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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. 

Abstract 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. 

We propose a classification scheme for nocturnal atmospheric boundary layers and apply it to investigate the spatio‐temporal structure of air temperature and wind speed in a shallow valley during the Shallow Cold Pool Experiment. This field campaign was the first to collect spatially continuous temperature and wind information at high resolution (1 s, 0.25 m) using the distributed temperature sensing technique across a 220 m long transect at three heights (0.5, 1.0, 2.0 m). The night‐time classification scheme was motivated by a surface energy balance and used a combination of static stability, wind regime and longwave radiative forcing as quantities to determine physically meaningful boundary‐layer regimes. Out of all potential combinations of these three quantities, 14 night‐time classes contained observations, of which we selected three for detailed analysis and comparison.

The three classes represent a transition from mechanical to radiative forcing. The first night class represents conditions with strong dynamic forcing caused by locally induced lee turbulence dominating near‐surface temperatures across the shallow valley. The second night class was a concurrence of enhanced dynamic mixing due to significant winds at the valley shoulders and cold‐air pooling at the bottom of the shallow valley as a result of strong radiative cooling. The third night class was characteristic of weak winds eliminating the impact of mechanical mixing but emphasizing the formation and pooling of cold air at the valley bottom.

The proposed night‐time classification scheme was found to sort the experimental data into physically meaningful regimes of surface flow and transport. It is suitable to stratify short‐ and long‐term experimental data for ensemble averaging and to identify case studies.