Length Scales in the Convective Boundary Layer

Lenschow, Donald H.; Stankov, B. B.

doi:10.1175/1520-0469(1986)043<1198:lsitcb>2.0.co;2

Cited by 276 publications

(269 citation statements)

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“…Only a few other measurements of the profiles of are available. Based on aircraft in-situ measurements, Lenschow and Stankov (1986) claimed that U/z i ∝ (z/z i ) 0.5 ; however, this could not be verified in our case due to the decrease of in the centre of the CBL. Kiemle et al (1997) studied several aircraft measurements over boreal forest and found U/z i ≈ 1.25.…”

Section: Discussioncontrasting

confidence: 66%

“…(Source A) Due to its physical meaning, atmospheric sampling error profiles for all higherorder moments and their combinations can be calculated after estimating the profiles of by application of turbulence statistics based on the theory derived in Lenschow and Kristensen (1985); Lenschow and Stankov (1986); Lenschow et al (1994), and Mann et al (1995). (Source B) Before higher-order moments of m are calculated, it is important to investigate whether the major part of the turbulent fluctuations was resolved by the remote sensing system.…”

Section: Fig 11mentioning

confidence: 99%

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Can Water Vapour Raman Lidar Resolve Profiles of Turbulent Variables in the Convective Boundary Layer?

Wulfmeyer

Pal

Turner

et al. 2010

Boundary-Layer Meteorol

111

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High-resolution water vapour measurements made by the Atmospheric Radiation Measurement (ARM) Raman lidar operated at the Southern Great Plains Climate Research Facility site near Lamont, Oklahoma, U.S.A. are presented. Using a 2-h measurement period for the convective boundary layer (CBL) on 13 September 2005, with temporal and spatial resolutions of 10 s and 75 m, respectively, spectral and autocovariance analyses of water vapour mixing ratio time series are performed. It is demonstrated that the major part of the inertial subrange was detected and that the integral scale was significantly larger than the time resolution. Consequently, the major part of the turbulent fluctuations was resolved. Different methods to retrieve noise error profiles yield consistent results and compare well with noise profiles estimated using Poisson statistics of the Raman lidar signals. Integral scale, mixing-ratio variance, skewness, and kurtosis profiles were determined including error bars with respect to statistical and sampling errors. The integral scale ranges between 70 and 130 s at the top of the CBL. Within the CBL, up to the third order, noise errors are significantly smaller than sampling errors and the absolute values of turbulent variables, respectively. The mixing-ratio variance profile rises monotonically from ≈0.07 to ≈3.7 g 2 kg −2 in the entrainment zone. The skewness is nearly zero up to 0.6 z/z i , becomes −1 around 0.7-0.8 z/z i , crosses zero at about 0.95 z/z i , and reaches about 1.7 at 1.1 z/z i (here, z is the height and z i is the CBL depth). The noise errors are too large to derive fourth-order moments with sufficient accuracy. Consequently, to the best of our knowledge, the ARM Raman lidar is the first water vapour Raman lidar with demonstrated capability to retrieve profiles of turbulent variables up to the third order during daytime throughout the atmospheric CBL.

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Section: Discussioncontrasting

confidence: 66%

Section: Fig 11mentioning

confidence: 99%

Can Water Vapour Raman Lidar Resolve Profiles of Turbulent Variables in the Convective Boundary Layer?

Wulfmeyer

Pal

Turner

et al. 2010

Boundary-Layer Meteorol

111

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“…To keep the statistical flux error, which depends on the inverse of the leg length (Lenschow and Stankov 1986), as small as possible, only flight legs above the largest patches of the main surface types (water, farmland and forest) were chosen during LITFASS-2003 (Bange et al 2006). The flight leg length is 9.7 km for water, 15.1 km for farmland, and 13.0 km for forest.…”

Section: Resultsmentioning

confidence: 99%

“…Generally, the 'true flux' is unknown in experiments and an estimation of F is required. Lenschow et al (1994) derived F on the basis of Lumley and Panofsky (1964), and Lenschow and Stankov (1986):…”

Section: Flux Calculation and Error Estimationmentioning

confidence: 99%

Heterogeneity-Induced Heat-Flux Patterns in the Convective Boundary Layer: Can they be Detected from Observations and is There a Blending Height?—A Large-Eddy Simulation Study for the LITFASS-2003 Experiment

