. Since the F-factor

experienced by the airplane is proportional to horizontal

wind gradient multiplied by groundspeed and down draft

divided by airspeed, the horizontal component of F-factor

tends to decrease with increasing altitude while the vertical

component tends to increase with altitude. At normal

approach speeds the change in the two components tend to be

of similar magnitude. The result is that the F-factor does

not vary greatly with altitude above the altitude of maximum

outflow up to altitudes where microbursts no longer pose a

safety threat (about I000 to 1500 feet). Below the altitude

of maximum microburst outflow both horizontal winds and

vertical winds decrease, leading to reduced F-factor. At the

high speeds used in the microburst flights, however, the down

draft contributes less to the total F-factor and the measured

F-factor does tend to decrease with altitude. The plot shows

variation in the altitude-corrected TDWR F-factor estimation

with altitude at a groundspeed of 70 and 115 meters per

second (136 knots and 223 knots) for a given microburst. The

two speeds approximate normal approach speed and the NASA

microburst penetration speed. The plot assumes that the

altitude of maximum outflow is 90 meters and that the radar

measurement is taken at that altitude. At 70 meters/second

the change in F-factor from 90 meters to 350 meters is less

than 0.01, while at 115 meters/second the change is nearly

0.04. The equation used to provide the TDWR altitude

correction is presented next.

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F-Factor Altitude Effect

The trend of relatively constant F-factor with variations in

altitude was used as an assumption in the TDWR F-factor

estimation algorithm. Although aircraft speed was used in

the F-factor algorithm, the altitude of the aircraft was not

included in any way. The divergence measured by the radar

was used directly and the altitude of the radar beam in the

microburst was used in the estimation of the vertical wind.

In effect, the F-factor estimate was assuming a penetration

at the radar beam altitude. In the events penetrated the

radar beam was typically at altitudes of 150 to 220 meters

above ground, depending on range of the event from the radar,

while the airplane typically flew through the event at 300 to

350 meters above ground. The analytical microburst models

described in references 4 and 5 include a shaping function

which describes the change in microburst outflow with

altitude. These models base the shaping function on mass

continuity, boundary layer friction, and vertical wind

profiles produced by the Terminal Area Simulation System

(TASS) numerical microburst model, which has been extensively

validated against observed microburst data (references 6 and

7). The shaping function p(h) provides the ratio of outflow

speed to maximum outflow speed at any arbitrary altitude.

Given this shaping function, the shear estimate (_) at any

altitude can be expressed as the shear at the altitude of

maximum outflow multiplied by p(h).

= I_' p(h) (i)

Where _' is the shear at the altitude of maximum outflow.

can express F at any altitude as:

We

and

F 1 = I_'P(hl) (V/g + 2hl/V )

F 2 = f_'P(h2) (V/g + 2h2/V )

(2)

(3)

or by rearranging 2 and 3:

F 2 = F 1 P(h2) (V/g + 2h2/V ) (4)

P(hl) (V/g + 2hl/V )

Equation 4 was used as an altitude correction algorithm where

F 1 is the uncorrected TDWR F-factor estimation, h I is the

TDWR radar beam altitude, and h 2 is the airplane altitude.

F 2 then becomes the F-factor estimate at the airplane

altitude.

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91
TDWR Based F-Factor and In Situ F-Factor

Shown in this plot is the F-factor estimated from TDWR data

for each of the five core penetrations compared to the

maximum in situ F-factor experienced during that event. Both

the uncorrected TDWR F-factor and the altitude-corrected

F-factor are shown. Also depicted are the alert thresholds

of each sensor (0.105) and the ideal "line of agreement".

The in situ and TDWR F-factors can be directly compared in

this manner since both are tuned to a scale length that

affects airplane performance. In the case of the in situ

measurement this scale length sensitivity is achieved through

gust-rejection filtering. With the exception of the

rightmost point (event 143) the TDWR F-factor overestimates

the in situ F-factor. When the altitude correction is

applied though, the lower four events agree well.

Considering that the two measurements are taken by different

sensors on different platforms, and at slightly different

times, the agreement is excellent. Of course much more data

is needed to begin to assign statistical significance to this

data. The reason for the relatively large TDWR underestimate

of the F-factor for event 143 is related to shear estimation

from TDWR products and will be discussed next.

92
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Shear Estimation from TDWR Data

Event 143 showed a substantial F-factor underestimate from

the airborne algorithms when subjected to the altitude

correction formula. The issue that arises is whether the

altitude correction formula was incorrect in this case or

whether another factor is involved. Examination of the

moving map display video tape showed that the peak in situ

F-factor was reached in the first third of the distance

through the icon, as opposed to the center of the icon as

would be expected. A plot of the along-track component of

inertial winds as recorded on the aircraft during the

penetration shows that the wind profile did not match the

assumed profile between the peak winds. In particular, an

intermediate peak in the wind was experienced about halfway

through the event. This peak was nearly as large in

magnitude as the peak outflow on the far side of the

microburst.

The ground rules associated with this experiment prohibited

changes to the ground system and led to shear estimation from

information about the peak wind points. This requires an

assumption about the wind profile between the peaks which, as

is demonstrated here, will not always be true. In

particular, pulsing microbursts may generate a microburst

within a macroburst. The shear between the peak-to-peak

winds may be low, but a smaller region of intense shear may

exist within the outflow. This pulsing phenomena is observed

both in field measurements and in TASS numerical simulation

microbursts and may be very common (references 2 and 8).

The TDWR system is capable of directly locating regions of

strong shear, as demonstrated by shear plots produced by MIT

Lincoln Laboratory for post-flight data analysis, but the

current alerting strategy does not require nor utilize this

capability. Properly implemented, shear-based alerting could

enhance the location of hazardous shears and improve the

quantification of the hazard to aircraft.

94
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Event 142 Along-Track Wind Profile and Shear Plot

Shown in the next graph and plot are the along-track

component of inertial winds experienced by the aircraft

during penetration of event 142 and the corresponding TDWR

shear map plot.

Superimposed on the inertial wind graph is the wind output of

the Oseguera-Bowles analytical wind model. The inputs to the

model are the AU and AR values provided by the TDWR for this

shear. Although the in situ winds were somewhat less than

predicted by the TDWR, the profile in the microburst core

matches the shape of the predicted profile. Event 142 is the

third data point from the left in the "TDWR Based F and

In Situ F" plot shown earlier, and produced excellent

agreement between predicted and actual F-factor when

corrected for altitude.

The shear (meters/second per kilometer) of event 142 and

airplane flight track are shown in the plot. This plot was

generated from TDWR velocity data and provided to NASA by MIT

Lincoln Laboratory. The shear plot agrees with aircraft

in sltu data in showing the region of strong shear in the

center of the microburst icon.
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Event 143 Along-Track Wind Profile and Shear Plot

The inertial winds experienced by the aircraft in event 143

are shown followed by the corresponding TDWR shear map plot.

This is the same microburst as in event 142 but penetrated

about four minutes later while traveling on a reciprocal

track.

The event has expanded and a new outflow surge has developed.

Event 143 is the rightmost data point in the "TDWR Based F

and In Situ F" plot shown earlier. The inputs to the model

winds in this case are the TDWR reported AU (corrected by the

altitude shaping function) and aR. Since the TDWR-reported

winds significantly overestimated the winds encountered, the

altitude-corrected AU is shown in order to more closely match

the inertial wind peaks and compare the wind profiles. This

plot shows a significantly greater than predicted shear in

the first half of the icon penetration. This intermediate

peak in the wind profile is responsible for the altitude

corrected TDWR F-factor underestimate.

The shear in event 143 and airplane flight track are shown in

the plot. The shear plot agrees with aircraft in situ data

in showing the region of strong shear in the southern portion

of the microburst icon. The flight data correlates very well

with the shear plots and suggests that the TDWR is capable of

accurately locating shear and measuring shear magnitude.

Detailed data about microburst shear is available in the TDWR

system but not made available in the current alerting

strategy and data link system tested. Provision of this type

of data to end users could better quantify the hazard and

eliminate the need to estimate shear from wind measurements

in airborne applications.

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Discrete Alerting Performance

To evaluate the overall alerting performance of the TDWR data

link system as tested, all 19 microburst icon penetrations

were considered. Out of these 19 events, 18 produced

airborne TDWR alerts while only 2 produced in situ alerts.

By far the predominant factor producing this nuisance alert

rate is the spatial effect of not penetrating the microburst

core in most of the events. The F-factor estimation is not

valid for any arbitrary path in the vicinity of the

microburst. Although a separate F-factor was computed for

each icon, the division of one microburst into multiple icons

was such that any given icon did not necessarily contain the

core of a microburst downflow. Penetration of these icons

resulted in a significantly lower in situ F-factor than

predicted. The second factor affecting alerting performance

was the altitude effect described earlier. When adjusted for

altitude, fewer icons exceed the alert threshold.

The final factor affecting alerting performance was temporal.

A microburst can grow or decay in the one minute interval

between updates. In the penetration of event 142 the

airborne TDWR alert was received after the airplane entered

the microburst event and a new data link update was received.

Since this event did not exceed the in situ alert threshold

the TDWR alert was counted as a nuisance alert rather than a

late or missed alert. Although nuisance alerts caused by

decaying events are probably inevitable with any remote or

forward look sensor, the issue arises as to the possibility

that an alert will be missed on a significant event. This

type of missed alert requires that the F-factor increase from

below threshold to a truly hazardous level between TDWR

updates, and that the airplane enter the event between those

updates. Insufficient data was gathered during the flight

tests to estimate the frequency of this occurrence, although

the potential for this situation was demonstrated in an

aborted microburst approach when the TDWR F-factor estimate

increased from 0.18 to 0.26 between updates.

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P]ans for 1992 Research Flights

Numerous changes are being made to the TDWR processing and

the flight operation to enhance system performance and data

opportunities during the planned 1992 wind shear flight

tests. The altitude correction technique developed during

this data analysis will be implemented onboard the aircraft

for real-time application. Appropriate limits will be set in

the altitude correction so that the algorithm functions

realistically while on the ground or when the airplane is

loitering at a higher altitude than would be used for

penetration. On the ground the algorithm will calculate an

F-factor applicable to an initial climb speed and altitude.

At high altitude the F-factor will be applicable to the

altitude range used for penetrations. The alert criteria

will also be modified to prevent alerts during ground taxi

due to microbursts near the airport as well as when airborne

above 1500 feet. In 1991 numerous alerts were received while

the aircraft was on the ground and microbursts were ahead

(none of these alerts are included in the analysis.) In 1992

these alerts will not be given unless the airspeed is at

least 60 knots, indicating that takeoff roll is in progress.

Of course the microburst icons will always be displayed.

To increase the number of microburst core penetrations, the

aircraft coordinator at the TDWR site will be provided with a

real-time range/azimuth disp]ay of shear. This display,

along with the waypoint feature of the data link, will be

used to communicate the most promising locations to the

airborne crew. At the suggestion of Dr. Steve Campbell of

MIT Lincoln Laboratory, a "waypoint-with-shear" data link

product will be tested. The concept is to make a direct

one-kilometer shear estimate at the TDWR site of a region

about the designated waypoint, and transmit this shear value

to the aircraft for use in F-factor estimation. This will

eliminate the process of estimating shear from the peaks of

the wind outflow for events marked with such a waypoint. The

normal F-factor processing of the microburst icons will

continue to be performed for all events.

Finally, the demonstration of an "automated pilot report"

capability on the data link is planned. In numerous events

(ref. 2) pilots have encountered wind shear and not provided

timely pilot reports to ATC. The controllers and subsequent

aircraft may not have the benefit of knowing why the earlier

aircraft missed the approach. The automated pilot report

will downlink the status of wind shear alerts from onboard

systems. In the NASA flight tests this alert information

will terminate at the TDWR site and will not actually be

provided to ATC.

I06
II II II II II

107
Summary

This experiment demonstrated the feasibility of transmitting

ground-based wind shear information to an aircraft via data

llnk, processing that information on the aircraft to estimate

the wind shear hazard index (F-factor), then providing the

information on a moving map display for operational use. In

the limited number of microburst core penetrations

experienced, the estimated F-factor compared very favorably

to the actual in situ F-factor. More cases are needed to

show statistical significance.

As the current system was implemented, the executive level

alerting performance was inadequate due to an excessive

number of nuisance alerts. These nuisance alerts were due to

inadequate data being available to the alerting process to

precisely locate the region of strong shear, and the aircraft

trajectory not intersecting those regions. The information

required to minimize this limitation is resident within the

TDWR system but not planned as an output product of

production TDWR systems. More complete use of the ground

system capabilities may greatly improve the utility of the

TDWR microburst information to the end users.

References

I. Federal Aviation Administration: Windshear Training Aid,

Example Windshear Training Program, February 1987.

2. Schlickenmaier, Herbert W.: Windshear Case Study: Denver,

Colorado, July II, 1988, DOT/FAA/DS-89/19, November 1989.

3. Bowles, Roland L.: Reducing Windshear Risk Through

Airborne Systems Technology, Paper Presented at the 17th

Congress of the International Council of the Aeronautical

Sciences, Stockholm, Sweden, September 9-14, 1990.

4. Oseguera, Rosa M. and Bowles, Roland L.: A Simple,

Analytic 3-Dimensional Downburst Model Based on Boundary

Layer Stagnation Flow, NASA TM-I00632, July 1988.

5. Vicroy, Dan D.: A Simple, Analytical, Axisymmetric

Microburst Model for Down draft Estimation, NASA

TM-I04053, DOT/FAA/RD-91/10, February, 1991.

6. Proctor, F. H.: The Terminal Area Simulation System,

Volume i: Theoretical Formulation, NASA CR 4046, 1987.

7. Proctor, F. H.: The Terminal Area Simulation System,

Volume 2: Verification Experiments, NASA CR 4047, 1987.

8. Proctor, F. H. and Bowles, R. L.: Three-Dimensional

Simulation of the Denver Ii July, 1988 MicroburstProducing Storm, Accepted for Publication in Meteorology

and Atmospheric Physics.
0

l l l I l

109
Air/Ground Wind Shear Information Integration - Flight Test Results

Questions and Answers

Q: Norm Crabili (Aero Space Consultants)- The shape of these icons sort of bothers me a

little bit. I was wondering if the racetrack pattenl has it's long axis along the radius vector from

the Doppler. Is that correct'?

A: Dave Hinton (NASA Langley) - In these cases it did.

Q: Norm Crabill (Aero Space Consullants) - So that is a limitation of the single site ground

determination of the velocity field. If you were making an approach to a runway that was at right

angles to that, you are not going to get a lot of information. What have you concluded about

TDWR siting relative to the runway? How do you use this information to help you site the

TDWR now that they are being deployed?

A: Dave Hinton (NASA Langley) - We did not try in our analysis to do that.. Our ground rules

were not to change the TDWR system, so we did not look into siting issues per say. There are

some very good historical reasons Ior why those shapes are the way they are, and I'll let Steve

Cambell cover that.

A: Steve Cambell (MIT Lincoln lmboratol'y) - That is a good point Norm: Basically, what we

originally started out with was a big region that we identified as a microburst. Then we decided

to do a better job of isolating where the strong velocity change was by dividing this shape up in

the azimuthal direction. I will talk a little bit about this on Thursday, and about some of the ideas

we have for doing a better job of localizing the region of peak shear. But you make a very good

point, and that is one of the things we are currently lcx_king at; how we can improve that shape

representation.

Q: Norm Crabill (Aero Space Consultants) - What about the question of TDWR siting, and

some practical situations?

A: Steve Cambell (MIT Lincoln Laboratory) - That is another issue too. We have done

extensive testing where we use dt, al Doppler Radars to determine the shear for approach or

departure paths and we have been able to show that we can do a pretty good job of estimating the

shear or the change in velocity along the flight path with a single TDWR. The deployed TDWR's

will be deployed in conjunction with the enhanced LLWAS system, which is a surface base

anemometer system. If you have a situatiotl with a highly asymmetric microburst then the

LLWAS system should be able to detect it. Now we have also studied this issue of how likely is

it that the outflow would be highly asymmetric. Generally, in the South East they are not very

asymmetric. You do see asylnmctric ones in places like Denver though; so in that case we think

that the surface sensor would be a fifil safe for making sure we detect the strong shear of any

region perpendicular to the radar beam.

Norm Crabiil (Aero Space ('onsultants) - Well of course the I,LWAS alarm at Dallas was after

the fact.

110 ORIGINAL PAQ_ IS

OF POOR QUALITY
Steve Cambeil (MIT Lincoln Laboratory) - I should point out that we arc integrating with the

enhanced LLWAS system. The current six station LLWAS is not really adequate for microburst

detection. The enhanced LLWAS system have something like a 13 to 16 stations. It's a much

more extensive LLWAS network which covers most of the approach and departure paths. It also

has a different algorithm than that used in the current Phase I LLWAS. We have been able to

show that when you integrate TDWR and the enhance LLWAS there is a very high probability of

detecting a hazardous wind shear along any arbitrary path. So we are very confident. Both

systems work very well and when you combine the two you have an extremely reliable system.

We have been able to verify that against our dual Doppler measurements, and other

measUrelT_nts.

Norm Crabili (Aero Space Consultants) - Dual Doppler is handy, but you won't have it at those

forty-six sites.

Steve Cambell (MIT Lincoln Laboratory) - The dual Doppler is for the purpose of generating

the truth so we know what actually happened. We are validating our single Doppler with the

surface sensors against dual Doppler.

Q: Sam Shirck (Continental Airlines)- With respect to the TDWR results, is it possible that in

the future airborne radar systems may data link their view of the wind shear situations to the

ground based TDWR, since the airborne systems have a better viewing angle and a much

enhanced update rate?

A: Dave Hinton (NASA Langley) - I think it certainly would be possible to down link the data

to the ground base system. The primary obstacle standing in the way is going to be the lack of a

system driver for doing that. Within the context of the program we are doing, we have a charter

not to change the ground system. Now, if there is a system requirement to do that, it could

possibly be done. There are a couple of technical issues involved; one is the data rate that would

be required to get that amount of information down, and secondly a lot of dual and triple Doppler

analysis' have been done of numerous events and that can take, I would expect, a significant

amount of post processing. To do the triple Doppler analysis in real time would probably be a

very large computational effort. So it is a question of a system driver plus the effort involved to

do it.

Jim Evans (MIT) - Where the ground systems are going in the relatively near future is toward

what is called integrated terminal weather systems, which in fact tries to integrate information

from all the available ground and airborne systems. We are already talking about ingesting winds

and temperature data out of planes. I don't think it is a big issue to transmit that information

down over a Mode S data link. ! think what you would do is that you would formulate it as a

message, it would then come up as an additional piece of alert information that could be passed

along in much the same style and thereby provided automatically to succeeding planes. I do not

think you would have to get into dual or triple Doppler analysis.

Dave Hinton (NASA Langley) - Obviously, you could operate such a system at various levels,

triple Doppler being the most complex. Another way would be to simply look at alert regions and

use those in some manner. Which Jim, if I understand, is what you are referring to.

Ill
Jim Evans (MIT) - I think I would try that for starters, because I think the others would be fairly

complicated. One of the issues that you would get into immediately on dual or triple Doppler,

with an airborne weather radar at X-band, would be the whole question about how well you had

unfolded your velocities. You don't have to unfold absolutely to get shear regions, but you could

be off by a whole fold without any trouble at all.

Q: Norm Crabill (Aero Space Consultants) - I would like to revisit the question of TDWR

siting. I don't know to whom I should address the question, but if you have an airport like O'Hare

with intersecting runways, where do you put the terminal Doppler radar?

A: Jim Evans (MIT) - It is fairly simple. What we have used as the criteria in siting the TDWR

is to look at the runway usage during circumstances when there is weather, and try to line up the

TDWR to look along those runways. We then do an adjustment in cases where there are split

runway regions. O'Hare is certainly the ugliest case one can point to. In most of the others, it is a

fairly reasonable site. We have tried to consider looking up the runways the maximum amount of

the time consistent with when the weather was going to be present. It is a lively task of course, in

a place like Leguardia, just trying to find a place to put the radar. I think we have been very

successful.

Q: Norm Crabill (Aero Space Consultants) - If you have intersecting runways and low

weather, and conditions conducive to microbursts, is there any intent to restrict the operations to

the runway that is favored by the terminal Doppler weather radar?

A: Jim Evans (MIT) - Again, we don't see the need to do that. What we have been trying to do

is assess the performance by taking dual Doppler measurements of the winds along the runway.

There is certainly ample reason to believe that dual Doppler does a very good job of estimating

the winds. What we do is we look at the winds along the runways no matter what there

orientation is, whether the TDWR has a good look angle or a bad look angle, and assess the

accuracy of the warnings. For example, if the actual wind along the center line of the path had

more than a 30 knot wind change over a suitably small distance we would check to see if we are

issuing an alert or not. It is a very high probability that we do, no matter what the orientation of

the runway is. That is what we have seen for Denver, Kansas City, and Orlando. In that process

we use runways that we have lousy look angles to. The reason that we know what the winds are

is because we have dual and in some cases triple Doppler data to tell us what the winds are. That

is the way we are trying to assess it. Are we giving it a timely warning for that runway? Sure, it

is a little better on the ones that you have a nicer look angle, but it doesn't mean that you are not

detecting, very reliably, all the ones at any angle you want to imagine a runway to be.

Norm Crabill (Aero Space Consultants) - Someone keeps bringing up in this discussion, dual

Doppler. My understanding is that there is only one TDWR per airport.

Jim Evans (MIT) - Let me again make it clear what is being done. From a research basis, we go

to airports with two and three radars :rod we evaluate quantitatively our performance. We score a

single radar's ability to give accurate warnings on all the runways. You say, how did you know

what was there. The reason we know what was there is that we had dual or triple Doppler. Now

when we go out in the actual operational system there is only one radar at the airport. But, we

112
believeweknow what its performance is going to be. This summer will be our seventh year of

testing with dual Doppler data. We think by doing this testing over a wide variety of airports and

geographic regions and having synthetic runways as well as real runways, we have a very good

handle on the performance of the system. That is the rule. It isn't that a operational system has

dual or triple Doppler, it is that we have done careful experiments with dual and triple Doppler

and supporting mesonet systems. Is there a microburst the radar can't measure. We have gone

out and tried to address that number in this phase. That is what we are quoting from and we hope

the past is a prediction of the future. Of course, the world may change. This is one of the most

carefully tested systems that I know of.

Q: Norm Crabill (Aero Space Consultants) - Yes, I agree there has been a lot of testing.

What has been the result for Chicago where will the radar be located?

A: Jim Evans (MIT) - We have not done dual Doppler testing yet at O'Hare. The radar will be

almost due south of O'Hare. There is an ARSR site down there it is roughly to the east of

Midway. It is a location that will give a good look at both Midway and O'Hare.

113
!14
Session I. NASA Flight Tests

Doppler Radar Results

E. Bracalente, NASA Langley Research Center

115
Doppler Radar Results

E. Bracalente, NASA LaRC

116
FourthCombinedManufacturers'&

Technologists'Airborne

WindShear ReviewMeeting4-14,16-92

NASA Flight Tests

Airborne Doppler Radar Results

PresentationOutline

1. Summary of Radar Flight Data Collected

2. Video of CombinedAft Cockpit, Nose Camera, & Radar

Hazard Displays

3. Comparisonof Airborne Radar F-factormeasurementswith

In Situ and TDWR F-factorsfor Some sample Events

4. Summary Wind Shear Detection Performance

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SIGNIFICANT WIND SHEAR EVENTS 1991 ORLANDO FLIGHT EXP.

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AirborneDopplerRadarResults

Performance Summary

. Datafrom over200clutterand150weathereventrunswere

collected. Theweathereventsincludedapproximately30

microbursts and20 gustfronts.

2. Nofalse hazardalertsresultedfromany cluttertargets.

. All microbursteventsweredetectedbythe airborneradar. For

the microbursts penetratedbytheA/C(approx.15),the airborne

radarderivedF-factorshowedexcellentagreementwiththe In

SitumeasuredF-factor.

4. Gustfrontswithapproximately5 dBzor higherreflectivitylevels

were alsodetected.

5. Samplecomparisonsof airborneradardatawithTDWRdata

showedcomparableresults.

11 Wetmicroburstscanbe accuratelydetectedinthe presenceof

severegroundclutter. Drymicroburstperformancewill be

evaluatedusingradarsimulationprogamwith dryU-Burstmodels

and possibleDenvergroundandflightexperiments.

139
Doppler Radar Results

Questions and Answers

Q: Anthony Berke (Mrr Lincoln Laboratory) - 1 am curious to know why you had the

antenna depressed two degrees or so when you were usually trying to do level flight penetrations?

A: Brac Bracalente (NASA Langley)- Primarily because we wanted to first look down into the

event, and secondly, to get some clutter into the signal. We were really doing it over a range of

tilt angles, 0, -1, -2, -3. We were collecting data with difl_rcnt conditions so we could evaluate

the effects of clutter under those conditions, and to get e×tra data down in the event. Obviously

in some of the comparisons with the In Situ wcrc the antenna was tilted down, the in Situ flew

above were we saw the measurement; there will be some differences there. We flied to compare

with the In Situ when we were as close Io the airplane as possible so the difference in altitude was

not great.

Q: Pat Adamson (Turbulence Ih'ediclion Syslelns) - To create total F-factor numbers you

estimate or infer the vertical compound the winds. Is that correct'? If so, how do you deal with

asymmetric events and with the different altitudes where vertical and horizontal winds trade-off.

A: Brac Bracalente (NASA I_angley) - That is correct, wc do that. Right now we are using an

algorithm that Dan Vicroy and Fred l+roctor ca,he up with. There is going to be a presentation

tomorrow by Dan on that vertical cstimalion. Basically we take the horizontal wind measurement

and multiply it by a factor which takes altitude into consideration. Basically, it is estimating the

vertical based on the horizontal COml×ment and the alliludc at which we made the measurement.

As far as the asymmetric events ;rod the dit'l_rcnl altitudes, l)an will talk about all that tomorrow.

It is pretty straightforward. I:vcrything I showed up here did included a vertical estimation in the

F-factors.

Q: Pat Adamson (Turbulence Prediction Systems) - What is the sensitivity of the radar? In

Denver, 10% of the dry microburst we,'¢ fi'om -1{) to 0 dBZ.

A: Brac Bracalenle (NASA Langley) - As 1 pt,inted out in the presentation, we did not see any

dry microburst, but we did see some low relleclivity gus! fronts. I showed one example where the

reflectivity was down in Ihe 5 to 10 dBZ ra,lge and wc were able to detect that. There wasn't

extremely strong clutter in that particular region. We think we will be able to work down into the

0 maybe 5 dBZ level, out to three or four kilom¢lcrs. Thal is what we are shooting for this

summer. Hopefully we will get those kind of events st, we can collect some data and see what we

can do.

Q: Pat Adamson (Turlmlence Prediclion Systems) - When flying at 230 knots, is it easier or

harder to suppress clutter thall at 140 knots?

A: Brac Bracalente (NASA i,angley) - I tlon'l know that we see much difference since we zero

out the velocity of the aircraft. The specmml width of the clutter might be a little bit wider at 230

knots. It doesn't really have much elfeel on our ahility to suppress Ihc clulter or to operate the

radar.

140
Session I. NASA Flight Tests N 9 3" _-_ _': _

Flight Test of an Infrared Wind Shear Detector

Dr. Burnell McKissick, NASA Langley Research Center

141
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,-/t,:,r_ _LANK r_o1 F|LMED pREGEDti',IG "' "_

143
COMMENTS ON "TOPICS DISCUSSED" SLIDE

The "TOPICS DISCUSSED" slide presents an outline of the

presentation. The 5 microburst core penetrations are presented because they

represent the only penetrations through the core of a microburst during the

Orlando and Denver deployments and therefore the greatest opportunity of

detecting a hazardous wind shear.

144
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COMMENTS ON "BACKGROUND" SLIDE

The central problem that is addressed in infrared wind shear detection is

the relatlonship between air temperature change and wind shear. Efforts to

draw a link between the two physical phenomena date back to 1954 to work

done by Fawbush and Miller. Sinclair, Kuhn and others measured air

temperatures around storms during the late 1970's. Sinclair has continued to

develop passive infrared technology to measure air temperatures and infer

wind shear hazards. Modelling of microbursts by Proctor produced an empirical

relationship between temperature change and maximum horizontal wind

outflow speed. Finally, Adamson developed a passive infrared wind shear

detector which is a part of the NASA/FAA wind shear program and the subject of

this presentation.

146
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149

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COMMENTS ON "MICROBURST TEMPERATURE

MEASUREMENTS, GLOBAL AND LOCAL" SLIDE

AWAS III computes a delta temperature (DT) which is a measurement of

a far field temperature (Tfar) minus a near field (Tnear): DT = Tfa r - Tnear.

Tnear is close to the aircraft while Tfa r is nominally 4 kilometers ahead of the

aircraft. If the DT measured by AWAS III is used in Proctor's relationship, for

example:

Umax =-5DT/2,

Umax becomes an estimate of maximum radial outflow. DT is a point (local)

measurement of Tfa r - Tnear. The AT in Proctor's equation is a temperature

difference between minimum temperature in the core of a microburst and air

temperature outside the microburst at the surface, a global difference. There is

no assurance that

DT=AT

or that Proctor's relationship will hold for every microburst. The next two slides

are pictures of AWAS III as it is installed on NASA Langley's Boeing 737. The

first of the two slides is an exterior view of AWAS III while the second slide

shows how AWAS looks from the inside of the airplane.

15o
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151
BLACK Af'.;D WN!TE PHQTOGRAP,_:

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152
I r ',r'_L. i".z, _j ,._

BLACK AND WHITE PHOTOGRAPH
COMMENTS ON "OUTPUT OF AWAS II1" SLIDE

AWAS III provides more parameters than are listed on this slide.

ones listed were used during the research presented in this talk.

The

154
155
COMMENTS ON "5 MICROBURST CORE PENETRATIONS AT

ORLANDO" SLIDE

Some pertinent information on the 5 core penetrations is presented on

this slide. For example, the penetration labeled as event 143 occurred on June

20, 1991. The in situ F-factor had a peak value of .167 and the thermal hazard

index had a peak value of. 14. Both indices gave a wind shear alert. The

thermal hazard index is an in situ Index based on air temperature measured

from aircraft sensors.

156
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157
COMMENTS ON *METHOD OF ANALYSIS" SLIDE

The basis of the analysis of the 5 events is the correlation between pairs

of important variables: OAT (outside air temperature), LLWSR (thermal hazard

index), LLWS2 (predictJve hazard index based on infrared measurements), D2

(infrared measured Tfar - Tnear, basis of LLWS2), Pitch (aircraft Euler angle)

and FE3 (in situ F-factor based on Inertial and air data measurements).

Estimating correlation coefficients and performing detailed comparisons of time

sedes can determine if AWAS III generated predictive wind shear indices. The

analysis of event 143 using these techniques will be presented in this talk.
C")

1B9
COMMENTS ON "RESULTS FROM 5 CORE PENETRATIONS" AND

"COMPUTED LOOK DIST:FLT TST 91" SLIDES

One of the first things that is noticeable from AWAS Iii generated data are

short computed look distances. The slide named "COMPUTED LOOK DIST:FLT

TST 91" shows this date for the Odando and Denver deployments. Estimates of

the correlation coefficients between D2 and temperature give evidence that D2

is measuring tnear. Because of this, D2 cannot provide a predictive response to

wind shear. Several possible explanations exist for the short look distances.

One of the first possible explanations is that the flights were through heavy rain

which resulted in shortened look distances. Flights at Denver were not through

rain, but the computed look distances were small for many of those events.

Another possible explanation was that the installation of AWAS III on the NASA

Boeing 737 resulted in short look distances. The NASA installation is different

than that of Amedcan and Northwest airlines, but no one knows how or if the

NASA installation affected look distances. In order to eliminate any possible

installation effect, TPS redesigned NASA's installation of AWAS III so that it is

more like that of Amedcen and Northwest aidiees for the 1992 deployments.

In the 5 core penetrations there were 4 thermal alerts given due to large

drops in measured ambient air temperature. For example,the measured

temperature drop for event 143 was approximately 10°C. A temperature drop of

this magnitude would correspond to a larger wind shear than experienced in

event 143. All of the microburst events of the Orlando deployment involved

flying through heavy rain and aircraft temperature probes are affected by rain.

Rain effects cause the measured temperature drops to be larger than the true

temperature drops. Large measured drops in temperature may have been a

contributing factor in the four thermal alerts in the five core penetrations.

16o
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COMMENTS ON "FE3...FOR A MICROBURST PENETRATION" SLIDE

This is the first of a series of slides that present a detailed analysis of

event 143. In this slide, time histories of the hazard indices FE3, LLWS2 and

LLWSR are shown. At approximately 95 seconds after the beginning of the

event FE3 alerts for a wind shear. There are peaks in LLWS2, but these peaks

are not a predictive response to wind shear. This will be shown in the

subsequent analysis. LLWSR generates an alert at about 50 seconds after the

beginning of event 143 or about 45 seconds before the alert caused by FE3.

This may be due to the rain effect on the temperature measurement since heavy

rain was encountered before penetrating the microburst.

164
03

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-0.10

-0.12

-0.14

FE3 , LLWS2 ,TLLWS2 AND LLWSR for #143

FLIGHT 612 ON 6/20/91 AT ORLANDO

FOR A MICROBURST PENETRATION

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SECONDS FROM 20:4-4:30

165
COMMENTS ON "WIND SPEED..." SLIDE

At approximately 90 seconds after the start of event 143 a substantial

down draft is encountered. Temperature begins to drop around 35 seconds

after the start of the event. As stated before, the drop in measured temperature

may be due to the rain effect on the aircraft temperature probe.

166
WIND SPEED, DIRECTION AND TEMP. for #143

FLIGHT 612 ON 6/20/ 1 AT ORLANDO

FOR A MICROBURST PENETRATION

40

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32 - . .. 200

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SECONDS FROM 80:44:30

167
COMMENTS ON "...CROSS CORRELATION OF LLWSR AND

TEMPERATURE" SLIDE

Normally the correlation at zero lag is much stronger than what is shown

in this slide. LLWSR is a function of temperature and usually has a correlation

coefficient of about .6.

168
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169
COMMENTS ON "...CROSS CORRELATION OF LLWSR AND FE3" SLIDE

The three peaks in the cross correlation coefficient correspond to the

three peaks in LLWSR correlating with the one peak in FE3. Peaks in LLWSR

occurred at 10, 34 and 48 seconds before the peak in FE3. LLWSR is based

upon measured temperature which may be affected by rain. Therefore, the

peaks in LLWSR may be due to rain effects.

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COMMENTS ON "...CROSS CORRELATION OF D2 AND PITCH" SLIDE

02 and pitch have a correlation coefficient of approximately .6 near zero

lag. This indicates a strong pitch effect in the D2 measurement. Also, this

positive correlation is evidence that D2 is following a near field temperature.

The reasoning goes as such: as the aircraft pitches up (increased pitch) a

colder temperature is sensed (temperature decreases) since the sensor is not

pitch stabilized. But, since D2 = tfar - tnear, pitch is correlating with -tnear which

gives a positive correlation coefficient. This is evidence that D2 was pdmadly

measuring near field temperature.

