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We show that the geometry of motions in atmospheric boundary-layer time series exhibits considerable independence from scale in spite of changing physics. The scaleindependence of structure shapes is shown by using a simple technique to extract basic shapes from the time series for timescales between 3 s and 2 h. A set of predefined basic shapes is chosen subjectively as those that occur most frequently in the time series: sine, step, ramp-cliff and cliff-ramp. The frequency of occurrence of shapes changes with the timescale, with a pronounced minimum at scales between 2 and 10 min depending on the stability and the shape function. This is in accordance with the minimum of kinetic energy between turbulence and mesoscales. However, the ratios of occurrences between different shapes are approximately scale- independent. What shapes are preferred depends only on the variable examined. The physics of different shapes and scales is examined from characteristics of individual shapes. Steep edges of shapes seem to be predominantly related to downward transport of heat and momentum, which weakens with increasing scale. Sine shapes on the other hand seem to be related to turbulent eddies and shear instability at small scales, and to internal gravity waves at larger scales with stable stratification. Therefore, the physics of individual shapes is shown to change with scale, while the geometry seems to remain approximately scale-independent.

At the end of the afternoon, when the surface heat fluxes start to sharply decrease, the CBL turns from a convective well-mixed layer to an intermittently turbulent residual layer overlying a stably-stratified boundary layer. This transition raises several observational and modelling issues. Even the definition of the boundary layer during this period is fuzzy, since there is no consensus on what criteria to use and no simple scaling laws to apply. Yet it plays an important role in such diverse atmospheric phenomena as transport and diffusion of trace constituents or wind energy production. This phase of the diurnal cycle remains largely unexplored, partly due to the difficulty of measuring weak and intermittent turbulence, anisotropy, horizontal heterogeneity, and rapid time changes. The Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST) project is gathering about thirty research scientists from the European Union and the United States to work on this issue. A field campaign (BLLAST-FE) is planned for spring or summer 2011 in Europe. BLLAST will utilize these observations, as well as previous datasets, large-eddy and direct numerical simulations, and mesoscale modelling to better understand the processes, suggest new parameterisations, and evaluate forecast models during this transitional period. We will present the issues raised by the late afternoon transition and our strategy to study it.

Mesoscale numerical models apparently do not capture small-scale non-turbulent motions in the boundary layer, and this seems to be the case regardless of the model resolution. The suspected causes are that the models miss necessary physics or miss the initiation of motions by small-scale terrain features and that the small-scale motions are filtered by implicit and/or explicit numerical diffusion. The absence of these submeso motions may underestimate shear-generation of turbulence in models. Also, the dispersion models may not work well with these motions omitted. Using the hierarchy of nested domains in the numerical mesoscale model WRF we evaluate the effects of numerical diffusion and boundary conditions on the amount of mesoscale variability at resolved scales. As certain previous studies indicate, there seems to be a significant absence of variability at smaller mesoscales using the usual stable model setup. Removal of the implicit and explicit numerical diffusion yields significant growth of the variability at all scales. Although this variability is apparently non-physical, the model spectra agree well with the measurements. We discuss different effects related to the associated upscale or downscale energy cascade at mesoscales.

Meandering of the wind vector can be attributed to a variety of mesoscale motions but its origin and dynamics are still unknown. It has been shown to affect horizontal dispersion under weak-wind conditions. However, the effects of meandering on the dispersion are not easily separated from the turbulence. Namely, using the fixed averaging length that separates turbulence from the mesoscale can incorporate part of the mesoscale spectrum into turbulence and artificially enhance the turbulence contribution to the dispersion. Here we show the separate contributions of the turbulence and meandering on the dispersion. The distinction between the turbulence and mesoscale motions is based on the multiresolution flux decomposition. The dispersion is studied using a Lagrangian stochastic particle model, where the wind field for the model is taken from the observations. The results show that the contribution of meandering to the time-averaged horizontal dispersion is dominant under all atmospheric conditions: weak and strong winds, and unstable and stable stratification. The meandering motions are also shown to be responsible for streaks and multimodal distributions of the time-averaged horizontal concentration patterns.

