Thermal Submeso Motions in the Nocturnal Stable Boundary Layer. Part 2: Generating Mechanisms and Implications

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

Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics

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

ERC DarkMix : Large Eddy Observatory, Voitsumra Experiment 2019 (LOVE19)

The weak-wind Stable Boundary Layer (wwSBL) is poorly described by theory and breaks basic assumptions necessary for observations of turbulence. Understanding the wwSBL requires distributed observations capable of separating between submeso and turbulent scales. To this end, we present the Large Eddy Observatory, Voitsumra Experiment 2019 (LOVE19) which featured 1350m of fiber optic distributed sensing of air temperature and wind speed, as well as an experimental wind direction method, at scales as fine as 1s and 0.127m, in addition to a suite of point observations of turbulence and ground-based remote sensing. Additionally, flights with a fiber optic cable attached to a tethered balloon provide an unprecedented detailed view of the boundary layer structure with a resolution of 0.254m and 10s between 1-200m height. We anticipate that these data will be of interest to boundary layer researchers, but also may be applicable to other communities that study the exchange between the atmosphere and the surface. The novelty of the DTS data, supported by additional observations, hopefully allows the investigation of research questions that could not be adequately addressed before. A pdf detailing the experiment documentation (LOVE19_AE-Documentation.pdf) is provided to give an overview of the experiment and data in addition to a submitted (and hopefully future) Earth System Science Data (ESSD) manuscript. The AE-Documentation is volume 65 of the Arbeitsergebnisse, Universität Bayreuth, Mikrometeorologie publication series. All data are provided as self-describing netcdfs. Two example scripts (as python-based Jupyter Notebooks) are provided, reconstructing the example figures from the ESSD paper. The examples demonstrate the unique capabilities of the LOVE19 data for examining boundary layer processes: 1) FODS observations between 1m and ~200m height during a period of gravity waves propagating across the entire boundary layer and 2) tracking a near-surface, transient submeso structure that causes an intermittent burst of turbulence

klapo/turbpy v1.0.2

First public and archived release with Zenodo

Constraining the Surface Energy Balance of Snow in Complex Terrain

Thesis (Ph.D.)--University of Washington, 2017-06Physically-based snow models form the basis of our understanding of current and future water and energy cycles, especially in mountainous terrain. These models are poorly constrained and widely diverge from each other, demonstrating a poor understanding of the surface energy balance. This research aims to improve our understanding of the surface energy balance in regions of complex terrain by improving our confidence in existing observations and improving our knowledge of remotely sensed irradiances (Chapter 1), critically analyzing the representation of boundary layer physics within land models (Chapter 2), and utilizing relatively novel observations to in the diagnoses of model performance (Chapter 3). This research has improved the understanding of the literal and metaphorical boundary between the atmosphere and land surface. Solar irradiances are difficult to observe in regions of complex terrain, as observations are subject to harsh conditions not found in other environments. Quality control methods were developed to handle these unique conditions. These quality control methods facilitated an analysis of estimated solar irradiances over mountainous environments. Errors in the estimated solar irradiance are caused by misrepresenting the effect of clouds over regions of topography and regularly exceed the range of observational uncertainty (up to 80Wm-2) in all regions examined. Uncertainty in the solar irradiance estimates were especially pronounced when averaging over high-elevation basins, with monthly differences between estimates up to 80Wm-2. These findings can inform the selection of a method for estimating the solar irradiance and suggest several avenues of future research for improving existing methods. Further research probed the relationship between the land surface and atmosphere as it pertains to the stable boundary layers that commonly form over snow-covered surfaces. Stable conditions are difficult to represent, especially for low wind speed values and coupled land-atmosphere models have difficulty representing these processes. We developed a new method analyzing turbulent fluxes at the land surface that relies on using the observed surface temperature, which we called the offline turbulence method. We used this method to test a number of stability schemes as they are implemented within land models. Stability schemes can cause small biases in the simulated sensible heat flux, but these are caused by compensating errors, as no single method was able to accurately reproduce the observed distribution of the sensible heat flux. We described how these turbulence schemes perform within different turbulence regimes, particularly noting the difficulty representing turbulence during conditions with faster wind speeds and the transition between weak and strong wind turbulence regimes. Heterogeneity in the horizontal distribution of surface temperature associated with different land surface types likely explains some of the missing physics within land models and is manifested as counter-gradient fluxes in observations. The coupling of land and atmospheric models needs further attention, as we highlight processes that are missing. Expanding on the utility of surface temperature, Ts, in model evaluations, we demonstrated the utility of using surface temperature in snow models evaluations. Ts is the diagnostic variable of the modeled surface energy balance within physically-based models and is an ideal supplement to traditional evaluation techniques. We demonstrated how modeling decisions affect Ts, specifically testing the impact of vertical layer structure, thermal conductivity, and stability corrections in addition to the effect of uncertainty in forcing data on simulated Ts. The internal modeling decisions had minimal impacts relative to uncertainty in the forcing data. Uncertainty in downwelling longwave was found to have the largest impact on simulated Ts. Using Ts, we demonstrated how various errors in the forcing data can be identified, noting that uncertainty in downwelling longwave and wind are the easiest to identify due to their effect on night time minimum Ts

Understanding how uncertainty in the forcing irradiances impacts simulations of snow

Thesis (Master's)--University of Washington, 2014Snowmelt in mountains is an important part of the water and energy cycles and provides water for 1/6th of the world's population. The downwelling irradiances are the primary drivers of this melt, however, they are rarely observed. The use of estimated irradiances, few observations, lack of evaluation of alternative sources of data, and the unique climate of mountain environments all lead to substantial uncertainties in the radiative fluxes used to force simulations of snow. The net irradiance of snow is determined by external forcing irradiances, the downwelling irradiances, and by the upwelling irradiances, which are functions of the internal model feedbacks. Errors in the forcing irradiances can be masked by errors in the internal processes that control the outgoing irradiances. The impact of uncertainties in the forcing irradiances for simulations of snow is evaluated in a series of idealized modeling experiment that split into two parts: 1) understanding errors in the forcing irradiances alone and 2) understanding the feedback and compensation between errors in the forcing irradiances and the internal processes that control the outgoing irradiances. In the forcing irradiances, it is shown that longwave biases of magnitude greater than 20 Wm-2 and shortwave biases of magnitude greater than 40 Wm-2, typical of methods for estimating irradiances in complex terrain, have substantial impacts on simulated snow water equivalent (SWE) and the simulated energy balance across a range of mountain climates. Random noise in the forcing irradiances has a negligible effect on modeled snowmelt and energy balance. The exception is warmer sites, which were found to be sensitive to nearly all errors in the forcing irradiances. The internal processes that control the outgoing fluxes can significantly impact the net irradiance of the snow. Two processes are explored: 1) albedo parameterization that controls the reflected shortwave irradiance and 2) the turbulence parameterization that controls the outgoing longwave irradiance through the surface temperature. Tuning of albedo parameters, an approach typically taken in modeling set-ups, can completely compensate for biases in the forcing irradiances when evaluating model performance using SWE. Varying turbulent flux parameters was found to have a much smaller impact on simulated snowmelt than albedo parameters - calling the role of the stability feedback into question. However, the surface temperature does depend strongly on the turbulence scheme selected. Finally, the application of these results is shown for a variety of mountain environments and methods. In general, the uncertainty in the albedo terms is larger than the uncertainty in the forcing irradiance terms. The structure of errors in the forcing irradiances is either uniform offsets that do not vary substantially throughout the year or shorter punctuated periods where the irradiance values are substantially different