172
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1?3
COMMENTS ON "...CROSS CORRELATION OF

D2 AND TEMPERATURE" SLIDE

in this slide D2 shows very strong correlation with temperature at the

aircraft. There also seems to be much weaker correlation of D2 with a far field

temperature 45 seconds ahead of the aircraft. This is additional evidence that

D2 was primarily measuring near field temperature.

174
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175
COMMENTS ON "...CROSS CORRELATION OF D2 AND T(t+45)-T(t)" SLIDE

A pseudo tfar - tnear is formed by computing T(t+45)-T(t), temperature 45

seconds ahead of the aircraft minus temperature at the aircraft. D2 shows a

very strong correlation with T(t+45)-T(t), but not as strong as the correlation with

temperature at the aircraft as shown in the previous slide. Strong correlation

between D2 and T(t+45)-T(t) may mean that D2 is measuring a far field

temperature 45 seconds ahead of the aircraft minus a near field temperature at

the aircraft. A look at the appropriate time series will show that 02 was

measuring near field temperature.

176
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1TT
COMMENTS ON "LLWS2, TEMPERATURE...FOR A MICROBURST

PENETRATION" SLIDE

The correlation of D2 and T(t-45)-T(t) does not represent a predictive

response to temperature changes. D2 was measuring near field temperature.

In the bottom graph of this slide D2 (the solid line) and T(t-45)-T(t) (the dotted

line) are plotted against time. The middle graph has outside air temperature

(solid line) and T(t+45)-T(t) versus time. Compairing the two graphs shows that

D2 varies inversely with temperature. After approximately 35 seconds from

beginning of event 143 the aircraft encounters the cold air outflow; D2 becomes

a measurement of near field temperature and LLWS2 is responding to near

field temperature changes. In the first 35 seconds of event 143 the temperature

and D2 are essentially constant and the variation in LLWS2 is system noise.

The positive correlation between D2 and T(t+45)-T(t) is due to their behavior

after 75 seconds from the start of event 143. During this period both variables

are increasing with time and D2 (between 75 and 90 seconds) correlates

positively with temperature beyond 120 seconds. Since the aircraft is in the

cold air outflow, this correlation does not represent a predictive response to

temperature but is termed a nonsense correlation.
LLWS2, TEMPERATURE, AND D2 FOR #143

FLIGHT 612 ON 6/20/91 AT ORLANDO

FOR A MICROBURST PENETRATION

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SECONDS FROM 20:44:30

179
COMMENTS ON "FREQUENCY...FOR ALL EVENTS" SLIDE

The in situ algorithm (FE3) alerted twice or 1.14% of the time. AWAS III

alerted 32 times or 18.18% of the events contained AWAS alerts. There was

one event (number 143) that had a common alert. The alert rates are

statistically different based on a ;[2 test.

180
FREQUENCY TABLE FOR

ORLANDO AND DENVER DEPLOYMENTS

FOR ALL EVENTS

t_

O

:m

ALERTS

FE3

NO ALERTS

1

.005682

1

.005682

31

.176136

143

.8125

.011364

.1818

181
COMMENTS ON "AWAS III THERMAL ALERTS = SLIDE

All of AWAS' alerts were thermal alerts. A large number of alerts

occurred during rain cell penetrations which may have been caused by rain

effects as previously stated and radar clutter runs which were low passes over

runways followed by go-srounds.

182
AWAS IIITHERMAL ALERTS

Event Type

Microburst

Number of Events

9

Rain Cell 11

Gust Front 1

Go-around 10

Other

Total

J.

32
COMMENTS ON "...FOR A GO-AROUND" SLIDE

This slide shows the typical behavior of AWAS III during a go-around.

The thermal hazard index is not compensated for the change in temperature

that occurs when the aircraft is climbing during a go-around.

184
RADIO ALTITUDE AND LLWSR FOR #190

FLIGHT 618 ON 7/I 1/91 AT DENVER

FOR A GO-AROUND

O

2000

1800

1600

1400

1200

1000

800

60O

400

200

0

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RADIO ALT ....

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ALERT THRESHOLD

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O3

SECONDS FROM 19:56:45

185
COMMENTS ON "AWAS III CONCERNS" SLIDE

A number of issues relating to AWAS III performance need to be

addressed. Some have already been mentioned during this presentation.

NASA's installation of AWAS Ul was different than that of other installations.

The TPS/NASA designed installation used a periscope and AWAS III was in a

pressurized passenger compartment of the aircraft. The other installations did

not use a periscope and were not in a pressurized part of the aircraft. No one is

sure if AWAS IIl's performance was affected by possible installation effects. All

of our penetrations were done with air speeds much higher than approach and

landing speeds. AWAS IIl's hazard indices are based on normal approach and

landing speeds of around 140 knots. AWAS IIl's performance during the goarounds points to the need for thermal hazard alerts are probably caused by

rain affecting temperature probe measurements. The hazard indices from

AWAS III appear to contain a lot of noise. Filtering of the indices would reduce

the noise level and possibly change the threshold for alerting.

186
AWAS III CONCERNS

NASA Installation of AWAS Ill

-Unheated mirror

-Mirror replaced twice

-Window (KR.S-5) had to be cleaned

-Rain in perlscope may lower

look distance

Airspeed of 230 kts. during

penetrations

Lapse rate compensation

Effect of rain on OAT measurements

Filtering of data

Threshold for alerting

187
COMMENTS ON "AWAS III CHANGES FOR 1992 DEPLOYMENTS" SLIDE

Numerous changes are being made to AWAS and to the NASA 737

installation. One of the biggest changes is a TPS redesigned periscope mount.

NASA's installation would be more like those of other AWAS III installations.

The KRS-5 window is being moved from the bottom of the periscope to the top

of the periscope. This will put the window in the same relationship to the

reflector as all other installations. Also the NASA installation will have a heated

reflector. TPS is developing hazard indices based on microburst penetration

speeds in excess of 200 knots. Also, AWAS III will have a new method of

compensating for pitch affects (lapse rate effects caused by aircraft pitching)

and compensation for lapse rate effects on OAT measurements. And finally,

filtering is introduced into the computations of the hazard indices.

]88
AWAS III CHANGES FOR

1992 DEPLOYMENTS

TPS Redesigned Periscope

TPS Developing Hazard Indices

Flight Test Airspeeds

for

New Lapse Rate Computation

Enhanced Lapse Rate Compensation

for OAT

Indices Based on Filtered Data

189
COMMENTS ON "CONCLUSIONS" SLIDE

The AWAS III system functioned according to specifications. Flight profile

modes changed when they should have and there were no system errors.

There is a need for compensating for rain effects on the thermal hazard index

and possible installation effects are uncertain. Various operational and

installation uncertainties do not allow NASA to make conclusive statements

regarding AWAS IIl's performance of the wind shear predictive function.

19o
CONCLUSIONS

AWAS III System Operated Without

Failures

Numerous Thermal Alerts From Rain

Contamination of OAT Measurements

Installation Effects On AWAS

Performance Are Unknown

III's

Results Are Not Fully Conclusive For

1991

191
Flight Test of an Infrared Wind Shear Detector

Questions and Answers

Bob McMillan (Georgia Tech) - I have more of a comment than a question. I would like to tell

you why I think the look distance was shorter in Orlando than in Denver. I think it was probably

water vapor. There are just thousands of water vapor lines scattered through the infrared. I am

sure that the humidity was higher in Orlando. So it was not liquid water so much as maybe water

vapor.

Burnell McKissick (NASA Langley) - There were lots of events that contained short look

distances for Denver too.

Pat Adamson (Turbulence Prediction Systems) - We had similar data in Orlando in 1990 and

we had considerably longer look distances. Two things that I think are important, one is that

these look distances are radically different than the installation on a research aircraft that we flew

in 1990, a Cessna Citation, and they are radically different from both the Northwest and the

American Airlines installations. The second one that I think I should mention, is when we talk

about OAT effects, the wet bulb/dry bulb effect on an OAT probe is radically increased as a

function of airspeed. So when we talk about overshoots fiom OAT at 230 or 240 knots it is

considerably different than the overshoot at 140 knots. They are quite different in that sense. As

far as look distance is concerned, these were very different. We had a meeting a few weeks ago

to look at these issues and the first thing we noticed was that we did not have any look distance in

this installation. That is one of the things that we will be looking at this year.

Q: Russell Targ (Lockheed) - In the very beginning and again at the end you said the jury is still

out as to whether the temperature sensing scheme will actually measure microburst. What is your

criterion going to be for you to determine whether or not this technology does what you want it

to do? What are you looking to see?

A: Burnell McKissick (NASA Langley) - We arc looking to see alerts at the appropriate time

which match up with alerts generated by our In Situ system. So we are sort of bottom lining the

whole thing with an alert at the right time, and the right event. Will it indicate a wind shear where

there is actually a wind shear. The issue of relating the temperature measurements to the wind

shear is one that people are still working on, and it ix very interesting, but we are sort of at the

bottom line of the whole thing.

Q: Russell Targ (Lockheed) - You show a mixture of missed alerts and false alamls and I am

wondering how much of that is acceptable ill your quantitative judgment?

A: Burnell McKissick (NASA Langley) - Well, certainly we would like to see less. No one

wants to see false alarms. 1 would like to see less alerts from my stand point, and just a clearer

picture of the whole thing. I am not going to say it won't work, there is indication that there is a

possibility for it. But there is also room for in31)rovemetat as there are in the other sensors too.

192
Session II. Hazard Characterization

193
194
Session II. Hazard Characterization N93-19597

Wind Shear Hazard Determination

Mike Lewis, NASA Langley Research Center

PRECEDt;_(] _:',4:3;.: _3LArJK l',iOf FILMED

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211

Session II. Hazard Characterization N93-]9

Three-Dimensional Numerical Simulation of tile 20 June I_Y-)1,Orlando Microburst

Dr. Fred Proctor, NASA [.angley Research Center

213
THREE-DIMENSIONAL NUMERICAL SIMULATION

OF THE 20 JUNE 1991, ORLANDO MICROBURST

FRED H. PROCTOR

NASA-LANGLEY RESEARCH CENTER

Flight Management Division

HAMPTON, VA 23665

14 APRIL 1992

FOURTH COMBINED MANUFACTURERS' AND TECHNOLOGISTS'

WIND SHEAR REVIEW MEETING

214
OUTLINE OF

ORLANDO 20 JUNE 1991 SIMULATION

I. INTRODUCTION

A. TASS MODEL DESCRIPTION

B. INITIAL CONDITIONS

H. RESULTS OF 3-D CASE STUDY

A. DESCRIPTION OF MICROBURST EVOLUTION

B. COMPARISON WITH OBSERVATIONS

C. EVOLUTION OF F-FACTOR FIELDS

D. COMPARISON OF TEMPERATURE VS

F-FACTOR FIELDS

HI. SUMMARY

215
EXTENDED ABSTRACT

On June 20, 1991, NASA's Boeing 737, equipped with in-situ and lookahead wind-shear detection systems, made direct low-level penetrations (300-350

m AGL) through a microburst during several stages of its evolution. This

microburst was located roughly 20 km northeast of Orlando International AirpoM

and was monitored by a Terminal Doppler Weather Radar (TDWR) located about

10 km south of the airport. The first NASA encounter with this microburst (Event

#142), at ~2041 UTC, was during its intensification phase. At flight level, in-situ

measurements indicated a peak 1-km (averaged) F-factor of ~0.1. The second

NASA encounter (Event #143) occurred at -2046 UTC, about the time of

microburst peak intensity. It was during this penetration that a peak 1-km F-factor

of ~.17 was encountered, which was the largest in-situ measurement of the 1991

summer deployment. By the third encounter (Event #144), at ~2051 UTC, the

microburst had expanded into a macroburst. During this phase of evolution, an insitu 1-km F-factor of 0.08 was measured. Details of these encounters from the

perspective of on-board radar, in-situ observation, on-board infrared sensor and

TDWR are discussed by various authors elsewhere in the conference proceedings.

The focus of this paper is to examine this microburst via numerical simulation from

an unsteady, three-dimensional meteorological cloud model. The simulated highresolution data fields of wind, temperature, radar reflectivity factor, and precipitation are closely examined so as to derive information not readily available from

"observations" and to enhance our understanding of the actual event. Characteristics of the simulated microburst evolution are compared with TDWR and in-situ

measurements.

The model used in the simulation is the Terminal Area Simulation 1 (TASS),

which has been previously applied to a number of microburst case

studies. 2'3'4'5'6'7'8 Characteristics of the model are listed in Slide 1 and Tables 1

and 2. The initial conditions for this simulation are listed in Slide 2, and the input

sounding for ambient temperature, humidity, and wind is shown in Slide 3. The

ambient sounding, observed near the location and time of the microburst, indicates

a moist, convectively unstable environment with weak and variable winds.

Results from the simulation are shown in the remaining figures and are

summarized in the final slide. The results indicate a high-reflectivity (wet)

microburst of moderate intensity whose evolution and structure compare favorably

with observations. This microburst, which is generated from the simulated parent

216
storm, may be characterized by three phases of evolution: 1) an intensification

phase, 2) a peak-intensity phase, and 3) a macroburst phase. The intensification

phase is initiated by rain forming through collection-coalescence and is associated

with increasing values of hazard and velocity differential. According to the model

simulation, and verified from "observations", the strongest region of wind-shear

hazard at this time is in the northern region of the outflow. The first NASA

encounter of the actual microburst took place during this phase of evolution.

Several minutes later during the peak-intensity phase, a second surge of heavy

rain shifted the strongest hazard regions to the southern portion of the outflow.

According to the simulation this second surge was associated with melting of

graupel aloft and generated the overall strongest downdraft speeds and windshear. During this phase of development, the microburst was again encountered

by NASA (Event #143), and in-situ and model data show a complex asymmetric

F-factor field. The complex hazard field exists, even though the simulation shows

a nearly symmetric region of outflow. The model data also indicates that regions

of upflow and performance-increase (positive F-factor) are embedded within the

microburst outflow, as was true in an earlier case-simulation of another Florida

microburst 8. Hence, hazard regions may be asymmetric and complex even in the

weak ambient wind conditions typical of Florida's summer season. Following the

time of peak outflow and wind-shear hazard, the outflow continues to expand

becoming a macroburst, although with embedded microbursts. The model

simulation, in-situ (Event #144), and TDWR data indicate that the embedded

microbursts are of weaker magnitude than the primary microburst during intense

phase (at least true for this case study).

Localcorrelation between F-factor and either temperature drop or temperature gradient is not apparent in the data from the simulation. However, as

predicted by the empirical formula for maximum wind differential from temperature

drop 5'9, the simulated temperature drop of about 6°C at the surface corresponds

to the simulated peak wind change (at 70 m AGL) of 32 m/s. At flight level

(roughly 325 m AGL) and at 37 min simulation time, the maximum temperature

drop was 3.50 C, almost half the magnitude of the temperature drop at the ground.

Hence as shown in the axisymmetric experiment of wet microburst, the magnitude

of temperature drop is greatest near the ground and markedly decreases with

altitude 4,5.

217
REFERENCES

. Proctor, F. H., 1987: The Terminal Area Simulation System. Volume h

Theoretical formulation. NASA Contractor Rep. 4046, NASA, Washington,

DC, 176 pp. [Available from NTIS]

2. Proctor, F. H., 1987: The Terminal Area Simulation System. Volume i1:

Verification Experiments. NASA Contractor Rep. 4047, NASA, Washington,

DC, 112 pp. [Available from NTIS]

. Proctor, F. H., 1988: Numerical simulation of the 2 August 1985 DFW

microburst with the three-dimensional Terminal Area Simulation System.

Preorints Joint Session of 15th Conf. on Sever_ Local Storms and E_qhth

Conf. on Numerical Weather Pred., Baltimore, Amer. Meteor. S,c.,

J99-J102.

J Proctor, F. H., 1988: Numerical simulations of an isolated microburst. Part

h Dynamics and structure. J. Atmos. Sci., _ 3137-3160.

5. Proctor, F. H., 1989: Numerical simulations of an isolated microburst. Part

Ih Sensitivity experiments. J. Atmos. Sci., _ 2143-2165.

6. Proctor, F. H., 1990: Three-dimensional numerical simulation of a Florida

microburst: The 7 July 1990 Orlando event. Airborne Wind Shear Detection

and Warning Systems, Third Combined Manufacturers' and Technologists'

Airborne Wind Shear Review Meeting, Hampton, VA, NASA Conf.

Publication 10060, Part I, 81-103. [Available from NTIS]

7. Proctor, F. H., and R. L. Bowles, 1990: Three-dimensional simulation of the

Denver 11 July storm of 1988: An intense microburst event. Preprints, !6th

Conf. on Severe Local Storms and the Conf. on Atmos. Electricity,

Kananaskis Park, Amer. Meteor. S,c., 373-378.

. Proctor, F. H., and R. L. Bowles, 1992: Three-dimensional simulation of the

Denver 11 July 1988 microburst-producing storm. Accepted for publication

in Meteorol. and Atmos. Phys., 47.

. Proctor, F. H., 1989: A relation between peak temperature drop and

velocity differentia/in a microburst. Preprints Third International Conf. on

the Aviation Wea. System., Anaheim, Amer. Meteor. S,c., 5-8.

218
TERMINAL AREA SIMULATION SYSTEM

(TASS)

[ALSO KNOWN AS THE NASA WINDSHEAR MODEL]

0 3-D TIME DEPENDENT EQUATIONS FOR

COMPRESSIBLE NONHYDROSTATIC FLUIDS

O PROGNOSTIC EQUATIONS FOR 11 VARIABLES

1. 3-COMPONENTS OF VELOCITY

2. PRESSURE

3. POTENTIAL TEMPERATURE

4. WATER VAPOR

5. LIQUID CLOUD DROPLETS

6. CLOUD ICE CRYSTALS

7. RAIN

8. SNOW

9. HAIL/GRAUPEL

O lst-ORDER SUBGRID TURBULENCE CLOSURE

WITH RICHARDSON NUMBER DEPENDENCY

O SURFACE FRICTION LAYER BASED ON MONINOBUKHOV SIMILARITY THEORY

O OPEN LATERAL BOUNDARY CONDITIONS

ALLOWING MINIMAL REFLECTION

O BULK PARAMETERIZATIONS

MICROPHYSICS

OF CLOUD

SLIDE 1

219
Table 1. Salient CharacterisUcs of TASS 2.4

Compressible, nonhydrostatic equation set

Non-Boussinesq formulation for density variations

Three-dimensional staggered grid with stretched vertical spacing

Movable, storm-centering mesh

Explicit time-split, second-order, Adams-Bashforth time differencing

and second-order quadratic-conservative space differencing for

velocity and pressure

Fourth-order quadratic-conservative space differencing and

third-order Adams-Bashforth time differencing for temperature

and water-vapor equations

Third-order time/space differencing with upstream-biased quadratic

interpolation for liquid and frozen water substance equations

Radiation boundary conditions applied to open lateral boundaries

Filter and Sponge applied to top four rows in order to diminish

gravity wave reflection at top boundary

No explicit numerical filtering applied to interior points

Surface friction layer based on Monin-Obukhov Similarity theory

Smagorinsky subgrid-turbulence closure with Richardson number

dependence

Liquid and ice-phase microphysics

Inverse-exponential size distributions assumed for rain, hail/graupel,

and snow

Raindrop intercept function of amount of rainwater 5

Snow treated as spherical, low-density graupel-like snow particles

Wet and dry hail growth

Accumulated precipitation advected opposite of grid motion, so as to

remain ground relative

_0
Table 2. Cloud Microphysical Interactions

Accretion of cloud droplets by rain

Condensation of water vapor into cloud droplets

Berry-Reinhardt formulation for autoconversion of cloud droplet

water into rain

Evaporation of rain and cloud droplets

Spontaneous freezing of supercooled cloud droplets and rain

Initiation of cloud ice crystals

Ice crystal and snow growth due to riming

Vapor deposition and sublimation of hail/graupel, snow, and cloud

ice crystals

Accretion by hail/graupel of cloud droplets, cloud ice crystals, rain,

and snow

Contact freezing of supercooled rain resulting from collisions with

cloud ice crystals or snow

Production of hail/graupel from snow riming

Melting of cloud ice crystals, snow, and hail/graupel

Shedding of unfrozen water during hail wet growth

Shedding of water from melting hail/graupel and snow

Conversion of cloud ice crystals into snow

Accretion by snow of cloud droplets, cloud ice crystals, and rain

Evaporation or vapor condensation on melting hail/graupel and snow

221
Orlando, FI, 20 June 1991, Simulation

INPUT DATA / ASSUMPTIONS

PHYSICAL DOMAIN SIZE

O X,Y: 15 KM x 15 KM

O Z: 18 KM

COMPUTATIONAL RESOLUTION

O HORIZONTAL

O VERTICAL

- 150 M (103 X 103 GRID POINTS)

- 70 M NEAR GROUND STRETCHING TO

440 M AT 18 KM (72 LEVELS)

CONVECTIVE INITIATION AT MODEL TIME ZERO

O SPHEROIDAL THERMAL IMPULSE

O DIMENSIONS- 7 KM HORIZONTAL x 1.25 KM VERTICAL

O AMPLITUDE - 1.5 ° C

SOUNDING OBSERVED NEAR TIME AND LOCATION OF STORM

(from special rawinsonde launch 2035 UTC)

SUB-CLOUD HUMIDITY AND TEMPERATURE MODIFIED USING

NASA AIRCRAFT MEASUREMENTS TAKEN NEAR THE TIME AND

LOCATION OF THE STORM

SLIDE 2

2_
Orlando, FI, 20 June 1991

Special sounding - 2035 UTC

t 00

.; /\ k._" rk, x/ \ Temperature /'X \/ \ xx' \

400

8oo

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l=

W 300

O_

W

r_

n

TEMPERATURE OC

HORIZONTAL

WINDS

SLIDE 3

223
20 JUNE 1991 MICROBURST

O MICROBURST ENCOUNTERED BY NASA AIRCRAFT 3 TIMES

1. FIRST ENCOUNTER (~2041 UTC) DURING INTENSIFICATION STAGE (EVENT #142).

2. 2ND ENCOUNTER (~2046) DURING PEAK INTENSITY

(EVENT #143).

3. 3RD ENCOUNTER (-2051) DURING MACROBURST

STAGE (EVENT #144).

O DURING INTENSIFICATION PHASE, MODEL AND OBSERVED

RESULTS SHOW STRONGEST SHEAR AND DOWNFLOW

IN NORTHERN REGION OF OUTFLOW.

O MODEL AND OBSERVED RESULTS INDICATE MAXIMUM

SHEAR AND DOWNFLOW IN SOUTHERN REGION OF

OUTFLOW DURING PEAK INTENSITY.

O MODEL RESULTS INDICATE MICROBURST INITIATED

BY RAIN FORMED THROUGH COLLECTIONCOALESCENCE.

O ACCORDING TO MODEL SIMULATION, THE MICROBURST IS

ENHANCED DURING PEAK-INTENSITY PHASE BY A

SECOND SURGE OF PRECIPITATION.

O THIS SECOND SURGE -- ASSOCIATED WITH RAIN

FROM MELTING GRAUPEL -- GENERATES STRONGEST

SHEAR AND DOWNDRAFT SPEEDS IN SOUTHERN

SECTOR.

224
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225
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TASS 3-D SIMULATION -- ORLANDO MICROBURST

3-D PERSPECTIVE OF STORM

10 DBZ RADAR REFLECTIVITY SURFACE VIEWED FROM NE

AT 36 MIN (2045 UTC)

STORM TOP A T 14 KM

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SUMMARY OF ORLANDO SIMULATION

O WET MICROBURST WITH HAZARDOUS WIND

SHEAR

O GOOD AGREEMENT BETWEEN SIMULATION AND

OBSERVATION OF EVENT

O COMPLEX MICROBURST STRUCTURE:

1. MULTIPLE DOWNDRAFT SURGES

2. MULTIPLE DIVERGENCE CENTERS

EMBEDDED WITHIN OUTFLOW

3. AREAS OF UPWARD MOTION EMBEDDED

WITHIN OUTFLOW

4. NONCLASSIC OUTFLOW AND F-FACTOR

PROFILES

O MODELED AV FUNCTION OF ALTITUDE AND

DIRECTION OF SEGMENT: PEAK AV OF 32.0 M/S

ALONG EAST-WEST SEGMENT AT 70 M AGL .VS.

21.1 M/S ALONG SIMULATED TDWR RADIAL (NNE -

SSW SEGMENT AT 190 M AGL)

O PEAK TEMPERATURE DROP OF ~6 ° C OCCURS AT

TIME OF MICROBURST PEAK INTENSITY

O SIMULATED RAINFALL RATES EXCEED 5 IN/HR AND

1-Km AVERAGED F-FACTORS EXCEED .15

O REGION OF PEAK WIND-SHEAR HAZARD DOES NOT

CORRELATE LOCALLY WITH PEAK TEMPERATURE

DROP

242
Three.Dimensional Numerical Simulation of the 20 June 1991, Orlando Microburst

Questions and Answers

Q: Not recorded

A: Fred Proctor (NASA Langley) - All my liclds are assumed to be horizontally homogenous,

in other words, they are constant horizontally but they vary in the vertical. There have been a lot

of studies that have shown that stomls are really detemfined by the vertical structure of the

atmosphere. That is really what is playing a larger role in creating all these complex fields. The

winds change direction with height as well as the tempe_uure and humidity and so forth. Exacdy

how it's doing that I can't answer.

Q: (Unknown) - Have you correlated the DT measurements you have with the downdraft

component of the F-factor as opposed to tile tot:d?

A: Fred Proctor (NASA l.,angley) - I haven't looked at that; I can't tell you.

Q: Kim Elmore (NCAR) - Did the downdrafl initiate at the minimum QE level, since it was an

area of a lot of coalescence? I was curious as to how deep it was?

A: Fred Proctor (NASA Langley) - 1 haven't looked at that yet, but usually in storms of this

type I find them to fonn really close to the freezing level, wherever that may be.

Q: Kim Elmore (NCAR) - But it is still the evaporation of rain drops that is the primary driving

force?

A: Fred Proctor (NASA Langley)- In this case yes.

Q: Kim Elmore (NCAR) - Is that common fi_r the southeastern storms?

A: Fred Proctor (NASA l,angley) - i would say it is probably a primary effect in most of the

storms, but certainly not in all of them. You could get one in an atmosphere that was somewhat

stable, relative to these, if you had relatively heavy rain fall rates, then you could probably drive

them by mass loading.

Q: Kim Elmore (NCAR) - 1 was going to ask you how much of a role precipitation loading

played?

A: Fred Proctor (NASA Langley) - I did not do that analysis for this storm, but I did for the

one I presented at the last conference and the mass loading was a pretty small percentage of the

total. Even though, in that storm, we had rainfall rates of 9 or 10 inches an hour. That was the

Orlando 1990 Storm.

243
244
Session II. Hazard Characterization _ 9 _ - _ _ _ C_ !_ .-. _j ,j

A "Numerical Field Experiment" Approach for Determining Probabilities of Microburst Intensity

Dr. Kelvin Droegemeier, University of Oklahoma

Terry Zweifel, Honeywell
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247

The

Numerical Field Experiment

Methodology

Use a Numerical Model to Simulate a Large

Population of Physically Plausiable

Scenarios Similar to What Might be Anticipated

During a Field Observing Program

249
Goals of Study

• Better understand how various physical

parameters interact to determine microburst

intensity

• Determine probabilities of microburst occurrence

under a variety of conditions

• Validate results against observations

_0
Experiment Design

• Axisymmetric Numerical Cloud Model

• Warm Rain Microphysics

• No Ambient Wind

• Zero Ambient Humidity

• Simulate Only the Sub-Cloud Region

• Continuous Influx of Rainwater at Model Top

(Cloud Base)

251
Distribution

J

IS km

Radial Coordinate

_2
Parameter Space

Cloud Base Height (0.5, 1, 2, 3, 4 km AGL)

Sub-Cloud Lapse Rate (70, 80, 90, 100% D.A.)

Surface Temperature (55, 65, 75, 85, 95, 105 F)

Cloud Base Reflectivity (20, 30, 40, 50, 60 dBz)

Rainshaft Radius (0.5, 1.0, 2.0 km)

All Combinations _ 1800 Simulations

• Each Run for 20 Minutes

• Max's & Min's of all Fields Saved Every 10 s

253
Model Validation

• NCSA Bakeoff- Tested Against Some 15 Codes

• Independent Tests with Krueger Axisymmetric

and Klemp & Wilhelmson 3-D

Umax Wmin

Droegemeier

Krueger

46.7 -35.0

45.1 -34.8

Droegemeier

K&W3-D

52.5 -37.9

47.4 -37.9

264
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Time (sec)

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286
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1200

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Perturbation Pressure (Pa)

258
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Perturbation Pressure (Pa)

259
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i

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261
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362
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• " _ _ _."/_'_.',,.'. _ _._,."_, C",_.." !.'.. i

•. °.i _'_ _'G,:...._:-%: * .. " • _

0 "* "_'- -- --_;*"*--_--'at!"-"., • . , , .

0 10 20 30 40 50

Radial Wind Speed (m s"t)

263
70

60

All 1804 $1mulationa a

20 mmmm.mm_Q °-i °

I(0 I0 20

hdlad Wbd Speed(mr j)

50

3

A 2.3

1.5

]

0.3

0

|

I0

All 1800 Simulations d

RIdild Wind Speed (n r n)

_0

i

IHO Shumimtlou !)

m =,, _ _* *'" "

j u I

Redid _1 Spud (m r _)

115

,os

1

l°

73

[-

! °

51

4$

0

L n

All 1800 Sinwhdlens e

• m eo. _ • _o • re.e, • • ee eo e..

e_e o_o _tte w * e • oeee • •

we_ ee. • *a ,...o_, • •

m.,,pm_t.e_-_- e* ...

mm .mw,_ e .o. • •

Rdlal WIeM Speed (m r')

30

ilo

o

v

I

M 7O

tt

0

All INO ,qimmlmtimu C

-- - m moe -. -...,

Wind Speed(m r ')

30

264

OF POOR QUP=Li.'P,,'
7O

60

=a'o 50

U

c= 3o

U

10

t

All 1800 Simulations a

i L - m_o.,

._o -;s -;o ._ .... O

Temperstsr, Detldt (K)

I

i

QI io$

i'

tl 0.$

0 " '

-25

All llMle Simmlatimm

._o .is ._o -_

Tempm_tsre Ddldt (K)

d

m_i 4

_3

| z

m

|,

¢.)

o

.25

• i . i ,, I . L ,

All llMle Simtdatlom b

• ,*oo°ie*mtm _ lilt I

Tmmlx.mmm _ (K)

il5

I U

ii 55

45

.2S

All 180e Simwlsttem e

*,_pe e_dmms_ _i* m qml _ _ _ -

_. omq,_ t

.is .io ":,' 'o

11o

i Ioo

[

q_ 6o

All 18M Si--,_l_bm C

, • . °, e_ ._. tll|

.;_ .is .io ._ '

Tmmpermmm _ (K)

266
Table 5. Slope of linear fit between radial and downdraft wind speeds for

all 1800 simulations as a function of rainshaft radius (R) and cloud base height (H), the ratio

of which is defined as the aspect ratio (R/H).

Rtinlhaft Radius R (kin) Cloud Base H (kin) Aspect Ratio (R/H) Slope

2.0 0.5 4.00 -0.60

1.0 0.5 2.00 -0.78

2.0 1.0 2.00 -0.75

2.0 2.0 1.00 -0.90

1.0 1.0 1.00 -0.93

0.5 0.5 1.00 -0.97

2.0 3.0 0.67 -0.95

2.0 4.0 0.50 -0.97

1.0 2.0 0.50 - 1.03

0.5 1.0 0.50 -1.10

1.0 3.0 0.33 - 1.04

1.0 4.0 0.25 -1.06

0.5 2.0 0.25 -1.16

0.5 3.0 0.17 -1.17

0.5 4.0 0.13 -1.17

2ee
Table 6. Probabilities (%) of radial wind speed classified according to model input parameters for

the sel of 1800 simulations.

ALL indicates that the associated parameter varies among all values used in the Subset.

Input Variable Wind Speed

LR Tsfc Refl CB Rad _>10.3ms 1 >12.9ms "1 >_15.4ms -1 >_lS.0ms "1

(> 20 kts) (> 25 kts) (>_ 30 kts) (> 35 kts)

ALL ALL ALL ALL ALL 44.50 35.17 28.22 22.61

100 ALL ALL ALL ALL 59.11 45.56 36.89 29.11

90 AIL ALL ALL ALL 45.33 37.11 29.78 24.00

80 ALL ALL ALL ALL 39.56 31.11 24.44 19.78

70 ALL ALL ALL AlL 34.00 26.89 21.78 17.56

ALL 105 ALL ALL ALL 48.67 40.67 33.33 26.33

ALL 95 ALL ALL ALL 48.00 39.33 31.33 26.00

AlL 85 ALL ALL ALL 46.33 37.00 29.67 25.33

ALL 75 ALL ALL ALL 45.33 34.33 28.00 23.00

ALL 65 ALL ALL ALL 42.33 31.67 25.33 20.00

ALL 55 ALL ALL ALL 36.33 28.00 21.67 15.00

ALL ALL 60 ALL ALL 98.89 85.28 78.61 70.56

ALL ALL 50 ALL ALL 74.17 62.50 46.94 35.00

AlL ALL 40 ALL ALL 36.39 22.50 13.61 7.22

ALL ALL 30 ALl., AlL ll.ll 5.28 1.94 0.28

ALL ALL 20 ALL ALL 1.94 0.28 0.00 0.00

AH., ALL ALL 4.0 ALL 49.44 45.56 39.72 34.44

AIJ., ALL ALL 3.0 ALL 53.06 46.39 41.11 35.56

ALl., ALl., ALL 2.0 ALL 56.11 46.94 38.33 31.67

ALL ALl, ALl., 1.0 ALL 45.00 31.67 21.94 11.39

ALL ALL ALL 0.5 ALL 18.89 5.28 0.00 0.00

ALL ALl, ALL AlL 2.0 52.00 43.33 35.50 29.00

ALL ALL ALL ALL 1.0 45.17 37.33 29.33 25.17

ALL ALl., ALL ALL 0.5 36.33 24.83 19.83 13.67

LR = Lapse Rate (% of Dry Adiabatic)

Tsfc = Surface Temperature (F)

Refl = Reflectivity Factor (dBz)

CB = Cloud Base Height (kin)

Rad = Rainshaft Radius (km)

267
(q6) _Cllllquqo.sd
Table 7. Probabilities (%) of radial wind speed classified according to model input parameters for

a subset of 114 simulations defined below.

ALL indicates that the associated parameter varies among all values used in the Subset.

