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UAS Data Collection for High-resolution
MET Modeling Ingest
Mr. Terry Jameson
Battlefield Environment Division
Army Research Laboratory, WSMR
COMM 575-678-3924
terry.c.jameson.civ@mail.mil
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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 upper
atmosphere
• Doppler weather radar (intensity and motion within storms) – good info but
only 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 the data just displayed to the operator? – can’t use in modeling
• Are the data recorded at the ground station? – probably not
• Are the data recorded on-board somehow? – probably not
• Are those data date/time/location-stamped?
• 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!
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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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Approved For Public Release; Distribution Unlimited
AirDat’s Commercial TAMDAR® System
GLOBAL
SATELLITE NETWORK
TAMDAR DATA
AIRBORNE SENSORS
TAMDAR DATA
TRANSPORT AIRCRAFT
SATELLITE
GROUND STATION
UAV / UAS
SECURE DATA CENTER
HIGH-RES
FORECAST
MET REPORT
DISPERSION
MODEL
FIRING
SOLUTION
FORECAST MODELING
NOWCAST
QA & FORMATTING
FORECAST / ANALYSIS
USERS
MET DATA USERS
TAMDAR SYSTEM ARCHITECTURE
LATENCY < 30SEC
GLOBALLY FROM TIME
OF OBSERVATION
Know the Weather
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Information
used with permission from AirDat, LLC
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Approved For Public Release; Distribution Unlimited
The Team
NMSU 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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Approved For Public Release; Distribution Unlimited
TAMDAR-U Sensor (Prototype)
Mounted on Modified
Aerostar Nose Cone
Prototype TAMDAR-U
CFD Analysis
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
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used with permission from AirDat, LLC
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Know the Weather
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TAMDAR-U Sensor (Prototype) - SWaP
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
40 in3
Internal
14.4 oz
(408 g)
(reductions possible)
(reductions
possible)
TOTALS
8.4W
(reductions
possible)
Know the Weather
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Information
used with permission from AirDat, LLC
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The Aerostar UAS
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The Airspace & Model Domain
32o 46.00’ N
107o 50.00’ W
40.00’ N
107o 50.00’ W
31o
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32o 46.00’ N
106o 30.00’ W
31o 40.00’ N
106o 30.00’ W
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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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Approved For Public Release; Distribution Unlimited
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Example “Test Card”
32o 46.00’ N
107o 50.00’ W
32o 40.00’ N
107o 34.00’ W
Point B
32o 46.00’ N
106o 30.00’ W
Normal climb to
10,000’ MSL
305O / 40 nm
125O
After T/O:
/ 40 nm
LRU A/P
32o 17.21’ N
106o 55.19’ W
Point A
Course 305o True
At 10,000 MSL, normal
descent to 7,000’ MSL
At Point B, standard rate
turn to 125o True
Return to Point A (LRU)
31o 40.00’ N
106o 30.00’ W
31o 40.00’ N
107o 50.00’ W
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SOUTHERN BORDER ADIZ
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At 65 kt IAS (approx. 75 kt
TAS), the R/T to Pt. B will
take approximately 1.15 hr.
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Example Results
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Approved For Public Release; Distribution Unlimited
What did we find?
 TAMDAR sensor could be adequately downsized/configured for UAV ops
 TAMDAR-U data successfully assimilated, formatted, ingested given erratic
flight patterns and altitudes of UAV missions
 From a qualitative standpoint, wind flow patterns looked more realistic
over 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 profiles
from 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
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