Meandering of the wind vector has been shown to affect horizontal dispersion under weak-wind conditions. It can be attributed to a variety of mesoscale motions but its origin and dynamics are still unknown. This study deals with the effects of meandering on dispersion using a Lagrangian stochastic particle model, where the wind field for the model is taken from the observations. The focus is on the behavior of flow due to meandering and on the relative contributions of meandering and turbulence to dispersion.

The focus is on analytical and numerical modeling of simple katabatic flows blowing over short and long cool slopes. Since it was verified that classical scaling via Monin-Obukhov length performs over such slopes rather poorly (a low-level jet usually determines the surface fluxes), a modified Prandtl model is used instead. This linear analytic 1D model is extended for 1.) almost any gradual eddy diffusivity K(z) via the WKB method, and 2.) it includes the Coriolis effect. Its asymptotic (WKB) solution is checked against its more complete numerical solution and versus a mesoscale model (MIUU model). The Prandtl model is further used to determine one of the coefficients in the MIUU model turbulence length-scale for stable “ z-less“ conditions, L ; L = min[2a(TKE)ˆ (1/2)/N, a(TKE)ˆ (1/2)/S] where N and S are buoyancy frequency and absolute vertical wind shear and 2a = 0.537 is the standard coefficient in this higher-order turbulence closure scheme. The result can be useful in parameterizing shallow persistent katabatic flows in NWP and climate models, in data interpretation and for improving characteristic length-scales in other numerical models.

Little is known about mesoscale motions in the stable boundary layer, except in special cases of monotonic gravity waves, solitons and density currents. Models are unable to capture the more typical mesoscale structures in the stable boundary layer. Before models can be seriously evaluated, more information on such structures are required. Analysis of twenty data sets reveal that mesoscale motions in the nocturnal boundary layer are spread continuously over a range of scales and do not follow any spectral or local similarity. The strength of the mesoscale motions (here on time scales less than one hour) can vary by an order of magnitude or more between nights at a given site. Transient mesoscale motions tend to be stronger in complex terrain compared to flat terrain. One exception is that transient mesoscale motions tend to be weaker with drainage flows. The large variation of the strength of the mesoscale motions between nights is largely unpredictable with present understanding, although large vertical shear of wind direction tends to correspond with greater temporal variation of the wind direction. The change of wind direction with time for weak winds is more often a sequence of abrupt events rather than gradual meandering of the wind direction back and forth. The frequency distribution of the one-minute wind direction for the one-hour records with weak winds is often bi-modal or multi-modal. Models are generally unable to capture such mesoscale motions apparently due to explicit and implicit horizontal diffusion. Simulations using COAMPS reveal the role of explicit horizontal diffusion on the degree of mesoscale motion in the model. Comparisons between the model and field results in complex terrain are presented for different strengths of the horizontal diffusion.

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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.

Submesoscale motions within the stable boundary layer were detected during the Shallow Cold Pool Experiment conducted in the Colorado plains, Colorado, U.S.A. in 2012. The submesoscale motion consisted of two air layers creating a well-defined front with a sharp temperature gradient, and further-on referred to as a thermal submesofront (TSF). The semi-stationary TSFs and their advective velocities are detected and determined by the fibre-optic distributed-sensing (FODS) technique. An objective detection algorithm utilizing FODS measurements is able to detect the TSF boundary, which enables a detailed investigation of its spatio–temporal statistics. The novel approach in data processing is to conditionally average any parameter depending on the distance between a TSF boundary and the measurement location. By doing this, a spatially-distributed feature like TSFs can be characterized by point observations and processes at the TSF boundary can be investigated. At the TSF boundary, the air layers converge, creating an updraft, strong static stability, and vigorous mixing. Further, the TSF advective velocity of TSFs is an order of magnitude lower than the mean wind speed. Despite being gentle, the topography plays an important role in TSF formation. Details on generating mechanisms and implications of TSFs on the stable boundary layer are discussed in Part 2.