InputVariable Wind Speed

LR Tsfc Refl CB Rad _>10.3 m s-I > 12.9 m s"I > 15.4 m s-I > 18.0 m s-I

(>_20 kts) (> 25 kts) (> 30 kts) (> 35 kts)

ALL ALL ALL ALL ALL 60.53 41.23 32.46 21.05

100 ALL ALL, ALL ALL 60.53 41.23 32.46 21.05

ALL 105 ALL ALL ALL 60.53 42.11 34.21 21.05

ALL 95 ALL ALL ALL 60.53 42.11 31.58 21.05

ALL 85 ALL ALL AIL 60.53 39.47 31.58 21.05

ALL ALL 60 ALL ALL I00.00 85.19 66.67 55.56

ALL ALL 50 ALL ALl, 66.67 55.56 48.15 22.22

ALL ALL 40 ALL ALL 55.56 22.22 22.22 11.11

ALL ALL 30 ALL ALL 28.57 14.29 0.00 0.00

ALL ALL 20 ALL ALL 25.00 0.00 0.00 0.00

ALL AlL AlL 2.0 ALL 85.71 64.29 57.14 42.86

ALL ALl., ALL 1.0 ALL 61.54 38.46 33.33 15.38

ALL ALL ALL 0.5 ALL 27.27 15.15 0.00 0.00

ALL ALL ALL ALL 2.0 60.00 46.67 33.33 26.67

ALL ALL ALL ALL 1.0 66.67 47.22 36.11 25.00

ALL ALL ALL ALL 0.5 54.55 27.27 27.27 9.09

Lapse Rate = 100% Dry Adiabatic

85 F g SurfaceTemperature < 105 F

20 dBz < Reflectivityg 60 dBz

0.5 km <:Cloud Base Height < 2.0 km

0.5 krn <:Rainshaft Radius g 2.0 km

Urad> 5 ms'l

269
Table l. Coeflqct_ts of muldv_iate linear regression equations for the radial wind speed (m s"1)

for aLl1800 simulations ( 1799 degrees of freedom), using various combinations of predictors.

Shown are the predictors, the estimated standard dev/adon of model error (m s"i), the leading

constant,and the five regressioncoeffic/ents. The umtsof the regressorsareas shown in Table 3.

except for the lapse rate. which is given by its decimal equivalent.

Predictor(s)

LR

Tsfc

dBz

CBHt

lhtd

all, LR

dSz, Talc

dIlz, CBHt

dBz, Rid

LR, Tsfc

LR, CBHt

LR, Rad

CBHt, Tsfc

CBHt, Rzd

........ . ...... ....

Tste, Rsd

LR, Talc, CBHt

LR, Tsrc. dBx

LR, Tsfc, Rad

LR, CBHt, dBz

LR, CBHt, Rad

LRo dBz, Rsd

LR, Talc, CBHt, Rsd

LR, Talc, CBHt, dBz

LR, Tsfc, ihtd, dBz

LR, CBHt, Rad, dBz

LR, Tsfc, CBHt,

dBz. Rsd

%

Variance

Explained

3.7 9.8

1.7 9.9

60.4 6.3

5.4 9.7

4,6+ 9.8

64.1 6.0

62.1 6.2

65.8 5.9

65.0 5.9

5.5

9.2

8.3

Est. Std. Rtqp'ession Coz4flciems

.......... 1...............

Modd E,ror "Con_ LR

-3.40 17.31 ....

5.15 -- 0.08 -- -- --

-t0.69 -- -- 0.55 -- --

7.50 -- -- -- 1.82 --

7.30 .... 3.43

i

•, .... . : ,, i. :,'I" ':' ' ,':::_l_'_:::'i:_i::'_'_ii:"::?l

-25.38 17.31 -- 0.55 -- --

-16.83 -- 0.08 0.55 -- ---

-14.51 -- -- 0.55 1.82 --

- 14.69 -- _ 0.55 -- 3.43

......... . ............................... -! ....... i- .......

9.7 -9.56 17.31 0.08 -- -- --

9.5 -7.22 17.31 -- -- 1.82 --

9.6 -7.41 17.31 -- -- -- 3.43

............ ;.h'"--'-'"

3.43

3.43

........ "+5........ L-3"3" .....-'"

I0.0 9.5 3.48 -- -- -- 1.82

6.3 9.7 !. 14 -- 0.08 -- --

, ,, , ,, ,, i

10.9 9.5 -13.38 17.31 0.08 -- 1.82 --

65.9 5.9 -31.57 17.31 0.08 0.55 -- --

10.1 9.5 - i 3.57 17.3 ! 0.08 -- -- 3.43

69.6 5.5 -29.22 17.31 -- 0.55 1.82 --

13.7 9.3 -11.23 17.31 _ -- 1.82 3.43

68.7 5.6 -29.41 17.31 -- 0.55 -- 3.43

9.2 -17.39 17.31 0,08 -- 1.82 3.43

5.4 -35.39 17.31 0.08 0.55 1.82 --

5.5 -35.57 17.31 0.08 0.55 -- 3.43

5.1 -33.23 17.31 -- 0.55 1.82 3.43

4.9 -39.40 17.31 0.08 0.55 1.82 3.43

15.5

71.3

70.5

74.1

75.9

27O
Table 9. As in Table 8, but assuming that the cloud base height is known (359 degrees of

freedom) and takes the values shown in the left column.

Cloud Base

Height

0.$ km

1.0 km

2.0 km

3.0 km

4.0 km

%

Variance

Explained

94.3

0.3

0.9

87.7

5.4

j=.

,.?i, _

95.8

1.4

1.2

86.5

6.6

92.7

4.2

1.7

79.9

6.9

89.3

5.8

2.6

74.8

6.0

_.::$ii<..!:i i. "

84.4

8.4

3.2

68.2

4.6

ulum I ilUn

Est. Std. Regression Coefficients

Dev. of ...............................................

Model Error ........ ....... ....... '.................. [ , ,.. .... .....

0.8

3.4

3.4

1.2

3.3

1.2

6.0

6.0

2.2

5.8

2.6

9.6

9.7

4.4

9.4

4.0

l 1.7

11.9

6.1

11.7

5.3

12.8

13.2

7.6

13.1

-6.61

5.35

5.17

-2.30

5.20

-17.31

4.47

6.89

-5.85

7.07

-37.66

-2.38

6.94

- l 1.75

8.04

-52_6

-8.46

4.46

- 15.86

8.08

-63.63

-15.99

2.29

-17.66

8.14

1.56

1.56

6.46

6.46

17.90

17.90

25.98

25.98

34.6

34.6

0.02

0.02

i I '

0.04

0.04

0.07

0.07

0.11

0.11

0.14

0.14

0.23

0.23

0.40

a

m

0.40

0.61

0.61

034

!

0.74

0.78

0.78

m

i

m

m

i

m

m

!

m

m

w

m

m

m

!

!

w

w

1.27

1.27

2.48

2.48

4.11

4.11

4.75

4.75

4.$7

4.57

271
TableI0.As inTable8,butassumingthatthereflectivity factorisknown (359degreesof

freedom)and takesthevaluesshown intheleftcolumn.

Radar

Reflectivity

% Est. Std.

Vsrisnce Dev. of

Explained Model Error

60.2 1.5

20 dBz 24.8 2.0

1.7 2,3

6,6 2.3

27.2 2.0

56.3 2.5

30 ¢IBz 37.6 3.0

1.8 3.8

0.0 3.8

16.8 3.4

., ,!,

56.5 3.7

40 dBz 30.1 4.6

4.7 5.4

2.5 5.4

19.2 4.9

66.7 4.0

50 dBz 9.6 6.5

9.3 6.5

32.2 5.6

15.7 6.3

":%,:, ;,

83.2 3.8

60 dBz 1.4 9.3

7.2 9.0

65.3 5.5

9.3 8.9

Regression Coefficients

Const LR i Tsfe dBz CBHt

.9.08 10.34 0.02 -- -0.46

-6.39 10.34 -- -- --

0.98 -- 0.02 --

3.37 .... 0.46

0.13 ....

-18.04 20.73 0.03 -- -0.01

-12.76 20.73 -- -- --

2.30 -- 0.03 -- --

4.89 .... 0.01

!.96 ....

-2&22 26.84 0,07 -- 0.67

-13.77 26.84 -- -- --

3.49 -- 0.07 -- --

7.62 -- -- -- 0.67

4.56 ....

-21.52 19.01 0.12 -- 3.04

-0.27 19.01 -- -- --

6.13 -- 0.12 -- --

9.51 -- -- -- 3.04

I0.81 ....

-13.11 9.65 0.14 -- 5.85

16.17 9.65 -- -- --

12.69 -- 0.14 -- --

12.08 -- _ -- 5.85

19.07 ....

Rad

1._

I._

2.49

2._

3.U

3.85

4.M

4.36

4.5

4.55

272
Table 12. As in Table 8, but assuming that the rmnshaft radius is known (599 degrees of freedom)

and takes the values shown in the left column.

Ralnshafl

Radius

0.5 km

1.0 km

2.0 km

%

Variance

Explained

77.9

4.6

0.9

69.7

2.7

77.1

4.0

1.5

65.9

5.7

_iiiJi:::iL!i:i_ii!i:i::LL :: mi:i:.

75.7

3.6

3.2

59.9

8.9

imm

Est. Std. Regression Coefficients

Model Error ..... ! ...... 1d,:.....

3.8

7.8

7.9

4.4

7.9

4.9

I0.0

10.2

6.0

9.9

5.4

10.7

10.7

6.8

10.3

-29.05 15.27

-4.60 15.27

4.86

-10.41 --

6.25

-37.28 18.33

-3.88 18.33

5.81 m

- I 1.80 --

7.69

.39.82

-1.73

4.78

-9.85

8.55

18.35

18.35

Rad

0.05 0.47 1.02 --

0.05 -- -- --

-- 0.47 -- --

-- -- 1.02 --

:.:::,:::..._._.:.:.:.;.:.: :-..:._:.;.:.;.:.:.:::

0.07

0.07

0.II

0.II

I

0.59

I

0.59

1.91 --

1.91 --

0.59

0.59

2.53 --

2.53 --

273
Table II. As in Table 8. but assuming that the surface temperature is known (299 degrees of

Freedom)and takes the values shown in the left column.

%

Surface Variance

Temperature Explained

75.1 4.1 .28.95 18.10

$$0 F 6.3 7.8 -6.21 18.10

63.8 4.9 -9.07 --

2.0 8.0 7.29 --

3.0 8.0 6.56 --

76.2 4.3 .30.71 17.67

65e F 5.0 8.6 -4.81 17.67

63.8 5.4 -9.73 --

3.5 8.7 7.48 --

3.9 8.7 6.97 --

75 ° F

85° F

95 ° F

I_°F

76.8 4.6

4.1 9.4

63.2 5.8

5.0 9.4

4.5 9.4

77.4 4.9

3.4 I0.1

62.5 6.3

6.4 I0.0

5.1 I0.0

Est. Std. i Regreuion Coefficients '

Oev.of l.......................... I......................... r.................. i.................... ,............................

ModelError ! Const I LR [,Tsfc[ dBz CBHt I Red

-32,42 17.30

-3.61 17.30

-I0.41 --

7.58 --

7.32

.34.07 16.93

-2.53 16.93

-11.08 --

7.60 --

7.53 --

77.8 5.2 -35.81 16.89

3.0 10.8 -1.85 16.89

61.4 6.7 -I 1.64 --

7.4 10.5 7.54 --

5.6 10.6 7.68 --

m

N

0.46 0.90 2.24

0.46 -- __

-- 0.90 --

-- -- 2.24

0.50 1.30 2.77

0.50 -- --

-- 1.30 --

-- -- 2.77

0.54 1.68 3.24

0.54 -- --

-- 1.68 --

-- -- 3.24

0.$7 2.03 3.72

0.57 -- --

-- 2.03 --

-- -- 3.72

0.60 2.37 4.14

0.60 -- --

-- 2.37 --

-- -- 4.14

0.63 2.65 4.50

0.63 -- --

-- 2.65 --

-- -- 4.50

77.7 5.5 -37.43 17.00

2.7 I 1.4 - 1.42 17.00

60.3 7.3 -12.18 --

8.7 ! l.O 7.47 --

6.0 11.2 7.78 --

274
Table 13. As inTable 8,butassuming thatthelapserateisknown (449 degrees offreedom) and

takesthevaluesshown m the Leftcolumn.

Ambient

Lapse

Rate

%

Variance

Explained

i iii

71.9 5.0

70% of 2.0 9.3

D.A. 64.6 5.6

0.7 9.4

4.6 9.2

74.7 5.0

80% of 1.9 9.8

D.A. 66.2 5.7

2.2 9.8

4.3 9.6

7&S 4.7

90% of 1.7 I0.I

D.A. 66.0 6.0

6.4 9.9

4.6 9.9

100% of

D.A.

82.8 4.1

1.5 9.8

55.0 6.6

20.7 8.8

5.6 9.6

i

Est. Std. Regression Coefficients

-23.63 -- 0.08 0.58 0.62 3.22

2.73 -- 0.08 -- -- --

- 12.29 -- -- 0.58 -- --

7.72 -- -- -- 0.62 --

5.26 .... 3.22

-25.10 -- 0.08 0.61 1.15 3.28

3.81 -- 0.08 -- -- --

-12.44 -- -- 0.61 -- --

7.79 -- -- -- 1.15 --

6.38 .... 3.28

-26.20 -- 0.08 0.62 2.01 3.51

5.49 -- 0.08 -- -- --

- 11.62 -- -- 0.62 -- --

7.55 -- -- -- 2.01 --

7.66 .... 3.51

.......... , i I:III:S:+::::

-23.78 -- 0.07 0.51 3.50 3.73

8.57 -- 0.07 -- -- --

-6.38 -- -- 0.51 -- --

6.92 .... 3.50 --

9.92 .... 3.73

i

275
Preliminary Comparison

with JAWS Observations

• Model Data Subset Satisfying the Following:

U _>10 ms-1

2.0 km < Cloud Base <_4.0 km

75 F < S fc Temp < 95 F

20 dBz < Reflectivity < 40dBz

90% D.A. < Lapse Rate < 100% D.A.

0.5 km_ Rainshaft Radius <_2.0 km

• 72 Simulations (vs 186 JAWS events)

2"/0
Table 6. Probabilities of horizontal wind speed computed from the model dam and JAWS

observations.

Wind Speed

Damset _>20 kts > 25 kts >_30 kts >- 35 k-ts

JAWS Observations 98.39 76.88 42.47 23.12

All 1800 Simulations 44.50 35.17 28.22 22.61

tModel JAWS Subset 98.61 66.67 40.28 25.00

t Cri_ia: radial wind _ 10 m s- 1

2.0 < cloud base height < 4.0 km

75 F < surface temperature < 95 F

20 dBz g cloud base reflectivity _ 40 dBz

90% dry adiabatic _; lapse rate < 100% dry adiabatic

0.5 kmg rainshaft radius < 2 km

(72 cases)

277
I

_,._\,I CqJ / |

|

(_5) _llllqeqoJd

278
(_.s m) p_mds pu!__ lelPe_

279
Preliminary Conclusions

• The Model Solution Space Contains Considerable

Variability

• Solution Behavior is Physically Consistent

• Reflectivity is the Dominant Influence Most of

the Time in Determining Radial Wind Speed

• Gross Statistical Comparisons with JAWS

are Encouraging

• The "Numerical Field Experiment" Approach

Appears Well-Suited to this Problem

• Several Avenues for Practical Application

280
Ongoing Work

• Inclusion of Ice Processes and Shallow Stable

Layers

• "Pointwise" and Overall Statistical Comparisons

with JAWS and Orlando TDWR OT&E Data

• Refinement of Parameter Space

• Evaluation of Probability Calculations for

Operational Forecasting

• Further Development of Parametric Relationships

Between Model Outputs and Inputs

281
A "Numerical Filed Experiment" Approach for

Determining Prohabililiesof Microburst Intensity

Questions and Answers

Q: Kim Elmore (NCAR) - 1 am fascinated by the work you did, and specifically how much of a

predictor reflectivity was in general. For the equivalent JAWS simulations, how good of a

predictor was reflectivity for intensity?. Do you remember?

A: Kelvin Droegemeier (University of Oklahoma) - We haven't actually done that break down

for the JAWS data yet. Based on the other results, i would say it was probably a very strong

influence.

Kim Elmore (NCAR) - Our experience in JAWS was that it wasn't a very good predictor of the

outflow we would see. That was one of the major conclusions. Reflectivity, for the JAWS data,

was not a good predictor.

Kelvin Droegemeier (University of ()klahoma) - Ijust had a student finish a Master's Thesis on

a study of Orlando cases. What we have found and what tended to make that conclusion seem

plausible in light of the fact that the reflectivity factor was the same and apparently similar

environments, was the fact that low level effects like low level inversions in stable air change the

outflow intensity. It does not take much stabilization in low levels to really change the outflow

intensity. In fact you will see that if you stay around and look at the animation sequence. Now I

know Fred and others of us who run models have actually dropped globs of rain into stable air for

a long time. What this student did, for the first time, with a 3-D cloud simulation with ice,

showed that the storms themselves forming beneath the low level stable air, were virtually

unaffected by it. Once the rain came down and the ot, tflow hit, that is when the radial winds were

really diminished by virtue of the stable air.

Kim Elmore (NCAR) - We have seen, when gust fronts go by, that often once you stabilize a

relatively deep part of the boundary layer that they do not make it through any more.

Kelvin Droegemeier (University of Oklahoma) - Yes; it doesn't have to be but maybe four or

five hundred meters. It can be pretty shallow. One of the limitations of our study is that we did

not put in the shallow stable layer. If you consider the parameters, you have to figure how thick

is a layer and how cold is it. That is two more parameters and that would run it up to 20,000

simulations.

Kim Elmore (NCAR) - In JAWS, we also did not stratify the cases as to was there cold surface

layer or not.

Kelvin Droegemeier (University of Oklahoma)- That is a tough thing to do. Usually they just

skim the mesonet and you just never know.

Kim Elmore (NCAR) - Our gut feeling was; once we had a cold surface layer, if it had been

around long enough to deepen, however much it had to deepen, and we did not even really know

how much that was, that tended to shut it off.

282
Kelvin Droegemeier(University of Oklahoma) - I completely agree, I think that is the

controlling influence for attenuating that stuff.

Q: Brac Bracalente (NASA Langley) - The reflectivity values you referred to are at the cloud

level right at the high altitude where the rain first starts to fall. Did you do any correlating with

the reflectivity levels at the outflow region? We have found that the peak shear did not

necessarily occur where the heaviest rain was.

A: Kelvin Droegemeier (University of Oklahoma) - No, we have not done that but we could.

In fact. if you stay around for the video tape you will see that the reflectivity near the ground or

not to far above the ground is less than it is at cloud base height. I should have mentioned, in the

absence of having ice in this case, we are assuming that once the precipitation falls below cloud

base the only stuff that is important for the forcing that occurs that drives that microburst is the

stuff that happens below cloud base. Obviously, that is not always the case, but that was the

assumption in this case here. The reason we did that is so we do not have to consider all the

possible soundings and wind profiles and everything that happens above cloud base where there is

a lot of variability. So that was the other assumption.

Q: Fred Proctor (NASA Langley) - Define what you mean by your cloud base height in your

model studies?

A: Kelvin Droegemeier (University of Oklahoma) - It is basically just the height at which the

rain begins to fall, that is the simplest explanation.

Q: Fred Proctor (NASA Langley) - Is that the top of your model then?

A: Kelvin Droegemeier (University of Oklahoma) - Yes, and the vertical velocity is zero

there.

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Session III. Reactive System Technology

PRE'C__DI['_G Po4.',_,E'_' ..... "_'_" 1"40"i FILMED 285
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SessionIII. Reactive System Technology N 9 3 - ] _5_ _ 0

An Approach to Evaluating Reactive Airborne Wind Shear Systems

Joe Gibson, Martin Marietta
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An Approach to Evaluating

Reactive Airborne Windshear Systems

in the Context of

Ground-based System Deployment Study

Joseph P. Gibson Jr

Martin Marietta

Air Traffic Systems

Washington, DC

ABSTRACT

An approach to evaluating reactive airborne windshear detection

systems was developed to support a deployment study for future FAA

ground-based windshear detection systems. The deployment study

methodology assesses potential future safety enhancements beyond

planned capabilities. The reactive airborne systems will be an

integral part of planned windshear safety enhancements.

The approach to evaluating reactive airborne systems involves

separate analyses for both landing and take-off scenario. The

analysis estimates the probability of effective warning considering

several factors including NASA energy height loss characteristics,

reactive alert timing, and a probability distribution for

microburst strength.
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An Approach to Evaluating Reactive Airborne Wind Shear Systems

Questions and Answers

Q: Dan Stack (ALPA) - I am curious whether or not you are including rejected landings or shall

we say go-arounds in your landing criteria? Perhaps rejected landings and go-arounds should be a

separate criteria.

A: Joe Gibson (Martin Marietta) - No I do not believe so.

Dan Stack (ALPA) - I can think of a couple of cases where a go-around was actually attempted.

One was a US Air that ended up in the grass at Detroit, and about 15 years ago there was another

one in Saudi Arabia where a go-around was attempted in a microburst and had gotten to 800 feet

above the ground before they were blasted back onto the ground by a second microburst. So

perhaps this rejected landing or a go-around concept should be included some place in your data.

A: Joe Gibson (Martin Marietta) - No we did not consider that, but it is a good point.

Q: Unknown - Could you expand on why you think the hazardous F-factor is higher in the

takeoff configuration?

A: Joe Gibson (Martin Marietta) - Well, because the aircraft is up near full power, and

therefore you can go through a higher strength microburst without it affecting your climb

performance. When you are going in at approach airspeed you are going in slower and a

microburst of comparable strength would effect you more on landing.

Mike Lewis (NASA Langley) - The curves that I showed in the last presentation would show

just the opposite of that?

Joe Gibson (Martin Marietta) - Yes, I realize that. That was the assumption when I went in. I

did this about six months ago.

Q: Bud Laynor (NTSB) - How did you consider the trade off of kinetic energy for potential

energy when you were going through your studies? You talked in terms of energy height loss,

but not in terms of airspeed, and yet you mentioned the pitching maneuver per the training aid?

A: Joe Gibson (Martin Marietta) - Yes, that energy height offset that I was talking about is

basically to account for the slowing of airspeed and then losing kinetic energy to save you actual

potential energy, which is the energy height, the height you are at. That was about a one hundred

foot offset; considering that you could slow, you could lose some airspeed with out getting to

stall.

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302
Session III. Reactive System Technology

Panel Discussion

Kirk Baker, Federal Aviation Administration

Dr. Roland Bowles, NASA Langley Research Center

Joe Gibson, Martin Marietta

Howard Glover, Sundstrand

Doug Ormiston, Boeing

Rosa Oseguera, NASA Langley Research Center

Dr. Paul Robinson, Lockheed Engineering & Sciences

Terry Zweife], Honeywell

i:'_ _.._ r, .:,

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Reactive System Technology - Panel Discussion

Panel Members;

Kirk Baker,

Roland Bowles,

Joe Gibson,

Howard Glover,

Doug Ormiston,

Rosa Oseguera,

Paul Robinson,

Terry Zweifel,

Los Angeles Certification Office, FAA

NASA Wind Shear Program Office, NASA Langley

Air Traffic Systems, Martin Marietta

Reactive Wind Shear Systems, Sundstrand

Reactive Wind Shear Systems, Boeing

NASA Wind Shear Group, NASA Langley

NASA Wind Shear Group, Lockheed Engineering

Reactive Wind Shear Systems, Honeywell

Roland Bowles (NASA Langley) - We composed some questions that we thought were

pertinent and would fuel some discussion, and distributed them to the panel members. I will just

review them briefly.

1) Current industry status of reactive technology.

- Percent equipage to date in the system for those who have opted to equip with

reactive system technology.

- Operational successes that any of you may want to relate.

- Operational problems and solutions as you see them.

We have an expert here from the FAA on equipage; his name is Frank Rock. In fact he is an

expert on all of this. Frank, what is the current fleet equipage percentage now?

Frank Rock (FAA) - I do not have the exact number, but it was quoted at the last meeting we

had and if I remember correctly it was about 50%.

Roland Bowles (NASA Langley)- Operational successes? You fellows who design and build

and implement them, is the technology out there working for you? Is it paying off?. Have you

saved lives as a result of this technology?

Howard Glover (Sundstrand) - I guess one way of looking at it is that I have not heard of a

fatal Wind Shear accident in a while. I asked around our place for some statistics, and I was

surprisedat what I found. I won't go into the gory details, but Sundstrand manufactures a couple

of different flavors of Wind Shear detection systems. One of them contains a Wind Shear

algorithm which is designed by Boeing and delivered on all new Boeing aircraft. The other is one

that we have designed ourselves and is essentially intended for older aircraft, the so-called analog

aircraft. There are approximately 2,500 of our systems flying, and they have probably been flying

in those numbers for about a year. That is an average I would say. That is several million flight

hours I believe, unless my arithmetic is totally hosed up. That is for a typical airline operation.

We haveonly heard of a single save in that whole time, and that is the Atlanta 767 that somebody

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mentionedthis morning. Thatis amazing.Soeitherit is happeningandwearenothearingabout

it, or thefrequencyof encounteringtheWindShearis considerablyless,becauseof theavoid

factor,whichall thepilotsarenow beingtrainedto do,ispayingoff. Thatis theonly conclusion

thatI cancometo. I suspecthatit is thecorrectone I guesstheconclusionthatoperatorsare

notreportingeventsis probablytruealso But onreflection,theavoidance,bothby useof

informationfrom the ground and also pilot reports, is paying off.

Unknown - What about invalid alerts?

Howard Glover (Sundstrand) - Nuisances We won't get too deep into the distinction between

system failure induced alerts and nuisance alerts. Originally, they were quite frequent the thing

said wind shear and there wasn't one. Well, actually there was a wind shear, but it was not

hazardous. Rosa Oseguera mentioned this morning several ways of compensating a reactive

system to take care of operational factors. One of the worst operational factors that we had

problems with was the down wind turn that she mentioned. When you turn from flying into the

wind to down wind, the aircraft sees an effective wind shear. We had compensated for that but

not enough. We found that at least one carrier during flight training operations was making forty

five degree bank turns in surface wind conditions of sixty knots or more. That will do it

everytime. So we had to tailor our system to that. Another factor was that pilots quite naturally

tend to carry excess airspeed when they suspect wind shear might be present. To a reactive

system that can look like an effective increase in the wind shear intensity. We also added

compensation for that excess airspeed to reduce the unwanted warning. Since we did all of those

things we have had relatively few nuisance alerts. In fact I could not find any reports, other than

sensor failures, in the last six months. Sensor failures is a whole other story. Obviously, if you

are depending on aircraft sensors and they fail then you can induce an apparent wind shear alert.

Roland Bowles (NASA Langley) - Terry, do you have any comments about your successes?

Terry Zweifel (Honeywell) - We have been out in the industry now since about 1985 or so and

have had on the order of 10 valid alerts by pilots. In some cases they were apparently quite

critical wind shears that the pilot was able to get out of and wrote us a nice report. I tend to

agree with the idea that having these devices coupled with the training in avoidance has made a

major impact on the number of alerts that has occurred, even possible accidents.

Paul Robinson (Lockheed) - Can I ask Howard Glover how you compensate for the wind shear

alert going off in the down wind turn?

Howard Glover (Sundstrand) - In the down wind turn we have bank angle as an input. We

assume that if a bank angle above a certain threshold is sustained for a while, the pilot or the flight

control system is doing it and the aircraft is in an intentional turn. Now I have heard it argued

that we know that the kind of turbulence you get in a wind shear encounter induces bankangles of

that order, I say yes, but the wind shear warning has gone off before that, in our simulations

anyway.

Paul Robinson (Lockheed) - So you are effectively reducing the gain on the system?

305
Howard Glover (Sundstrand) - Exactly, yes.

Roland Bowles (NASA Langley) - I think that introduces the next question. Is there design

space remaining to improve reactive system technology? Is it possible to look at perhaps better

performance at lower cost? Is the gust rejection or turbulence problem solved? Are the time to

alert performance of these systems optimized? And the last one relates exactly to Paul's question;

do you need three axis implementation? You can let the physics do the walking through the

yellow pages for you there, rather than degaining the system as a function of bank angle. Would

that be a worth while improvement or would it be considered excessive cost? Any comments

along those lines?

Paul Robinson (Lockheed) - I would like to present something which backs up what Mike

Lewis presented this morning. This deals with the gust rejection filters, the time to alert, and the

parameter on which the system should alert. Mike mentioned that the hazard is defined as a one

kilometer averaged F-factor. I would like to show you this chart here. The red line is the Ffactor that was experienced by the In Situ system while penetrating Event 143. It is filtered using

a second order filter. If we take the raw unfiltered data and calculate the backward one kilometer

average of the F-factor we get the black line. As you can see it is a lot noisier than the In Situ Ffactor we are using, but it illustrates two points. One is that if you are going to work on

anaverage F-factor of 0.105 or more you require some filtering in order to get the alert at the

correct time and of the correct volume. The other point is that the filter does a lot better job at

noise suppression. So you are really gaining two things here, noise suppression, and you are

actually calculating an averaged F-factor on which to alert. This might put up a new spin on the

gust rejection filter problem.

Roland Bowles (NASA Langley) - We are asking this question from the perspective of the

technologist. Let me ask this, is there any drivers on the airline side to improve performance, or

are they perfectly happy with the product they have? Do they want improved performance,

perhaps even at lower costs? Or is the customer clambering for something better? Any other

comments about design space remaining for this technology to improve situations?

Howard Glover (Sundstrand) - I think I would get fired if I said there was no design space left.

Yes, of course, there is. But you hit it right on the head a moment ago. Are the improvements

necessary? No, the customers are not beating on us for that. They are beating on us for all the

usual things that customers do. More reliability of the equipment, etc.

Terry Zweifel (Honeywell) - I would like to make a few comments relative to the turbulence

rejection and time to alert. The real problem with reactive systems is that you have to have the

heavy filtering. The filter that Paul just showed is not a light filter, it is a very heavy filter, and

you have to have that for the turbulence suppression. Conversely, that filter is the very thing that

keeps it from alerting faster. A lot of studies that we have done at Honeywell and I know that

others have done throughout the industry show, that unfortunately, with the simple type filters

that we are looking at this is apparently as good as we can do. There are other concepts that we

have looked at, but have not really got to the point of production readiness. Smart filters for

example, using some of the atmospheric parameters that Kelvin Droegemeier was talking about.

Maybe you can make those filters a little less heavy in certain conditions. So I think there is some

306
roomto grow in thatarea.But right nowasI seeit thatis abouttheonly areayouaregoingto

getafasterdetectiontime outof.

Roland Bowles (NASA Langley) - You are really beating your head against a vicious trade off.

The physics is compelling. You are either going to get a lot of false alerts and good response

time, or a lot of delay?

Terry Zweifel (Honeywell) - The filter itself determines how many nuisance alerts you are going

to have.

Roland Bowles (NASA Langley) - To what extent do you think the application of reactive

system technology can be made aircraft independent or aircraft non specific. In other words, can

one box work for all airplanes? Would that be useful? Are there manufacturers that are thinking

along those lines?

Howard Giover (Sundstrand) - I think all of the reactive system manufacturers have essentially

one system, for all transport category aircraft anyway. In fact, I think inherent in the reactive

system is an independence from the aircraft performance; as far as wind shear detection is

concerned anyway. As far as recovery guidance is concerned, obviously it has to be somewhat

specific to the aircraft. Terry, do you disagree with that?

Terry Zweifei (Honeywell) - I agree one hundred percent. In fact, we have done studies on that

and have actually submitted a report to the FAA comparing L-101 l's, DC-9's, 727's, 737's, the

whole gambit, and the detection times just do to the algorithms are virtually identical. There are

of course differences in what sensors they have, how the boxes are mounted on the airplane, that

sort of thing. The basic detection algorithms have not changed for any of the airplanes so

naturally you would hope that it would detect the same. So yes, I would agree with that. There

is physically no reason that they should be different, given roughly the same long period

frequencies and that sort of thing. It is not necessarily true, as you mentioned, on the lighter

airplanes, the Gulf Streams perhaps, and some of those.

Roland Bowles (NASA Langley) - I think for heavy airplanes, 100,000 pound category or

heavier, you are in good shape. I think the light ones could pose a challenge. I guess that leads

us into the one that I think a lot of people are about ready to engage in, and that is, the industry

view of the FAA certification criteria as exemplified in the TSO C-117. Is theTSO a useful

standard? Is the TSO content technically sound? What are the current problems in applying the

TSO? And, are industry and government willing to modify the TSO where appropriate? If that is

a reasonable thing to do, what king of process would you have to go through to do that?

Kirk Baker (FAA) - Right now the requirement documents that we use to certify reactive

systemsare AC 25-12, AC 120-41, and TSO C-117. The minimum performance requirements for

the system are referenced in TSO C-117. One of the problems that we have seen is a

discontinuitybetween AC 25-12 and TSO C-117. AC 25-12 brings out the way you demonstrate

the system,but it does not specifically have hard requirements like the TSO does. Today we've

only had one application for TSO C-117 that we are considering in the Long Beach Office. What

we have looked at so far has demonstrated to us that there is a definite lack of interpretation of

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whattheTSOreally says,andhow youinterprettheperformancerequirements.Thisis ourgoal:

thatall applicationsfortypecertificateor typecertificateandsupplementaltypecertificateSTC's

will berequiredto meettheminimumperformancestandardsof theTSO. Noonehas

demonstratedthatto usyet. I think this isbecauseof thecomplexityof theTSO. I think the

FAA hasanobligationto makethishappen;to clearlydefinetheFAA policiesfor reactive

systemsandapprovalunderTSOC-117. Oneof thewaysthatRolandhadindicated,was

possiblyamendingtheTSO. If wecan'tuseit andwecan'tseemto implementit thenmaybewe

needto changeit. Well, I amdoingsomethingfight now,in theACO,to try andbetterprovide

theFAA'sinterpretationof whattheTSOrequirementsare. I havesentthatdocumentout andI

believesomeof youhavereceivedthatdocumentalready.If youhaven'tI dohavecopieshere

andyouarewelcometo takesome.This policystatementis in adraftform right now andwe

havecoordinatedit throughall theACO's. It specificallyspellsoutour interpretationof the

requirements,andhowyou shouldmeettherequirementsin performanceandguidance.We are

solicitingyour commentsright nowon ourinterpretation.I think thatis onewayof determining

whetherweneedto changetheTSO. What I wouldproposeis to cometo anagreementon what

thosepoliciesandinterpretationsshouldbe. Then I wouldlike to amendAC 25-12to includea

statementswhichreferencestheTSO astheminimumperformancestandard.But,in orderto do

thisweneedyour commentsassoonaspossible.Thatis basically,I think, theFAA'sposition

right nowoncertifyingandapprovingsystemsundertheTSO. Wewill needthosecommentsin

byApril 30,1992.

Terry Zweifel (Honeywell) - One obvious comment there would be on your statement to amend

AC 25-12 to include a statement which references the TSO. That kind of makes the TSO an

advisory circular, doesn't it? What if I have this wonderful box and I want to go certify it are you

going to certify it under AC 25-12? If so, do I have to go out and run the whole TSO? For those

of you who may not be familiar with it, the TSO is not a small test. It takes approximately four

weeks to run this set of tests. So if you are trying to build boxes for all kinds of airplanes you are

getting into some very involved testing. Obviously we as manufacturers would just as soon not

do that unless we had to. That is my concern. In essence the TSO is just becoming a part of an

AC and there is no TSO.

