Classifying the nocturnal atmospheric boundary layer into temperature and flow regimes

Abstract

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.

Small-Scale Variability in the Nocturnal Boundary Layer

“…Wind speeds at 1 m that exceed about 2 m s −1 on the valley floor seem to eliminate the transient cold pools and maintain warmer air at the surface, although LST this "threshold" appears to vary between stations. This threshold wind speed is higher than the threshold wind speed for the "hockey stick" (Sun et al 2012), which is about 1.2 m s −1 based on the dependence of the 1-m friction velocity on wind speed for the current SCP measurements outside of the valley (Pfister et al 2019). The higher threshold for the cold pool compared to that for the usual hockey stick is probably due to the influence of sheltering by the topography and associated strong stratification.…”

“…On nights with significant small-scale variations of temperature, even perfect forecasts of the nocturnal trend may fail to identify important short periods of frost or fog formation. The relation of such temperature variations to the wind vector, stratification, downward longwave radiation, and the general magnitude of the small-scale motions must be investigated in more detail, both in terms of cases studies and classification of nights (Pfister et al 2019).…”

“…The side slopes of the valley are on the order of 10% or less. We analyze the 1-m sonic anemometer observations from 20 stations and from the main tower at 0.5, 1, 2, 3, 4, 5, 10, and 20 m. The SCP measurements include fiber-optic distributed temperature sensing (DTS) (Thomas et al 2012;Zeeman et al 2015;Pfister et al 2017Pfister et al , 2019, which provides fine horizontal resolution of 0.3 m and time resolution of 5 s at 0.5, 1, and 2 m above the ground surface along a 250-m cross-valley transect. Times are expressed in local standard time (LST = UTC − 7 h).…”

“…Guerra et al (2018) found that the microtopography induces horizontal variation of temperature that systematically increases with decreasing wind speed. Based on fine-scale measurements of the horizontal structure, Pfister et al (2019) found that spatial and temporal variations of temperature on horizontal scales of a few 100 m or less in gentle topography tended to organize into several prototype regimes: windy with limited formation of cold air; windy but with cold air drainage and pooling modulated by large shear-induced mixing and lee turbulence; and low wind speeds with more robust cold-air drainage and cold pool formation. This organization was formulated in terms of the general wind speed, stratification, and downward longwave radiation.…”

Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables

“…In particular, FODS is ideally suited for observing turbulence, especially during weak-wind conditions. Weak-wind boundary layers break many of the assumptions that underlie eddy covariance techniques (Thomas, 2011;Cheng et al, 2017;Pfister et al, 2019), which forms an obstacle for understanding the dynamics of turbulence during these conditions. For instance, eddy covariance relies on the ergodic hypothesis, the assumption that time and space averages converge under horizontally homogeneous and stationary conditions (Taylor, 1938).…”