Ch Challenges of Mi Micro-We Weather Fo Forecasting for UAS - - PowerPoint PPT Presentation

Ch Challenges of Mi Micro-We Weather Fo Forecasting for UAS Skyways

Dr James Pinto Deputy Director, Research Applications Laboratory NCAR, Boulder 29 April 2019

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With key contributions from: Sean Bailey and Suzanne Weaver Smith (UKY) Pedro Jimenez, Anders Jensen Tracy Hertneky (RAL) Gijs De boer (University of Colorado)

Ch Challenge

• Small UAS very susceptible to finescale weather

variability

• Smaller scale phenomena are inherently more

uncertain to predict.

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Uncertainty Predictive Scale

What are the requirements for a particular UAS operation?

How do we translate uncertain wx info into decisions?

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Finescale Prediction Impacts Translation UAS Sensor Calibration Gap Analysis Partnerships User Interactions

NC NCAR UA UAS/UA UAM Wx Wx Pr Program

Improve Guidance/ UAS Wx Integration

Re Realtime Fi Finesc scale Pr Prediction for LAPSE-RA RATE TE

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100 m grid spacing 3km grid spacing NOAA/NCEP High Resolution Rapid Refresh

100 km

Meso-to-microscale coupling research

• ngoing at NCAR (Munoz-Esparza et al. 2018)

LA LAPSE SE-RATE TE Fi Fiel eld d Exper Experimen ent

• Period: 15-21 July 2018
• Location: San Luis Valley,

South-central Colorado

• WRF-based system - run

twice per day

• Used in next day mission

planning

• 100 m grid spacing – can

resolve gusts and thermals

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LES Domain

100 km

Dr Drain ainag age F Flo low C Cas ase S Study

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CU lidar Saguache Airport (04V) UKY BCT5D

• 75 UKY flights
• 2 quadcopters
• 3 fixed-wing platform
• 2 long duration orbits – CU TTwistor

UKY BCT5E UKY DJI ASOS CU TTwistor UKY S1000

Asset Deployment in Saguache Canyon 19 July 2018

BlueCat Fixed Wing UAS Drainage

Ev Evolution of Drainage Winds in San Luis Valley

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~300 ft AGL *04V 12 hour run valid: 06:00 – 18:00 UTC (00:00 – 12:00 LT) 04V = ASOS at Saguache Airport

Evaluation

• n of M
• f Mod
• deled W

Wind V Variability w y with UA UAS

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Model

UKY-5D UKY-5B

Wind Direction Wind Speed UAS Model UAS SE NW Drainage Drainage

Impact on Low-level Winds

UA UAS Data Assimilation

UAS DA results in 25%-50% reduction in mean error DA Direction Speed noDA DA Period ASOS 10 m winds ASOS 04V vs WRF LES

In Inter erac activ tive e Tools ls

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• Adapted technologies to make products specific for UAS planning
• Used for daily weather briefings for LAPSE RATE : 14-21 July 2018

150 m AGL

Futur Future e of UAS Wea eather ther Guidanc uidance

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Predicted Winds, Turbulence, Battery Charge along Flight Path with Uncertainty UAS sensed data

Futur Future e of UAS Wea eather ther Guidanc uidance

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Simulation for Oklahoma City 10 m grid spacing using FastEddyTM Sauer and Domingo-Esparza

Questions?

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Backup Slides

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Mod Model Con Configuration

• n

Domain 1

• 1 km resolution
• 487 x 637 x 45 gps

Domain 2

• 100 m resolution
• 1008 x 972 x 45 gps

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D01 D02

Model Physics

• WSM Microphysics
• MYNN2 PBL – D01 Only, D02 = WRF_LES
• NOAH LSM
• Builds on Munoz-Esparza et al 2017, 2018

Next Day Fcst GFS 12Z 18Z WRF D01 WRF D02 (avail. by 4pm LT) 10Z HRRR 04Z Day of Fcst 22Z WRF D02 (avail. by 4am LT) WRF D01 ( 6 hr spinup) WRF D00

Ev Evaluation of Fi Finesc scale Mo Model el Winds

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ASOS at Saguache Airport WRF LES at Saguache Airport Obs drainage flow periods

• 2.5

Bias Max Wspd: Timing: +0.8 +1.0

• 1.0
• 1.5 m s-1

L L L