Long-Term Vertical Velocity Statistics Derived from Doppler Lidar in the Continental Convective Boundary Layer June 16, 2016 1 LARRY BERG 1 , ROB NEWSOM 1 , DAVE TURNER 2 , RAJ RAI 1 1 Pacific Northwest National Laboratory 2 NOAA National Severe Stoms Laboratory Contact: [email protected]
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Long-Term Vertical Velocity Statistics Derived from ......Long-Term Vertical Velocity Statistics Derived from Doppler Lidar in the Continental Convective Boundary Layer June 16, 2016
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Long-Term Vertical Velocity Statistics Derived from Doppler Lidar in the Continental Convective Boundary Layer
June 16, 2016 1
LARRY BERG1, ROB NEWSOM1, DAVE TURNER2, RAJ RAI1
1Pacific Northwest National Laboratory 2NOAA National Severe Stoms Laboratory
! Distributions of vertical velocity (w) are needed to understand both PBL turbulence and boundary-layer clouds
! Most studies of w statistics are derived from short-term aircraft observations or tower based studies ! BAO (300 m) ! Cabaw (213 m)
! Long-term measurements of w statistics are lacking, but are needed for evaluating model results and developing new parameterizations ! ARM LASSO (LES ARM
Symbiotic Simulation and Observation) project
! Data collected by Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility provide a unique opportunity to address this need.
24 Rai and Berg et al.
data from a single point at the center of domain), but not resolved in both time and626
space. Vertical profiles of wind speed and potential temperature have been prepared627
for the seven spatial locations within D06 for half-hour period 1300-1330. The loca-628
tion of SW and S are exactly due south of location W and C, whereas the location629
of N and NE are due north of the C and E. These four additional locations (SW,630
N, E, and S) are located approximately 660 m inside the south and north boundary631
of domain D06. Figure 17a shows the TKE profile at the seven locations; the rela-632
tive locations are shown in the inset box. Note that the solid-lines, dashed-line, and633
dotted-lines represent the region with the higher elevation (W (544 m), SW (506 m),634
S (555 m), and C (443 m)), center point, and lower elevation (N (305 m), E (318 m),635
and NE (226 m)) of the domain D06. Similarly, the legend lines with red, blue, and636
black represent the locations that are generally aligned with the predominant flow637
(i.e., southwesterly). The TKE profile shows a great deal of variability between these638
seven locations. In general, the TKE observed at the lower elevations is smaller than639
the TKE at higher elevations. However, the TKE profile between the two consecutive640
locations along the mean flow (i.e., E and S; N and W) are comparable except be-641
tween the two locations NE and C. The smallest TKE value observed for location NE642
may be due to its relatively large offset distance from the wake region (refer to Fig.643
7). It is interesting to note that the maximum TKE at locations C and N are found644
close to the surface. Figure 17b shows the standard deviation sw
profile for the ver-645
tical wind component at all locations. The shape of the profile is almost similar to646
that of the TKE profile and peak values of sw
can be found anywhere from 300 to647
600 m above the surface. Given the relative shape of the TKE and sw
profiles, the648
low-altitude maximum TKE at points C and N is most likely due to variations in the649
horizontal wind components. The mean horizontal and vertical wind components are650
shown in Fig. 17c and Fig. 17d, and the figure highlights the variability occurring in651
the seven locations. The horizontal wind is systematically slower in the locations hav-652
ing lower elevation (dotted lines) and exhibit smaller vertical wind shear compared653
������ ��� ����
0 1.5 3
���
��
0.6
1.2
1.8 a)
�� �� ���0 0.9 1.8
b)
� �� ���-0.7 0 0.7
c)
��� � ��� �� ���
0.5 2.5 4.5
d)
��� � ��
0 0.15 0.3
e)
SW S
W C E
N NE
Fig. 17 A half-hour (1300-1330 PST) averaged profile of a) TKE, b) standard deviation of vertical compo-nent of wind, c) mean of vertical component of wind, d) horizontal wind speed, and e) variance of potentialtemperature at seven locations SW, S, W, C, E, N, and NE. The green box in the inset shows the sevenlocations in domain D06.
Motivation
June 16, 2016 2
TKE and σw generated from LES of region of complex terrain.
