P1.14
Detecting Boundary Layer Turbulence through Raman Lidar
Water Vapor Retrievals using Autocorrelation Analysis
Erin P. Wagner, David D. Turner, and Steve Ackerman
Cooperative Institute for Meteorological Satelite Studies, University of Wisconsin-Madison
Motivation: Climate and weather
model grids are too coarse to resolve
turbulent boundary layer processes
and must use parameterizations to
represent them. Comparisons of
model-derived boundary layer top
heights and vertical diffusions with
observations are rare, possibly
because long-term, high-resolution
turbulence data sets did not exist until
recently. The goal of this project is to
detect turbulent structure and
boundary layer heights using water
vapor mixing ratios derived from
measurements taken near Lamont,
Oklahoma at the Southern Great
Plains Cloud (SGP) and Radiation
Test-bed (CART) using the CART
Raman Lidar (CARL). Boundary layer
heights and vertical fluxes derived
using CARL measurements can then
be compared to similar
parameterizations in climate and
weather models.
CARL and Turbulent
measurements: The Raman Lidar
located near Lamont, Oklahoma
has observed the boundary layer at
10-second resolution for
approximately 3 years. Among
other constituents, it is sensitive to
nitrogen and water vapor, which
enables the retrieval of water vapor
mixing ratios. Because nitrogen is
proportional to the total amount of
dry air in the atmosphere, it can be
used in place of total dry air
measurements.
In the absence of rain and
boundary layer clouds the water
vapor mixing ratio is essentially
conserved. Turbulent structures
can then be obtained from these
measurements as any changes in
water vapor mixing ratio must be
due to transport through turbulent
processes.
Figure 1: Water vapor mixing ratios
(top) and the turbulent component of
water vapor mixing ratios (bottom)
retrieved by the Raman lidar at the
ACRF SGP site in Lamont, Oklahoma
on June 8, 2007.
Case Study: Water vapor mixing
ratios retrieved from CARL data
measured on June 8, 2007 has
been separated into mean and
turbulent components. Only the
well-developed boundary layer is
considered here. Before
separation, the top of the
boundary layer is clearly visible at
approximately 1.5 km, as the
signal drops off quickly with
height. Looking at the turbulent
component of mixing ratios
reveals a great deal of dry and
moist air exchange through
turbulent circulations (circled).
Figure 4: Autocorrelations for all times plotted against height.
The horizontal line denotes the approximate height of the
boundary layer at 1.5 km.
Autocorrelations with height: The figure above shows
autocorrelations plotted with height for the entire time series.
Lag-correlations peak slightly near the center of the boundary
layer and increase significantly at the top of the boundary layer.
This indicates that fluctuations in water vapor mixing ratio near
the top of the boundary layer are not only relatively strong, but
also have measurable periodicity.
Future work: Case studies will be performed on several types
of boundary layers, such as cloudy, stable, and developing.
Analyses will include more quantifiable boundary layer top
identification and higher moment analysis as well as
autocorrelation analysis. Ultimately, we intend to create a
comprehensive boundary layer height and turbulent climatology
in order to identify variables associated with distinct turbulent
conditions and associated fluxes. These results can then be
compared to different model parameterizations.
Figure 2: Estimation
of the integral scale
of water vapor mixing
ratio with and without
noise correction.
(Courtesy of Volker
Wulfmeyer)
References:
Figure 3: Autocorrelation coefficients of water vapor mixing ratios at lags from 10 to
1500 seconds plotted for several heights
Adequate Resolution: The CARL sampling resolution (10 seconds) must
be much smaller than the integral time scale of the moment of the water
vapor mixing ratio if this lidar is to be used to measure turbulence. The
accompanying figure (courtesy of Volker Wulfmeyer) illustrates that this
condition has been sufficiently met. The integral time scale of the majority
of turbulent structure, as indicated by water vapor mixing ratio
measurements, is indeed much larger than the lidar sampling resolution.
Autocorrelation in the upper boundary layer: Nothing immediately striking is visible in
autocorrelations in the middle and lower boundary layers (above-left). The upper boundary
(above-right) experiences higher lag-correlations and an expected decrease in correlation
with increased lag. Of most interest is the increase in autocorrelation between lags of 400
seconds and 800 seconds. This approximately coincides with the Brunt-Vaisala frequency
and indicates updraft and downdraft couplets near the top of the boundary layer. It is also of
interest to note that the peaks of these autocorrelations shift to higher lags, suggesting an
increase in eddy width with height. This is in agreement with the findings of Lenshow et al
(2000). This feature is not visible at heights immediately above the boundary layer.
1. D.H. Lenshow, V. Wulfmeyer, and C.Senffr, Measuring secondthrough fourth-order moments in noisy data. Journal of
Atmospheric and Oceanic Technologies, 17, 1330-134. (2000).
2. T.L. Acker, L.E. Buja, J.M. Rosinski, and J.E. Truesdale, User’s
Guide to NCAR CCM3. NCAR Technical Note. NCAR/TN-421+IA,
Boulder, Colorado, 210pp (1996).
Acknowledgments:
Department of Energy
Global Change Education Program
Volker Wulfmeyer