Microfronts in the nocturnal boundary layer

Mahrt, L.

doi:10.1002/qj.3451

Cited by 24 publications

(15 citation statements)

References 41 publications

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“…The circumstance that the COSMO-CLM model, which was utilized with a horizontal grid resolution of 3 km performs better with regard to the wind-direction (BIAS) in the Feldbach region than GRAMM-SCI using a resolution of 200 m, is probably due to the underlying comparison with gridded data from WPG and not with the observations themselves. As mentioned before, observed wind directions often exhibit a large variability in low-wind speed conditions, which cannot be captured by mesoscale models (e.g., [37]).…”

Section: Wind Speed and Directionmentioning

confidence: 99%

Development of the Mesoscale Model GRAMM-SCI: Evaluation of Simulated Highly-Resolved Flow Fields in an Alpine and Pre-Alpine Region

Oettl

2021

Atmosphere

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In this study, new developments implemented in the mesoscale model GRAMM-SCI are presented. GRAMM-SCI has been specifically developed for providing flow fields in the sub-kilometer range. A comprehensive model evaluation using wind, temperature, radiation, as well as soil moisture and soil temperature observations in an alpine valley and in a hilly pre-alpine region in Styria (Austria) is presented. Three one-way nested model domains were used, whereby the coarse model run (5000 m horizontal resolution) was initialized and forced using ERA5 reanalysis data. The grid sizes for the two inner domains were set to 1000 m and 200 m, respectively. Comparisons were carried out for a five-day period in October 2017 which was dominated by clear-sky conditions. Though, the observations reveal quite complex flow structures governed by interactions between synoptic flow and thermally-driven local flows, GRAMM-SCI was able to reproduce the main features satisfactorily. In addition, the new version of GRAMM-SCI shows significant improvement with regard to simulated air temperature compared with the previous one. Finally, microscale flow-field simulations were carried out for some monitoring sites that are apparently influenced by nearby buildings or vegetation.

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Section: Wind Speed and Directionmentioning

confidence: 99%

Development of the Mesoscale Model GRAMM-SCI: Evaluation of Simulated Highly-Resolved Flow Fields in an Alpine and Pre-Alpine Region

Oettl

2021

Atmosphere

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“…Any field site with a similar geometry featuring a valley deep enough to provide some mechanical sheltering and with a relatively pronounced elevation change of 6 • at the shoulder can potentially form TSFs. As an example, Mahrt (2019) describes submeso motions at three different field sites including TSFs within the SCP experiment.…”

Section: Topographymentioning

confidence: 99%

“…According to this definition, a range of motions fall into this category including meandering, solitary waves, gravity waves, wave-like motions, and microfronts. The detection of submeso motions is usually performed based on tower data by analyzing case studies of meandering (Cava et al 2019a), by using the Haar-wavelet (Mahrt 2019), by analyzing spectra and thus their temporal scale (Stiperski and Calaf 2018), by identifying meandering with autocorrelation functions (Anfossi et al 2005), or by using autocorrelation functions to determine oscillations within several parameters, like horizontal and vertical wind speed, temperature, and other scalars (Kang et al 2014(Kang et al , 2015Mortarini et al 2017;Stefanello et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Thermal Submeso Motions in the Nocturnal Stable Boundary Layer. Part 2: Generating Mechanisms and Implications

Pfister

Lapo

Mahrt

et al. 2021

Boundary-Layer Meteorol

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In the stable boundary layer, thermal submesofronts (TSFs) are detected during the Shallow Cold Pool experiment in the Colorado plains, Colorado, USA in 2012. The topography induces TSFs by forming two different air layers converging on the valley-side wall while being stacked vertically above the valley bottom. The warm-air layer is mechanically generated by lee turbulence that consistently elevates near-surface temperatures, while the cold-air layer is thermodynamically driven by radiative cooling and the corresponding cold-air drainage decreases near-surface temperatures. The semi-stationary TSFs can only be detected, tracked, and investigated in detail when using fibre-optic distributed sensing (FODS), as point observations miss TSFs most of the time. Neither the occurrence of TSFs nor the characteristics of each air layer are connected to a specific wind or thermal regime. However, each air layer is characterized by a specific relationship between the wind speed and the friction velocity. Accordingly, a single threshold separating different flow regimes within the boundary layer is an oversimplification, especially during the occurrence of TSFs. No local forcings or their combination could predict the occurrence of TSFs except that they are less likely to occur during stronger near-surface or synoptic-scale flow. While classical conceptualizations and techniques of the boundary layer fail in describing the formation of TSFs, the use of spatially continuous data obtained from FODS provide new insights. Future studies need to incorporate spatially continuous data in the horizontal and vertical planes, in addition to classic sensor networks of sonic anemometry and thermohygrometers to fully characterize and describe boundary-layer phenomena.

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“…Their spatio-temporal scales and origin are not generalized, but a multitude of processes are commonly lumped under the denomination of sub-mesoscale motions. Besides meandering, sub-mesoscale motions can include internal gravity waves (Zaitseva et al 2018), density currents (Sun et al 2002), drainage flows (Mahrt et al 2001), or microfronts (Mahrt 2019). While parametrizing these scales is challenging due to partly unknown physics, exploratory data analysis can help to discover new concepts.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Shear Forcing of Turbulence in the Nocturnal Boundary Layer: A Statistical Analysis

Boyko¹,

Vercauteren²

2020

Boundary-Layer Meteorol

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The lower nocturnal boundary layer is governed by intermittent turbulence which is thought to be triggered by sporadic activity of so-called sub-mesoscale motions in a complex way. We analyze intermittent turbulence based on an assumed relation between the vertical gradients of the sub-mean scales and turbulence kinetic energy. We analyze high-resolution nocturnal eddy-correlation data from 30-m tower collected during the Fluxes over Snow Surfaces II field program. The non-turbulent velocity signal is decomposed using a discrete wavelet transform into three ranges of scales interpreted as the mean, jet and sub-mesoscales. The vertical gradients of the sub-mean scales are estimated using finite differences. The turbulence kinetic energy is modelled as a discrete-time autoregressive process with exogenous variables, where the latter ones are the vertical gradients of the sub-mean scales. The parameters of the discrete model evolve in time depending on the locally-dominant turbulence-production scales. The three regimes with averaged model parameters are estimated using a subspace-clustering algorithm which illustrates a weak bimodal distribution in the energy phase space of turbulence and sub-mesoscale motions for the very stable boundary layer. One mode indicates turbulence modulated by sub-mesoscale motions. Furthermore, intermittent turbulence appears if the sub-mesoscale intensity exceeds $$10 \%$$ 10 % of the mean kinetic energy in strong stratification.

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Microfronts in the nocturnal boundary layer

Cited by 24 publications

References 41 publications

Development of the Mesoscale Model GRAMM-SCI: Evaluation of Simulated Highly-Resolved Flow Fields in an Alpine and Pre-Alpine Region

Development of the Mesoscale Model GRAMM-SCI: Evaluation of Simulated Highly-Resolved Flow Fields in an Alpine and Pre-Alpine Region

Thermal Submeso Motions in the Nocturnal Stable Boundary Layer. Part 2: Generating Mechanisms and Implications

Multiscale Shear Forcing of Turbulence in the Nocturnal Boundary Layer: A Statistical Analysis

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