1 Approved For Public Release; Distribution Unlimited Mr. Terry Jameson Battlefield Environment...

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



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



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!



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



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



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



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



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



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



The Aerostar UAS



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



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



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”



Example Results



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



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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