Abstract. Highly distributed temperature data are used as input and as calibration data for a temperature model of a first order stream in Luxembourg. A DTS (Distributed Temperature Sensing) fiber optic cable with a length of 1500 m is used to measure stream water temperature with a spatial resolution of 0.5 m and a temporal resolution of 2 min. With the observations four groundwater inflows are found and quantified (both temperature and relative discharge). They are used as input for the distributed temperature model presented here. The model calculates the total energy balance including solar radiation (with shading effects), longwave radiation, latent heat, sensible heat and river bed conduction. The simulated temperature along the whole stream is compared with the measured temperature at all points along the stream. It shows that proper knowledge of the lateral inflow is crucial to simulate the temperature distribution along the stream, and, the other way around stream temperature can be used successfully to identify runoff components. The DTS fiber optic is an excellent tool to provide this knowledge.

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.

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Nocturnal variations of temperature and wind are examined at three contrasting sites. After the early evening period of rapid cooling, the magnitude of the variations of temperature on a time scale of 10 min to an hour often become larger than the corresponding temperature change due to the nocturnal trend. These shorter-term temperature variations are forced by wave-like motions and more complex modes. Observations from a network of stations across a shallow valley at one of the sites are analyzed in more detail. Typically, decreasing wind speed corresponds to less mixing and lower temperature at the surface followed by increasing wind speed, increased mixing, and higher temperatures. The flow may continue to switch back and forth between these two states for much of the night. These non-stationary motions interact with motions induced by the gentle local topography, leading to intermittent local drainage flows, transient cold pools, and both propagating and semi-stationary microfronts.

Abstract. In recent years, the spatial resolution of fiber-optic distributed temperature sensing (DTS) has been enhanced in various studies by helically coiling the fiber around a support structure. While solid polyvinyl chloride tubes are an appropriate support structure under water, they can produce considerable errors in aerial deployments due to the radiative heating or cooling. We used meshed reinforcing fabric as a novel support structure to measure high-resolution vertical temperature profiles with a height of several meters above a meadow and within and above a small lake. This study aimed at quantifying the radiation error for the coiled DTS system and the contribution caused by the novel support structure via heat conduction. A quantitative and comprehensive energy balance model is proposed and tested, which includes the shortwave radiative, longwave radiative, convective, and conductive heat transfers and allows for modeling fiber temperatures as well as quantifying the radiation error. The sensitivity of the energy balance model to the conduction error caused by the reinforcing fabric is discussed in terms of its albedo, emissivity, and thermal conductivity. Modeled radiation errors amounted to −1.0 and 1.3 K at 2 m height but ranged up to 2.8 K for very high incoming shortwave radiation (1000 J s −1 m −2 ) and very weak winds (0.1 m s −1 ). After correcting for the radiation error by means of the presented energy balance, the root mean square error between DTS and reference air temperatures from an aspirated resistance thermometer or an ultrasonic anemometer was 0.42 and 0.26 K above the meadow and the lake, respectively. Conduction between reinforcing fabric and fiber cable had a small effect on fiber temperatures (< 0.18 K). Only for locations where the plastic rings that supported the reinforcing fabric touched the fiber-optic cable were significant temperature artifacts of up to 2.5 K observed. Overall, the reinforcing fabric offers several advantages over conventional support structures published to date in the literature as it minimizes both radiation and conduction errors.

Abstract. The weak-wind boundary layer is characterized by turbulent and submesoscale motions that break the assumptions necessary for using traditional eddy covariance observations such as horizontal homogeneity and stationarity, motivating the need for an observational system that allows spatially resolving measurements of atmospheric flows near the surface. Fiber-optic distributed sensing (FODS) potentially opens the door to observing a wide range of atmospheric processes on a spatially distributed basis and to date has been used to resolve the turbulent fields of air temperature and wind speed on scales of seconds and decimeters. Here we report on progress developing a FODS technique for observing spatially distributed wind direction. We affixed microstructures shaped as cones to actively heated fiber-optic cables with opposing orientations to impose directionally sensitive convective heat fluxes from the fiber-optic cable to the air, leading to a difference in sensed temperature that depends on the wind direction. We demonstrate the behavior of a range of microstructure parameters including aspect ratio, spacing, and size and develop a simple deterministic model to explain the temperature differences as a function of wind speed. The mechanism behind the directionally sensitive heat loss is explored using computational fluid dynamics simulations and infrared images of the cone-fiber system. While the results presented here are only relevant for observing wind direction along one dimension, it is an important step towards the ultimate goal of a full three-dimensional, distributed flow sensor.

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.

Abstract. The weak-wind boundary layer is characterized by turbulent and submeso-scale motions that break the assumptions necessary for using traditional eddy covariance observations such as horizontal homogeneity and stationarity, motivating the need for an observational system that allows spatially resolving measurements of atmospheric flows near the surface. Fiber-Optic Distributed Sensing (FODS) potentially opens the door to observing a wide-range of atmospheric processes on a spatially distributed basis and to date has been used to resolve the turbulent fields of air temperature and wind speed on scales of second and decimeters. Here we report on progress developing a FODS technique for observing spatially distributed wind direction. We affixed microstructures shaped as cones to actively-heated fiber-optic cables with opposing orientations to impose directionally-sensitive convective heat fluxes from the fiber-optic cable to the air, leading to a difference in sensed temperature that depends on the wind direction. We demonstrate the behavior of a range of microstructure parameters including aspect ratio, spacing, and size and develop a simple deterministic model to explain the temperature differences as a function of wind speed. The mechanism behind the directionally-sensitive heat loss is explored using Computational Fluid Dynamics simulations and infrared images of the cone-fiber system. While the results presented here are only relevant for observing wind direction along one dimension it is an important step towards the ultimate goal of a full three-dimensional, distributed flow sensor.