identification of tower wake distortions headline in sonic … · 2016-06-19 · identification of...
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HeadlineIdentification of Tower Wake Distortions
in Sonic Anemometer Measurements
during XPIA
KATHERINE MCCAFFREY, PAUL T. QUELET, ADITYA
CHOUKULKAR, W. ALAN BREWER, JAMES WILCZAK,
STEVEN ONCLEY, DANIEL WOLFE, JULIE K. LUNDQUIST
American Meteorological Society
22nd Symposium on Boundary Layers and Turbulence
Session 16, Contribution 16A.6, 24 June 2016, 4:45 PM
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Opposing direction booms enable
characterization of BAO tower wake
2
• Campbell
Scientific CSAT3
sonic
anemometers
• Tilt correction
(Wilczak et al.
2001)
• 20 Hz data, 2-min
WS & 20-min TKE
averaging period
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Sonic anemometer observations are
affected by upstream tower wakes
3
50 m
50 m
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2-min SE/NW mean wind speed ratios
reveal distinct wakes from BAO tower,
along with speed-up regions
NW wake
speedup regions
SE wake 4
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Sonics’ 2-min mean wind speed show
agreement in free stream regions
NW wake SE wake
Free stream
550 m
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20-min SE/NW turbulent kinetic energy
(TKE) reveal wider angular swaths of
BAO tower wake
NW wake SE wake 6
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50 m sonics’ 20-min TKE shows (log)
agreement in free stream regions
NW wake SE wake
Free stream
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𝑹𝟐 between sonics for wind speed & TKE
vs. wind dir. precisely defines wake edges
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Compass summary of wake boundaries
shows BAO tower asymmetry
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Recall the example wind direction time
series
The free stream zones show a
consistent sign of difference in wind
direction measured from the two sonics10
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SE-NW wind direction bias varies
sinusoidally with wind direction
Δ𝜃𝑆𝐸 𝑡𝑜 𝑁𝑊 = 10 sin(𝜃𝑆𝐸 − 154) (deg) 11
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Vector
representation of
tower winds vs.
free stream
winds shows
greatest
differences near
the
perpendicular
direction
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Sonics
MAE %
comparison
of 2-min
WS or 20-
min TKE
increases
rapidly if
any or
nearly all
data points
are waked
any nearly all
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BAO tower wake characterization is
vital for successful XPIA data usage
• Wake effect up to 50% wind speed reduction,
with adjacent speedup regions up to 5%
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BAO tower wake characterization is
vital for successful XPIA data usage
• Wake angular swath wider in TKE than WS
15
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• Sinusoidal variation of sonic wind direction
differences
BAO tower wake characterization is
vital for successful XPIA data usage
• Wake
asymmetry due
to tower
geometry
• Averaging period
not significant, any
or nearly all waked
points in time
period important
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Time for Questions…
Photo Courtesy: Bill Brown, NCAR
Watershed School (Boulder, CO) 6th & 7th grade MGAUS Radiosonde Launch,
BAO Tower During, XPIA 17 Mar 2015 Bill Brown
McCaffrey K., P.T. Quelet, A. Choukulkar, J. Wilczak, D.E. Wolfe, A. Brewer, S.
Oncley, J.K. Lundquist, 2016: Identification of Tower Wake Distortions in
Sonic Anemometer Measurements. Atmospheric Measurement
Techniques. Submitted.
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Extra Slides
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I. Sonic Anemometers (“Sonics”) Comparisons and
Correlations
II. Independent Identification: Sonic Anemometer vs.
Lidar Observations
III. Flow Deflection around Tower
IV. Time Averaging and Temporal Extent of Wake Impacts
V. Conclusions
Presentation Outline
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XPIA Lidar Super Site (LSS): WCv2
Vert. Prof. Horiz. Wind
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Scanning lidars: VTS ≈ 10m, LSS 130m,
south of BAO Tower
LSSLundquist et al. (2016), BAMS, accepted. 21
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100 to 200 m lidar comparisons agree
as independent measurements
Wind Speed Wind Direction22
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Ratio of a) NW and b) SE compared to
WCv2 and VTS also shows tower wakes
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Sonic wind direction differences are
asymmetric compared to independent
WCv2 measurements
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Sonic wind direction differences are
asymmetric compared to independent
VTS measurements
Mean of sonic wind dir. reduces differences.25
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Increasing averaging interval length
decreases prominence of tower wake
Wind
Speed
TKE
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