Outline • Data descrip:on • Composites of w stats • Sor:ng the results • Conclusions
ARM Doppler Lidars and Analysis Criteria
! Halo-Photonics DL Deployed at ARM fixed sites and AMF ! Near-IR (1.5 µm) ! Range is typically less than 2 km for clear-air
retrievals ! Value Added Product (VAP) has been
developed that includes wind profiles and key statistics: variance, skewness, and kurtosis
! Utilize ARM meteorological and flux data, wind profiles from 915 MHz radar wind profilers
! Data selection criteria ! Require SNR to be ≥ 0.03 (more stringent
criteria used for higher order stats) ! Define zi using a threshold of variance
(0.04—based on Tucker et al. 2009) ! zi > 0, w* > 0 (implies positive surface heat flux) ! Cloud Fraction < 0.001
June 16, 2016 3
50
45
40
35
30
25
-120 -110 -100 -90 -80 -70
ARM SGP Site
Representative Case: 18 July 2015
June 16, 2016 4
2.5
2.0
1.5
1.0
0.5
0.0
z or
zi (
km)
12:007/18/15
16:00 20:00 00:007/19/15
Date and Time (UTC)
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Heat Flux (K m s -1) or w
* (m s -1)
2.01.51.00.50.0
σw
2 (m2 s -2)
ECOR w* ECOR Sensible Heat Flux zi
ECOR=Eddy Covariance System
Composite Statistics
June 16, 2016 5 12:00 15:00 18:00 21:00 00:00
Time (UTC)
2.5
2.0
1.5
1.0
0.5
0.0
z (k
m)
2.5
2.0
1.5
1.0
0.5
0.0
z (k
m)
2.5
2.0
1.5
1.0
0.5
0.0
z (k
m)
Skewness
Kurtosis
1.21.00.80.60.40.20.0σw
2
1.21.00.80.60.40.20.0
43210Kurtosis of Gaussian Distribu:on = 3
Day:mes values posi:vely skewed
Peak value smeared by averaging
More peaked
Less peaked
Sensitivity of Statistics to Parameters
! The w statistics can be sorted by many different ways—think of factors that could influence the PBL turbulence ! Time of day ! Wind direction ! Season ! Surface shear stress/friction velocity (u*) ! Wind Shear at PBL top ! Static stability
! Use Kolomogorov-Smirnov test to determine if differences are statistically significant ! Look to see if values could be from the same parent distribution
June 16, 2016 6
Change in Variance Profile with Time
! All values of σw2 have been
normalized by w*
! Values tend to be smaller than short-term results presented by Lenschow et al. (2012):
! Normalization works best in late-morning and afternoon ! PBL approximately steady
state
June 16, 2016 7
0.50.40.30.20.10.0
Median σw2/w*
2
1.2
1.0
0.8
0.6
0.4
0.2
0.0
z/z i
18:00
Time (UTC)
14:30
22:30
2015 All Months, Southerly Winds
Limit analysis to 17 to 23 UTC
€
σw2
w∗2 =1.8 z
zi
$
% &
'
( )
2 3
1− 0.8 zzi
$
% &
'
( )
2
Sensitivity of σw2/w*
2 to Wind Direction
! Differences in land use and surface roughness associated with wind direction could lead to differences in velocity statistics
June 16, 2016 8
1.2
1.0
0.8
0.6
0.4
0.2
0.0
z/z i
0.50.40.30.20.10.0σw
2/w*2
5000# of Obs.
Easterly Southerly Westerly
Limit analysis to periods with southerly winds
Error bars: 75th and 25th percen:les
ARM CF
Dependence on Season
June 16, 2016 9
σw2/w*
2 changes with season, differences in surface heterogeneity? Skewness larger in PLB top during warm seasons
1.2
1.0
0.8
0.6
0.4
0.2
0.0
z/z i
0.60.50.40.30.20.10.0σw
2/w*2
0.80.40.0Skewness
65432Kurtosis
DJF MAM JJA SON
Dependence on u*
! Values of u* from surface flux measurements were sorted to determine critical values ! u* in Lenschow et al. (2012) ranged from 0.16 to 0.52 ms-1
June 16, 2016 10
1.2
1.0
0.8
0.6
0.4
0.2
0.0
z/z i
0.60.50.40.30.20.10.0
σw2/w*
21.00.80.60.40.20.0-0.2
Skewness65432
Kurtosis
Large u* Small u*
Large values of u* lead to small values of σw2/w*
2 and kurtosis
Large: 0.59 ms-‐1 Small: 0.27 ms-‐1
Dependence on Stability
! Stability defined using –zi/L ! Values greater than 30 are unstable, less than 30 moderately unstable
June 16, 2016 11
1.2
1.0
0.8
0.6
0.4
0.2
0.0
z/z i
0.60.50.40.30.20.10.0
σw2/w*
21.00.80.60.40.20.0-0.2
Skewness65432
Kurtosis
Unstable Moderately Unstable
Unstable cases have smaller values of σw2/w* and kurtosis
Dependence on Wind Shear Across the Boundary-Layer Top
! Wind shear determined from radar wind profiler ! Based on wind speed differences between z/zi of 1.1 and 0.9
June 16, 2016 12
1.2
1.0
0.8
0.6
0.4
0.2
0.0
z/z i
0.60.50.40.30.20.10.0
σw2/w*
21.00.80.60.40.20.0-0.2
Skewness65432
Kurtosis
Large Shear Small Shear
Large: 1.4 ms-‐1 Small: -‐0.6 ms-‐1
Cases with large amounts of shear are less skewed over much of the BL
Conclusions
! Data from ARM Doppler lidars provide a unique opportunity to look at long-term vertical velocity statistics
! Scaling with w* works best for cases when BL is quasi-stationary ! There as systematic differences in the σw
2/w*2, skewness, and
kurtosis associated with differences is: ! Season ! Surface stress: smaller
values of u* lead to large values of σw
2/w*2
! Stability: Moderately unstable conditions lead to larger values of σw
2/w*2 and kurtosis
! Wind Shear across the BL top: small values of shear lead to larger values of skewness
! Future efforts will extend work to stable conditions
June 16, 2016 13
Less intense mixing at surface leads to larger variance
Less intense mixing at boundary-‐layer top leads to larger skewness
Acknowledgments: This work was supported by the DOE Atmospheric System Research (ASR) Program and Atmospheric Radia:on Climate Research Facility
Variance largest in spring; Skewness larger in warmer seasons
June 16, 2016 14
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