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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 [email protected]

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Weather Prediction Models

2 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 3 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?

4 • 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 HIGH-RES FORECAST DISPERSION MODEL NOWCAST FORECAST / ANALYSIS USERS SECURE DATA CENTER MET REPORT FIRING SOLUTION FORECAST MODELING QA & FORMATTING TAMDAR SYSTEM ARCHITECTURE MET DATA USERS LATENCY < 30SEC GLOBALLY FROM TIME OF OBSERVATION Approved For Public Release; Distribution Unlimited

Know the Weather

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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 7 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) Prototype TAMDAR-U Mounted on Modified Aerostar Nose Cone CFD Analysis

Measures and Reports -Ice presence -Median and peak turbulence -Relative Humidity -Indicated and True Airspeed -Static pressure and pressure altitude -Air temperature (Mach corrected) -Winds Aloft (Speed and Dir) -GPS Position and Time -Additional sensing possible (CBRN) -Encryption Possible

Know the Weather

Approved For Public Release; Distribution Unlimited

Approved For Public Release; Distribution Unlimited TAMDAR-U Sensor (Prototype) - SWaP LRU

Probe (External)

Dimensions (Volume) Weight Max Power (Estimated)

2.6”x2.5”x0.7” 3.6” Pitot 2.2 oz (62 g) N/A Data Acquisition, Processing, and Communications (Internal)

TOTALS

40 in 3 40 in 3 Internal (reductions possible) 12.2 oz (346 g) 14.4 oz (408 g) (reductions possible) 8.4W

8.4W

(reductions possible)

Know the Weather

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The Aerostar UAS

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The Airspace & Model Domain

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32 o 107 o 46.00’ N 50.00’ W 32 o 106 o 46.00’ N 30.00’ W 31 o 107 o 40.00’ N 50.00’ W Approved For Public Release; Distribution Unlimited 31 o 106 o 40.00’ N 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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32 o 107 o 46.00’ N 50.00’ W Approved For Public Release; Distribution Unlimited

Example “Test Card”

32 o 107 o 40.00’ N 34.00’ W Point B 32 o 106 o 46.00’ N 30.00’ W

After T/O:

125 O / 40 nm 305 O / 40 nm LRU A/P 32 o 17.21’ N 106 o 55.19’ W Point A 31 o 107 o 40.00’ N 50.00’ W 31 o 106 o 40.00’ N 30.00’ W SOUTHERN BORDER ADIZ Approved For Public Release; Distribution Unlimited

Normal climb to 10,000’ MSL Course 305 o True At 10,000 MSL, normal descent to 7,000’ MSL At Point B, standard rate turn to 125 o True Return to Point A (LRU) 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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What did we find?

16  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 17

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