Abstract
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.
Highlights
Submesoscale motions, called ‘submeso motions’ hereafter, are defined by their typical temporal and spatial scales, and due to their significance are added to the classification established by Orlanski (1975)
We further investigate the implications of thermal submesofronts (TSFs) on the stable boundary layer (SBL), as well as their relation to commonly used alternative approaches to classify the boundary layer, including wind regimes defined by the relation of wind speed to a turbulence statistic (Sun et al 2012), or thermal regimes defined by the relation of the Obukhov length to the sensible heat flux (Mahrt 1998)
As TSFs are formed by two competing air layers (Sect. 3), a passing TSF generates abrupt changes in point observations, such as of temperature, wind speed and direction, as well as of turbulence characteristics (Fig. 3)
Summary
Submesoscale motions, called ‘submeso motions’ hereafter, are defined by their typical temporal and spatial scales, and due to their significance are added to the classification established by Orlanski (1975). 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 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, 2015; Mortarini et al 2017; Stefanello et al 2020).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
