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Evaluation of WRF LES using UAS observations and Doppler lidar in a
high sub-alpine desert valley Dr James Pinto
Deputy Director, Research Applications Laboratory
NCAR, Boulder
1
Anders Jensen1, Pedro Jimenez1, Domingo Munoz-Esparza1, Katherine Glasheen2, Sean Bailey3,8, Phillip Chilson4, Jamey Jacob5,8, Corey Dixon2,6, Gijs de Boer2,7, Suzanne Smith3,8, Julie Lundquist2
1NCAR 2University of Colorado 3University of Kentucky 4University of Oklahoma 5Oklahoma State Univeristy 8CLOUD-MAP
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Motivation
• Increasing need for microscale weather info
2
What are the requirements for micro weather information?
ISTP : Toulouse France May 2019
Planning time scales: 0-12hr Resolution: < 1 km Parameters: sensible weather in BL Domain: O(100 km) Uncertainty Information
From Zipline
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Realtime Finescale Prediction Systems
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Rhonda et al. 2015
WRF coupled with fire model - Colorado
WRF coupled with Urban LSM – 4 day nighttime T anomaly – for Amsterdam heat wave
Munoz-Esparza et al. 2018
15
km
~100 m grid spacing
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LAPSE-RATE Field Experiment
• Period: 15-21 July 2018
• Location: San Luis Valley, South-central Colorado
• Boundary layer variability
• Drainage flows
• Valley CI
• Flight Information
1287 flights
262 flight hours
50 UAS platforms
4 ISTP : Toulouse France May 2019
04V
4000m
4000m
BLV
Drainage
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Model Configuration Domain 1
• 1 km resolution
• 487 x 637 x 45 gps
Domain 2
• 100 m resolution
• 1008 x 972 x 45 gps
5
D01
D02
Model Physics
• WSM Microphysics
• MYNN2 PBL – D01 Only, D02 = WRF_LES
• NOAH LSM
• Builds on Munoz-Esparza et al 2017, 2018
10Z
HRRR
04Z 22Z
WRF D02 (avail. by 4am LT)
WRF D01 ( 6 hr spinup)
HRRR
ISTP : Toulouse France May 2019
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Model Sensitivities
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(UTC) WRF LES Wspd: 10 60 120 240 m
D1 Spinup Period: 0h Test: 6h
Physical Parameterizations: Sensitivity of fog layer to microphysics
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Boundary Layer Variability
7
UKY
UNL
University of Colorado Ttwistor
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CU Ttwistor
UKY Bluecat
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Boundary Layer Variability
8
CU Ttwistor – 300 m – Outbound (1215-1235 MDT)
CU Ttwistor – 100 m – Return (1240-1320 MDT)
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UKY Bluecat (0700-1215 MDT)
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Boundary Layer Variability
9
WRF 100 m WSPD - Return (1300 MDT)
Model
Obs
WRF 100 m Vertical velocity
ISTP : Toulouse France May 2019
12:50 MDT 11:00 MDT
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LAPSE-RATE Field Experiment
• Period: 15-21 July 2018
• Location: San Luis Valley, South-central Colorado
10
LES Domain
10
0 k
m
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04V
4000m
4000m
Boundary layer variability Drainage winds Valley CI
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Evolution of Drainage Winds in San Luis Valley
11
~300 ft AGL
*04V
12 hour run valid: 06:00 – 18:00 UTC (00:00 – 12:00 LT)
04V = ASOS at Saguache Airport ISTP : Toulouse France May 2019
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Evaluation of Finescale Model Winds
12
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 0 0
ISTP : Toulouse France May 2019
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WRF LES vs Lidar
13
Comparisons of Wind Speed Profiles at Saguache Airport (SLV)
2018-07-14 2018-07-18
CU Lidar
WRF LES
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Drainage Flow Case Study
14
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
ISTP : Toulouse France May 2019
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Evaluation of Modeled Wind Variability with UAS
15
Model UKY-5D
UKY-5B
Wind Direction Wind Speed
UAS
Model
UAS
SE
NW
Drainage
Drainage
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Drainage
Drainage
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Temporal Variations in Wind Speed
16
UKY-5D
UKY-5B
Movie time period
100 m AGL
ISTP : Toulouse France May 2019
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Impact of AMDAR Data on Fcst Error Reduction
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Peterson 2015
Error Reduction per Observational Dataset (24 hour forecast)
(5 model mean and STD
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Impact on Low-level Winds
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
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Summary Points
• Meso-to-microscale coupling is critical for properly representing finescale evolution of boundary layer structures.
• Care must be taken to optimize configuration of M2M system.
• Assimilation of local scale observations will be needed to better constrain mesoscale forcing and to quantify uncertainties.
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Ensemble Kalman Filter (EnKF)
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Temporal Variations in Wind Direction
20
UKY-5D
UKY-5B
100 m AGL
Movie time period
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Drainage flow Erosion
21
WRF LES Wdir 0830 MDT
40 m
90 m
215 m
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