Meteorological Measurements made during RxCADRE

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Meteorological Measurements made during RxCADRE Craig B. Clements, Daisuke Seto, Jon Contezac, and Braniff Davis Fire Weather Research Laboratory Department of Meteorology and Climate Science San José State University San José, CA 4 th Fire Behavior and Fuels Conference Raleigh, NC

Overview Instrumentation and Experimental Design - Overview of CSU-MAPS Data Processing Preliminary Results - Tower measurements: Fire front micrometeorology - Doppler wind lidar measurements Summary and Conclusions

1. 32-m extendable meteorological tower. 2. 2012 Ford F250 4x4 Crew Cab 3. Halo Photonics, Streamline 75 Doppler Lidar 4. Radiometrics, MP3000A Microwave Profiler 5. Vaisala DigiCora MW31 radiosonde system Lidar Halo Microwave Profiler California State University

TowerWorx, MAG-106 steel tower-trailer - GVW: 8200 lbs - 32 m maximum height - deploys with 2-persons, ~30 min - uses outriggers, no guying - instruments prewired

CSU-MAPS Tower instrumentation Up to 6 measurement levels: four fixed: 9,15, 22, 32 m AGL Sensors: - HMP45C Temp/RH sensor - Gill 2-D windsonic anemometer - CSI CSAT 3-d sonic (two) - Licor 7500 CO 2 /H 2 O analyzer - CSI CR1000, CR3000 loggers - Powered by 70 W Solar panel

Experimental Design L2G L1G L2F

CSU-MAPS: Ambient Meteorology (S7-S9)

Sounding: 4 Nov 2012 Potential Temperature (K) Water Vapor Mixing Ratio (g kg -1 ) Height (m AGL) Height (m AGL) Wind Speed (m s -1 ) Wind Direction ( )

Sounding: 7 Nov 2012 Potential Temperature (K) Water Vapor Mixing Ratio (g kg -1 ) Height (m AGL) Height (m AGL) Wind Speed (m s -1 ) Wind Direction ( )

Sounding: 10 Nov 2012 Height (m AGL) 1000 1410 Potential Temperature (K) Water Vapor Mixing Ratio (g kg -1 ) Height (m AGL) Wind Speed (m s -1 ) Wind Direction ( )

Sounding: 11 Nov 2012 Height (m AGL) 0945 1549 Potential Temperature (K) Water Vapor Mixing Ratio (g kg -1 ) Height (m AGL) Wind Speed (m s -1 ) Wind Direction ( )

Micrometeorology of Fire Front Passage (FFP) Meteorology at the fire front do not represent ambient conditions. The FFP is characterized by: Increase in velocity field u, v, w, and temperature, T. Surface wind reversal Peak in turbulence and sensible heat flux. Minimum in atmospheric pressure Strength of each determines fire-atmosphere coupling.

Tower Instrumentation Two 3-D sonic anemometers: (ATI, SATI-Sx probe) 2 m and 6 m AGL Fine-wire thermocouples (Omega, Inc), every 1 m, 1-9 m AGL 6 m ATI sonic Thermocouples 1-6 m Hukseflux SBG-01: total heat flux radiometer: 2.8 m Medtherm 64: radiative heat flux radiometer: 2.8 m 2.8 m radiometers 2 m ATI sonic Campbell Scientific Inc, CR3000 data logger, CFM card reader, 2 GB card Clock locked to GPS Raw data sampled at 10 Hz; TCs at 5 Hz

Data Processing: 10 Hz time series Raw 10 Hz time series of u,v,w,t s are: 1. Despiked using 3σ for pre, and post FFP 1. FFP is visually inspected (or despiking is applied). 2. 30 min u, v rotated into stream-wise, cross-wind directions; w is tilt-corrected (planar-fit method). 1. 15 min average is used to calculated perturbations for turbulent statistics

Data Processing: Turbulence Spectra Wind velocity and temperature (10 Hz): U: mean wind direction V: lateral wind direction W: tilt-corrected, vertical velocity Ts: sonic temperature Define Pre-, During-, Post-Fire Front Passage (FFP) Fast Fourier Transform (FFT) / Wavelet Transform Temperature ( C) 110 100 90 80 70 60 50 Ts Pre-FFP During-FFP Post-FFP fs(f) 10 1 0.1 raw smoothed 40 0.01 30 20 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 0.001 0.001 0.01 0.1 1 10 f = frequency

Data Processing: Turbulent Statistics Turbulent Kinetic Energy (TKE) is the sum of velocity variances: Turbulent Sensible Heat Flux (Hs) is calculated by eddycovariance from To isolate FFP, 1 min averages are calculated

Tower Time Series: 10 Hz processed (m s -1 ) S3 S4 S5 U (m s -1 ) V (m s -1 ) W Temperature (C) Ts Time (CST)

Time Series: 10 Hz processed

Tower Time Series: 10 Hz processed

Turbulence Kinetic Energy and Heat Flux

Convective and Radiative Heat Fluxes

Comparison of Heat Fluxes: Eddy Covariance vs. Radiometric Measurements Eddy covariance

Near-Surface Thermodynamic Plume Structure (Plot S3) 6 Fine-Wire TCs, 1 Hz 120 Height (m AGL) 5 4 3 2 100 80 60 40 (Temperature (C) 1 16:26:00 16:27:00 16:28:00 Time (CST) 20

Doppler The Lidar RxCADRE deployed Project during S7

Doppler Radial Velocity and Backscatter: S7

Doppler Radial Velocity and Backscatter: S8 Elevation angle: 2.5 18:37:05 UTC 18:39:22 UTC Thank you! 18:41:51 UTC 18:44:08 UTC

Doppler Radial Velocity: LG2

Doppler Radial Velocity: LG2 18:24:47 18:25:10 18:26:35 18:30:12

Conclusions Overall, data quality is high! In situ tower data shows FFP structure and fire-atmosphere coupling Doppler Lidar scans are able to track plume boundaries Lidar beam able to penetrate plume, but some attenuation occurs downstream Future work 1. Correlate lidar plume boundaries with UAS fire front 2. Calculate turbulent spectra from time series data 3. Compare heat fluxes to others measured

Acknowledgements Dave Grimm and Keith Hawk (Eglin AFB FD) Joint Fire Science Program (JFSP #11-2-1-11) National Science Foundation (AGS# 0960300)

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