On the Behavior of the Stable Boundary Layer and the Role of Initial Conditions

2006 ◽  
pp. 1811-1829
Author(s):  
Xingzhong Shi ◽  
Richard T. McNider ◽  
M. P. Singh ◽  
David E. England ◽  
Mark J. Friedman ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Initial Conditions ◽  
Stable Boundary

On the Behavior of the Stable Boundary Layer and the Role of Initial Conditions

2005 ◽  
Vol 162 (10) ◽  
pp. 1811-1829 ◽  
Author(s):  
Xingzhong Shi ◽  
Richard T. McNider ◽  
M. P. Singh ◽  
David E. England ◽  
Mark J. Friedman ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Initial Conditions ◽  
Stable Boundary

scholarly journals The role of snow-surface coupling, radiation, and turbulent mixing in modeling a stable boundary layer over Arctic sea ice

2013 ◽  
Vol 118 (3) ◽  
pp. 1199-1217 ◽  
Author(s):  
H. A. M. Sterk ◽  
G. J. Steeneveld ◽  
A. A. M. Holtslag
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Turbulent Mixing ◽  
Stable Boundary Layer ◽  
Arctic Sea Ice ◽  
Snow Surface ◽  
Stable Boundary ◽  
Arctic Sea ◽  
Surface Coupling

scholarly journals Role of land-surface temperature feedback on model performance for the stable boundary layer

2007 ◽  
Vol 125 (2) ◽  
pp. 361-376 ◽  
Author(s):  
A. A. M. Holtslag ◽  
G. J. Steeneveld ◽  
B. J. H. van de Wiel
Keyword(s):  
Boundary Layer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Stable Boundary Layer ◽  
Model Performance ◽  
Stable Boundary ◽  
Temperature Feedback

The role of surface heterogeneity in modelling the stable boundary layer

2006 ◽  
Vol 122 (3) ◽  
pp. 517-534 ◽  
Author(s):  
A. McCabe ◽  
A. R. Brown
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Surface Heterogeneity ◽  
Stable Boundary

Detecting regime transitions in the stable boundary layer

2021 ◽  
Author(s):  
Nikki Vercauteren ◽  
Amandine Kaiser ◽  
Vyacheslav Boyko ◽  
Davide Faranda ◽  
Sebastian Krumscheid
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Stable Boundary Layer ◽  
Warning Signal ◽  
Statistical Modelling ◽  
Polar Regions ◽  
Stable Boundary ◽  
Combination Of Methods ◽  
Regime Transitions

<p>Predictability of the atmospheric boundary layer is impaired by possible rapid transitions between fully turbulent states and quiescent, quasi-laminar states. Such rapid transitions are observed in Polar regions or at night when the atmospheric boundary layer is stably stratified, and they have important consequences in the strength of mixing with the higher levels of the atmosphere.<span> </span></p><p>In some cases, perturbations of the flow can play an important role in triggering transitions. Using different randomised models of the stable boundary layer, we will investigate the role of natural fluctuations of atmospheric processes to trigger regime transitions.<span> </span></p><p>We then apply a combination of methods from dynamical systems, statistical modelling and information theory to study and detect those regime transitions. A statistical-dynamical indicator is developed as an early-warning signal of regime transitions that can be applied to highly non-stationary field data.<span> </span></p>

scholarly journals The Influence of Submeso Processes on Stable Boundary Layer Similarity Relationships

2013 ◽  
Vol 71 (1) ◽  
pp. 207-225 ◽  
Author(s):  
Otávio C. Acevedo ◽  
Felipe D. Costa ◽  
Pablo E. S. Oliveira ◽  
Franciano S. Puhales ◽  
Gervásio A. Degrazia ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Time Scale ◽  
Stable Boundary Layer ◽  
Local Stability ◽  
Observational Studies ◽  
Surface Coverage ◽  
Stability Parameter ◽  
Stable Boundary ◽  
Similarity Relationships

Abstract Previous observational studies in the stable boundary layer diverge appreciably on the values of dimensionless ratios between turbulence-related quantities and on their stability dependence. In the present study, the hypothesis that such variability is caused by the influence of locally dependent nonturbulent processes, referred to as submeso, is tested and confirmed. This is done using six datasets collected at sites with different surface coverage. The time-scale dependence of wind components and temperature fluctuations is presented using the multiresolution decomposition, which allows the identification of the turbulence and submeso contributions to spectra and cospectra. In the submeso range, the spectra of turbulence kinetic energy range increases exponentially with time scale. The exponent decreases with the magnitude of the turbulent fluctuations at a similar manner at all sites. This fact is used to determine the smaller time scale with relevant influence of submeso processes and a ratio that quantifies the relative importance of such nonturbulent processes with respect to turbulence. Based on that, values for the local stability parameter that are unaffected by nonturbulent processes are found. It is shown that the dimensionless ratios do not usually converge to a given value as the time scale increases and that it is as a consequence of the locally dependent submeso influence. The ratios and their stability dependence are determined at the time scales with least influence of nonturbulent processes, but significant site-to-site variability persists. Combining all datasets, expressions for the dependence of the dimensionless ratios on the local stability parameter that minimize the role of the submeso contribution are proposed.

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