Kirk Baker (FAA) - Well the AC is one way to certify a system. I don't think you are creating

an AC out of the TSO. The TSO has minimum performance requirements listed in it, times to

detect. That is something that the AC doesn't have. There shouldn't be a disconnection there.

What I see happening in the industry, is everybody wants to continue to certify their systems

under the AC 25-12 to establish practices with various ACO's. One thing that promotes is non

standardization and ACO shopping. Because one ACO does not treat an applicant the same as

another. That is one of the reasons that I think the TSO is a valuable document. It could create

some standardization. It is a minimum performance standard. I think that goes right along with a

generic type, airplane independent system. I know the industry feels that they are generic, but

they have never demonstrated it. We have always gone and demonstrated on the type airplane

that needed it.

Terry Zweifel (Honeywell) - My concern is that if you continue doing it that way, what you end

up with is that we AC every possible type of airplane. There is no longer any need for the TSO.

It doesn't accomplish anything. If you take that approach, you think why TSO to begin with.

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Howard Glover (Sundstrand) - I would invert that. Our experience is that we had to do the

same amount of testing to certify, in addition to TSO certification, whether or not meet we the

TSO. In other words, when we go to the certification office, they still want to see it on the

simulator specific to the type of aircraft were going on. We still have to ground and flight test on

the aircraft that we are applying for the supplementary type certificate for. The biggest potential

factor in variation of performance from installation to installation, is the sensors on the aircraft,

and that is not addressed by the TSO at all.

Kirk Baker (FAA) - Those are installation specific requirements.

Terry Zweifel (Honeywell) - That is why you need the AC.

Kirk Baker (FAA) - Exactly, I was going to add that point. That is why I think, if we can

somehow be convinced in the FAA that the systems are generic detection wise, than the TSO

could become a useful document. Because once you demonstrated your performance and

function of detection then again you would still have to go out and demonstrate installation

specifics like sensor combinations and guidance requirements. But the detection portion would be

taken care of under the TSO. That is one advantage I see of the TSO. I am not a real fan of the

TSO myself, but it is with us and we have to try and use it. That is why I am trying todisseminate

and get the FAA to make some interpretations of what the requirements are of the TSO and

standardize those. Then disseminate them to industry for your review and comment. If we can

live with our interpretation, then we will go ahead and publish that as a memorandum policy letter

to all of the ACO's along with probably some guidance on the installation specifics. Again, just

because you have the TSO doesn't mean you can just go stick it on an airplane.

Terry Zweifel (Honeywell) - No, admittedly. The problem from the manufacturers point of view

is that building simulators to certify all these systems on is a very expensive proposition. Where

do you find DC-8 simulators that you can try your reactive system on. They are very hard to

come by and you end up struggling with that. The idea of the TSO, I thought, would be that you

could take this box which was TSO'd and I wouldn't need to build that DC-8 simulator. I know

the detection laws are OK. That to us was the big advantage. I was afraid you were trying to tell

me, "that is nice but we won't do that, you are still going to have build the DC-8 simulator."

Kirk Baker (FAA) - No, I think we can tackle the detection issue. Guidance is another question

altogether, obviously. The rule has been changed. Guidance is not a requirement on older

airplanes. DC-8 being one of them.

Howard Glover (Sundstrand) - Kirk, if you are willing to revisit AC 25-12 are you willing to

revisit TSO C-117, and reconvene the committee for one session?

Kirk Baker (FAA) - Certainly, if the comments from industry strongly oppose our interpretation

and convey to us that there is a need to amend the TSO then that is what we will do. That is a

lengthy process.

Terry Zweifel (Honeywell) - That is the concern I have. If it will take 50 years then it is going

to be of no value to anyone.

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Kirk Baker (FAA) - Thatis why I amtrying topromotethisotherway. That is, bycomingto an

agreementon theinterpretationof therequirementsin theTSO. With thatknowledgebase,then

amendtheAC 25-12. Wewantto enforcetheideathatanycertificationof a reactivesystemhas

to meettheminimumperformancestandardsof theTSO. Thatis standardFAA policy. Right

now thatis nothappening.

Terry Zweifei (Honeywell) - I am not sure what you are referring to. We, by the way, happen to

be an applicant for the TSO, in case you haven't deduced that?

Kirk Baker (FAA) - We haven't seen that yet and you may convince us otherwise. From thedata

that I have looked at so far, I don't think you demonstrated the system the way we interpreted it.

For instance, the wind axis separately as opposed to in combination.

Terry Zweifel (Honeywell) - I don't know if you want to get into the details of the TSO?

Kirk Baker (FAA) - I don't think we really do.

Roland Bowles (NASA Langley) - I don't think we have time today The intent here is to

expose a willingness on the FAA's part to sit back and garner comments from the industry, look at

what your comments are, and if appropriate, put a process in place Lo alter or at least amend the

TSO. In terms of you who have actually used it. What are the areas that are sensitive to you?

What is most difficult? If it takes four weeks, what are the stumbling blocks in the TSO?

Terry Zweifel (Honeywell) - One thing is this running the turbulence test.

Roland Bowles (NASA Langley) - I totally disagree with the whole turbulence approach. That

is one we are going to purpose.

Howard Glover (Sundstrand) - It isn't just the time taken for the turbulence test, Roland, which

is excessive. I think it is 250 hours, something like that. It is the fact that each run of the

turbulence test is, for example done at constant altitude. We have to have a system which takes

into account aircraft performance instantaneously. If the aircraft is in takeoff configuration it

should be climbing. If it is in an approach configuration it should be descending at 700 feet per

minute, roughly. None of that is taken into account by the turbulence model. In fact it is totally

artificial for our system to fly level at 500 feet above the ground. That does not make sense, and

yet here you are doing it as a test.

Terry Zweifel (Honeywell) - I don't argue that It does give you an indication of what your

turbulence sensitivity is.

Paul Robinson (Lockheed) - But it doesn't have to be done like that. The turbulence testing

process can be simplified based on the work that Roland has been doing at NASA. A follow onto

that is the inputs for Wind Shear detection, using actual microburst F-factor inputs or windfields.

There doesn't seem to be too much representative of what we saw in the field in 1991 ,that goes

through the system in terms of predicting time to alert, and missed alerts in the TSO. That would

be another problem, the weather inputs.

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Terry Zweifel (Honeywell) - Are you saying that the wave forms are not realistic?

Kirk Baker (FAA) - They are not realistic.

Terry Zweifel (Honeywell) - The problem is you would have to come up with an infinite number

to represent every conceivable microburst thing. How do you say this five is representative and

this five isn't?

Roland Bowles (NASA Langley) - What we would like to contribute to your process is to

suggest a significant revision, though not complicated or more complex, turbulence realization for

consideration in the TSO. And then "also, to consider the applicant complying with the nuisance

criteria by a direct calculation of a performance number compared to a curve. Is he above it or

below it? This ability to predict what the nuisance rate or level exceedance rate may be is based

on well founded scientific and accepted aviation computational principles. Let me put it another

way. I think it makes no since to sit there and run at one constant altitude an airplane simulation

for 2500 hours with a fixed RMS turbulence, and count an exceedance. What I think we want to

do is ask the question, "how often will you get a nuisance due to operational turbulence based on

well founded available data, per operation." Use three minute approaches and three minute

departures, and Monte Carlo that. That is the operational number you want. Not whether or not

one turbulence realization run for 2500 hours will give you an exceedance. Howard, do you see

what I am suggesting?

Howard Glover (Sundstrand) - I agree with you entirely.

Roland Bowles (NASA Langley) - And, we can do that problem by calculation, rather than

simulation. We would like to lay this out for you Kurt, and see how the industry responds to it.

Here is some data that shows how well we predicted it as compared to measurements. The

measurements are based on data that was provided courtesy of Boeing in their Southwest

program. Our problem is getting this kind of operational data to compare the predictions to. This

is what our system looked like, and notice in both cases, the measurements are falling under the

tail of the calculation. Which says if we had a higher population or a higher statistical sample we

are likely to pull this up and they would agree even more closely. We can't pursue it here because

of the complexity of it. But this is the approach we will recommend to you. It could really cut

down on the cost of complying with that nuisance demonstration that you have in the TSO.

Terry Zweifel (Honeywell) - Why do you need actual operational data? That is really just a

mathematical exercise of running through the process?

Roland Bowles (NASA Langley) - Strictly to convince us that the prediction is true.

Terry Zweifel (Honeywell) - How do you get that? How do you gather all that data

operationally, turbulence levels and all that?

Roland Bowles (NASA Langley) - That will be embodied in the turbulence model and we base

that on literally 3000 hours of low altitude turbulence measurements by the whole B-52 fleet.

About 7000 hours of data out of Canadian and US turbulence measurements in different terrain

311
anddifferentatmospheric stability at low altitude. That statistical model has been put together.

Terry Zweifel (Honeywell) - Is that the one in the TSO?

Roland Bowles (NASA Langley) - No. It came out of Slick's deck.

Terry Zweifel (Honeywell) - I guess what I was trying to get at is that once you define what the

turbulence is then all you have to do is sit down and run it through the filters.

Roland Bowles (NASA Langley) - Precisely, you define the turbulence realization, you define

your gust rejection filter network and by calculation show that with that stochastic input across

that ensemble of statistics you fall either above or below the exceedance line. I think it could cut

the cost considerably, and it would be actually more valid than what we are doing now. This is

the way we prove that the wings won't come off of airplanes due to extreme gust loads. TheTSO

may be useful but it could be more useful, and less costly to apply. I think there is some room for

improvement, or maybe it's clarification, of the technical content in the TSO. So, we will respond

as will the industry and we will see where we go.

Kirk Baker (FAA) - Yes, I think that is very important.

Roland Bowles (NASA Langley) - The final subject. How does industry view hybrid systems

technology based on the integration of reactive and predictive technology? What are the expected

problems, expected improvements in safety, and perhaps cost? I have heard people say that

reactive system technology is non throw away. I have said it. I think that is where I heard it,

actually. I do believe it is non throw away technology. So, is there some industry sense that

hybrid devices may be useful if the cost can be controlled. Would that not get us out of some of

the dilemmas on certifying predictive systems as a stand alone device. Any comments along that

line?

Howard Glover (Sundstrand) - I think one of the keys to convincing airlines to put money into

anything other than the reactive system is the benefit to cost ratio. If you can make the predictive

system do something other than detect this Wind Shear event, which is going down in frequency

as far as encounters are concerned, then that is the best way to do it. Things such as was

mentioned today like clear air turbulence detection, wake vortex detection, perhaps even terrain

detection. If you can do that and demonstrate it, then I think it is probably something that the

industry would go for. If you can't do that then ! don't believe that they will. Except in rare

occasion.

Sam Shirck (Continental Airlines) - I think 1 would be remiss if I left anyone with the

impression that our airline in particular, and I think American and Northwest also, are really

enamored with the reactive systems. I think it was the best technology that we were able to

produce at the time, but I don't think it is the answer at all. Joe Gibson presented some facts there

that would indicate that on takeoff we have only got one chance out of ten of survival in the right

type of shear. That is what I got out of what you said. As a pilot, I can tell you I am not wild

about those odds. And I think I can speak for the airline, that I don't think we would stay in

business with those type of odds. So we are hoping that we can get certification of a predictive

312
typesystemthatis standalonefor atleasttheretrofit aircraft. The betterof all worldswouldbe

to backit upwith areactivesystem.In thefutureI don'tthink youwill beableto buyanairplane

withoutareactivesystemthatisembeddedin theflight controllawsof theaircraft. I don'tthink

it is goingto bepossibleto doit. But apredictivesystemis somethingthatweare definitely

striving for and I think we are very close. One of the things that kind of bothers me, I guess it is a

comment more than a question, I heard just a few minutes ago that we were using a two second

pilot response time and a two second engine spool time. I think accepted pilot response time in

the past on RTOs which is a critical situation, has been 2 1/2 seconds. I would like to see at least

some recognition that we stick with the same ground rules. And, for those that think that the

engines on the ground loving 727 are going to spool in two seconds have got another thought

coming. That won't happen.

Roland Bowles (NASA Langley) - Your point is well taken Sam. We have had some successes

out there with this technology, but there is one around the comer where we may dig a whole and

have to go back to the drawing board.

Russell Targ (Lockheed) - I was worried about the exact thing that Sam just spoke to and that

is the data that Mr. Gibson just presented. In the pilot's wind shear handbook we learn that a

average wind shear encountered in the JAWS study had an F-factor of about 0.2 and they warned

that a heavy weight jet encountering such an average wind shear had about a 50% chance of

experiencing undesirable contact with the ground. Now that is three year old analyses, and we

have a whole panel of experienced reactive investigators here. I wondered, if you consider this

F-factor of 0.2 to be an average wind shear that one might encounter, what is the likelihood of

having an accident even after you have enunciated a wind shear occurrence that you are flying

into.

Roland Bowles (NASA Langley) - Well, this is a rare opportunity for me, this is a time I get to

challenge Russell. No statistic came out of the JAWS program with regard to an average F-factor

of 0.2 at all. What we do have is a statement based on a 498 microburst sample with a lot of data

provided by Lincoln and the NCAR guys, using the same algorithm that we were using to uplink

the F this past summer, that suggest that this is the probability of equaling or exceeding a given

level of F. So, about half of them are greater than 0.12. A 0.2 or bigger, based on this data,

would occur at a frequency of one in one hundred, roughly. I don't know where you got that

number but it was clearly not true.

Russell Targ (Lockheed) - Where I got the number was from the histogram that appears in the

JAWS study where they say that an average Denver-Stapleton dry microburst would correspond

to a 40 or 50 knot headwind change.

Roland Bowles (NASA Langley) - That is not an F-factor, that has nothing to do with airplane

energy loss.

Russell Targ (Lockheed) - If you say that you lose that over a kilometer.

Roland Bowles (NASA Langley) - But that was not the case. Some of them were five

kilometers wide.

313
Kim Elmore (NCAR) - I amtryingto rememberwhatthenumberswere,butI thinka typical

sizewasalittle overthreekilometersandtypicalintensitywassomethingon theorderof 14

metersper second,or somethinglike that. Of course,wedid notknow whatF-factorwasback

then. 0.2would befar biggerthanwhatwesawon theaverage.

Russell Targ (Lockheed) - I have no de_ire to quarrel over what the average F-factor in the

universe is. What I would like to know is if I encounter such an average F-factor as is indicated

here, what is my likelihood of surviving?

Roland Bowles (NASA Langley) - 1 think Joe gave you the answer to that in terms of a system

effectiveness number. Again effectiveness of a system is not the ability to detect it, it is the ability

to prevent an accident. Let's face it, there are some events in this world that I don't care if you

get the reactive system alert in two seconds or four seconds or a millisecond, if you are in it you

may not survive. There are some out there that big. But these tend to be relatively rare.

Joe Gordan (Safe Flight) - I think everybody is missing something here and that is what the

accident studies have shown. The fact is that given all the accident studies, any reactive system

would have saved that accident. You can pick numbers out, Mother Nature does some funny

things, but that is not what the evidence has shown.

Roland Bowles (NASA Langley) - You can make a compelling case that for the vigil pilot who

is right on top of it, knowing what is coming, he can survive. The point is, nobody knows what

the effectiveness of the system is. As we heard, we have only gotten a few alerts out there in the

system and we sort of know what the false alert problems are.

Terry Zweifel (Honeywell) - I will just make a few more comments relating to that. When you

say it is a 0.2 F-factor shear, the question is not is it in a 0.2 F-factor shear, the question is for

how long? There is another assumption that is being made and that is, if you are in a shear and

the shear is 50 knots that you will lose 50 knots of airspeed. That does not happen in reality. You

do lose airspeed, but you will not lose the entire amount of the shear. So that means your kinetic

energy relative to the air is not as bad as it appears in some of these studies.

Roland Bowles (NASA Langley) - That's right, and that is why we need to change that curve in

the TSO from wind speed change to something more meaningful in terms of performance impact

on airplanes.

Terry Zweifel (Honeywell) - One more comment on the accident models. What Joe said is

exactly true. There is nat an accident case that anyone's reactive system can't detect and fly out

of. In fact, in many cases we ran them 2 and 2 1/2 times the actual value and you still could fly

out. So if there was a conception somehow that the reactive systems are just totally ineffective, it

clearly is not true. They are not the final answer, I don't think anyone is promoting that. But they

also can do a lot more than seemed to be coming out in some of the papers here.

314
Session IV. Airborne Doppler Radar /Industry

315
316
Session IV. Airborne Doppler Radar / Industry

RDR-4B Doppler Weather Radar With Forward Looking Wind Shear Detection Capability

Steven Grasley, Allied-Signal Aerospace

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With Forward Looking Wind Shear Detection Capability

Questions and Answers

Q: Roland Bowles (NASA Langley) - Do you feel that you understand and have a clear path in

mind for certification as per the industry government activities on the interim standards document

and other certification related questions? Secondly, do you plan in the next six months to move

forward with the certification program?

A: Steve Grasley (Allied-Signal) - I think we understand what has been done to date. It

certainly is not absolutely clear how certification will ultimately be accomplished. There is still a

number of issues that remain open. The MOPS is being firmed up and that is one of the critical

things that we are going to need. It is going to take another meeting or two I believe. John

Wright is kind of leading up that activity and he shaking his head in agreement. It is going to take

a little bit longer before that is done. As far as moving forward within the next six months to do

some certification, it is quite dependent on those issues. There is also some of the exempted

airlines who are quite interested in moving forward. We'll support them if we are in a position to

do that. If they want to move forward and get going with it then we will certainly support

whatever they would like in that area.

Q: Kirk Baker (FAA) - You mentioned in your talk that you used some inputs for antenna tilt

management, could you elaborate on what those are?

A: Steve Grasley (Allied-Signal) - The key issue is to steer the antenna beam in the outflow

areas so we can get the measurements that we need and limit the amount of ground clutter that

we get through the main beam. The inputs are defined by the new and evolving 708-A interface

specification, radio altimeter is really the key one. We know how high we are above the ground

and approximately where we want to be looking, in terms of tilt angle, so we can steer the beam

into that region. As I mentioned, one scan did weather and one scan would do wind shear

processing. You are looking at two different types of phenomenon in that case. You want to see

the weather in front of you as well as the wind shears. The idea being that we could steer the

beam during weather based upon what the pilot has selected and the weather of interest to the

pilot, but then to get back down and do the scan in the wind shear mode right where we want it,

through the region of interest in the microburst event. The radio altimeter data is primarily used

to know our height above the ground, so we can steer the beam properly.

Q: Bruce Steakley (Lockheed) - What is the residual sensitivity of your system after your

clutter cancellation techniques?

A: Steve Grasley (Allied-Signal) - I do not know the numbers right off the top of my head. We

can certainly give you a little more background on that a little bit later. Certainly, we are not

seeing things drastically different from what was seen in the NASA flight test in terms of

sensitivity. It is very similar. It was encouraging.

Q: Ernie Baxa (Clemson University) - Can you say anything about the clutter rejection

336
algorithms for the wind shear detection mode?

A: Steve Grasley (Allied.Signal) - We have verified that the way that we are doing it is

working. I am probably not at liberty in this particular forum to talk about specifics of that, we

might be able to do that in another way. So far we are satisfied with the way our clutter rejection

processing is working and we have good evidence to show that it does a good job. We can see

the wind shears and get rid of the clutter data. I am sure that is not really the answer you were

looking for, but it will have to do for today.

Q: Bob McMillan (GTRI) - You mentioned earlier in your talk that the Bendix radar can detect

turbulence. Given the tenuous nature of back scatter from clear air atmospheric inhomogenities,

what is the reliability of detecting turbulence at useful ranges?

A: Steve Grasley (Allied-Signal) - We certainly make no claim to be able to detect clear air

turbulence. You need something to see and something to bounce energy off of. Our objective in

turbulence detection is to detect it in weather conditions. We are not attacking the clear air

turbulence problem at this time, not from a radar perspective anyway.

Q: Pete Saraceni (FAA) - How well do the you predict the 4B radar will see a dry microburst?

A: Steve Grasley (Allied-Signal) - I think we pretty much agree with what the NASA folks

have said about the capability of detecting a dry microburst, as well as what the Collins and

Westinghouse folks have said. You are basically into physics and the technology available today

Somewhere in the zero, down in the fairly low dBZ range we can get useful detection at

reasonable detection ranges. What exactly can we see and how far away can we see it? That is to

be determined this year. That is going to be a major objective of our activity and testing this

summer, this storm season. We can see something that is currently classified as dry, but exactly

how much? That is what we will find out.

Q: Dave Hinton (NASA Langley) - You suggested that pilots wanted to have the option of

manually selecting the wind shear mode above 2500 feet. Do you believe that there is any

operation requirement for wind shear avoidance above 2500 feet? Is there any safety hazard from

wind shear at those altitudes?

A: Steve Grasley (Allied-Signal) - No, we don't think there is any issue of hazard at those kind

of altitudes. When you start getting into shear type conditions above those things people tend to

say that is turbulence of some sort more than wind shear. I did suggest that pilots wanted the

option of manually selecting it. You get a wide range of inputs and desires on capabilities when

you start talking to pilots. They want all kinds of neat stuff. Are we going to end up providing

that option to be able to select wind shear above 2500 feet? No, that is not the intention at this

point and time. We have just gotten inputs that said it would be kind of neat to look.

Q: Dave Hinton (NASA Langley) - Do you believe that Doppler technology can support wind

shear detection at those altitudes given that downdraft estimation may be unreliable above 2500

feet and there may be little or no microburst outflow for the Doppler system to detect.

337
A: Steve Grasley (Allied-Signal) - That is true. We don't necessarily believe the radar Doppler

technology can really provide you any benefit at that altitude. As I mentioned earlier, we do not

believe there is really a wind shear hazard at those kind of altitudes.

338
Session IV. Airborne Doppler Radar / Industry N93- 1_:_G 0 2

Airborne Doppler Radar Research at Rockwell International

Roy Robertson, Rockwell International

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Airborne Doppler Radar Research at Rockwell International

Questions and Answers

Q: Roland Bowles (NASA Langley) - How did you estimate the downdraft, or did you estimate

it in your total hazard calculation? Secondly: you post processed the airplane data through the In

Situ algorithm; what did you think about the veracity of the algorithm?

Roy Robertson (Rockwell) - I will answer the second question first. Yes, we did post process

the data. We collected the aircraft data from a different set of sources than what is on the B737.

We had to piece together some of the In Situ inputs. The air data came from one source and the

accelerometer data came from a different source, so we had to do a little work getting the filtering

constant of the input data to agree. We also had some effort getting the angle of attack input

calibrated. The algorithm seemed to have a fairly high sensitivity to angle of attack. Once we got

those initial things worked out, we felt that the algorithm was doing very well. For downdraft

estimation we used the gust cut estimate that Dave Hinton had provided to the Lincoln Labs guys

that had the altitude of the radar beam as a input parameter to the hazard factor calculation.

Q: Branimir Dulic (Transport Canada) - What is the price range and when will the system be

in full operation, and the weight?

A: Roy Robertson (Rockwell International) - Price range? I am in engineering not marketing,

so I would be stepping into some really deep problems if I said anything about that. Availability,

we expect the system to be operational and finished with certification in 1993, and weight is

roughly 30 pounds for the RT and something less than that for the antenna.

371
372
Session IV. Airborne Doppler Radar / Industry N93-196(}3

Acquisition and Use of Orlando, Florida and Continental Airbus Radar Flight Test Data

Mike Eide, Westinghouse Electric

Bruce Mathews, Westinghouse Electric

PREGEDING P,-_CIE r3,..,A.,:_," "':/ NOT FILMED

373
Fourth Combined Manufacturers' and Technologists'

Airborne Wind Shear Review Meeting

April 14-16, 1992

Fort Magruder Inn, Williamsburg, Virginia

by

Mike Eide

Flight Test Pilot

Bruce Mathews

Radar Systems Engineering

Westinghouse Electric Corp.

Electronic Systems Group

Baltimore, Maryland

Abstract

Westinghouse is developing a lookdown pulse Doppler radar for production as the sensor and processor

of a forward looking hazardous windshear detection and avoidance system. A data collection prototype of

that product was ready for flight testing in Orlando to encounter low level windshear in corroboration

with the FAA-Terminal Doppler Weather Radar (TDWR) . Airborne real-time processing and display of the

hazard factor were demonstrated with TDWR facilitated intercepts and penetrations of over 80 microbursts

in a three day period, including microbursts with hazard factors in excess of .16 (with 500 ft. PIREP

altitude loss) and the hazard factor display at 6 n.mi. of a visually transparent ("dry") microhurst with

TDWR corroborated outflow reflectivities of +5 dBz. Range gated Doppler spectrum (I,Q,FFT) data was

recorded for subsequent development and refinement of hazard factor detection and urban clutter rejection

algorithms.

Following Orlando, the data collection radar was supplemental type certified for in revenue service

on a Continental Airlines Airbus in an automatic and non-interferring basis with its ARINC 708 radar to

allow Westinghouse to confirm its understanding of commercial aircraft installation, interface realities,

and urban airport clutter. A number of software upgrades, all of which were verified at the ReceiverTransmitter-Froeessor (RTP) hardware bench with Orlando microburst data to produce desired advanced

warning hazard factor detection, included some preliminary loads with automatic (sliding window average

hazard factor) detection and annunciation recording. The current (14-APR-92) configured software is free

from false and/or nuisance alerts (CAUTIONS, WARNINGS,etc.) for all take-off and landing approaches, unde_

2500 ft. altitude to weight-on-wheels, into all encountered airports, including Newark (NJ), LAX, Denver,

Houston, Cleveland, etc.

Using the Orlando data collected on hazardous microbursts, Westinghouse has developed a lookdown

pulse Doppler radar product with signal and data processing algorithms which detect realistic microburst

hazards and has demonstrated those algorithms produce no false alerts (or nuisance alerts) in urban

airport ground moving vehicle (GMTI) and/or clutter environments.

374
Introduction

The Westinghouse Technical Direction is to provide a forward looking detection

and avoidance system of low level windshear based upon a pulse-Doppler lookdown radar

sensor to the commercial air transport market. The design of this system is for a

"quiet, dark cockpit" with low false alert and nuisance alert rates. To be used, its

warnings must be trusted and its hardware performance reliable.

Detection of microbursts employs an X-Band radar sensor designed to criteria

which has made Westinghouse a leader in reliable lookdown airborne radar. Specific

for this application are the demands of operating in an urban clutter environment and

its attendant moving vehicle background. Our design approach is to temper initial,

analytic designs based on experience with data from encounters with both microbursts

and airport urban clutter.

However, it is difficult to obtain simultaneously interesting/stressing

microbursts and appropriate clutter. These two series of flight tests have been

respectively concerned to record radar data for microburst detection algorithm

refinement and to observe and develop clutter rejection processing into a robust

variety of urban airports with an in-revenue service aircraft reality. Signal and

data processing algorithms subjected to input data collected in flight against actual

microburst hazards verifies the detection capability of software upgrades to a radar

in revenue service and demonstrate by superposition both hazard detection and low

false alert criteria.

Overview

The Westinghouse involvement with airborne forward looking windshear detection

radar (see figure I) began in 1989. After talks with NASA LaRC, a flight test into

nearby urban airports was conducted using a modified APG-68 (F-16) radar [I]. Data

was collected along approach glideslopes using NASA "typical" waveforms. A number of

antenna lookdown angles were examined to establish a baseline on antenna sidelobe

rejection and appearance of ground moving discrete_and traffic. Airport selection

excluded stressing second time around urban clutter.

Westinghouse initiated a major development program.

Receiver/transmitter/processor (RTP) units were designed, assembled, and software

equipped to gather microburst data at the tailend of the microburst season. The

design included pre-prototype component and design techniques. RTP configuration

conformed to ARINC 708. The design included an FFT based signal processor and real

time data processor.

375
The data collection radar was delivered to the Westinghouse owned and operated

BAC-I-II at the end of August with signal processing algorithms installed. Vectored

by TDWR to areas of evolving or potential microburst activity, pilot decisions about

fly-through utilized a realtime hazard factor display. Pre-processed radar data (FFT)

was collected on over I00 microbursts over a 3 day period, including a run which

produced 500 ft. loss in altitude.

The Orlando flights served as a checkout for installing the R/T into a

Continental Airbus. Unlike Orlando, only VCR format data would be collected on the

Airbus. The radar operated in an autonoumous, non-interfering basis with the

installed ARINC 708 type antenna system. VCR format data has been collected on 682

flight (1682 flight hours from 4-SEP-91 thru 8-APR-92) for take-off and approaches

over altitudes from weight-on-wheels to 2500 ft. into a variety of urban airports

including Cleveland, Denver, Newark, L.A., San Fransisco, Houston.

The initial software configuration on the Continental Airbus included only the

signal processing algorithms as configured in the Orlando flights. These included

neither complete clutter rejection nor total hazard factor algorithms. The software

has been recently updated to include (i) refinement in the signal processing designed

to reject GMTI-clutter while not impairing windshear detection, (2) computation of a

total (vertical plus horizontal) hazard factor and (3) detection logic for total

hazard factor. Effectively, the equipment is nominally configured for false alert

scoring.

Radar and Instrumentation Desiqn Considerations

Hazard factor accuracy may seem like an abstract and inaccessible quantity, but

first order estimates of hazard factor accuracy can be controlled in sensor design by

examining the sensitivity of the hazard factor to various measurement accuracies,

particularly the accuracy of measuring outflow radial velocity and the distance over

which the change in outflow velocities take place.

In fact, once these sensitivities are recognized, budgets for controlling the

contribution from any single source can be allocated into the design. While the

effects of sidelobe clutter, GMTI discretes, and other "clutter residue"

contributions may be analytically elusive, accuracy limits of the hardware, the

algorithmic processes, and/or waveform design may be established early in the design

process.

In general, there may be several contributors to the Doppler velocity accuracy

budget besides the signal-to-noise limitation, but the signal-to-noise limitation on

Doppler velocity accuracy is most fundamental to when (at what range) the radar

algorithmic processes can be expected to produce good velocity maps which produce

good hazard factor maps. Minimal outflow reflectivities (and Doppler velocities)

which produce marginally accurate hazard factors can be small if larger amplitude

sidelobe/mainlobe ground moving vehicles or sidelobe discretes are inhibited from

entering the velocity map.

376
Imperfect accuracy in the_dimension over which the winds change will produce

errors. Over-resolving sensors like radar will cut the microburst outflow into

several pixels on a fine range grid, making the measurement of the outflow diameter

relatively accurate in comparison to non-resolving sensors (e.g. infrared).

When the microburst is well resolved in range, a series of velocity measurements

for the range pixels along an azimuth line may be used to construct an approximation

to the horizontal windshear. Least mean square type approximations will be accurate

over linear regions of shear (i.e. hazards) if the velocity measurements for each

pixel are accurate.

Limits on velocity accuracy are usually set by the Doppler filter 3 dB.

bandwidth. Non-resolving (i.e. pulse pair) Doppler sensors must resort to large

signal-to-noise ratios to maintain accurate velocity measurements. Resolving (i.e.

FFT spectrum analyzer) Doppler sensors can provide accurate Doppler velocity

measurements at low signal-to-noise ratios.

The importance of low signal-to-noise ratio velocity accuracy is that the

reflectivity of the outflow may be correspondingly less reflective, i.e. "dry".

According to NASA-LaRC, a minimally small, hazardous microburst will have the

hazard area extend over about D_ = I000 meters. Alowing some overlap by the

approximating ensemble, the least mean square type slope estimator may begin to

operate when diameter of the microburst hazard is subtended by the LMS window

(population) of n e points,

D - n e dR

_R - D,/n e

Substituting for the range gate AR, with (V/g) = (80/9.81) = 8.15 sec, a 10% hazard

factor accuracy on a nominal hazard factor of .105 yields [2],

bF/F = .10 - ((8.15)/[2(i000) (.I05)]} In e 6v/(ne-2) "5]

Re-arranging, the velocity accuracy _v_er point must be small,

6v - 2.58 (ne-2)'5/ne

For signal-to-noise limits, the velocity resolution Av contributes to defining the

velocity accuracy, 6v - Av/(2 S/N) "5 Squaring both sides of the equation, the

relationship between Doppler resolution and signal-to-noise becomes, approximately :

(_v)2/(S/N) - 2(2.58)2(ne-2)/ne 2

Consequently, range gated FFT spectrum analyzers can furnish fine Doppler resolution

and, hence, accurate hazard factors at low S/N ratios due to minimal outflow

reflectivities.

377
Accurate hazard factor processes at low S/N must avoid larger S/N returns, e.g.

mainbeam clutter, sidelobe clutter, ground moving taffic, spurious, etc. The RTP

assembly contains a stable oscillator (STALO), Receiver, Signal Processor, Solid

State Transmitter, and Low Voltage Power Supplies. The STALO and receiver provide

stability and spurious free operation in the presence of large mainbeam clutter. The

receiver and STALO are departures for their attention to minimun detectable velocity

and interference. The transmitter is solid state, based upon GaAs MMIC power

amplifiers. A powerful signal processor is provided. This furnishes the numerical

signal processing engine to accomplish an FFT spectrum analysis with proprietary

algorithms to reject/sort clutter and GMTI with no consequence to windshear. Such

algorithms are demanding because they must be executed at input data rates.

The signal processing effectively furnishes velocity (range x azimuth arrayed

pixel) maps of the horizontal wind fields before and along the glideslope of the

aircraft. Map data is available at a reduced rate. Data processing of these maps

furnishes total hazard factor estimates along the aircraft approach or departure

altitude profile. The final output stages of the warning system utilize a graphic

processor to transform the radar coordinate maps into PPI formatted data as well as

colour code the VCR displays. The processor design also supports high speed porting

of the I&Q input data, the FFT data, etc. for instrumenting/data collection purposes.

"The Name of the Game" (see figure 2) for low level windshear warning is to sort

windblown rain returnK*_ other returns, including mainbeam urban (STAE) clutter,

sidelobe distributed clutter, sidelobe and/or mainlobe GMTI. "Conditioning" preserves

the signal integrity and minimizes spreading of mainbeam clutter through the

downconversion process to analog-to-digital (ADC) conversion. "Signal Processing"

includes those algorithms which are accomplished at the coherent processing interval

(input data rate). With FFT spectrum analysis processing, there is a whole filterbank

of Doppler candidates to describe the Doppler of the wind in a single range x azimuth

beam (velocity map) pixel. The signal processor chore must smartly reduce the data

entering the subsequent data processing stages by orders of magnitude. Pulse-pair and

spectral averaging processes are simple and less demanding largely because they

accept/include as eligible many Doppler returns which may not be windshear. "Data

Processing" means the processing of the wind velocity maps to produce a total (i.e.

both horizontal and vertical component) hazard factor map. It also may include the

detection of average hazard factor areas. These different levels of radar data become

the principal intermediate stages for observing radar performance and

recording/instrumenting/displaying data.

