1 approved for public release; distribution unlimited mr. terry jameson battlefield environment...
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![Page 1: 1 Approved For Public Release; Distribution Unlimited Mr. Terry Jameson Battlefield Environment Division Army Research Laboratory, WSMR COMM 575-678-3924](https://reader035.vdocuments.mx/reader035/viewer/2022062314/56649e615503460f94b5d536/html5/thumbnails/1.jpg)
1 Approved For Public Release; Distribution Unlimited
Approved For Public Release; Distribution Unlimited
Mr. Terry JamesonBattlefield Environment DivisionArmy Research Laboratory, WSMR
COMM [email protected]
UAS Data Collection for High-resolution MET Modeling Ingest
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Approved For Public Release; Distribution Unlimited
Weather Prediction Models
Numerical Weather Prediction (NWP) Models
• Predictions of basic Met parameters (winds, temperature, pressure, humidity)• Predictions of derived parameters (turbulence, visibility, cloud layers, etc.)• Predictions at 3-D grid points ( ~ 30 mi. down to ~ 8 mi. horizontal spacing)• Predictions out several hours - up to many days• Research-grade models (one-hour predictions – 0.6 mi. grid spacing)
Models require Met data observations input for initialization
• Surface weather stations (manned and automated) – little help for upperatmosphere
• Doppler weather radar (intensity and motion within storms) – good info butonly when storms are present
• Satellite observations of winds and temps (very coarse vertical resolution)• Vertically-pointing wind profiling radars – few locations even in U.S.• Weather balloons (winds, pressure, temperature, humidity)
~ 70 stations in Lower 48, ~700 world-wide Twice-daily balloon launches Mainstay of NWP model input since its inception in late ‘50s-early 60’s
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But there’s a Problem
In the U.S. all of the above are available, but…..
• Problem is: All of the above leave many gaps (time/space), especially for high-resolution models
• Problem is: In/near the battlefield, only a very few weather balloon and surface observation stations exist
• Problem is: Those few stations can be sporadic in their observations
Bottom line:
WE NEED MORE INPUT MET DATA!
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In-situ Obs from UAVs
Data collected from UAVs - What are we up against?
• Certainly many UAVs have a temperature sensor/readout, plus GPS winds
BUT…
• Are those data date/time/location-stamped?
• Are the data just displayed to the operator? – can’t use in modeling
• Are the data recorded on-board somehow? – probably not
• What about pressure and humidity? – need those parameters as well
• How to QC the data? – bad data or wrong time/place = poor performance.
• How to format the data? – models are very picky!
• Are the data recorded at the ground station? – probably not
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TAMDAR-What is it?
TAMDAR: (Tropospheric Airborne Met DAta Reporting)
• Small meteorological (Met) data sensing/transmitting instrument• AirDat, LLC• Installed on ~150 regional commuter airliners• Collects Met data for ingest into Numerical Weather Prediction (NWP) Models
TAMDAR-U (TAMDAR-UAV)
• TAMDAR downsized for installation on UAVs• Stringent restrictions on Size, Weight, and Power (SWaP) requirements
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AirDat’s Commercial TAMDAR® System
Know the Weather
QA & FORMATTINGFORECAST MODELING
SECURE DATA CENTER
MET DATA USERSFORECAST / ANALYSIS USERS
AIRBORNE SENSORS
GLOBAL SATELLITE NETWORK
TAMDAR DATA
TAMDAR DATA
SATELLITEGROUND STATION
LATENCY < 30SEC GLOBALLY FROM TIME
OF OBSERVATION
FIRING SOLUTION
HIGH-RES FORECAST
DISPERSION MODEL
NOWCAST
MET REPORT
TAMDAR SYSTEM ARCHITECTURE
UAV / UAS
TRANSPORT AIRCRAFT
Information used with permission from AirDat, LLC
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The TeamNMSU PSL/Technical Analysis & Applications Center (TAAC)
• The Aerostar-B UAV• Established COA in southern NM• Substantial experience in conducting instrumentation flight tests
AirDat, LLC • The TAMDAR• Instrumentation facilities (Lakewood, CO)• Data ground station and NWP modeling facilities (Florida)• Substantial experience in instrumenting commercial airline fleets• Substantial experience in ingesting TAMDAR data into models
ARL
• Long-term history of DOD weather research and support• High-resolution, battlefield-scale NWP model development• Substantial experience in assessing model performance
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TAMDAR-U Sensor (Prototype)
Measures and Reports
-Ice presence -Relative Humidity
-Median and peak turbulence -Indicated and True Airspeed
-Static pressure and pressure altitude -Winds Aloft (Speed and Dir)
-Air temperature (Mach corrected) -GPS Position and Time
-Additional sensing possible (CBRN) -Encryption Possible
Prototype TAMDAR-U CFD AnalysisMounted on Modified Aerostar Nose Cone
Know the WeatherInformation used with permission from AirDat, LLC
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TAMDAR-U Sensor (Prototype) - SWaP
Know the Weather
LRU Dimensions (Volume)
Weight Max Power (Estimated)
Probe(External)
2.6”x2.5”x0.7”3.6” Pitot
2.2 oz(62 g)
N/A
Data Acquisition, Processing, and Communications
(Internal)
40 in3 12.2 oz(346 g)
8.4W
TOTALS 40 in3
Internal(reductions possible)
14.4 oz(408 g)(reductions possible)
8.4W(reductions possible)
Information used with permission from AirDat, LLC
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The Aerostar UAS
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32o 46.00’ N106o 30.00’ W
31o 40.00’ N106o 30.00’ W31o 40.00’ N
107o 50.00’ W
32o 46.00’ N107o 50.00’ W
The Airspace & Model Domain
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Experimental Approach
Collect TAMDAR-U data within model domain for three-hour flight
Reformat and archive data for later analyses
Run model in data-ingest mode for 3-hrs, simulating ingest during flight
Continue model run after data ingest cutoff – generate 6 hr forecast
Compare output charts with/without TAMDAR-U ingest
Compare against any available observations
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LRU A/P32o 17.21’ N106o 55.19’ WPoint A
32o 46.00’ N106o 30.00’ W
31o 40.00’ N106o 30.00’ W
31o 40.00’ N107o 50.00’ W
32o 46.00’ N107o 50.00’ W
32o 40.00’ N107o 34.00’ WPoint B
SOUTHERN BORDER ADIZ
305O / 40 nm125O / 40 nm
After T/O:
Normal climb to 10,000’ MSL
Course 305o True
At 10,000 MSL, normaldescent to 7,000’ MSL
At Point B, standard rateturn to 125o True
Return to Point A (LRU)
At 65 kt IAS (approx. 75 ktTAS), the R/T to Pt. B willtake approximately 1.15 hr.
Example “Test Card”
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Example Results
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What did we find?
TAMDAR sensor could be adequately downsized/configured for UAV ops
TAMDAR-U data successfully assimilated, formatted, ingested given erraticflight patterns and altitudes of UAV missions
From a qualitative standpoint, wind flow patterns looked more realisticover and near mountain slopes with TAMDAR-U data ingest
Few observations within most of the domain for quantitative evaluation
Weather balloons launched at LRU airport compared against vertical profilesfrom the models were inconclusive
Very benign weather case-study days were not conducive to finding clear distinctions between models
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What’s next?
Collect TAMDAR data within a data-rich model domain (commuter fleet)
Run model ingesting or withholding data as before
Select some “bad weather” case-study days (rainfall, strong winds, etc.)
Conduct quantitative statistical analyses, observation points versus forecasts