Sühring

Raasch

2013

Boundary-Layer Meteorol

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An understanding of how the convective boundary layer (CBL) is mixed under heterogeneous surface forcing is crucial for the interpretation of area-averaged turbulence measurements. To determine the height and degree to which a complex heterogeneous surface affects the CBL, large-eddy simulations (LES) for two days of the LITFASS-2003 experiment representing two different wind regimes were undertaken. Spatially-lagged correlation analysis revealed the turbulent heat fluxes to be dependent on the prescribed surface flux pattern throughout the entire CBL including the entrainment layer. These findings prompted the question of whether signals induced by surface heterogeneity can be measured by airborne systems. To examine this question, an ensemble of virtual flights was conducted using LES, according to Helipod flight measurements made during LITFASS-2003. The resulting ensemble-averaged heat fluxes indicated a clear dependence on the underlying surface up to the top of the CBL. However, a large scatter between the flux measurements in different ensemble runs was observed, which was the result of insufficient sampling of the largest turbulent eddies. The random and systematic errors based on the integral length scale did not indicate such a large scatter. For the given flight leg lengths, at least 10-15 statistically independent flight measurements were necessary to give a significant estimate of heterogeneity-induced signals in the CBL. The need for ensemble averaging suggests that the observed blending of heterogeneity-induced signals in the CBL can be partly attributed to insufficient averaging.

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“…Ensemble-averaging: For an aircraft flux meaurement w'c' the relative error £ w c = [-('"' 2 +l)X wc ] Wl LW C where r wc is the correlation coefficient between w and c, X wc is the integral scale of we, and L wc is the length of the flight segment (Lenschow and Stankov, 1986). 20 km segments yield errors of 33% of mean, in the best case (sensible heat).…”

Section: Sources Of Uncertainty In Flux Estimatesmentioning

confidence: 99%

How well can regional fluxes be derived from smaller‐scale estimates?

Moore

Fitzjarrald

Ritter³

1993

J. Geophys. Res.

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Regional surface fluxes are essential lower boundary conditions for large-scale numerical weather and climate models and are the elements of global budgets of important trace gases (Stewan. et al, 1989). Surface properties affecting the exchange of heat, moisture, momentum and trace gases vary with length scales from one meter to hundreds of kilometers. A classical difficulty is that fluxes have been measured directly only at points (towers) or along lines (from aircraft). The process of "scaling up" observations limited in space and/or time to represent larger areas has been done by assignings properties to surface classes and combining estimated or calculated fluxes using an areaweighted average. It is not clear that a simple area-weighted average is sufficient to produce the larye scale from the small scale, chiefly due to the effect of internal boundary layers, nor is it known how important the uncertainty is to large-scale model outcomes. Simultaneous aircraft and tower data obtained in the relatively simple terrain of the western Alaska tundra were used to determine the extent to which surface type variation can be related to fluxes of heat, moisture and other properties. Surface type was classified as lake or land with aircraft-borne infrared thermometer, and flight-level heat and moisture fluxes were related to surface type. The magnitude and variety of sampling errors inherent in eddy correlation flux estimation place limits on how well any flux can be known even in simple geometries. Because of the presence of intrinsic and site-specific uncertainties, regional-scale flux of heat and moisture using aircraft observations in our study area can be reasonably verified to be estimated correctly from linear combinations of smaller-scale estimates to within a factor of 1.5. Rights at lower levels or in a more comprehensive or systematic pattern might be able to resolve the contributions from individual surface types better, but an experiment to test any scaling-up hypothesis is difficult to devise.

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Length Scales in the Convective Boundary Layer

Cited by 276 publications

References 0 publications

Can Water Vapour Raman Lidar Resolve Profiles of Turbulent Variables in the Convective Boundary Layer?

Can Water Vapour Raman Lidar Resolve Profiles of Turbulent Variables in the Convective Boundary Layer?

Heterogeneity-Induced Heat-Flux Patterns in the Convective Boundary Layer: Can they be Detected from Observations and is There a Blending Height?—A Large-Eddy Simulation Study for the LITFASS-2003 Experiment

How well can regional fluxes be derived from smaller‐scale estimates?

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