Prior to the Orlando flights, Westinghouse assembled and delivered the data

collection radar hardware to the software/systems integration bench. Real beam map

and supporting modes were first developed and checked out at the bench and in local

flight tests. Windshear mode development proceeded with several local flights through

August. Initial development of the windshear signal processing utilized NASA-LaRC

FORTRAN computer models of microbursts [3] and glideslope geometry, modified by

Westinghouse to include its own models of multiple time around echo (MTAE, STAE) and

distributed sidelobe clutter.

378
The objectives of the ORLANDO flights were to collect data on microbursts and to

demonstrate airborne real-time processing and hazard factor display. The Orlando

Flights were conducted with signal and data processing operating (loading the

'timeline) but with _ cursory signal proceesing enabled only. In general, the many

thresholds and adjustable processing parameters in the signal processing algorithm

were "de-sensitized" to insure that any and all Doppler reports would be passed to

the VCR map displays. The objective was to unhinderdly collect any available data on

microbursts. Real time processing of the wind blown rain return into velocity maps

and hazard factor maps would allow the pilot and test crew to penetrate the

microbursts, collect in situ (SUNDSTRAND) data, and otherwise corroborate the

airborne displayed and recorded data with TDWR.

The installation on the BAC-I-II (see figure 3) utilized a configuration in

anticipation of the Continental Airbus installation to follow. A typical ARINC 708

(i.e. retrofittable) 30 inch flat plate phased array antenna was controlled through

the sequencer. Data collection would include I&Q pulse and gated FFT radar data, INU,

and air data input to the SUNDSTRAND reactive device in addition to the VCR formatted

displays.

The BAC-I-II operated in a fashion with the air traffic controllers and TDWR

radar operators not unlike the preceeding NASA flights. Safety of flight

considerations included minimum altitude limitations and air space restrictions.

Using the voice and data link established by NASA earlier, the TDWR operators would

vector the aircraft to the vicinity of the microburst. Based on pilot observations

and TDWR radar reflectivity, Doppler, and/or hazard factor, the aircraft might

penetrate the microbursts.

The Westinghouse flights were greatly aided by the fact of the real time

airborne radar instrumentation display (see figure 4). The aircraft was directed to

the vicinity of microburst activity by the TDWR, and the pilot used the radar display

to locate a particular cell, assess the flight safety, and navigate through with

little problem. As the data collection proceeded and the radar demonstrated its

abilities to locate microbursts at long range, Westinghouse could approach general

areas of activity and pick among evolving events. The VCR display format for both the

Orlando and the Continental flight tests was constructed to the arguable convenience

of engineers, and crowded a lot of instrumentation into a small space. Range (out to

8 n.mi. in range gates of 300 m.) x Azimuth (±23") (B Scope) maps were provided for

two bars of azimuth data, one bar at a lower elevation angle than the other. Each

pixel on the screen represents a range gated angle cell of 16 colour shade coded

data.

The maps at the top of the VCR format are unscaled amplitude (i.e. S/N) . The

"bland" colour palette employs red as a large amplitude signal and blue-green as

minimal (near noise). The upper bar is on the right and the lower bar is on the left.

Below these amplitude maps are the velocity maps for the respective bars. Green

indicates zero velocity, yellow-red indicate tailwinds of increasing magnitude and

blue indicates increasing headwinds (±24 m/s or 3 m/s per colour shade). The odd

rectangular window on the left is a B-scope lower bar horizontal hazard display. Most

379
people will find the PPI format of "total" hazard factor in the lower right corner

most assessible. Hazard factor colour quantization spanned z.2 (0.025 per shade). The

space not used by the colour coded maps allows numerical discrete data. Activity of

the signal processing numeric words provides engineers indications of proper activity

of critical stages of the process. Along the bottom are indications of azimuth and

elevation antenna position, aircraft location (lat.-long.), altitude, etc. Space was

also allocated for SUNDSTRAND reactive hazard factor display and alphabetic

annunciation.

The BAC-I-II was vectored to some 80 different microburst events by the TDWR

operators. Many of those events included multiple "isolated" cells and complex "line"

events. In all, the radar collected data on over 100 microbursts in three afternoons

of flight.

VCR tapes of the instrumented VCR format and views of the intercepts out the

windshield will be shown.

The first video begins with a full screen display of the VCR instrumentation

format. The amplitude, velocity, and hazard factor maps at the start of this run are

full of activity in progress at near and very far (8 n.mi.) ranges. The cells of

interest are being discussed by the pilot and TDWR. The airborne radar operator

begins directing the pilot's attention to a beginning event. The audio contains

conversation between the pilot and crew over the intercom and with the air traffic rf

communication including the TDWR. The video transitions to a view out the pilot's

windscreen with the instrumentation shrinking into the lower left corner of the

screen. Subsequently, only the total hazard factor PPI map (true perspective) is

shown. The visual shows little sign of outflow in the rain cell. As the penetration

evolves, the microburst developes hazard factor displays portraying many shades of

colours, including nearly .2 (top red). TDWR corroboration (post-flight de-briefing)

placed the hazard factor along the flight path at .16, and the audio includes a pilot

report of 500 ft. altitude loss for a penetration which began at an altitude under

2000 ft.

The second video segment begins as the plane (windscreen visual) emerges from a

prior run on a rain core. The plane manurers slightly under TDWR direction,

approaching a lake. Careful visual inspection of the lake surface will reveal an

outflow. The air volume above the lake is clear. The radar display picks up indicated

outflow activity in both the upper and lower bars of its scan patterns, and the

hazard display shows a weak hazard factor at about 6 n.mi. as the aircraft turns and

steadys under radar operator/radar display direction. Post flight de-briefing with

TDWR corroborated a microburst forming with an outflow of +5 dBz. reflectivity. As

the BAC-I-II approaches, pilot comments indicate little or no visual _Lof a

reflective rain core. The final audio remarks indicate the physical encounter with

the windshear.

380
An outflow reflectivity of +5 dBz. at 6 n.mi. offers a rough calibration of the

minimum detectable outflow reflectivity performance of the westinghouse radar. As we

introduced earlier, the RTP was installed aft of the pilot cockpit with additional

waveguide run losses. We should expect to see lower_reflectivities at shorter ranges,

so, together with the range scaling and _ne a .... :_n:_ losses of a typical air

transport installation, we may interpret an equivalent detectable reflectivity at 1.5

n.ml. (30 seconds of warning) of -5 dBz. This particular microburst happens to be the

least reflective outflow which we encountered, and the minimum detectable outflow

reflectivity the Westinghouse radar system may expect is considerably) smaller

(better) than -5 dBz.

Continental Airlines Airbus Flight Testing

The Continental Airbus installation has given Westinghouse a opportunity to

collect data and observe radar operation in the commercial airframe environment. The

object of the Continental flight test was to place a radar of expected performance

into a typical airline installation environment and observe its performance in the

clutter and ground moving target/traffic environments as provided by the approaches

and rake-offs of its schedule. This objective was not in principle concerned with

encountering microbursts and verifying/evaluating equipment detection performance.

The salient design reasons for the flight test addressed the false alert and accuracy

aspects of the radar design. Certainly, the interest was to perceive how and to what

extent clutter, including mainbeam clutter, sidelobe clutter, ground moving

traffic, etc. and any other phenomena encountered within the operational conditions

of the aircraft approach and/or departure, including rf interference, will be evident

to the radar. Such perceptions may allow some assessment of the false alert

potential, but more likely, they furnish opportunities to Westinghouse to refine or

add to its design.

Radar systems are dependent upon other systems on the aircraft for their

satisfactory operation. Radomes and radome maintainence, mounting, vertical

reference, altitude, etc. are furnished by the aircraft. Independent of any urban

clutter - false alert concerns, there is much to be observed to insure a sensitive

pulse-Doppler radar can properly operate, come what may with clutter.

Given that suitable hosting is provided, the regular flight patterns of an inrevenue service aircraft expose the radar to a variety of mainlobe, sidelobe, and

second time around (STAE) urban and airport vicinity ground moving vehicular clutter.

The data collection radar system was supplied to Continental for installation.

After supplemental type certification [4], the radar began supplying VCR display

formatted video tapes at regular intervals. The installation of the Westinghouse

equipment allowed non-interferring operation of the data collection radar with the

on-board radar transparent to the pilot/crew. Whenever the radar was not being used,

the westinghouse radar would turn on automatically at altitude or takeoff using

supplied aircraft discretes and altitude data. The installation is largely an

exploitation of the dual RTP operation expected for ARINC 708 equipment.

381
After returning to Baltimore, a different, more vivid colour palette was

introduced to highlight activity. In general, the velocities of the outflows did not

begin to approach 24 m/sec, so the velocity scale was reapportioned to _16 m/sec. The

I and Q data recorded during the BAC-I-II Orlando flights could be re-played through

the RTP to produce new VCR displays and maps. The new palette uses a black background

for zero activity. The amplitude scale indicates max (saturating) amplitudes by white

decreasing to red, yellow, blue, green. The new velocity scale uses black for zero

doppler with yellow, red as increasing magnitude tailwinds and green, blue, purple as

increasing magnitude headwinds. The hazard factor uses black as zero, with yellow,

red, magenta as increasing hazardous windshear and green to blue as increasing

performance enhancing windshear.

The Orlando flights collected a mountain of radar data on microbursts. In

general the clutter background was not worst case urban clutter, but some data was

collected in/over the Orlando airport when it was closed to air traffic by the

storms. This data allowed empirical studies of signal processing thresholds to reject

non-windshear and ensure that windshear-like returns are retained without apparent

loss. In situ data collection was limited. Air data collection was included at the

last moment and its quality/collection is under examination and is questionable. TDWR

radar data, available each day immediately after the respective flights, was used to

"calibrate" the reflectivity/sensitivity of the radar, Doppler, and horizontal hazard

processes of the data collection hardware and signal processing algorithms. Given

their often differing perspectives on the events, the airborne and ground based

radars produced excellent agreement in velocity and hazard factor and time and

physical registration.

The Continental installation was initially equipped with unmodified Orlando

signal processing algorithms. These algorithms were tailored to ensure that windshear

would not be inadvertently editted/rejected, etc. Hence, the initial installed

software configuration furnished only the simplest of mainbeam clutter processing as

a means of rejection. Subsequent software updates included total hazard factor

construction and a sliding window detection (400 m. range window with an window

average F=.I05 threshold) and optimized signal processing. All subsequent software

loads were developed in the signal processing lab using the spare RTP unit as a test

bed. The range gated in-phase and quadrature A/D data recorded during the flights for

particular (i.e. hazardous) cases was played through the unit to check the

performance of the PROMS (programmable read only memory chips) _ destined for the

Continental Airbus. Hence, the signal and data processing algorithms updating the

Continental were verified to produce hazard factors, cautions, and warning alerts in

correspondence to the corroborated Orlando microbursts. The software updates retain

detection performance during periods of urban airport approach clutter false alert

rejection algorithm observation, experimentation, and refinement.

The latest software load included parameters and thresholds for the signal

processing algorithms as determined empirically from reprocessing the Orlando flight

test data.

382
a&-

The video segment shows_sample in-revenue service landing approach for two

different software loads in slde-by-side comparison into the same (Newark) airport.

Although the PPI total hazard factor display of the earlier (incomplete) software

load shows some caution and hazard factor activity (from the spurious returns

entering the velocity map from sidelobe leakage of discrete targets), it might well

be considered remarkably "clean" were it not for the other PPI display being

absolutely free of any such false cautions and/or alerts, even down to minimum

altitude (weight on wheels). This video short indicates the power of the combined

signal and data processing of the final configuration.

The map/instrumentatlon displays of these two runs were not, of course,

collected simultaneously. However, the results portrayed are representative of the

false alert performance to be viewed on all the landing approaches and takeoffs of

the respective configurations.

The Continental flight tests have allowed Westinghouse to observe the commercial

air transport operating and clutter environments. The equipment has performed largely

as expected. Software loads have demonstrated by superposition the power of signal

processing in rejecting sidelobe/vehicle traffic leakage while fully retaining

microbursts, i.e. the signal processing algorithms and data processing algorithms

operated satisfactory on the collected microburst data without any detection losses.

The thresholds for sidelobe/GMTI rejection were empirically determined to retain

microburst windshear by training with the Orlando microburst data. The Continental

flight test data argues that a combination of modern signal and data processing

algorithms can eliminate false alerts without compromising necessary detection

performance..

383
Summar7

i. Westinghouse has provided a new design pulse-Doppler lookdown radar for the air

transport market.

2. With the help of the FAA and TDWR and the procedures established with them during

the NASA LaRC flights, unprocessed quantitative (FFT) airborne data was collected in

Orlando on over 100 separate microbursts, including real time hazard factor maps.

3. Westinghouse demonstrated the first airborne real-time detection of microburst

windshear using airborne radar signal, data and hazard factor processing.

4. With the help of Continental Airlines, clutter data on many urban airports has

been sampled within the context of the Westinghouse design.

5. Westinghouse has used the raw (I,Q, FFT) data collected in Orlando on hazardous

microbursts to verify that its subsequent software loads have retained the necessary

hazard detection performance. [False alert suppression has not been achieved at the

expense of detection performance.]

6. Westinghouse has demonstrated airborne real time sidelobe/GMTI clutter rejection

and a potential for satisfactory false alert operation. [Demonstration of 100,000

flight hour false alert times takes a long time.]

References

384

I. B.Mathews, "Sabreliner Flight Test for Airborne Windshear Forward Looking

Detection and Avoidance Radar Systems", Airborne Wind Shear Detection and Warninq

Systems, Third Combined Manufacturers' and Technologists' Conference, Oct. 16-18,

1990, NASA Conf. Pub. 10060, pt2, DOT/FAA/RD-91/2-II, p 715-754

2. I.Miller and J.Freund, Probability and Statistics for Enqineers, Prentice Hall,

1965, p 233-235

3. C.L.Britt, User Guide for an Airborne Windshear Doppler Radar Simulation (AWDRS)

Proqram, NASA Contractor Report 182025, DOT/FAA/DS-90/7, June 1990

4. G.Tsoucalas, "Continental Airlines Certification Plan", Westinghouse Predictive

Windshear Radar Installation on Continental Airbus A300-B4 Aircraft", REF: FAA

Project No. ANMI001-2145, (undated) 1991
Success by Empirical Refinement

Concerns/Expectations

'_ 8190

Lookdown

Data

"Total" Hazard

Detecllon 2

Production

Certification

Detection With Rejection

"Detection"

Display

fig. 1 Overview of Approach

Low false alarm rate rada_ design must address mainbeam and sidelobe realities,

particularly for sensitive detection near urban airports. Westinghouse has

stressed the empirical detailed understanding of both microburst and urban airport

clutter radar return in its design approach.

The Name of the Game ... Separate

the Wind Return from Clutter

Target _G,,,,

Extraction

Rar,_ 0_tl S_cm_ A_'=or S|lu,al,O.

i_l

It Amplitude

FFT _ Conditioning

and

Processing

0 +3#kll ,_Dktl

(_1 filllll

fig. 2 the Name of the Game... Separate the Wind Return from Clutter

Clutter and ground moving vehicular traffic returns must be separated from

microburst outflow reurns. This begins with a hardware design attendant of pulse

Doppler realities and continues through digital algorithms to keep the wind blown

rain and disregard non-windblown rain-like returns.

385
BAC1- 1 1 Data Collection

Configuration

Cockpit Camera

Antenna

Operator

DR

Cockpit Recorder

Radar Video

Recorder

Instrumentation

Recorder

INS

Engineering nmluu ._ GPS

Station

Aircraft Data

fig. 3 BAC-I-I1 Installation

The BAC-I-II installation includes ports for recording __ variety of radar

instrumentation and aircraft data.

Lower Bar

Amplitude

Lower Bar

Velocity

Horizontal

Hazard

Upper Bar

Amplitude

Upper Bar

Velocity

Horizontal

Hazard

fig. 4 VCR Instrumentation Format

The VCR format allowed collection of a great quantity of data of differing types.

The discrete words included aircraft data and general processor health/activity

parameters. The velocity display covered ±24 m/s in Orlando with 16 colour shades

(z16 m/s on Continental Airbus). The hazard factor map covered -0.2 _ f S +0.2
Acquisition and Use of Orlando, FL and Continental Airbus Radar Flight Test Data

Questions and Answers

Q: Roland Bowles (NASA Langley) - Do you consider the ground clutter problem, both fixed

and ground moving, solved?

A: Bruce Mathews (Westinghouse) - Yes, I do. The only thing I see remaining is a

demonstration of the hazard factor accuracy in the presence of competing clutter.

Q: Roland Bowles (NASA Langley) - On a couple of the charts I saw the words "proven

performance." On what kind of scientific basis do you claim proven performance, and would that

be admissible in your certification initiative?

A: Bruce Mathews (Westinghouse) - I am not sure what is admissible to certification. We are

engineers we are not scientist, we are not doing science. We have a great deal of faith and

understanding in the principles of radar. We believe what we see, and it correlates very well with

the TDWR. When they say they have an outflow reflectivity and velocity and we get the same

thing, that is what we expect, and we are getting it. We do have a limited amount of In Situ data

that we collected. We do not have a great deal of faith in it and there is not much we can do

because it is limited.

Q: Roland Bowles (NASA Langley) - Do you plan to get it?

A: Bruce Mathews (Westinghouse) - We plan to get it this summer.

Q: Roland Bowles (NASA Langley) - Can you show us how your radar correlated, in your one

hundred events, with the TDWR data?

A: Bruce Mathews (Westinghouse) - I think we can show that, yes. But, I don't have a

viewgraph to show it right now.

Q: Roland Bowles (NASA Langley) - Dave Hinton talked about this yesterday, and I think

Steve Campbell will further elaborate on it. Depending on how you flew and where you were

relative to the divergent center, the TDWR could be viewed as significantly overestimating. We

went through a very careful selection criteria to pull out the microburst encounters that really

warranted detailed inspection. I would appreciate it if you could show us sometime what you

have done, maybe later in the conference.

A: Bruce Mathews (Westinghouse) - Well, we are hoping to get that data. We do not have In

Situ data, so we can't give you that kind of analysis. That is all there is to it. The data we have

from our Sundstrand is very unsatisfactory.

Q: Jim Evans (MIT) - First, I would like to make a comment on the value of In Situ data. One

of the key issues is the altitude dependence of the outflows, and where you are measuring versus

where you should be measuring. We are flying our tests up at 1000 feet or above and we think

the threat is a lot worse at lower altitudes. I think that is the first point we ought to recognize.

387
The value of In Situ is somewhat limited here because in fact you are not totally realistic as to

where you should be flying. But that leads to another question. At what altitude where you

attempting to measure in the measurements that we saw here? That is a very important issue in

terms of your overall system performance and it has important implications. You did not really

say at what altitude your antenna measures?

A: Bruce Mathews (Westinghouse) - We showed a two bar scan. We have one bar which we

call an upper bar which points up and its principle purpose is to look at the reflective core and to

make a higher altitude measurement of the outflow. As you can see in some of the displays, there

was a stronger outflow in that upper bar than in that lower bar. The lower bar looks as near to

the glide slope as a function of altitude as we dare. We tend to pick the beam up to keep the

receiver from saturating, to stay in linear operation and to avoid unwanted clutter and saturation

effects in the receiver. We picked the beam up as we come down in altitude. Now for these flight

test in Orlando that beam was probably not doing a lot because we were flying fairly level at 1000

feet. Whcn we land into Newark we are picking the beam up as a function of altitude controlling

the beam with aircraft data. That is why the Continental Air Bus flight is important, to see how

well that algorithm works. Some of the adjustments we wave made were to pick that up a little

bit faster, because we saw a little bit more three sigma chatter in the elevation accuracy of the

antenna than we had anticipated. Summarizing, we seek to make an estimate or a statement of

the hazard factor along the glide slope that the pilot is flying. We look with two beams, one well

above the glide slope and one very near to the glide slope to make that estimate.

Q: Pete Sinclair (Colorado State University) - How was the vertical motion determined?

A: Bruce Mathews (Westinghouse) - Westinghouse determines the vertical hazard factor using

an algorithm which we would say is an extension of the NASA work that Dan Vicroy has

reported. Because we have a two elevation bar scan, we measure the outflow velocities at two

altitudes. Now, if you have two points you can draw a line between them. If you have a linear

polynomial and you integrate it like you would for a conservation of mass principle, like Dan uses

in his treatment of vertical estimation, you would get a quadratic polynomial, and that is what we

do.

Q: Pete Sinclair (Colorado State University) - Was aircraft data or radar data used in this

calculation?

A: Bruce Mathews (Westinghouse) - It is all radar data.

Q: Pete Sinclair (Colorado State University) - At what altitude is the calculation valid?

A: Bruce Mathews (Westinghouse) - The altitude is the altitude along the glide slope, that is

what the calculation is made for. It is for every range gate along the glide slope. There is a

separate vertical hazard factor calculated for each one of those range gates.

388
Session V. Doppler Related Research

389
390
Session V. Doppler Related Research

N98-19604

Vertical Wind Estimation from Horizontal Wind Measurements

Dan Vicroy, NASA Langley Research Center

PREGEDING P_GE _LAr,_Y, NOT FILMED

391
392

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°_

-cO

=_., ,,<

0 Z
Outline

• The Downdraft Measurement Problem

• Initial Research Activities & Results

• Current Methodologies

• Summary and Future Activities

This presentation will begin with a brief description of the downdraft

measurement problem for airborne Doppler based systems and the

importance of the downdraft in assessing the hazard posed by a

microburst wind shear. This will be followed by a review of research

on the feasibility of using simple microburst models to compute the

downdraft from horizontal wind measurements. The current

methodologies for computing the vertical wind will then be discussed.

A summary of the results and the plan for future research will conclude

the presentation.

393
Downd raft Measuremelti hi_Pr°blem t_

Doppler type sensors measure I

line-of-sight velocities ._ ,_ ---,-"_"

.......:_:..,:_:!_ 11Jli_ _ I _ _ _ _'" '

Unable to measul e velocities perpendicular to line-of-sight

Two of the airborne forward-look sensor technologies being tested

to provide advanced warning of wind shear are Doppler RADAR and

LIDAR. Both measure the Doppler shift of reflected light or radio

waves from the aerosols, rain drops and other debris in the air, to

determine the line-of-sight relative velocity of the air. An inherent

limitation of this type of system is its inability to measure velocities

perpendicular to the line-of-sight. The presence of a microburst can

be detected by measuring the divergence of the horizontal velocity

profile, yet, the inability to measure the downdraft can result in a

significant underestimate of the magnitude and spatial extent of the

hazard.

394
NASA I.aRC

Wind Shear Hazard Index

The "F-factor"

For straight and level flight

F= __---w

8 V

Related to the potential rate of climb

The magnitude of the hazard posed by a microburst to an airplane

can be expressed in terms of the "F-factor "t. The F-factor is a

nondimensional hazard index that is directly related to the potential

rate of climb capability of the airplane in wind shear. For straight and

level flight the F-factor is a simple function of the rate of change of the

horizontal wind (u), the vertical wind (w), the acceleration due gravity

(g), and the airplane's airspeed (V). Positive values of F indicate a

performance-decreasing situation, and conversely, negative values

indicate a performance-increasing condition.

Bowles, Roland L.: Reducing wind shear Risk Through Airborne Systems

Technology. 17th Congress o! the International Congress of Aeronautical

Sciences, Stockholm, Sweden, September 1990.

395

COLOR pH©i OGIV, PH

This chart shows F-factor contour plots and the wind velocity

vectors for an axisymmetric microburst at four stages in its life cycle.

This microburst was generated with the Terminal Area Simulation

System (TASS) high-fidelity atmospheric model, t The F-factor

contours were computed for an airplane flying level at 130 knots. The

contours on the left include the vertical wind in the F-factor calculation

while the contours on the right do not. The contours on the right

represent the detectable hazard from solely horizontal wind

measurements. The magnitude and spatial extent of the detectable

hazard is clearly diminished. This chart illustrates the need for some

means of estimating the magnitude of the vertical winds from the

horizontal wind measurements.

t Proctor, F. H.: The Terminal Area Simulation System. Volume I: Theoretical

Formulation. NASA CR-4046, April 1987.

NASA LaRC

Initial Research Activities

• Focused on downdrafts in microbursts

• Tried three microburst downdraft models of varying complexity

Linear model

Empirical model

Ring Vortex model

The initial research objective was to determine the feasibility of

computing the downdraft of a microburst from horizontal wind

measurements using simple microburst models. No attempt was

made to compute updrafts or vertical winds from other weather

phenomena, such as gust fronts, since these were considered

performance increasing and thus were not hazardous. Three

microburst downdraft models were tested. The three models

represented varying degrees of complexity. The linear model was the

simplest and the ring vortex model was the most complex.

397
NASA I.aRC

Linear Downdraft Model

Based on:

Conservation of mass Linear variation with altitude

au +aw+u (au ul aw

W,= --Z ¥ r=o w--z

Downdraft Is linear near core

6001

(n

3

1013

Nonlinear near outflow vortex

The "linear model" is the simplest of the three models tested. It is

based primarily on the principle of conservation of mass, which is

expressed on this chart in cylindrical coordinates. If the vertical wind

is assumed to be zero at the ground and vary linearly with altitude,

then the vertical wind can be expressed as a simple function of the

radial velocity profile. The linear assumption appears reasonable in or

near the core of the microburst but poor near the outflow vortex.

398
Empirical Model

Model based on gonorlc shape of mealuurod mlcroburst ovonts

Radial shaping functions

Verticalvelocity

1

0 rm

-_--.Increasing radius .--...-->

Model variables

rm

G

Zm

Vertical shaping functions

Shaping function

Radius of peak radial velocity

Shaping variable

Scale factor"

Altitude of max radial velocity (Set to 60 meters)

As the name implies, this model is based on measurements of

several microburst events. The empirical model is an axisymmetric,

steady-state model that uses shaping functions to satisfy the mass

continuity equation and simulate boundary layer effects t. The shaping

functions are used to approximate the characteristic profile of the

microburst winds. The empirical model is fully defined through four

model variables: the radius and altitude of the maximum horizontal

wind, a shaping variable, and a scale factor.

t Vicroy, Dan D.: A Simple, Analytical, Axisymmetric Microburst Model for

Downdraft Estimation. NASA TM-104053, DOT/FANRD-91/lO, February 199i.

399
Ring-Vortex Model

Model based on theoretical simulation

of mlcroburat flow characteristics

Prirnc, y Vorte) Mod_ variables

Ring Rv Radius of vortex ring

Zv Ntilude of vortex ring

d Diameter of ring core

r Vortexstrength

The ring-vortex model is a theoretically derived model based on

the assumption that the flow field generated by a vortex ring near a

flat plate is similar to that of a microburst. This model has a primary

vortex ring located above the ground and a mirror image ring located

equidistant below the ground plane. The mirror image ring is used to

satisfy the no-flow through the ground boundary condition. The vortex

ring model is defined by four model variables: the radius and altitude

of the primary vortex ring, the diameter of the viscous core, and the

circulation strength.

4OO
An example of the mean and standard deviation of the downdraft

estimate errors from the three models is shown here for the TASS

axisymmetric microburst presented earlier. The errors are shown for

each altitude at which a downdraft profile was estimated. Also shown

is the error that results from assuming no downdraft (w=0). The errors

were computed in the downdraft region of the microburst as the actual

minus the estimated value. The errors increased with altitude for all of

the models and all worked well below 300 meters. The empirical

model worked particularly well in this example but had less favorable

results in other test cases.

401
NASA LaRC

12

10

8

6

4

2 0

_-4

-6

-8

-10

-12

Downdraft Estimate Errors

(In Microburlt Core)

0 Assumew=O

i I-ILinearModel

A EmpiricalModel

0 Ring.Vortex Model

9 11 13 15

Time,

-0.15

-0,12

-0.09

-0.06

-0.03

0.00

0.03

0.06

0.09

0.12

0.15

(V= 130 knoll)

The total mean and standard deviation of the downdraft error over

the full altitude range (0 to 600 meters), is shown here for each of the

four stages of the microburst. Also shown in the figure is the

corresponding F-factor error for an airspeed of 130 knots. None of the

models had significantly better performance than the others. The

linear model worked well for all the cases at altitudes below 200

meters. The empirical model produced the best results for the 11 and

13 minute cases. The 11 minute case is near the time of maximum

shear and is perhaps the most critical from a hazard perspective.

402
NASA LaRC

Initial Research Results

• Downdraft estimation errors increased with altitude

• No significant improvement with increased model complexity

• Model fitting technique requires knowledge and tracking of

divergence center

The primary result of this initial study was to establish that simple

microburst models could be used to estimate the downdraft from

horizontal wind measurements. For the three models tested the

downdraft estimate errors increased with altitude and there was no

significant improvement with model complexity. One difficulty of the

model based downdraft estimation technique is the requirement that

the model be referenced about the divergence center of the

microburst. This requirement poses system implementation issues

such as identification and tracking of the divergence center, which

were not addressed in this study. Details of this initial study can be

found in AIAA paper 91-2947 "Assessment of Microburst Models for

Downdraft Estimation" by Dan D. Vicroy.

403
NASA LaRC

Current Research Efforts

• Transformation of radial shear from microburst to sensor

referenced coordinate system

• Development of new vertical wind estimation techniques

• Application of new techniques to '91 flight test data

The new wind shear hazard criterion, which was introduced by

Mike Lewis (NASA LaRC) in an earlier presentation, defines the

hazard as the F-factor averaged over one kilometer. Since the

F-factor is now being averaged, the updrafts as well as the downdrafts

must be computed. This required a restructuring of the techniques

discussed earlier. This was accomplished by first translating the

microburst-referenced wind field to a sensor referenced coordinate

system. Simplifications were made to this transformation which

manifested new vertical wind estimation techniques from Doppler

sensor measured winds. These techniques were then tested using

measured winds from the '91 flight tests to determine their viability.

4O4
NASA IJRC

Mi=o.bu1_V rs

Ongm_ or: therefore:

"_ Sensor Origin

Radial Shear Transformation Equation

(In Microburst Core)

Assuming a symmetrical microburst

with no rotational velocity

_= _u_co,_(Om-O,)+Um._(Om-O,)

ar, arm _ sm

In the core of a microburst:

au_.__= Ur.

ar m rm

This chart shows the radial shear transformation equation from a

microburst-centered coordinate system to a sensor-referenced

coordinate system under some simplifying assumptions. If the radial

shear is assumed to be linear in the microburst core, then the

transformation equation becomes a simple equality. If this equality is

then applied to the mass conservation equation, a simple equation for

the vertical velocity gradient as a function of the sensor measured

radial shear is obtained.

405
Radial Shear Transformation Equation

r s

Sensor Origin

(Outside Microburst Core)

Assuming a symmetrical microburst

with no rotational velocity

-_-r= co_2(o,,. o,)+ .=n urm sin2 (Ore-O,)

Outside microburst core:

--_0 as rm --> oo

therefore: o_u_.__. = o_u.__m mcos2(Ore"0=)

_r s o_rm

or: °_u_.._L= ;_ °_Um

c_r, o_rm

For large rm

This chart uses the same transformation equation as the previous

chart but assumes that the measurements are made outside the

microburst core. As the distance from the microburst core increases,

simplifying assumptions can be made which result in an inequality

relationship between the vertical wind gradient and the sensor

measured radial wind.

4o6
Vertical Shear Approximation

r, >0

Assume Inside microburst core:

aw .2au,

-_= at,

-_- < 0

dr,

Assume outside microburst core:

_w.__

_z at,

or

By combining the results of the previous two charts a simple

approximation for the vertical wind gradient as a function of the sensor

measured radial wind can be postulated.

407
Vertical Wind Estimation Methodology

Linear Method Empirical Method

w - zaw aw

-_ w .-_(z) _-

1_o q(z)

0

100 200 300 400 500 600

z, meters

With an estimate of the vertical wind gradient in hand, the next step

was to develop methodologies for computing the vertical wind from the

vertical wind gradient. Two methodologies were developed. The

simplest was the previously tested linear method. The other method

was a derivation of the empirical model used in the initial study. The

vertical shaping functions were used to define an aflitude dependent

function for computing the downdraft in the microburst core, and the

linear method is used to compute the updrafts outside the microburst

core.

408
NASA LaRC

25

Event 143 In Situ Data Winds

| , | - .

"250 2 4 6

, • , . , ..... :-I_,0:;_

8 10 12 14 16 18

Range, nm

A quick test of the two new methodologies was conducted using the

In Situ measured winds from microburst and gust front penetrations

during the '91 flight tests. Presented on this chart are the horizontal

(U) and vertical (W) wind measurements of microburst event 143. The

horizontal wind was used as input into the vertical wind estimation

methodologies. The measured vertical wind was used to compare with

the esitmated value.

4O9
In Sltu Data Results for Event 143

The vertical wind estimation results are shown on this chart for the

new linear and empirical methods. As can be seen there is very little

difference between the two methods for this particular case. The

difference between the two methods only manifests itself at altitudes

above 400 meters. This data was obtained at an altitude of about 300

meters. In general the vertical wind estimate follows the measured

vertical wind profile. However, localized fluctuations in the horizontal

wind profile resulted in spikes in the vertical wind estimation. This

would indicate that the horizontal wind profile may need to be filtered to

provide a smooth input for vertical wind estimation.

410
Event 175 In Situ Data Winds

10

¢n

50

-10

-2O

0 2 4 6

Range,

As mentioned earlier, the vertical estimation methods were also

tested using gust front data. Presented on this chart are the horizontal

and vertical wind measurements of gust front event 175.

411
5

4

3

2

_io

-1

-2

-3

-4

I I

"50 1 2

In Situ Data Results for Event 175

"----- In Situ Data

Empirical

_/tr_ A

3 4 5 6 7

Range, nm

1200

1000

80O

6O0

40O E

2oo_

o _/

-200

-g0O

-800

-1000

8

Once again, the difference between the two methods is small at the

altitude at which this data was collected. The methods estimated the

updraft fairly well, but considerably over estimated two downdrafts.

The current methodologies assume any divergence is a microburst and

compute the downdraft accordingly. This can lead to the large

downdraft estimates shown here. Some signal processing may be

required to test the extent of the divergence and classify as a

microburst or a local fluctuation accordingly.

412
I

NASA IJRC

Results Summary

• Simple analytical models are sufficient for computing vertical

winds at altitudes below 600 meters (-2000 ft).

• May need to tailor the vertical shear approximation to signature of

radial shear measurement (how linear;is the shear measurement

over a given range?)

• Estimate of vertical wind is sensitive to "noise" in radial shear

value

The preliminary data obtained to date would indicate that the simple

analytical methods discussed here should be sufficient for estimating

the vertical winds from horizontal wind measurements. However, there

is still some signal processing research required to improve the vertical

wind estimates and reduce the sensitivity to local fluctuations in the

horizontal wind profile.

413
Future Activities

FOCUS:

System implementation issues

- Clutter

- Resolution

Signal prOCessing?

Approach:

Use sensor simulations with high fidelity asymmetric

microburst models

Compare simulation results with flight test data

Future research efforts will focus on the system implementation

issues for utilizing the two vertical wind estimation methodologies.

The signal processing required to distinguish small scale vertical wind

fluctuations from larger scale microbursts will be a large part of this

research. The forward-look sensor characteristics, such as

signal-to-noise ratio and range gate resolution, must be accounted for

in the signal processing.

Sensor simulations with high fidelity asymmetric microburst models

will be used to develop the signal processing. Once developed, the

simulation results can be tested against flight test data to assess the

"real world" performance.

414
COLOR PHOTOGRAPH

This last chart is used to illustrate the signal processing problem.

Shown here is a surface plot of the horizontal wind measurement from

a range/azimuth scan of an airborne Doppler radar. Included on the

surface plot are the F-factor contours. Clearly, the signal processing

will play an important role in hazard identification.

415

Vertical Wind Estimation from Horizontal Wind Measurements

Questions and Answers

Q: Craig Wanke (MIT) - I have a question about determining whether you are inside the core

or outside the core. Do you need somehow to estimate in real time where the core of the

microburst is or to know your distance from it somehow, to apply this?

A: Dan Vicroy (NASA Langley) - I probably wasn't very clear on that. Part of the problem

with the model base approaches that I showed early on was that they were all referenced to the

center of the microburst. Consequently, you did have to track the microburst and determine

where the center of divergence was. We decided that was definitely not a good approach. The

second methodology that I showed, which is the current implementation, just looks at the sign of

the divergence, and if it is a positive divergence then you assume that you are in a microburst core

and if it is a negative divergence then you are outside of the microburst core. That is probably too

simplistic. Perhaps what you need to do is tailor the vertical shear approximation by doing a

linearity check. If it is a positive divergence and that divergence is fairly linear over a given range,

then perhaps you can assume that you are in a microburst core and then estimate the vertical wind

accordingly. If it is not very linear over the appropriate range, then you can say that is just

turbulence or a small downdraft and you would not want to treat it as a microburst.

Q: Pat Adamson (Turbulence Prediction Systems) - From the dual Doppler analysis,

particularly in the Denver are, it was not uncommon to have 2:1 asymmetric events, as well as dry

events with a low signal the noise. Have you done any error calculations on the estimation of

vertical winds under those conditions?

A: Dan Vicroy (NASA Langley) - I haven't yet. That is part of that future work that we hope

to wrap up by the end of the summer. The microburst simulations that I will be using from Fred

will all be asymmetric, they will not be axisymmetric.

Fred Proctor (NASA Langley) - I have looked at a couple of very asymmetric events using this

technique and it does surprisingly well.

416

Session V. Doppler Related Research

N93-19605

Microburst Characteristics Determined from 1988-91 TDWR Testbed Measurements

Paul Biron, MIT Lincoln Laboratory

Mark Isaminger, MIT Lincoln Laboratory

417
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419
Vugraph Text - Biron/Isaminger Papers

Vugraph #1 "Outline"

Under FAA sponsorship, the MIT Lincoln Laboratory has conducted experimental

windshear measurements at a number of locations since 1985:

1985: Memphis, TN

1986: Huntsville, AL

1987-88: Denver, CO

1989: Kansas City, MO

1990-91: Orlando, FL

The principal sensor was the TDWR testbed radar (S-band with 1° beamwidth through

1990, C-band with 0.5 ° beamwidth since 1991). Supporting sensors have included the

UND C-band Doppler radars (1986-91) the MIT C-band Doppler radar (1991) and a

sizable surface mesonet (measuring average and peak temperatures, humidity and winds

1/minute).

This paper presents some recent results (extending those in the paper by Wolfson, et al. at

the 19th Conference on Decision and Control) germane to airborne windshear system

design and certification. We will first discuss the data analysis procedure and the

associated caveats. The relative frequency, severity and duration of microburst hazards at

the various locations is important for determining the tradeoffs between safety and

operational impact of false alerts which are encompassed in detection system thresholds.

We next consider radar/lidar design issues such as reflective in mierobursts and the

vertical structure of outflows. A companion topic, gust front characteristics, is discussed

in a paper by Klingle, et al. at the vugraphs end of this talk. Finally, we provide recent

surface thermodynamic data associated with microbursts.

420
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421
Vugraph #3 "Assessment Procedure"

The TDWR testbed radar meteorologists have compiled gross microburst structure

information on a large number of the microbursts. The meteorologist inspects the TDWR

surface scan radial velocity field with the TDWR microburst detection algorithm overlaid

on the image. The meteorologist clicks the mouse on the radial velocity image pixels

characterizing the maximum and minimum velocity associated with a rnicroburst as well

as the midpoint. The velocity values, locations and corresponding reflectivity values are

stored in a computer data base. The shear is estimated by the equation S = AV / AR

where AV is the velocity difference and AR (i.e.., the "width" ) the distance between

maxima and minimum velocities.

Thus, localized high shear regions such as discussed by Campbell and Proctor in this

conference are not captured by this approach, (i.e., the shears computed generally are a

lower bound to shear averaged over typical distances such as 1-2 kin).

Since only horizontal velocities are considered, and the degree asymmetry is not known,

the vertical component is not directly considered. It should also be noted (see Campbell

paper in this conference) that the altitude at which the surface tilt measured may have

biased velocities downward. The data base considers microbursts at ranges out to at least

30 km and, one expects (from geometrical arguments) that the bulk of the data is a

relatively long range where horizon effects tend to create a beam volume at higher

altitude.

In some cases, we have high resolution vertical profiles from RHI scanning on

microbursts at close range. The 1991 data is particularly useful in this respect due to the

0.5 ° beamwidth.

422
423
Vugraph #4 "Dist. of MB Strength"

The number of microbursts detected in real time (shown in parentheses next to the bar

code) varies considerably between the various locations. The Huntsville results were

biased low (by a factor of approximately 2) by lack of real time automatic detection

algorithm outputs. The Kansas City data reflects a year with far fewer thunderstorms

than normal. Orlando was clearly the most active location with a total of over 1600

microbursts observed through October 1991.

The next three vugraphs show the observed AV AR converted the F factor estimates using

the equation

F -- K*AV / AR

corresponding to a flight at a ground speed of approximately 130 knots. We see that all

locations have at least 100 such events with Orlando having over 300 such events in

1991.

424
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The Denver microbursts axe seen to have almost an uniform distribution of duration out

to 35 minutes duration. By contrast, Orlando appears to have a bimodal distribution with

modes centered at durations 8 minutes and 20 minutes respectively.

429
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Vugraph #9

The range in outflow reflectivities at the velocity maxima and minima at individual

locations vary 40-50 dB. Denver is seen to have a median reflectivity of 10 dBZ which is

some 25 dB lower than Kansas City or Orlando. For a single microburst, the outflow

reflcctivities can differ by some 35 dB. This typically occurs when a microburst downdraft that initially was in the middle of a heavy rain region migrates to the edge of the rain

region so that one portion of the microburst outflow is in a region of little or no rain. It

should be noted that detection of the low reflectivity region will be very difficult for

Doppler radars which have extended range sidelobes (e.g., due to the use of pulse

compression). Note also that a significant fraction of the Denver microbursts have

reflectivity less than 0 dBZ (the TDWR testbed has a sensitivity of approximately -5 dBZ

at a range of 50 kin).

432
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The core reflectivities are seen to be higher (e.g., by 10 - 15 dB for median levels) than

the outflow reflectivities. Note also that the range of core reflectivities is much less than

the outflow reflectivity. Most of the literature to date has focused on core reflectivities.

Thus, although most microbursts in Orlando are very wet, (nearly all core reflectivities >

40 dBZ), over 10% of the outflows are fairly dry (< 20 dBZ).

The rain rates shown at the top of the figure were computed from the relationship

Z = 295 R 1.43

where R is the rainfall rate in mm/hr and Z is the reflectivity factor in mint/m3. Note:

the rain rates sketched in on the corresponding vugraphs during the verbal presentation

were erroneously labeled as inches/M, but were actually mm/hr. At the meeting, the

threshold of rain that would raise concerns about attenuation for laser systems was stated

to be about I inch/hour. We see that approximately half of the Orlando microbursts will

have rain rates exceeding 1 inch/hour. Thus, Orlando testing will be useful in addressing

the ability of laser systems to work in heavy precipitation.

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The spatial scale of microburst outflows is important for design of spatial filtering

algorithms. The outflow size is seen to be quite similar with Orlando having slightly

larger widths. A number of outflows are less than 1 nmi wide.

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Since small events pose a difficult detection challenge, the magnitude of the wind

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correspond to a wind change of less than 30 knots. However, there are some strong (> 40 knot) small events.

438
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The vertical structure of microburst events is a key issue in forward looking sensor

design. The bulk of the reported results on vertical profiles have been flawed by the prior

vertical resolution associated with:

1) the use of PPI scans with relatively widely spaced elevation angles and/or

2) the inclusion of data from long ranges where the radar beam vertical extent is

large relative to the microburst variation with height.

In Orlando, we are attempting to improve this situation by taking advantage of the narrow

beamwidth (0.5 o) of the TDWR testbed. Microbursts within 10 km (corresponding to a

beamwidth vertical extent < 80m) are scanned using RHI scans to provide closely spaced

vertical measurements.

We see a wide variation in vertical profile between events and during an individual event.

The drop offin velocity from the surface to 300m AGL is about 6 knots (15-30%) for the

9 August event at 1938 GMT and for the 28 September event. By contrast, we see

similar drop off between the surface and 100m AGL for the 3 October event and the 9

August event at 1956 GMT. The August 1991 events show a 33-50% drop off in velocity

at 150m AGL.

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The surface mesonet system used with the TDWR testbed measures temperature every 7

seconds and records the 1 minute average and peak values. The figure shows the

temperature changes associated with all microbursts (AV > 10 m/s) which impacted the

mesonet. We see that there is a wide variation in temperatures at the surface with a

significant fraction of the events having temperature drops less than 2° C.

It should also be noted (see attached article by Klingle-Wilson, et al) that most gust fronts

have temperature changes of -7 ° .

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446
CHARACa_RISTICS OFGUSTFRONTS *

Diana Klingle-Wilson and Michael F. Donovan

Lincoln Laboratory, Massachusetts Institute of Technology

P. O. Box 73; Lexington, MA 02173

1. INTRODUCTION

A gust front is the leading edge of a thunderstorm

outflow. A gust frontal passage is typically characterized by

a drop in temperature, a rise in relative humidity and pressure, and an increase in wind speed and gustiness.

Gust front detection is of concern for both Terminal

Doppler Weather Radar (TDWR) and Next Generation

Weather Radar (NHXRAD) systems. In addition, airborne

systems using radar, lidar, and infrared sensors to detect

hazardous wind shears are being developed (Bowles and

Hinton, 1990). The automatic detection of gust fronts is desirable in the airport terminal environment so that warnings

of potentially hazardous gust front-related wind shears can

be delivered to arriving and departing pilots. Information

about estimated time of arrival and accompanying wind

shifts can be used by an Air Traffic Control (ATC) supervisor to plan runway changes. Information on expected wind

shifts and runway changes is also important for terminal capacity programs such asTerminal Air Traffic Control Automation (TATCA; Spencer, et aL, 1989) and wake vortex advisory systems.

In addition, the convergence associated with gust

fronts is often a factor in thunderstorm initiation and intensification. Knowledge of gust front locations, strengths, and

movement can aid forecasters with thunderstorm predictions.

Current gust front detection systems generally are reliable in that the probability of false alarms is low. However

the probability of detecting gust fronts with these systems

is less than desired (Evans, 1990). Improved characterization of gust fronts is a key element in improving detection

capability.

Typically, the basic products from the algorithms are

the location of the gust front (for hazard assessment) and

its propagation characteristics (for forecasting). This paper

discusscs the thermodynamic and radar characteristics of

gust fronts from three climatic regimes, highlighting regional differences and similarities of gust fronts. It also compares propagation speeds, estimated by two techniques, to

measured propagation speeds.

*The work described here was sponsored by the Federal Aviation Administration, The United States Government assumes no

liability for its content or use thereof.

2, DATA AND METHODOLOGY

Measurements made as a part of the Federal Aviation

Administration (FAA) TDWR operational demonstrations

held in Denver, CO (1988); Kansas City, MO (1989); and

Orlando, FL (1990) are used to characterize gust fronts. To

support the operational demonstrations, a 30- to 40-station

mesoscale network (mesonet) of automatic weather stations,

with an average inter-station spacing of 1.4 - 2.1 kin, was

sited at each airport to measure surface winds, temperature,

relative humidity, pressure, and rainfall amounts every minute (Wolfson, 1989). Only gust fronts that passed through

the mesonet were considered in this study.

The requirement that a gust front pass over the mesonet limited the number of gust fronts available for analysis.

Ten Denver, nine Kansas City and 13 Orlando gust fronts

were chosen. Mesonct data were used to determine the surface thermodynamic and kinematic characteristics of gust

fronts, while reflectivity thin line characteristics were

derived from the TDWR testbed radar (FL--2). Wolfson, ct

al. (1990) present statistics on gust front strength, length,

duration, propagation, depth, and temperature difference

between the ambient and outflow air. This paper extends

that analysis by characterizing the thermodynamic structure

and radar reflcctivity thin line signatures of gust fronts from

the different climatic regimes.

Gust front temperature and relative humidity were

taken from the mesonet data. Figure I shows a time series

30

15

Gust Frontal Passage

r--

./

22OO 2210 2220 2230 224O 225O

Time 0JTC)

FiBure 1. Time seriea of typical temperature (°C) and relative humidity associated with a Bust frontal passase.

ii

plot of the typical temperature and relative humidity associated with a gust frontal passage over a mesonet station.

The sharp decrease in temperature and rise in relative humidity at 2215 UTC mark the passage of the gust front. For

447
this gust front, the ambient temperature was 23°C, the outflow temperature was 18°C, and the temperature difference

was 5oc. The ambient relative humidity was 50%, the outflow relative humidity was 100%, and the relative humidity

difference was 50%. These data were tabulated for each station that experienced the passage of a gust front. The data

were then averaged to derive characteristic temperatures

and humidities for each gust front.

Gust front propagation speeds and refleetivity thin

line characteristics were derived from single-Doppler radar

data. The average and peak reflectivities, as well as the average reflectivity ahead of and behind the thin line, were extracted from each gust front event that exhibited a thin line.

An event is a single observation of a gust front on a radar

volume scan as determined by subjective analysis. Thus, a

single gust front scanned five times by the radar would result

in five gust front events.

3. GUST FRONT CIIARACrERISTICS

Figure 2 provides the distribution of some temperature and relative humidity characteristics of Denver, Kansas

City, and Orlando gust fronts. Negative temperature differences indicate that the outflow air was cooler than the ambient air. Averages computed from these data are presented

in Table 1. For one Kansas City gust front the outflow was

silghtly warmer and less moist than the ambient air.

Table 1. Averages of maximum outflow temperature (max_"_),

minimum outflow temperature (min'T'H_), outflow temperature ('_),

ambient temperature (Tamb), ambient-outflow temperature difference (AT), maximum outflow relative humidity (maxRH#), minimum outflow relative humidity (minRH_), outflow relative humidity

(RHIj), ambient relative humidity (RHamb), and outflow-ambient

relative humidity difference (A"R--H). Temperatures are in *C and

relative humidities are in percent.

Denver

maxTsr (°C) 30

minTer (*C) 18

T# (*C) 24

T, mb (*C) 29

AT (*C) -5

maxRHsf (%) 82

minRl-lgf (%) 23

RI-18f (%) 50

RH, mb (%) 30

i_ (%) 20

Kansas City Orlando All

27 29 30

14 20 14

21 25 23

25 32 29

-4 -7

100 100

53 65

86 84

74 58

12 26

-6

100

23

74

54

20

Kansas City outflows exhibit the greatest range in

outflow temperatures (13"C), followed by Denver and then

Orlando. Kansas City average ambient and average outflow

temperatures are colder than Denver and Orlando temperatures, but the average temperature difference between the

outflow and ambient air is smallest in Kansas City.

The relative humidity data show that outflows are

driest in Denver. On average, the largest difference in ambient-outflow relative humidity is associated with Orlando,

followed by Denver and Kansas City.

Outflows from thunderstorms have been shown to be

dynamically similar to density currents (Charba, 1974). A

density (gravity) current is generated whenever a fluid of

greater density moves through a fluid of lesser density. The

motive force of the gravity current is the hydrostatic pressure difference between the two fluids. Equation 1 expresses

gust front propagation speed in terms of the depth of the

outflow head and the difference in virtual temperature between the warm and cold air (Seitter, 1983). This equation

1/2

k,FH 'l "(Eqn. 1)

where:

V = gust front propagation speed

k' = redefined Froude number ('l)

g = acceleration of gravity

H = depth of gust front head

ATv = difference in virtual temperature between warm and

cold air

Tv = vinual temperature of the warm air.

was used to estimate the propagation speed of the Denver,

Kansas City, and Orlando gust fronts for comparison to

measured propagation speeds, as deduced from radar data.

Head depth was estimated from radar data and virtual temperature was estimated from temperature and relative humidity. The comparison of propagation speeds computed

from Seitter's technique and measured propagation speeds

is given in Figure 3. In two Denver and three Kansas City

cases, the gust fronts did not propagate away from the leading edge of the parent storm and outflow depth could not

be estimated. These gust fronts are not represented in

Figure 3.

3O

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0

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o s ,o ,'5 3o

X Denver

• Kansas Cit_

+ Orlando

Measured Speed (m/s)

Figure 3. Estimated versus measured gust front propagation speea

Estimated values were computed from Seitter's technique. In cases

where data points overlap, the numbers of points for each location

(D: Denver, K: Kansas City, O: Orlando) are shown in parentheses.

448
70-

60-

50-

40-

3O

20-

10-

70

50

30

20

0

Denver 1988

12.5 22.5 32.5 42.5

12.5 22.5 32.5

GUST FRONT THERMODYNAMIC CHARACTERISTICS

Kansas City 1989 Orlando 1990 ALL

a) Average Ambient Temperature (°C)

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5O _

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e) Averal[e Outflow Humidity (%_

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f) Average Relative Humidity Difference (%) (outflow minus ambient)

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-35 -15 5 25 45 -35 -15 5 25 45 -35 -15 5 25 45 -35 -15 5 25 45

Figure 2. Relative frequency (%) of the average (a) ambient temperature (*C); (b) outflow temperature (*C); (c) temperature difference

(*C) between the outflow and ambient air; (d) ambient relative humidity (%); (e) outflow relative humidity (%); and (f) relative humidity

difference (%) between the outflow and ambient air. The values on the abscissa are the midpoints of the interval.

449
Goff (1976) found that propagation speed was roughly 67% of the maximum wind speed in the outflow. This

estimate of propagation speed is compared to the measured

speeds in Figure 4.

30

X Denver

• Kansas City

_- 2s 4" Orlando

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Figure 4. Estimated versus measured propagation speed. Estimated values were derived from Goff's technique. In cases where

data points overlap, the numbers of points for each location (D: Denver, K: Kansas City, O: Orlando) are shown in parentheses.

Propagation speed is generally overestimated using

Seitter's technique, although the estimated speeds for Kansas City gust fronts were less than the measured values.

Gofrs technique also tends to overestimate propagation

speed, but to a lesser degree than Seitter's technique. The

average differences and average absolute differences bev, veen the measured and estimated speeds are given in

Table 2. The two techniques provide about the same performance for Denver gust fronts, but Golf's estimate is better for Kansas City, Orlando, and over all.

Table 2. Average and average absolute differences between estimated and measured propagation speed for Denver, Kansas City,

Orlando, and All locations.

Average

Average Absolute

Location Difference Difference

Seitter's Technic ue

Denver 3.3 4.0

Kansas City 0.8 5.2

Orlando 6.3 6.3

All 4.2 5.4

Gott's Technique

Denver 3.0 3.2

Kansas City 0.1 3.0

Orlando 0.8 2.4

All 1.3 2.8

Figure 5 shows gust front duration, propagation

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Figure 5. Gust front duration, propagation speed, and outflow

depth as functions of the average ambient-outflow temperature difference (-A'T) and relative humidity difference (_"_-_).

flow temperature and relative humidity differences for gust

fronts at the three sites. Since the gust front motive force

is the hydrostatic pressure difference between the outflow

and ambient air, one would expect those outflows exhibiting

the largest temperature differences to move fastest and last

longest. The data do not support this expectation, possibly

because the velocity of the opposing ambient flow is not considered. In addition, gust front strength is determined from

Doppler velocities. Since the radar senses only the alongthe-beam component of the flow, strength estimates may

be incorrect.

Reflectivity data from gust front events is provided

in Figure 6. For detection algorithms, it is important to know

not only the reflectivity characteristics of the thin line, but

also the reflectivity characteristics of the air on either side

of the thin line. For this reason, reflectivities ahead of and

behind the gust front are given. Mean values for the measured variables are shown in the upper right corner of each

plot. There appears to be no strong regional influence on

the peak and average reflectivities in the thin line or in the

average reflectivity behind the thin line (i.e., in the cold air).

However, the reflectivities of the air ahead of the thin line

(i.e., in the warm air) are lower in Denver (-7 dBZ) than

in Kans,ls City (-A.dI_Z) and Orlando (-3 dBZ), although

these differences are small. If the thin line is visualized as

a "wrinkle in a rug" then the wrinkle is higher, and therefore

possibly easier to detect, in Denver.

450
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Kansas City 1989 Orlando 1990

a) Peak Reflectivity in Thin Line

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Reflectivity (dBZ) Reflectivity (dBZ)

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Reflectivity (dBZ)

Figure 6. Reflectivity characteristics of gust fronts represented by relative frequency of events at three airports (Denver, Kansas City, and

Orlando) for the measured variable. The rightmost graph in each row shows the relative frequency of the measured characteristic for all gust

fronts (ALL).

451
4. SUMMARY

The key to detecting gust fronts is the accurate characterization of the phenomena. Some algorithms rely heavily on radar signatures of gust fronts, while others are based

upon sensors that measure temperature changes across the

gust front. Regardless of the sensor used to detect gust

fronts, it is important to understand the differences and similarities in gust fronts over a variety of climatic regimes.

This paper has shown for the cases studied here that

Kansas City outflows are colder than Denver and Orlando

outflows; and that Denver outflows are driest. However, the

ambient-outflow temperature and relative humidity differences are greatest in Orlando.

Two techniques were used to estimate gust front

propagation speed. Seitter's method, which used virtual temperature and outflow head depth, overestimated propagation

speed. Goff's method also overestimated propagation speed,

but to a lesser degree.

Reftectivity thin lines were also analyzed. The values

of reflectivity in the thin lines showed no regional bias. However, the reflectivity of the ambient air was lowest in Denver,

which may make Denver thin lines easier to detect.

5. REFERENCES

Bowles, R. L. and D. A. Hinton, 1990: Windshear detection: airborne

system perspective, Windshear - One Day Conference, London,

England, 1 November 1990.

Biron, P.J., and M.A. Isarninger, 1989: An analysis of microburst

characteristics related to automatic detection from Huntsville, AL

and Denver, CO. Preprints, 24th Conference on Radar Meteorology, Tallahassee, Amer. Meteor. So¢., 269-273.

Charba, J., 1974: Application of g-current model applied to analysis

of squall-line gust front, Mon. Wen. Rev.. 102: pp. 140-156.

Evans, J. and D. Turnbull, 1990: Development of an automated windshear detection system using Doppler weather radar. IEEE Proceedings, 77, 1661-1673.

Evans, J.(ed.), 1990: Results of the Kansas City 1989 Terminal

Doppler Weather Radar (TDWR) Operational Evaluation Testing,

DOT/FAA/NR-90/1 (ATC-171), 78 pp.

Golf, R. C., 1974: Vertical Structure of Thunderstorm Outflows,

Wen. Rev.. 104; pp. 1429-1440.

Merritt, M.W., D.L. Klingle-Wilson, and S.D, Campbell, 1989: Wind

shear detection with pencil beam radars. The Lincoln Laboratory

Journal, 2, 483-510.

Seitter, K, L., 1983: Numerical simulation of thunderstorm gust fronts,

Air Force Geophysics Laboratory Rept. No. AFGL-TR-83-0329,

Envir. Res. Paper No. 862, 34 pp.

Spencer, D. A., J. W. Andrews and J. D. Welch, 1989; An Experimental Examination of the Benefits of Improved Terminal Air

Traffic Control Planning and Scheduling, The Lincoln Laboratory

Journal, V. 2, No.3, Fall 1989, pp. 527-536.

Wolfson, M., D. Klingle-Wilson, M. Donovan, J. Cullen, D. Neilley,

M. Liepins, R. Hallowell, J. DiStefano, D. Clark, M. Isaminger,

P. Biron, B. Forman, 1990: Characteristics of thunderstorm-generated low altitude wind shear: A survey based on nationwide terminal doppler weather radar testbed measurements, Proceedings

of the 29th Conf. on Decision and Control, Honolulu, HI, pp.

682-688.

Wolfson, M.M., 1989: The FLOWS automatic weather station network. J. A¢mos. Oceanic Technol., 6, 307-326.

452
Microburst Characteristics Determined from 1988-91 TDWR Testbed Measurements

Questions and Answers

Q: Roland Bowles (NASA Langley) - In the discussion of your vertical structure charts, for

those two events, where was the event relative to the radar?

A: Jim Evans (MIT) - The top event is at a range of seven kilometers. This is scanned

vertically and you can see the little x's on all the data points that are actually measured as

individual measurements. In the blue event the radar range is 2.7 kilometers, and this is a half

degree beam. What we have told them to do is when they see a microburst within about 7 or 8

kilometers to go into an alternative scan pattern and mix in RHI with PPI so that we get very high

resolution on the outflows. We have been concerned ourselves about what altitude should we be

setting our beams for the TDWR. So we have been trying to understand this whole issue of what

the structures are. You have to do it at close range and the fact that we have a half degree

elevation beam helps a lot. When we do RHI scanning we measured a whole bunch of angles, so

they are pretty closely spaced, particularly at the bottom.

Q: Roland Bowles (NASA Langley) - There is a least one publication that came out of Lincoln

that was excellent, were you published a great deal of your findings on half velocity point

disuibution; the altitudes at which the velocity was half peak. Do you have plans to publish, for

those data that you can resolve the peak outflow, those distributions?

A: Jim Evans (MIT) - I think we probably need to put out a yearly report that takes all the ones

from the preceding year and just reports them so that people in the community can use it. There

is a very thick report that has data all the way up through about 1989 and maybe a little bit of

1990, and contains everything we knew about outflow structure in the vertical domain. We will

continue to put out that report and we will continue to try to scan these things as best we can.

Roland Bowles (NASA Langley) - That would be valuable for people working with airborne

systems.

Jim Evans (MIT) - It is an absolutely key parameter both for ground based and airborne systems.

Q: Mike Lewis (NASA Langley) - It looks like you have a lot of good data there. When

talking about the summary of F-factor values, over what distance are those values taken or are

they in fact variable distances?

A: Jim Evans (MIT) - The radar range is keeping track of all the microburst at least all the way

out to 30 kilometers. The point I made was that if you start saying that the probability of the

microburst occurring is proportional to area you find out that you tend to be weighted to long

distances as opposed to short distances.

Q: Mike Lewis (NASA Langley) - Not range from the radar, but over what length were those

F-factors values calculated?

453
A: Jim Evans (MIT) - It is simply taking the difference of the maximum and minimum velocities

over whatever distance that occurred. So it is variable. That is an average shear over the outflow

region. You may have localized hot spots, which Steve Campbell will talk about. That is one of

the caveats and I want to emphasize that this is really a lower bound on what the Fs would be if

you looked over say one kilometer.

Mike Lewis (NASA Langley) - Essentially, any time you talk about F you need talk about both

magnitude and length.

Jim Evans (MIT) - Yes, I understand. The advantage of this particular data is that it is a very

large data base. You could take some selective events and go back and reprocess and probably

work out a correction.

Q: Mike Lewis (NASA Langley) - You made some points about the core reflectivity versus the

outflow reflectivity. Perhaps the message is not quite so bad for radar manufacturers trying to

measure that low reflectivity area, because, while the maximum velocities would perhaps be in

that outflow area the maximum shears are still in the core. That is the kind of region that we are

trying to measure and trying to protect from, and that perhaps is the region of somewhat higher

reflcctivity.

A: Jim Evans (MIT) - Well, I think Steve will be showing some examples of where the highest

shears are in his paper, which I believe is tomorrow. You can decide for yourself whether or not

they are in the core.

Steve Cambeli (MIT Lincoln Laboratory) - In general, is not necessarily true that the strongest

shear is were the strongest refleetivity is. When Jim said cores, he meant reflectivity cores and

that is not necessarily where the highest shear is.

Mike Lewis (NASA Langley) - O.K. I thought you were talking about the downdrafi core.

Jim Evans (MIT) - The other thing that you have to understand is that in an awful large fraction

of the events, particularly in a place like Denver, have multiple outflows bumping into each other.

That is why there is asymmetry. Nobody can make an asymmetrical microburst by itself, but they

tend to occur in families and that is what is ugly about the whole process.

Mike Lewis (NASA Langley) - My last point is that you mentioned a couple of times about the

differences of measuring at a flight test altitude of 1000 feet versus lower altitudes. It seems to

me, for the research purpose of determining if your shear detection system is measuring real shear,

it is perfectly O.K. to measure at 1000 feet and confirm or deny the measurement with either In

Situ measurement, or an estimate from TDWR, at the same altitude. It is not necessarily a flawed

flight test to measure at a 1000 feet even if the maximum shear is at 300 feet or so, as long as you

confirm your data by other 1000 feet measurements. If the shear from that confirrnation equals

the shear that your detector is predicting then you are doing a good job.

Jim Evans (MIT) - I guess I disagree. I think we are going beyond that. It isn't the proof that

you can measure velocity, I could do it at 3000 feet. The key issue that you the airline buyer and

454
theair passengeroughtto askis; doI havea systemthatcanmeasurethehazardwheretheplane

hasgot to fly or inferit properly. Thatis thekeyissue.My pointis this,if youtry to point your

antennadownatminusthreedegreestheclutterchallengegoesupdramatically.You mayhavea

systemthatis viableatmeasuringshearandvelocitiesat 1000feetandit isnotviableat

measuringdownat50feet. Thatis thequestionI think youhaveto askthesystemdesigner.

Mike Lewis (NASA Langley) - If you are talking about the clutter differences, then I agree

completely.

Jim Evans (MIT) - I am talking about the clutter. Low reflectivity microburst have cross

sections that are typical of what the military talks about as low observable vehicles. It is not easy

to build look down shoot down fighters.

I showed some probability distributions of microbursts as a function of outflow reflectivity and

some people asked about the ones that are low reflectivity events with high F values, what would

be their distribution? We will try to do that. I thought maybe we could do it for the conference,

but I think that is a little to much to promise. We do have it stored in a database. In principal

there ought to be no problem in just putting in some more side conditions. That is one of the nice

things we have been able to do by having these in a computerized database. Anyone who is

interested in finding out about low reflectivity events with high Delta V's or high F-factors and

want the characteristics of those, give me your business card and as soon as we get the results run

off we will get it to you. We will give it to Roland as well.

Q: Branimir Dulic (Transport Canada) - Why do we think the number of events in Huntsville

was underestimated?

A: Jim Evans (MIT) - It was not due to post processing. The number we showed for

Huntsville was from the real-time log of microbursts. Subsequently, there was a limited replay

operation where we were trying to decide if the radar missed microbursts. We compared the

radar to surface wind measurements. We would pick certain days that they had found microbursts

by looking at the surface wind measurements and they go back and look at the radar data. What

they discovered in doing that was that we did not miss very many events, but the real-time log

was missing about half the events that had been picked up in the post processing. It had missed

about half of those that had came down over our mesonet, our wind sensors. So on the basis of

that, I would presume that we missed about half. What happened was the humans watching the

displays in real-time you will see a really strong microburst over here and they might miss another

one over some other place that wasn't so distinct. That is the king of thing that a computer does

very well and humans get distracted. Because it was a careful but limited after the fact analysis

that show we missed about half, I think that is probably true in general.

455
456
Session V. Doppler Related Research

N 9 3 - 1 9 6 0 '_'i

Algorithms for Airborne Doppler Radar Wind Shear Detection

Jeff Gillberg, Honeywell

Mitch Pockrandt, Honeywell

Peter Symosek, Honeywell

Earl Benser, Honeywell
ALGORITHMS FOR AIRBORNE

DOPPLER RADAR

WIND SHEAR DETECTION

Jeff Gillberg

Mitch Pockrandt

Peter Symosek

Earl T. Benser

Honeywell Inc.

Systems and Research Center

3660 Technology Drive

Minneapolis, Minnesota $$418

ABSTRACT

Honeywell has developed algorithms for the detection of wind

shearlmicroburst using airborne Doppler radar. The Honeywell algorithms

use three dimensional pattern recognition techniques and the selection of

an associated scanning pattern forward of the aircraft. This "volumetric

scan" approach acquires rcflectivity, velocity and spectral width from a

three dimensional volume as opposed to the conventional use of a two

dimensional azimuthal slice of data at a fixed elevation. The algorithm

approach is based on detection and classification of velocity patterns

which arc indicative of microburst phenomenon while minimizing the

false alarms due to ground clutter return. Simulation studies of microburst

phenomenon and X-band radar interaction with the microburst ha_ e been

performed and results of that study arc presented. Algorithm performance

in detection of both "wet" and "dry" microbursts is presented.

SLOE1

Title Slide

SLmE2

The development of algorithms for detection of wind shear/microhurst using airborne

Doppler radar is a part of a larger Honeywell effort for the development of an Enhanced

Situation Awareness System (ESAS). This multifunction system will increase pilot

situation awareness through provision of landing guidance in adverse weather, detection

of severe weather, detection of severe weather, detection of microburst/wind shear,

detection of wake vortices and clear air turbulence. This integrated system seeks to

provide the above functionality with minimal impact to the aircraft and minimal

requirements for additional hardware.

SLIDE 3

The Honeywell remote windshear detectionresearchhas concentratedon development of

algorithms which arc not based on any particularsensor technology. The algorithms

4_
require measurements of air mass velocities that could be provided by laser doppler

velocimeters, other types of laser radar as well as Doppler weather radar. The algorithm

approach is based on detection and classification of velocity patterns which are indicative

of microburst phenomenon while minimizing the false alarms due to ground clutter

return. These algorithms have been developed and tested using the NASA-Langley

Airborne Windshear Doppler Radar Simulation (AWDRS) and modeling of X-band

Doppler radar characteristics.

SLIDE 4

The core of the Honeywell approach is the use of three dimensional pattern recognition

techniques and the selection of an associated scanning pattern forward of the aircraft.

This "volumetric scan" approach acquires reflectivity, velocity and spectral width from a

three dimensional Volume as opposed to the conventional use of a two dimensional

azimuthal slice of data at a fixed elevation. For each volume element (voxel) in azimuthelevation-range space the radar provides measurements of reflected power, mean velocity

and spectral width. Receiver noise characteristics are measured and used to enhance the

quality of the received data. Four separate types of features are then identified including

regions of positive and negative divergence, regions of high reflectivity, regions of

rotation and regions with similar spectral width. These regions are then collectively

assessed by a three dimensional association algorithm which identifies three dimensional

features which axe associated clusters of the four basic feature types listed above. These

three dimensional features are then compared with known attributes of microburst

phenomenon and temporally tracked to identify those three dimensional features which

are indications of microbursts.

SLIDE 5

These algorithms have been developed and tested using the NASA-Langley Airborne

Windshear Doppler Radar Simulation (AWDRS) and modeling of X-band Doppler radar

characteristics. We have modified the software for operation on our Sun workstations. It

has also been modified to provide data from a simulated volumetric scan pattern and

provides simulated output characteristic of that expected from nominal X-band Doppler

radar using a Honeywell 12-inch fiat plate antenna which produces an 8 ° beamwidth. We

have been using the axisymmetric models developed in the past and are now upgrading

the software to include the newer three dimensional models recently released.

SLOE6

The Honeywell approach was developed to minimize probability of false alarms at an

acceptable detection capability. This is accomplished by the use of full three dimensional

correlation approaches which reject those features which are characteristic of ground

clutter and are not supported by measurements throughout the three dimensional scanning

volume. Temporal tracking is used to assure the rejection of transient phenomenon.

Additional information is also used to reduce false alarm rate including thresholding

using the F-Factor hazard level, assessment of spectral width characteristics and

comparison of the physical size of the hazard with known phenomenological

understanding and aircraft upset requirements.

SLIDE 7

The volumetric velocity and hazard region data for a "wet" microburst (core reflectivity

of 60 dBz) event is shown for three elevation scans at 0 °, 20 ° and 40 ° elevations. This

459
data is simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse

repetition frequency of 6kHz and a pulse width of 11.tsec. As is shown there is a large

region at near ground level where significant hazard is identified. Note the extension of

velocity and hazard features above in the 20 ° and 40 ° slices.

SLmE8

The reflectivity, Doppler velocity, intermediate data and final identified hazard region

data for a "wet" microburst (core reflectivity of 60 dBz) event is shown. This data is

simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse repetition

frequency of 6kHz and a pulse width of ll.tsec. As is shown there is a large region at near

ground level where significant hazard is identified. Note the rejection of spurious hazard

regions surrounding the actual hazard region.

SLIDE 9

The volumetric velocity and hazard region data for a "dry" microburst (core reflectivity

of 25 dBz) event is shown for three eleva:ion scans at 0 °, 20 ° and 40 ° elevations. This

data is simulated for a IkW radar utilizing a Honeywell 12-inch antenna with a pulse

repetition frequency of 6kHz and a pulse width of llasec. As is shown there is a large

amounts of clutter at near ground level. Note the extension of hazard features above in

the 20 ° and 40 ° slices which eliminates most of the ground clutter as potential

microbursts.

SLIDE 10

The reflectivity, Doppler velocity, intermediate data and final identified hazard region

data for a "dry" microburst (core reflectivity of 25 dBz) event is shown. This data is

simulated for a lkW radar utilizing a Honeywell 12-inch antenna with a pulse repetition

frequency of 6kHz and a pulse width of 11.tsec. As is shown there is a large amounts of

clutter at near ground level. Note the rejection of the significant level spurious hazard

regions surrounding the actual hazard region

SLIDE 11

Honeywell is continuing the development of algorithms for detection of wind

shear/microburst using airborne Doppler radar as well as developing data fusion

approaches to utilize data provided from remote sensors as well as in-situ sensors for an

overall integrated wind shear detection system. We anticipate testing of the algorithms

using flight test data in 1992 and continued development of optimal guidance approaches

exploiting data from remote sensors and in-situ sensors.

46O
461
462
463
464

466
467
468
469
470
471
Algorithms for Airborne Doppler Radar Wind Shear Detection

Questions and Answers

Q: Kim Elmore (NCAR) - What did you use as a basis for using spectrum width estimates as an

indicator of a hazard? Also, simply as a caveat, we found that in the Denver environment the use

of rotation and convergence aloft does not seem to work very well as an estimation of whether

the storm is actively producing a microburst or will produce one. It does work in the wet kind of

environments in the South East, but it doesn't seem to work very well for us. Jim Wilson and Rita

Roberts spent quite some time on that and finally threw up their hands in despair. How do you

plan to address that, if this is going to be a primary constituent of the hazard determination.

Secondly, what did you use as the basis to utilize spectrum width as a hazard estimation?

A: Earl Benser (Honeywell) - Right now there is a minimum and maximum spectral width

threshold set that are used. We also look for areas that are of common spectral width for the

volumetric feature recognition. So those are the two approaches that we have been using in terms

of spectral width thresholding for detection. In terms of the correlation of all the volumetric

features simultaneously, we do not necessarily demand that they are all simultaneously present,

but we use the lack of all features as a part of the clutter rejection approach.

Q: Kim Eimore (NCAR) - I am curious as to where you got your information to form a

hypothesis about spectral width associations.

A: Earl Benser (Honeywell) - I guess I am not necessarily familiar with those details of the

activity.

Kim Elmore (NCAR) - Again, I wound up spending a lot of time chasing spectral width on

Denver storms, and we found that it was next to useless. It was extremely viewing angle

dependent. It may well be that you have done some sort of correlation with your beam width and

what kind of spectral width you can expect from a meteorological aspect. That may have some

utility. But, the work we did with our radars showed that it was not necessarily a good indicator.

Earl Benser (Honeywell) - As I understand it, and again I am not necessarily familiar with the

details of that particular part of the algorithm, there are spectrum widths that are consistent with

the type of phenomenon we are looking at. Things that have very little spectral width tend to be

point targets as opposed to distributed targets that have relatively moderate spectral widths. The

large spectral widths, as I understand it, are somewhat noisy. Anyway, the basic point is that

there is activity going on in that area. I am not really familiar with the details.

Q: Paul Robinson (Lockheed) - From your slides it looks like you could estimate the hazard at

zero degrees relative to the horizon, but you had the airplane flight path that looked like it was

three degrees down. Does that not mean that you are looking at a hazard that the airplane may

not encounter and perhaps underestimating that hazard?

A: Earl Benser (Honeywell) - The analysis that was presented today showed results where we

looked at zero up to 40 degrees in 10 degree slices. Our activity right now is looking at specific

scan pattern issues with respect to overall scan rate, scan range, and scan resolution.

,*72
Q: paul Robinson (Lockheed) - Is there some reason why the program may not work looking

three degrees down?

A: Earl Benser (Honeywell) - We do not have any reason to believe that it should not work.

We have not completed the testing. Zero degrees was the initial completed activity.

Q: Dan Vicroy (NASA Langley) - Are you estimating a vertical winds? The fact that you are

doing multiple vertical scans allows you to do some pretty interesting things in the vertical

domain.

A: Earl Benser (Honeywell) - We have not gotten that far in our efforts. To date we have been

looking at merely the in-plane velocity information, for detection of areas with consistent shear

numbers for feature identification. We have not gotten to the point to either map out the vertical

velocity structure within the events or to develop an F-factor estimate based on that initial data.

473
474
Session VI. Airborne Doppler Radar /NASA

PRE_DkNG P_GE _LA,_K r,,_OTFILMED

475
476
Session VI. Airborne Doppler Radar / NASA N93-19G07

NASA Experimental Airborne Doppler Radar and Real Time Processor for Wind Shear Detection

P. Schaffner, NASA Langley Research Center

Dr. M. Richards, Georgia Institute of Technology

W. Jones, NASA Langley Research Center

L. Crittenden, Research Triangle Institute

477
NASA Experimental Airborne Doppler

Radar and Real-Time Processor

for Windshear Detection

P, Schaffner, NASA LaRC

Dr. M. Richards, GTRI

W, Jones, NASA LaRC

L. Crittenden, RTI

478
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N93-

Session VL Airborne Doppler Radar / NASA

Ground Clutter Measurements Using the NASA Airborne Doppler Radar: Description of Clutter

at the Denver and Philadelphia Airports

S. Harrah, NASA Langley Research Center

Dr. V. Delnore, Lockheed Lockheed Engineering & Sciences

M. Goodrich, Lockheed Lockheed Engineering & Sciences

C. Von Hagel, NASA Langley Research Center

499
Ground Clutter Measurements Using the

NASA Airborne Doppler Radar:

A Description of Clutter at the

Denver and Philadelphia Airports

S. Harrah, NASA LaRC

Dr. V. Deinore, Lockheed

M. Goodrich, Lockheed

C. Von Hagel, NASA LaRC

5OO
GROUND CLUTTER MEASUREMENTS USING

THE NASA AIRBORNE DOPPLER RADAR:

A DESCRIPTION OF CLUTTER AT DENVER AND PHILADELPHIA AIRPORTS

STEVEN D. HARRAH *

DR. VICTOR E. DELNORE **

MICHAEL S. GOODRICH **

CHRIS YON HAGEL *

Detection of hazardous wind shears from an airborne platform, using commercial sized

radar hardware, has been debated and researched for several years. The primary

concern has been the requirement for "look-down" capability in a Doppler radar during

the approach & landing phases of flight. During "look-down" operation, the received

signal (weather signature) will be corrupted by ground clutter returns. Ground clutter at

and around urban airports can have large values of Normalized Radar Cross Section

(NRCS) producing clutter returns which could saturate the radar's receiver, thus

disabling the radar entirely, or at least from its intended function.

The purpose of this research was to investigate the NRCS levels in an airport

environment (scene), and to characterize the NRCS distribution across a variety of radar

parameters. These results are also compared to results of a similar study 1,2using

Synthetic Aperture Radar (SAR) images of the same scenes. This was necessary in

order to quantify and characterize the differences and similarities between results

derived from the real-aperture system flown on the NASA 737 aircraft and parametric

studies which have previously been performed using the NASA airborne radar simulation

program.

This presentation describes the research and results obtained to date. These results

were derived from data collected during the 1991 NASA Wind Shear Flight Experiment

and include: the collection of data, analysis of incidence angle effects and polarization

sensitivity, a comparison of NRCS statistics derived from the NASA radar and the ERIM

SAR, an examination of intra-image features and inter-image repeatability, and an

engineering summary of these results.

* NASA Langley Research Center, Hampton, VA 23665

** Lockheed Engineering and Sciences Company, Hampton, VA 23666

1 D. Gineris, S. Harrah, and V. Delnore, "Analysis of Synthetic Aperture Radar (SAR) Data for Wind

Shear Radar Clutter Modelling," Proceedings of the Airborne Wind Shear Detection and Warning

Systems: Second Combined Manufacturers' and Technologists' Conference, Williamsburg, VA,

October 18-20, 1988, pp. 225-244.

2 S. Harrah, V. Delnore, and R. Onstott, "Clutter Modelling of the Denver Airport and Surrounding

Areas," Proceedings of the Airborne Wind Shear Detection and Warning Systems: Third Combined

Manufacturers' and Technologists' Conference, Hampton, VA, October 16-18, 1990, pp. 785-836.

501
• Summary of Ground Clutter Flights

Locations & Measurements

Research Objectives

• Landing Scene NRCS Statistics

Measurement Repeatability

Incidence Angle Effects

Polarization Sensitivity

• Specific Terrain NRCS Statistics

Measurement Repeatability

Correlation to SAR Images

Incidence Angle Effects

• Dynamic Range of Clutter Returns

Antenna Pointing

Incidence Angle Effects

Polarization Sensitivity

Conclusions

Application for Radar Design

FY' 92 Flight Plans

5O2

OF pOOR O.UA_I"r¥
* Denver

22 Approach/Landings (Runway 26 & 35)

Over 1 Hour ot Recorded Final Approach Time

22 Level Flights (-1000' AGL) (Runway 26 & 35)

Approx. 2,000,000,000 I&Q Samples

• Philadelphia

31 Approach/Landings (Runway 27)

Over 1 Hour of Recorded Final Approach Time

Approx. 1,500,000,000 I&Q Samples

Research Objectives

Evaluate Ground Clutter NRCS

Evalutate AGC Performance

Polarization & Antenna Tilt Management

RADAR EQUATION

GROUND CLU'I-rER CALCULATIONS

-- p2_2 =o 2

P(R) = -3a"IW(/ I,/,

(4,.,, .;;; l_(o.¢,)d_/

o ° = Normalized Radar Cross Section

-8"

ELEVATION "t 2"

ANGLES -16"

-20"

ELLIPSE

INCIDENCE

ANGLES

(78" - 86")

(74"- 82")

(70"- 78")

(66"- 74")

ELEVATION INCIDENCE

ANGLE ANGLES

-3" (84" - 89")

PARABOLA

ELEVATION

ANGLES

0"

-1"

INCIDENCE

ANGLES

(86"- 8g')

(85"- 89")

HYPERBOLA

503
ORIGINAL FA_JE

BLACK AND WHITE PHOTOGR_OH

:!:i:

i_,

504

ORP-_NC.L r_,_F IS

OF POOR QUALITY
NASA WINDSHEAR RADAR

FINAL APPROACH GROUND CLUTTER NRCS (DENVER)

INCIDENCE ANGLE EFFECTS : HH POLARIZATION

0.07

0.06

0.06

0.04

0.01

0.02

0.01

INC. ANGLE II (dB) o (dB)

• 05-80 -20.1 8.1

I_J 83-89 -23.0 7.8

-46 -40 -30 .30 -25 -20 -1§ -I0 -S 0 6 10 16

NRCS (da)

0.07

FINAL APPROACH GROUND CLUTTER NRCS (DENVER)

INCIDENCE ANGLE EFFECTS : VV POLARIZATION

0.06

0.06

0.04

0.03

0.02

0.O1

INC. ANGLE p (dB) o (de)

• ,_ -U.0 7.3

-45 -40 .36 -30 -25 .30 -16 -10 -5 0 5 10

NRCS (dB)

15

5O5
FINALAPPROACH GROUNDCLUTTERNRCS (DENVER)

POLARIZATION SENSITIVITY: -1 ELEVATION

0.07

0.06

0.05

0.04

0.03

0.02

0.01

Im W -22.9 7.3

-.46 -410 -3S -30 -25 -20 -15 -10 -5 0 5 10 16

NRCS (dB)

0.07

0.0(I

0.06

0.04

0.03

0.02

O.0t

FINAL APPROACH GROUND CLUTTER NRCS (DENVER)

POLARIZATION SENSITIVITY :-3 ELEVATION

POLARIZATION p (dB) o (dB)

• HH -23.0 7.8

[] VV -20.S 7.6

-45 4O .-35 -25 -20 -15 -I0

NRCS (dB)

-5 0 § 10 16

5O6
0.07

FINAL APPROACH GROUND CLUTTER NRCS (DENVER)

POLARIZATION SENSITIVITY : -3 ELEVATION

0,06

0.05

0.04

0.03

0.02

0.01

POLARIZATION F (de) <x (dB)

• HH -23.0 7.6

i VV -20.6 7,6

-41; -40 -3S -30 -2S -20 -11; -10 -S 0 5 10 16

NRCS (dB)

o.o45

FINAL APPROACH GROUND CLUTTER NRCS (PHILADELPHIA)

POLARIZATION SENSITIVITY :-3 ELEVATION

0.04

O,O_IS

0.03

0.025

0.02

0.015

0.01

0,006

0

POLARIZAllON H (dB) o (dB)

• HH -25.8 10.7

• VV -24.5 10.6

NRCS(dB)

507
508

Ji

1

a

T

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+++ -

+,' "!i,

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il!P

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NASA WINDSHEAR RADAR

0.1

0.09

0.08

0.07

0.06

0.06

0.04

0,03

0.02

0.01

0

NRCS HISTOGRAM (DENVER TERMINAL)

WSRAD & SAR COMPARISON

INC. ANGLE p(dB) o(dB)

WSRAO • 78-86 -20.8 12.4

SAR il 77-79 -29.O 12,8

-45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15

NRCS (dB)

0.1

0.09

0.06

0.07

0.06

0.06

0.04

o.o3

0.02

0.01

0

NRCS HISTOGRAM (DENVER PARKS)

WSRAD & SAR COMPARISON

-35 -30 -25 -20

INC. ANGLE It(dE) o(dB)

WSRAO • 70-78 -20.S 8.3

liAR • 71-76 -23.0 6.4

-lS -10

NRCS (dB)

-5 0 S IO 15

509
NASA WINDSHEAR RADAR

0,08

0.0"/

0.06

0.06

0.04

0.03

0.02

0.01

NRCS HISTOGRAM (DENVER URBAN)

MEASUREMENT REPEATABILITY

INC. ANGLE I_ (dB) o (dB)

• a,_o -17.4 -14.$

-45 -40 -35 -30 -25 -20 -t5 -10 -5 0 5 10 15

NRCS (dB)

NRCS HISTOGRAM (DENVER RESIDENTIAL)

INCIDENCE ANGLE INSENSITIVITY

• 83-89 -22.1 5.0

78-86 -2S.6 6.3

-40 -35 *30 -25 -20 -15 -I0 -5 0 8 10

NRCS (dB)

510
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511
NASA WINDSHEAR RADAR

AGC HISTOGRAM (PHILADELPHIA - APPROACH 27)

MEASUREMENT REPEATABILITY & POLARIZATION SENSITIVITY

O.11 POLARIZATION la(dS) o (dB)

0.1 ANT. EL. • HH ...... _3 15_4

0.09 -3 [] HH 20.4 1S.2

[] VV 26.4 lS.O

0.0e

0.07

0.06

0.05

0.04

0,03

0.0_.

0.01

0

6 10 1E 20 25 30 35 40 45 50 55 60 65 70 75 80

APPLIEO AGC (dB)

0.12

AGC HISTOGRAM (DENVER - APPROACH 26)

ANTENNA TILT MANAGEMENT : HH POLARIZATION

0.1

0.06

0.o6

0.o4

0.o2

o

5

EL, ANGLE (DEG) ll(dB) o(dB)

• o 19.2 e.s

24.3 10.7

lo is 20 25 30 3s 40 4s r,o ss

APPLIED AGC (dB)

512
NASA WINDSHEAR RADAR

1991 FLIGHT EXPERIMENT __'_

0.06

AGC HISTOGRAM (DENVER - APPROACH 35)

POLARIZATION SENSITIVITY : -1 ELEVATION

0.05

0.04

0.03

0.02

•.•1

POLARIZATION p(dB) o(dB)

• HH 27.1 10.4

[] VV 27.0 10.5

5 10 15 20 25 30 35 40 45 60 55 60 6S 70 75 80

APPLIED AGC (dB)

0.07

AGC HISTOGRAM (DENVER - APPROACH 35)

POLARIZATION SENSITIVITY :-3 ELEVATION

0,06

0.05

0.04

0,03

0.02

0.01

• 10 11 lO II IO |1 40

POLARIZATION p(dB) o(dB)

• HH 29.7 12.4

[] w 31.a 12.e

|11 7O |0

513
NASA WINDSHEAR RADAR

0.25

0,2

BIN-TO-BIN d(AGC) HISTOGRAM (DENVER - APPROACH 35)

_ ANT. EL. {DEGI I_(dB)____q{dB)__

• -t 4,4 3.9 HH

I -3 4.4 4.1

-t 4'I 3.7

VV

[] -3 4.3 3.9

0.15

0,1

0,05

0 5 10 16

AGC BIN-TO-BIN CHANGE (dB)

20 25 30

STAPLETON TERMINAL

Airport

Runway 90 -

80 -

70 -

60 -

01'

0 50 - ='

40 - _,,

30 -

20 -

10 -'-'_-.--J:

0

.87

i ii

i ii

i it

i ii

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.885 .9

Residenlial

1-3Blocks

So_hol

BId0s,

Roads,

Pa_inglois,

(Maj_duller

so_ce)

8

(.9

"r

i I m

I ' I ' ' I .,

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Residential/

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Industrial North of

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> R

9' n= -

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i i

I 0

I !

'I ' i i

• I ! i

I i i

I' I ', I' I

•96 .975 .99 1.005

West LON (plus 104 deg.)

514 1lnl n_ln nn2 _n2 A
Conclusions 1991 Flight Experiment

NRCS Incidence Anale Effects

Use Uncorrected NRCS from SAR Maps in Simulation

Man-Made Clutter Insensitive to Incidence Angle

• NRCS Polarization Sensitivitv

Angular Dependency to Polarization Sensitivity

6 dB or Less of Seperation HH - VV

• ComDarison with $AR Derived NRCS Statistics

Natural Targets Show Good Agreement with SAR

SAR Maps Should Produce Realistic Clutter in Simulation

Reasonable Fidelity (Dynamic & Spatial Variations)

AG_C Incidenc.e Angle Dependency

6 dB/1 = Lower AGC Mean at Angles of Interest

2 dB/1 ° Lower AGC Std. Dev. at Angles of Interest

• AG(_ Polarization Dependency

1-3 dB Reduction Using VV (@ -1=)

1-3 dB Reduction Using HH (@ -3 =)

• Bin-To-Bin AGC Independent of Tilt & Polarization

Implications for 1992 Fliq,ht Experiment

Re-Investigate a Few Key Terrain Features

Increase Database for Polarization Study

Continue to Examine Moving Clutter

515
Ground Clutter Measurements Using the NASA Airborne Doppler Radar:

Description of Clutter at the Denver and Philadelphia Airports

Questions and Answers

Q: Jim Evans (MIT) - What is the instability residue of the radar transmitter? What is the signal

wave form which has been used to obtain data? What arc the antenna side lobes, in elevation,

with the radome on? How is the data below the receiver sensitivity represented in the clutter

histograms?

A: Steve Harrah (NASA Langley) - After talking to Collins, we would prefer not to openly

disclosethe instabilityresiduevalues. Ifyou would liketotalktoCollinsthey arcmore than

willingtosharethatinformationwith you. The wave form isbasicallya simple rectangularpulse.

The antcnna sidelobesarcbasicallya halfof a dB below what they arcwith theradomc off.

Those Icvclsaretypically30-35 dB down forthefirstsidelobe. In my clutteranalysisI made

sure,through the equationsthatwere implemented, thatwe only looked atground cluttertargets

which fcllwithin four dcgrccsof thecenterof the beam. Inthatrespect,Idon'tbelievewc saw

anythingthatdid not have a significantamount ofAGC appliedtothem. By that,ittellsme that

they wcrcn'tdown inthc noise.

Q: Jim Evans (MIT) - Will clutter measurements be conducted with realistic profiles at ugly

clutter locations?

A: Steve Harrah (NASA Langley) - We are planning on making some additional measurements

this year as I stated in my conclusions and future work statement. We are going to try and look at

the urban clutter in Denver. As you suggest further on down in your comments to use runway 8.

We will try and work that into the schedule and as long as we can get ATC to agree with it. In

addition to that, we are going to make a trip to Washington this year we think, and maybe some

other uglier clutter sites.

516
Session VI. Airborne Doppler Radar / NASA N 9 3 - 1 9 G 0 9

Spectrum Characteristics of Denver and Philadelphia Ground Clutter and the Problem of

Distinguishing Wind Shear Targets from Moving Clutter

A. Mackenzie, NASA Langley Research Center

517
518
Spectrum Characteristics of

Denver and Philadelphia Ground Clutter

and the Problem of Distinguishing

Windshear Targets from Moving Clutter

A. Mackenzie, NASA LaRC

PRE'_EDINO _;_;,':.,EBLAr'K r_Ol" F|LMEL)

519
Abstract

Spectral analysis of 1991 wind shear flight data has provided information

about the power spectral density, spectral width, and velocity of Eround

clutter detected by the wind shear radar at several major airports.

Ground clutter must be recoEnized and separated from weather tarEets

before wind shear can be computed. Information will be presented

characterizln E and comparing Eround clutter and weather target spectra.

The information includes: (t)spectral widths of stationary 8round clutter

seen at various scan snd tilt anEles, {2)power spectral density and

velocity of movin E ground clutter relative to the stationary ground

clutter, and {3)spectral widths and velocities of weather tarEets. The

presentation will also include summary numerical results in the form of

histograms and example numerical results in the form of spectral plots.

52O
ILl

mm

I--

0

ILl

r_

cn

X _ _'-

ILl rr' Im"-'.,-' mm_ml#

C--

ILl mmm r_

ne _

F- Fm _

_ m

¢_ II:

zm _

a. °_

_ n- _0

o_ _ oo

c_ _0, , _ _, , '

521
Data Frame and Range Bin Geometry

In the usual mode of operation, the radar antenna scans in azimuth while

keeping a fixed tilt. During the transmission of 128 pulses, the antenna

moves through 0.5 degrees of its scan. Data collected from these

transmitted pulses are called one data frame. Range _bins are spherical

shells concentric about the radar. Each bin is ]44 meters thick. During

one data frame, 128 samples are collected from each range bin.

522
re ,-

0
Comparison of Weather and Clutter Doppler Spectral Shapes

After an FFT has been performed on a 128-point I,Q voltage time series, a

128-point Doppler- velocity spectrum may be drawn from the magnitudes of

the results. The spectrum represents power spectral density versus the

detected radial velocities of targets in one range bin. A pulse

repetition frequency of 3755 Hertz yields a velocity Nyquist interval of

-30 to +30 meters per" second. The lower plot shows a typical stationary

ground clutter spike, which appears as a tall, pointed peak. 'rile upper

plot shows a typical weather spectrum, which looks like a collection of

adjoining peaks tn one area of the total spectrum. Moving clutter may

appear as one or more distinct velocity peaks. Clutter may have higher or

lower power spectral density than the weather target.

524
O0

Z_

_o

0

CD

CL

IZ)

>

0

+

_ J

._J

0 _

±_/

> _ ____A

0 _

0

O-3

LJ

>

c_

IZ)

>

525
Th___eEffect o__f/Velocity Compensation on Stationary Ground Clutter

The radar receiver compensates in hardware for the apparent motion of the

ground toward the airplane at the center of the antenna beam. For most

range bins, this compensation puts the stationary ground clutter at the

center of the velocity spectrum, which is zero meters per second. The

velocities o[ stationary ground clutter not near the beam center will

appear with an offset from zero, given by the equation at the bottom of

the opposing page. The spectra shown are from a landing approach to the

Philadelphia Airport. In most cases, stationary ground clutter from the

near range bins, such as bin 6, is too far down on the antenna beam power

pattern to be seen above the noise. However, an occasional very highly

reflective object may appear in the spectrum with a velocity offset. Case

3 shows one of these occurrences.

526
Z

0

0

m

_m

ZE_

_0

0

OA

Z

_0

Hm

O<D

0

Hm

Z

_0

OH

P_

P_

U_

.......................... i

I

(

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F'-

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o

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"° _

©

_N

Z _

m r,5

•. = _

c,,1 ,--1 [q

,<[-,

H _ [-, _ r.n

_ __] [._-] H
Spectral Width Versus Scan

An initial look at Denver stationary ground clutter has yielded these

spectral widths estimated by pulse pair processinE. The data were taken

from slx level flights in clear weather over the Stapleton Airport, each

flight with the antenna set at a different tilt angle. Average spectral

wldths were calculated versus scan and tllt angle, using those range bins

where the antenna boresIEht intersected the ground. Data frames were

excluded if their spectral width was more than 3.5 meters per second,

since higher widths indicated the presence of moving clutter. The plot

shows the width Increasing toward the edges of the scan and decreasln E as

the antenna is tilted further downward.

528
0

1,1,1

O.

g/uJ 'qlP!M

0

0

!

529
Movtn_ Clutter Seen in 30 Seconds of FliRht Over Philadelphia:

-33 De_ree Tilt, ±30 DeE_ree Scan"

A moving clutter velocity histogram was produced by counting instances of

moving clutter detected during 30 seconds of level flight over

Philadelphia. In one range bin, each frame was searched for the largest

clutter peak on each side of zero where the power spectral density of the

peak was more than four times the average power spectral density for the

entire frame. A power spectral density histogram was produced by

comparing the power spectral density of each peak to the power spectral

density of the stationary clutter in the same frame. In over 90 per cent

of cases, the moving clutter was less reflective than the stationary

clutter. In the other I0 per cent of cases, the moving clutter was up to

16 times more reflective than the stationary clutter.

530
Z

E_ _

0 o

co

_ +1

E_

od

Z_

c_

Z I

°"

c_

I,/,,/,4

ZC_

0

:X>0

0 if') 0

S39N3_noo0 30 %

0

0

S33N3_I_InO90 30 o,,

531
Wind Shear Viewed Alon_ the 0.25-De__ Azlmuth Line

Looking along the 0.25-degree azimuth frame from range bins 10 to 55, we

see weather velocities changing from near zero meters per second to -7

meters per second to +9 meters per second. Stationary ground clutter is

also present in each bin. Pulse pair processing estimates of mean wind

velocity are biased by stationary clutter velocity if no filtering is

done. Improved wind velocity estimates are obtained by filtering out the

stationary clutter prior to pulse pair processing. The filtered velocity

map labeled "frm 1054" in the upper right corner shows weather velocities

calculated for the above mentioned range bins in the center of the scan.

5_
WIND SHEAR VIEWED ALONG

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Comparison of Doppler Spectra from Ban_e Bin 55

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Looking at range bin 55 across a series of frames at -2.25 to +3.75

degrees in azimuth, we see weather accompanied by stationary ground

clutter in every frame. Moving clutter appears, grows stronger, and fades

as the antenna scans across a highway. In the flltered velocity map

labeled "frm 142" in the upper right corner, the highway appears as a

series of contrasting recatangular areas in a line down the center of the

scan. Since only stattonary clutter was filtered out before calculating

the map velocities, some of the velocity estimates on the map are biased

by the moving clutter. However, the areas of moving clutter are

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unbiased weather velocities are readily discernable in large areas of the

map, despite the presence of moving clutter in other areas.

536
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539
Spectrum Characteristics of Denver and Philadelphia Ground Clutter and the Problem of

Distinguishing Wind Shear Targets from Moving Clutter

Questions and Answers

Q: Jim Evans (MIT) - One of the important questions in reviewing the ability to reject ground

clutter by filtering is what the base noise of your system is. What is the base noise of the system

you are using in terms of instability residues?

A: Ann Mackenzie (NASA Langley) - You mean the noise of the receiver system?

Q: Jim Evans (MIT) - Well, it has to do with your transmitter system and the amplitude in

phase variations it may apply as it puts out pulses, plus and noise in your local oscillator. What

you will see when you analyze the spectrum, if you just sat on the ground and bounced the signals

off of a nice target, is a big spike at zero velocity and then you will see a noised floor from

anywhere from twenty to fifty or sixty dB down, that is almost flat. It turns out that is one of the

expensive items in trying to build a pulse coherent radar, and it is an important element in terms of

trying to understand the significance of your results. That is why I asked the question; what is the

instability residue of your system? You can not build a system that puts out exactly to a

thousandth of a dB the same pulse amplitude every time it transmits.

A: Brac Bracalente (NASA Langley) - I would like to address that. This is a design that was

provided to us by Collins. All I can tell you is that it has a very stable low noise level. Our noise

sensitivity is down around minus 110 dBZ and we see signals down that low. I can't tell you what

the exact number is, that is something you will have to talk to Collins about. All I know is that it

is a low number.

Q: Jim Evans (MIT) - That is not the right number. The number I am asking about is signal

dependent noise?

A: Brac Bracalente (NASA Langley) - Are you talking about the clutter noise generated by

jitter phase instability.

Q- Jim Evans (MIT) - Phase and amplitude instabilities either at the transmitter or the receiver.

A: Brac Bracalente (NASA Langley) - I can not give you the exact number on that. All I know

is that it is pretty low. I think it is at a low enough level to not be a problem in the operation of

the system.

540
Session VI. Airborne Doppler Radar / NASA N93-19Gi0

Comparison of Simulated and Actual Wind Shear Radar Data Products

Dr. C. Britt, Research Triangle Institute

L. Crittenden, Research Triangle Institute

541
Comparison of Simulated and Actual

Windshear Radar Data Products

Dr. C. Britt, RTI

L. Crittenden, RTI

542
Comparison of Simulated and Actual Windshear Radar Data Products

Charles L. Brltt and Lucflle H. Crittenden

Research Triangle Institute

Research Triangle Park. NC 27709

Abstract for Proposed Technical Talk for the

Fourth Combined Manufacturers' and Technologists'

Alrborne Wind Shear Review Meeting

Williamsburg, VL_gUUa

April 14-16, 1992

Abstract

Prior to the development of the NASA experimental wind shear radar system, extensive

computer simulations were conducted to determine the performance of the radar in combined

weather and ground clutter environments. The simulation of the radar used analytical

mlcroburst models to determine weather returns and synthetic aperture radar (SAR) maps to

determine ground clutter returns. These simulations were used to guide the development of

hazard detection algorithms and to predict their performance.

The structure of the radar simulation will be reviewed. Actual flight data results from the

Orlando and Denver tests are compared with simulated results. Areas of agreement and

dlsagreement of actual and simulated results are pointed out.

543
COMPARISON OF SIMULATED AND ACTUAL

WINDSHEARRADAR DATA PRODUCTS

Charles L. Britt, Ph.D

Lucille H. Crittenden

VIEWGRAPH TITLES

Slide 1 -

Introduction - Comparison of Simulated and Actual Windshear

Radar Data Products by Charles L. Britt and Lucille H.

Crittenden.

Slide 2-

This is an overall flow chart of the Radar Simulation

program developed for NASA by RTI personnel. The simulation

inputs include: i) a NASA-developed microburst data base for

simulation of microburst radar returns; 2) synthetic aperture

radar (SAR) maps from the Environmental Research Institute of

Michigan (ERIM) for calculation of stationary ground clutter; and

3) a discrete target data base for simulation of moving ground

clutter. A Monte Carlo technique is used to calculate the inphase (I) and quadrature (Q) signals for each range cell of the

radar. These signals are processed to power, velocity and hazard

index using various signal and data processing algorithms.

Slide 3-

An example of a synthetic aperture radar (SAR) map of the

Denver area. This map is used to determine the ground clutter

level in the simulation.

Slide 4 -

This chart shows how the NASA flight test data is used to

drive the simulation to permit direct comparison of simulated and

actual radar data products.

Slide 5-

An example of a Denver ground scattering coefficient

(sigma-zero) map obtained from NASA flight test data in July

1991. The location of runway 26R at Denver Stapleton airport is

shown on the map. It should be noted that although the radar map

is in sigma-zero units, the actual values of sigma-zero are valid

only in the region where the antenna beam center intercepts the

ground. Slides i0 and ii show true sigma-zero levels corrected

for antenna pattern effects.

544
Slide 6-

An example of a simulated Denver ground scattering

coefficient (sigma-zero) map using the NASA flight test data to

provide aircraft position data for the simulation. This map is

plotted for the same instant of time slide 5.

Slide 7-

This is a plot comparing simulated ground clutter levels

with ground clutter levels obtained from flight tests. An ERIM

supplied algorithm was used in the simulation to correct for the

difference in incidence angles between the angles used in

obtaining the SAR data and the angles required by the simulation.

Slide 8-

Plot similar to slide 7 except the ERIM incidence angle

correction is no__ttused for the simulation. Better correlations

between flight and simulated data are obtained in this case. In

both cases, an antenna tilt of -3 degrees is used.

Slide 9-

Plot of the frequency of occurrences of various clutter

levels for flight and simulated data. The simulation used the

SAR maps with no correction for incidence angle differences.

Slide 10-

Values of ground scattering coefficient (sigma-zero)

obtained from a sample of flight data on a Denver approach to

runway 26R. The aircraft altitude was 620 feet when the data

were taken.

Slide llData taken under conditions similar to slide i0 except

obtained from the simulation. The SAR clutter maps used in the

simulation were uncorrected for incidence angle differences.

Note that the mean value of sigma-zero is somewhat larger in this

simulated case.

Slide 12-

Correlation calculations to determine if the simulation and

flight data are properly registered spatially. The highest

correlation is obtained with a lag of 2 range bins (288m) in the

simulation using the SAR clutter maps. This indicates a

difference of this magnitude in the coordinate systems used. For

future comparisons of flight and simulated data, this

registration error will be corrected.

Slide 13-

Conclusions from the results to date in the comparison of

simulated and flight test data products. The comparison of

simulated and flight data will continue.

545
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Comparison of Simulated and Actual Wind Shear Radar Data Products

Questions and Answers

Q: Bruce Matthews (Westinghouse) - I think you have made a case that you have a good

simulation of clutter, but how would you include that simulation of clutter into a radar? When

would that be adequate?

A: Les Britt (RTI) - You mean to check the radar to certify it or something like that?

Q: Bruce Matthews (Westinghouse) - In your summary you state: "Simulation is an excellent

tool for prediction of radar performance." You have just talked about a clutter model. How does

that clutter model reflect what the radar equipment is? Do you have models for that also?

A: Les Britt (RTI) - We have in our simulation a baseline radar system which is basically our

experimental system. That is what we use. We tried to simulate the flight system as well as we

could, and that is our radar model that is in the simulation. The same number of A to D bits and

that sort of thing.

Q: Brac Braealente (NASA Langley) - So I guess you could add somebody else's design in

there by proper modifications for their particular radar?

A: Les Britt (RTI) - Yes, if we knew all the parameters we could put someone else's radar

model in there.

Q: Bruce Mathews (Westinghouse) - Does NASA recommend the use of the ADWRS clutter

simulation with manufacturer furnished parameters as an adequate or reasonable alternative to

other means of simulation, such as an RF injection driven by the SAR clutter maps?

A: Les Britt (RTI) - I think this has to do with the certification or system evaluation. I can't

speak for NASA, but I doubt if they recommend either one. I don't think anybody knows. I think

it is up to the manufacturer or perhaps the RTCA to determine how to evaluate the system.

Q: Jim Evans (MIT) - The clutter power not at the aircraft velocity is a key element of radar

simulation performance. How are you modeling transmitter receiver instability residues, and the

antenna side lobes with radome on, especially those at negative elevation angles? What

experimental measurements have been or will be done to validate the assumptions?

A: Les Britt (RTI) - I will take the second part first. The antenna model used in the simulation

is actually a table of measured data taken with the antenna and radome, over plus or minus 90

degrees, in the NASA anechoic chamber. So it includes all the side lobes. The data was taken in

two principle planes but it was searched in three dimensions for any spurs or little peaks, and we

did find one small peak which is in the data. We modeled a full 3-D pattern using the two

principle planes with an interpolation scheme to go between the two principle planes. The first

part of the question was the transmitter receiver instability. I brought some slides to show how

we do that in the simulation. For each range bin we generate a series of I&Q depending on how

many pulses we simulate. Currently it is running around 128. We model the clutter in the return

559
with aMonteCarlotechniquewhichuses a set of random phased scatterers. Each range bin is

divided up into five or six thousand incremental areas, each one assigned a random phase which is

held fixed over the 128 pulse variation. The transmitter error is modeled with a random phase

error which is currently a white noise model. In other words, it is changed from pulse to pulse in

accordance with a normal distribution, which is an input parameter. You input the variance and it

pulls out a transmitter phase error which is modeled as a linear function. You can also put a

frequency drip in there, if you want to. It is modeled from pulse to pulse. You can get more

elaborate with the phase model but that is the one we are currently using. We use an RMS phase

error now of 5 degrees. We have run it up to 10 or 20 to see what effect it has, but that is

currently what we are using. How is it validated? Basically by estimates from Collins and what

have you. We talked to some tube manufacturers when we went through this two or three years

ago, to get some number to put in there. It does not represent every transmitter, but we feel like

it represents ours fairly well.

560
Session VI. Airborne Doppler Radar / NASA

NASA Airborne Radar Wind Shear Detection Algorithm and the Detection of Wet Microbursts in

the Vicinity of Orlando, Florida

Dr. C. Britt, Research Triangle Institute

E. Bracalente, NASA Langley Research Center

561
NASA Airborne Radar

Windshear Detection Algorithm and

the Detection of Wet Microbursts

in the Vicinity of Orlando, Florida

Dr. C, Britt, RTI

E. Bracalente, NASA LaRC

562
NASA Airborne Radar Windshear Detection Hazard Algorithm and the

Detection of Wet Microbursts in the Vicinity of Orlando Florida

Charles L. Britt

Research Triangle Institute

Research Triangle Park, NC 27709

and

Emedio Bracalente

NASA Langley Research Center

Hampton, VA 23665

Abstract for Proposed Technical Talk for the

Fourth Combined Manufacturers' and Technologists'

Airborne Wind Shear Review Meeting

Williamsburg, Virginia

April 14-16, 1992

Abstract

The algorithms used in the NASA experimental wind shear radar system for detection,

characterization and determination of windshear hazard are discussed. The performance of

the algorithms in the detection of wet microbursts near Orlando is presented. The talk will

also review various suggested algorithms that are currently being evaluated using the flight test

results from Denver and OrLando.

563
NASA AIRBORNE RADAR WINDSHEAR DETECTION HAZARD ALGORITHM

AND THE DETECTION OF WET MICROBURSTS IN THE VICINITY

OF ORLANDO FLORIDA

Charles L. Britt, Ph.D

Reseach Triangle Institute

Emedio Bracalente

NASA Langley Research Center

VIEWGRAPH TITLES

Slide 1 -

Introduction - NASA Airborne Radar Windshear Detection

Hazard Algorithms and the Detection of Wet Microbursts in the

Vicinity of Orlando Florida

Slide 2-

Example of a hazard index display from the NASA experimental

windshear radar system. The algorithms to be discussed are

designed to provide for timely windshear hazard alerts in the

presence of ground clutter with no false alarms triggered by the

ground clutter.

This hazard map is from flight data taken at Orlando,

Florida on 6/20/91 and represents a wet microburst (Event #143)

with a peak hazard factor of approximately .15. The subsequent

flight of the aircraft through the microburst confirmed the

hazard index through in-situ measurements. Agreement between the

radar predictions and in-situ measurements was excellent.

Slide 3-

Techniques used in the NASA experimental radar to enhance

the detection of a windshear hazard.

Slide 4-

Example of a plot of received power level vs. radar range

showing the operation of the fast-acting radar AGC and the wide

dynamic range seen along a range bin of the radar. Data from

Orlando flight through microburst event #143.

Slide 5-

Chart showing various signal processing techniques that are

being evaluated to separate the ground clutter and weather

signals. The flight data is processed using various combinations

of these processing techniques.

5_
Slide 6-

Example of the Doppler spectrum obtained in range bin #47,

frame 366 of Orlando event #143 (a wet microburst penetration).

The mean wind velocity is approximated 6 m/s.

In processing the radar data, a 2-pole time-domain IIR

filter was used with no data weights. The ground clutter is

located at frequency line #65.

Slide 7-

Doppler spectrum taken under the same conditions as slide 6,

except that Hann data weighting is used.

Slide 8-

Doppler spectrum taken under the same conditions as slide 6,

except that FFT processing, Hann data weighting and spectral line

editing is used.

Slide 9-

Doppler spectrum taken under the same conditions as slide 6,

except that autoregressive (AR) processing, Hann Data weighting,

and spectral line editing is used.

Slide 10-

Radar velocity map of microburst event #143 using a 2-pole

IIR filter with Hann data weights and time-domain pulse-pair

velocity estimation.

Sl_de llRadar velocity map similar to slide I0 except using a

spectral domain (FFT) filter (line editing) with spectral domain

pulse-pair velocity estimation.

Slide 12-

Criteria for determining a valid velocity measurement in

each range bin.

Slide 13 -

Algorithms used for calculation of the hazard factor.

Slide 14-

Plot illustrating the technique of least-squares hazard

estimation. Five wind velocity measurements along a range line

are used to estimate the slope of the velocity/range line which

is proportional to the radial hazard index. In some cases, a

weighted least squares technique is used whereby the velocity

measurements are weighted by the value of spectral width.

Measurements with smaller values of spectral width are given more

weight in the slope calculation. This calculation is made for

each range bin along a range line to provide an estimate of

hazard for each range bin.

565
Slide 15-

Algorithms used to determine the extent of the hazard.

Slide 16-

Technique for determining the area and centroid of a hazard

region as it appears on the radar map. The hazard region is a

region of the radar map where the total hazard index is above a

threshold value ('.105)

Slide 17-

Criteria used to display windshear alert on the radar

display.

S_ide 18-

Summary of the various thresholds used in processing flight

data. The set of baseline thresholds given in the chart provide

good results in the detection of windshear hazards and the

elimination of false alerts. These thresholds are still under

evaluation.

Slide 19-

Conclusions from evaluation of signal and data processing

algorithms to data.

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585
NASA Airborne Radar Wind Shear Detection Algorithm and the Detection of Wet

Microbursts in the Vicinity of Orlando, Florida

Questions and Answers

Unknown - When you weight the least squares fit for shear on spectral width, isn't that going to

make you unduly sensitive to any clutter that does get through your other filtering? I assume a

false return would have very low spectral width wouldn't it?

A: Les Britt (RTI) - Yes, you are right. There have been suggestions to threshold on both ends

of the spectral width, on very narrow spectral widths which may be a moving target and on the

high end too, which is noise.

586
Session VL Airborne Doppler Radar / NASA

N93- _I9G _

Signal Processing for Airborne Doppler Radar Detection of Hazardous Wind Shear as Applied to

NASA 1991 Radar Flight Experiment Data

Dr. E. Baxa, Clemson University

587
Signal Processing for

Airborne Doppler Radar Detection of

Hazardous Windshear as Applied to

NASA 1991 Radar Flight Experiment Data

Dr. E. Baxa, Clemson University

588
Outline

• Ratform Stability Analysis - aircraft attitude variations

Microburst Detection Without Conventional Ground Clutter Rejection

-autoregressive modelling

-microburst tracking

Adaptive Filtering for Ground Clutter Rejection With Low SCR

- adaptive noise cancelling

- simulated microburst in real clutter data

• Analysis of Out-of-Range Returns

Groundspeed Corrections From Radar Returns

-identification of error

-asimuthal bias

Additional On-going Research Work

4th CMTAW meeting

Radar Slrstems ][.sbocato¢lr

Elo¢Urlcml -_,4 Compiler v,,KJLaoetriall

C3emson g_vorsl_r

Apr. 15, lg02

Abstract

Radar data collected during the 1991 NASA flight tests have been selectively

analyzed to support research directed at developing both improved as well as new

algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft

attitude data from several flights indicated that platform stability bandwidths

were small compared to the data rate bandwidths which should support an

assumption that radar returns can be treated as short time stationary. Various

approaches at detection of weather returns in the presence of ground clutter

are being investigated. Non-conventional clutter rejection through spectrum

mode tracking and classification algorithms is a subject of continuing research.

Based upon autoregressive modelling of the radar return time sequence this

approach may offer an alternative to overcome errors in conventional pulse-pair

estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with

emphasis on low signal-to-clutter ratio situations, particularly in the presence of

discrete dutter interference. An analysis of out-of-range clutter returns is included

to illustrate effects of ground clutter interference due to range aliasing for aircraft on

final approach. Data are presented to indicate how aircraft groundspeed might be

corrected from the radar data as well as point to an observed problem of

groundspeed estimate bias variation with radar antenna scan angle. A description

of how recorded clutter return data are mixed with simulated weather returns is

included. This enables the researcher to run controlled experiments to test

signal processing algorithms. In the summary research efforts involving

improved modelling of radar ground clutter returns and a bayesian approach

at hazard factor estimation are mentioned.

589
6

Roll Angle vs. Time PHL f3r3s2.m6

Rail _nule R'IL -F'3_'3._.._6 ADm'cx_h FI ighb

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Ra4eurSl_em Lebormory

4th CMTAW meeting _ee_i_l u4 Co,,puter EalLaea_u_ Apr. 15, 1902 Clemso= University

Q

NOTES

Roll angle variation with time during approach to

runway 27 at PHL. Data were recorded from DATAC

wil_each frame of radar data at a frame rate of 29.25

frames per second.

590
R

D

L

em

I

v

I

I=

D

w

r"

Roll Angle Spectrum PHL f3r3s2.m6

Rot L ¢r_gtn 51=,=L-'br'um PHL F_3,=2,m6

i ! i i

41h CMTAW meeting

Radm" STztemz I.eboFstory

nectricat end Co_puter E,tirbseeFltq[

Clemzoa U_'mss'ztty

Apr. 15, lgg2

NOTES

Frequency spectrum of roll angle time variation

during approach to runway 27 at PHL.

Data were recorded from DATAC with each

frame of radar data at a frame rate of 29.25

frames per second.

591
Crab Angle vs. Time PHL f3r3s2.m6

4,5 i

4

3,5

d

8

e 3

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1,5

1

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! i s

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4th CMTAW meeting

f.trle retcdciv@ t:o Frarre 7i_ (sees)

ea4er Systems Le_¢atory

Elecu'iced msd Computer Ymgiaeertag

Clutsoo U_verzity

Apr. 15, 1982

NOTES

Crab angle variation with time during approach to

runway 27 at PHL. Data were recorded from DATAC

wit_each frame of radar data at a frame rate of 29.25

frames per second.

592
Crab AngleSpectrumPHL f3r3s2.m6

Prpcum,',--y (Hz I

Rsder Syste_u [.abot'_dory

Eloct_L¢81 emd Computer EngineorinK

4th CMTAW meeting tim=ram Unfverdty Apr. 15, lgG2

Q

NOTES

Frequency spectrum of crab angle time variation

during approach to runway 27 at PHL.

Data were recorded from DATAC with each

frame of radar data at a frame rate of 29.25

frames per second. Crab angle mean was removed

prior to spectral analysis.

593
PitchAnglevs.Time PHLf3r3s2.m6

P;sr,,=h A,,QI=- PI-tL F3r3=2.,,,r_ App,-=,=:h i:::[ ;_,6

| i I I I

D

B

I"

D

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+.tr,_ "r'=tal::;v,:

£edar Systems Laboratory

llectrlc¢ =4 Compuler l_gLueerins

4th CMTAW meeting Cle=soa Uat_rs|ty Apr.15,1992

r_

NOTES

Pitch angle variation with time during approach to

runway 27 at PHL. Data were recorded from DATAC

wit_each frame of radar data at a frame rate of 29.25

frames per second.

594
Pitch Angle Spectrum PHL f3r3s2.m6

Pi_ch AngLe S_cZ_rum PHL f':Jr3-_.,da_ (d_krL-nd_dl

| I ! |

I I I

0.2 1.¢ 0.6 0,8

Frec_e_ (Hz I

4thCMTAW meeting

Ele©U'lcel end Compumr Engtnee_

Clestson University

Q

Apr. 15, 1982

NOTES

Frequency spectrum of pitch angle time variation

during approach to runway 27 at PHL.

Data were recorded from DATAC with each

frame of radar data at a frame rate of 29.25

frames per second. The mean value of pitch angle

was removed before spectral analysis.

595
Orlando Flights Though Events 142,143,|44

I'1

O

r"

(

|

fit

)

Dr'Lor_ Dr'_w71 ar-9_8 I_ or'_ (BIO) =b TDNR ES.3442/St.32:SB

T ; u

144 ,.:

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25

10

143//6."micr r= ,.

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_r, jTDWR ,

_,=-=4=C|¢.)

4th CMTAW meeting

RaiderSystem= Laboratory

ElecU'tcel and Compmer F_KLaoerin8

Clemsol Unf_rst_ Apr. 15, lg_

NOTES

Ground tracks of three legs of the NASA flight through

the microburst in Orlando on day 171. This event

was numbered 14l on the first pass, 14_ on the second,

and 14_on the third. The aircraft was at about 1100 feet

traveling in excess of 200 knots. The indicated position

of the microburst is an estimate of the core position

based upon radar data. The TDWR is at 0,0 on this plot.

596
Event 143 Orlando 1991

11.

a

11

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Of

Spec=4=r_un _=E¢,_ plat; ar, aJwBs4, mG -F'r,,'-me |_5

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511

4.11

311

In

ve[ac;_=v ,!" _/g..= ] , _hre-=hoLd = gS; =IB

Redes" Sy_toms Labocatot-lr

I[loctr|cal end Compmm" F.JIBiaooring Apr. 15, 1992 4th CM'I'AW meeting Clemzoa U,,t,mrsity

NOTES

During the flight through event 143 at Orlando a

snapshot of the radar return looking into the

microburst is analyzed using a Fourier transform

of the I & Q sequence (96 samples) taken at an

antenna azimuth of -0.25 degrees. The aircraft

was on the track labeled 143 on the previous slide

and located at about 8 km west and 22 km north of

the TDWR. The range cell at the zero crossing of

the "s-curve" characteristic is about range cell 35

which is appoximately 5 km ahead of the aircraft.

In the presentation the Doppler power spectrum

has been thresholded and then a point density

plot is used to indicate spectrum intensity. Doppler

windspeed is on the abscissa. Range cells from 6

to 96 are indicated on the ordinate. No clutter

rejection filtering has been used.

59'7'
Autoregressive (AR) Modelling of Radar Return

Radar

I&Q

data

Low-Order AR Model

Algorithm

- for N=2: R(Ts), R(2Ts)

Ts= llprf

,l

1

Tracker

DopplerSpectrum

mode estimates

for N==2: vel 1, vel2

Mode Classifier

(expertsystem)

veil -0" Gound Clutter

vel2 • clutter or weather

RMm" Syrtuu Laboratory

Zlmctrical ,,,-,4 ColputeT Inllnem'Ing

4th CMTAW meeting Clemseo Ooi_aarslty Apr. 15, 1992

NOTES

Linear modelling of the I & Q sequences as if they

were the output of a linear all-pole model driven by

white noise is being used to investigate the feasibility

of detecting modes in the return Doppler spectrum

without the use of clutter rejection filtering. In

situations where the clutter is particularly strong

or may tend to bias spectrum mean estimates even

when attempts at clutter rejection are made, this method

is viewed as a possible alternative. It also provides

a method for estimating a spectrum as an alternative

to the FFT. A second order AR model is comparable

to the pulse pair algorithm in terms of processing load

and can give a useful spectrum estimate for further

processing.

598
i

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3O

2O

I0

0

-I0

-2O

-3O

41h CMTAW memlng

AR Based Velocity Estimates for Event 143

(_o OO'

O

o

oe o

o

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O

oOo o o :_ _._ O0 i 000

I I I I I0 I I I0 20 30 40 5 60 70 80 90 I00

Range Cell

R_m" =brae m=

nectrlcet rand Comp-*mr grist8 Apr. 16, IgS_

NOTES

The snapshot of event 143 in Orlando at a time very

near to that shown in the earlier scatter point plot is

shown here after autoregressive modelling. Again no

clutter rejection filtering has been used. The bubble

center locations indicate the spectrum mode Doppler

velocity estimates for each range cell. The size of the

bubbles indicate relative mode streng_fs. The small

bubbles near + 30 m/s and - 30 m/s in range cells 65

and above are indicative of returns at ranges where

the return is weak with very little speclular structure.

Investigation is continuing to use these methods

coupled with an expert mode classification algorithm

to detect hazardous windshear.

Ref: M.W. Kunkel, "Spectrum Modal Analysis for the

Detection of Low-Altitude Windshear with Airborne

Doppler Radar," Radar Systems Lab TR-15, ECE Dept.,

Clemson University, Feb. 1992.

599
n

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--5

DEN rwy 26 Flyover

DBrwur-dn3,-l=4-.mE CII,_| c_. T[]J_ EFJ.B7B4/L_4. TST9

I i l i I

\

r_s 28

I I I J &

NC_ (km)

4th CMTAW meeting

Rmler Syslems Leber_ery

Elec_l_d en4 Compmer Znlli.n.e,e_UZlE

Clemson U_rsity

Apr. 15, 1902

NOTES

This is a ground track plot of one of several clutter

flights over runway 26L at Denver Stapleton.

Generally these flights were at altitudes of 1400

to 1500 feet at groundspeeds near 200 knots. Various

antenna elevations were used. Results here include

data from horizontal elevation ( 0 degrees) and -1 and

-3 degrees (below horizontal). The position plot is

relative to the location of the TDWR (0,0).

6OO
Ground Clutter Doppler Spectrum (DEN rwy 26 Flyover)

1440ft all AZ=0,EL=-1

10o

Spectrum of Den_cr flight, frame ]23, RC//50

9O

8O

7O

50

40

_-30 -20 -|0 0 10 Z0 30

Velocity, ( metcrs/sec )

Spectrum of o/p error teq. from LMS-adpn¢ o| order 2, RC de 50

i

I I t l

-20 -10 0 10 20 30

/

fliEht#: Denver, frame//: 123 , mu - 0.1

5

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-10

-15

-20

-25i

-3_!o

4th CM'IAW meeting

Radar SyJtems Laboratory

Electrical and Computer Engineering

Clomzon University Apr. 15, 1992

NOTES

A typical clutter FFT spectrum is shown which includes

"discrete clutter" modes at about -18 m/s and +25 m/s

in addition to the main lobe clutter near zero Doppler.

These modes away from zero are due to returns from

interstate highway 1-70 which passes under runway 35.

Also shown is the impulse response of a second order

adaptive clutter rejection filter with the I &Q sequence

used for the illustrated spectrum also used as a training

sequence for the adaptive filter. Notice that the adaptive

filter places notches at each of the strong clutter modes

even with only a second order filter. The filter is an

adaptive noise canceller using the LMS algorithm and

is shown in block diagram form in the next figure.

601
Adaptive Noise Canceling with LMS Filter

LMS AIBonthm: wz(n + I) = _w_(n')+,_e(n)vz(n - i), 0 _ i < M

where £(n) = _x(n) + _ol(n) -. g(n) = x_(n) + _ul(n) - w Tvz...

Primary

=(.) + __,(n)_

Tapped-delay-line model

Adaptive noise-canceler contiguration summary.

-.. _=(.)

4th CMTAW meeting

Radar Syztems Laboratory

Electrical and Computer Engineering

Clemson University

r_

Apr. 15, 1992

NOTES

An adaptive noise canceller can be used to optimally

reject clutter from a radar return if a reference clutter

sequence is available and that sequence is highly

correlated with the ground clutter po rtion of the

primary return and uncorrelated with the weather

portion of the primary return. As shown earlier a

low order tapped delay type adaptive filter may be

very effective and is being investigated as an

alternative for very low signal to clutter ratio situations.

602
Analysis of Modelled Microbursts in Real Clutter

.,[" dutteronlyl -I- -I- _

"nitialization J -I-| analysis

parametersJ

AWDRS weather only I-_ level I

computer I & Q data j

program

Rede_ System= LeJbox_ory

nectctcaZ end Computer

4th CMTAW meeting Clemxon Unlyet_itt_y meeting Apr. 15, lgg2

et --

NOTES

To investigate very low signal to clutter ratio situations

the NASA simulation model is being used in conjunction

with actual recorded ground clutter returns. The

simulation model is set up with a microburst windfield

from a previously observed microburst. It can be

placed at any location for which clutter data have

been recorded. Simulated I & Q data are then simply

added to the archived clutter I & Q data after proper

scaling and used for analysis of signal processing

algorithms. The weather signal to clutter ratio can

be controlled as desired by the user.

603
Adaptive Noise Canceling Clutter Rejection Results

post clutter rejection filter pulse-pair mean estimate error

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simulated July 1988 microburst in real clutter

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4th CMTAW meeting

Rsdar STztems Laboratory

Electrical ud Computer _|neering

Clem.zon University Apr. 15, 1992

6O4

NOTES

The LMS based adaptive noise canceller described

earlier was used to investigate detection of the

July 11 1988 Denver microburst in the presence

of actual radar ground clutter data recorded in 1991.

The weather return levels were scaled to maintain

a constant average signal to clutter ratio of -20 dB

in each range cell. The pulse pair estimate of Doppler

spectrum mean was then computed after filtering

with an adaptive noise canceller using the recorded

and time delayed clutter data as a reference input.

Results were compared to similar processing after

filtering with a fixed 1.5 m/s notch Butterworth filter.

The adaptive filter is much better where discrete

clutter interference is present (near range cells 50

and 64). This should improve hazard factor estimates.
DEN nNy 26 Flyover, EL--0, 3755 prf, 1-14km

rlr_2=l¢==l From. 153 r= G-gG 9G, _o_pl.= =E PRF

norm 80-100_B -30,+]Om/m Oapplmr vm(oc;Ey mpec_ro

4th CMTAW meeting

Radar Syziomz Laboratory

Electrical and Computer Engineering

Clemson University

Apr. 15, 1992

NOTES

This and the next 8 slides analyze the effect of out-of-range

returns in the Denver data when the aircraft was flying

over runway 26 headed west with the radar antenna

oriented toward the mountains.Three views show the

radar Doppler spectra for range cells 6-96 at the 3755

prf. Data at 1877.5 prf were also recorded and analyzed

to show what is in range cells 6-96 without second

time around returns and what is in the extended range

cells that aliases into the closer range cells when

operating at the higher prf. Three different antenna

elevation angles are shown. Out-of-range interference

is significant at the horizontal elevation (EL=0) and is

reduced to a negligible level at EL=-3 degrees. Some

spectrum aliasing is also noted in the reduced prf

plots since the Doppler range is halved to -15,+15 m/s.

6O5
DEN rwy 26 Flyover, EL=0, 1877.5 prt, 1-14km

dn2¢1==I Fram. ]_3 rc G-gG IG .=mpl.. ob PRF/2

r_rm 70-J_dB -:]_;,+iSm/_ DDppler vml_;Ey =_Er=

4th CMTAW meeting

Uadeur Systems Laboratory

Electrical and Computer Engineering

Clemson U_|_mrs|ty

DEN rwy 26 Flyover, EL=O, 1877.5 prf, 42-56km

dr_=L==l Fr=== 1_3 r= 284--374 IS ==_pL=. =E PI_:/2

Apr. 15, 1992

_orm 70-100dB -_,*LS_J= DoppLer vatoc;Ey .p_=_r=

6O6

4lh CMTAW meeting

Radar Systemz Laboratory

Electrical and Computer Engineering

Ciemzon Uni_nsrsity Apr. 15, 1992
DEN rwy 26 Flyover, EL=-1, 3755 prt, 1-14km

dr_Iclu_;Fr_ )23 r_" G-96 9_ sc,11_le_ al_ PRF

7B-LI_ -30,+30./. Doptnl.r velo=;_y II:_c_ro

4th CMTAW meeting

Radar Systems Laboratory

ZJectr|cal and Computer EnlineerinB

Clomson Uni_raity

DEN rwy 26 Flyover, EL=-I, 1877.5 prf, 1-14km

dn4clt_ Fr_ 123 rc g-9t; l_ ._IH _6 PRF/2

Apr. 15, 1992

4th CMTAW meeting

Ra4ar SySlam# Laboratory

Eloctrlc_d and Computer Engineering

Clamson University Apr. 15, 1992

6O7
DEN rwy 26 Flyover, EL=-I, 1877.5 prf, 42-56km

cln4cLmS Frum. [23 P= 284-374 t_ iamplem oE PRF/2

70-L_B -L_,'15_/_ Oopplip viLo=;_y spmclr_

41h (_rlrAW m_ttno

Radeur Syztems Laboratory

Electrical and Computer Engineering

Clomzon Univorsity Apr. 15, 1992

DEN rwy 26 Flyover, EL=-3, 3755 prf, 1-14kin

dr_.q_l_3 Frclnm 21g rc G-gG 9(; l_plms oE PRF

7B-i_ -30,*J0=1. OopptIP v.to=;_y ipm=Er=

608

4th CMTAW meeting

RadeLr Syztemz Laboratory

Electrical and Computer Engineering

Clomzon Univorzity Apr. 15, 1992
DEN rwy 26 Flyover, EL=-3, 1877.5 prf, 1-14km

driP=It3 Fr=m_ 2JB r= G-96 LG s_lms ob PRF/2

7B-L(_ -L_,+JSm/w Doppl=r vtLa=;_y 5m==_ra

41h CMTAW meeting

_edar Syzteml Laboratory

gleclti_:el and Computer I_ngineerlng

Clemzon On|v_rxity

DEN rwy 26 Flyover, EL=-3, 1877.5 prf. 42-56km

dn3ct=3 Fra_ 21B r= 294-374 t5 .amplem 06 PlC_

Apr. 15, 1992

7_-t_l_ -t_,+J++/. D_ppler velac;_y =pBc_ra

41h CMTAW meeling

Radar Systems Leboralory

£1eclric_l _d Computer Engineerin K

Ciemson OuiTerz/ty Apr. 15, 1992

609
Mean Goundspeed Error from Radar Return -DEN

dn2c6esl.m6, EL=0, all times with corrected AZ " 0, rc 696

=h,_= m_'r=ar- Dot- F_=n== Clui i=l), ¢_d QvBroLt av=w,oma (d=r,_nd)

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Eloctricel n4 Computer Es_ieeeriss E

4th CMTAW meeting Ctem_m,,O_rsity Apr. 15, 1992

¢1

NOTES

One of the Denver runway 26 overrflights is analyzed

to determine any difference from zero of the Doppler

location of the ground clutter mainlobe. This difference

is interpreted as a difference between the groundspeed

used to determine Doppler zero in the radar

demodulation and the groundspeed as measured by

the radar. In each frame of 128 radar pulses the

spectrum peaks in all range cells have been averaged

to get a "frame" mean. These are then plotted for

each frame when the corrected antenna azimuth

was 0 degrees. A slight bias of aproximatedly 0.055 m/s

is noted.

610
St. Dev.GoundspeedErrorfromRadarReturn-DEN

dn2c6cslJn6, EL=O, all times with corrected AZ ,, O, rc 6-96

_i-==-=1==-=1d_v;=.+-;=,_ I:==r F1"==r= (==_L;=I). =_1 =v.=raLL r.E. =l=,v. (d==h-d)

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4th CMTAW meeting

R_lar Sysmmz Laboratory

noctrlcal end CompmerZnl_fmeecb_

Clemz_ University

Q

Apr. 15, lgg?

NOTES

The situation described in the previous slide has

been analyzed to estimate the standard deviation

in the "groundspeed error" as determined from

the radar measurement.

611
Groundspeed Correction Bias vs. Antenna Azimuth

AZ--30

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-)m _ -le g lm _| _la

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4_h CMTAW mo_in9

Broils" Sl,*nemm I,._f'al_'T

nocu_cai --d ¢oapmmr F,_mrlNl_ Apr. 16, 1982

NOTES

During each flight experiment the airborne radar

antenna was in a scanning mode, typically covering

-30, +30 degrees azimuth relative to the A/C longitudinal

axis. A small bias error in the ground speed that

varied linearly as a function of azimuth angle was

noted in virtually all data sets. This error remained

after routine correction for the geometric variation

of zero Doppler as a function of azimuth angle. In

addition, the slope of this bias varied depending on

the antenna scan direction. This figure illustrates

a counterclockwise scan from a DEN rwy 26 flyover.

FFT spectra of data records from 61 frames at 1

degree increments across the scan were thresholded

so that the central ground clutter peak is represented

as a cluster of points showing how the Doppler spectrum

peak varies with azimuth angle. All data are from the

range cell where the antenna boresight intersects

the ground.

612
Additional On-going Research Work

1. Real time algorithm development

2. Adaptive clutter rejection filtering

. Modelling of weather radar ratums

- autorogressive modelling

- linear models based on fractals

- non-linear models based on chaotic systems theory

4. Optimal Bayesian methods for Hazard Factor estimation

- use Doppler spectra as windspeed probability density

- transform to F-factor probability density

- estimate "most probable" F-factor map in protection volume

4th CMTAW meeting

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_on Un|'Ye_lty Apr. 15, lgg2

References

Baxa, E.G., Jr., " Airborne Pulsed Doppler Radar Detection

of Low-Altitude Windshear- A Signal Processing

Problem," Digital Signal Processing 1, pp. 186-197,

Academic Press, October 1991.

Kunkel, M.W., "Spectrum Analysis for the Detection of

Low-Altitude Windshear with Airborne Doppler Radar,"

Radar Sys. Labs. TR-15, ECE Dept., Clemson Univ.,

Clemson, SC 29634-0915, February 1992.

Baxa, E.G., Jr. and Lee, J., "The Pulse Pair Algorithm

as a Robust Estimator of Turbulent Weather Spectral

Parameters Using Airborne Pulse Doppler Radar,"

NASA CR4382, DOT/FAA/RD-91/17, 1991.

Keel, B.M. and Baxa, E.G.,Jr., "Adaptive Least Square

Complex Lattice Clutter Rejection Filters Applied to

the Radar Detection of Low Altitude Windshear,"

Proc. Int. Conf. Acoust., Speech, Sig. Proc., ICASSP-90,

Albuquerque, pp. 1469-1472, Apr. 1990.

613
614

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September 1992 Conference Publication

4, TITLE AND SUBTITLE S. FUNDING NUMBERS

Airborne Wind Shear Detection and Warning Systems - Fourth WU 505-64-12-01

Combined Manufacturers' and Technologists' Conference

e. AUTHOR(S)

Dan D. Vicroy, Roland L. Bowles, and Robert H. Passman,

compilers

7, PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23665-5225

9. SPONSORING 1 MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

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NASA CP-10105, Part 1

DOT/FAA/RD-92/19-I

11. SUPPLEMENTARY NOTES

Dan D. Vicroy and Roland L. Bowles: NASA Langley Research Center, Hampton, Virginia

Robert H. Passman: Federal Aviation Administration, Washington, DC

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13. ABSTRACT (Max;.mum 200 words)

The Fourth Combined Manufacturers' and Technologists' Conference was hosted jointly by

NASA Langley Research Center (LaRC) and the Federal Aviation Administration (FAA) in

Williamsburg, Virginia, on April 14-16, 1992. The meeting was co-chaired by

Dr. Roland Bowles of LaRC and Bob Passman of the FAA. The purpose of the meeting was

to transfer significant ongoing results of the NASNFAA joint Airborne Wind Shear Program to

the technical industry and to pose problems of current concern to the combined group. It

also provided a forum for manufacturers to review forward-look technology concepts and for

technologists to gain an understanding of the problems encountered by the manufacturers

dunng the development of airborne equipment and the FAA certification requirements. The

present document has been compiled to record the essence of the technology updates and

discussions which follow each.

14. SUBJECT TERMS

Microbursts; Wind Shear; Aircraft Hazards;

Doppler Radar; Infrared; LIDAR

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