Low-level buoyancy as a tool to understand boundary layer transitions

Advancements in remotely piloted aircraft systems (RPAS) introduced a new way to observe the atmospheric boundary layer (ABL). Adequate sampling of the lower atmosphere is key to improving numerical weather models and understanding fine-scale processes. The ABL’s sensitivity to changes in surface fluxes leads to rapid changes in thermodynamic variables. This study proposes using low-level buoyancy to characterize ABL transitions. Previously, buoyancy has been used as a bulk parameter to quantify stability. Higher resolution data from RPAS highlight buoyancy fluctuations. RPAS profiles from two field campaigns are used to assess the evolution of buoyancy under convective and stable boundary layers. Data from these campaigns included challenging events to forecast accurately, such as convective initiation and a low-level jet. Throughout the daily ABL transition, results show that the ABL height determined by the minimum in vertical buoyancy gradient agrees well with proven ABL height metrics, such as potential temperature gradient maxima. Moreover, in the cases presented, low-level buoyancy rapidly increases prior to convective initiation and rapidly decreases prior to the onset of a low-level jet. Low-level buoyancy is a function sensitive in space and time, and with further analysis could be used as a forecasting tool. This study expounds on the utility of buoyancy in the ABL and offers potential uses for future research.

Flux-mediated dark matter

We propose a new mechanism to communicate between fermion dark matter and the Standard Model (SM) only through the four-form flux. The four-form couplings are responsible for the relaxation of the Higgs mass to the correct value and the initial displacement of the reheating pseudo-scalar field from the minimum. We show that the simultaneous presence of the pseudo-scalar coupling to fermion dark matter and the flux-induced Higgs mixing gives rise to unsuppressed annihilations of dark matter into the SM particles at present, whereas the direct detection bounds from XENON1T can be avoided. We suggest exploring the interesting bulk parameter space of the model for which dark matter annihilates dominantly into a pair of singlet-like scalars with similar mass as for dark matter.

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.

Estimation of groundwater CO2 concentrations on a catchment scale using Random Forest

<p>Water-rock-interactions in the saturated and unsaturated zone govern the natural variability of CO<sub>2</sub> in groundwater. However, anthropogenic pollutions such as excessive input of organic and inorganic fertilizers or sewage leakage can cause shifts in the carbonate-pH system in an aquifer. Additional dissolution of minerals and associated mobilization of harmful heavy metals are possible consequences. Anthropogenic groundwater pollution is especially an issue where a protective confining layer is absent. On the other hand, addressing an environmental hazard such as fertilizer input to a single parameter remain intricate due to the high number of possible competing reactions such as microbial-controlled redox reactions. To overcome these obstacles, machine learning based statistical methods become increasingly important.</p><p>This study attempt to predict the CO<sub>2 </sub>concentration in groundwater from a multi-feature selection by using Random Forest. For this purpose, groundwater chemistry data (in situ measured bulk parameter, major ions, nutrients, trace elements and more) from more than 23000 wells and springs in Germany were collected and homogenized in a single database. Measured or calculated CO<sub>2 </sub>concentrations<sub></sub>are used to train the Random Forest algorithm and later to validate model results. Beside chemistry data, features about hydrogeology, soil characteristics, land use land cover and climate factors serve as predictors to build the “forest”. The intention of this study is to establish comprehensive CO<sub>2 </sub>predictions based on surface and climate features and to identify trends in local CO<sub>2 </sub>production. Gained knowledge can be used as input for groundwater quality management processes and adaptation policies.</p>

Evaluating the effect of variable lithologies on rates of knickpoint migration in the Wutach catchment, southern Germany

<p>The topographic evolution of landscapes strongly depends on the resistance of bedrock to erosion. Detachment-limited fluvial landscapes are commonly analyzed and modelled with the stream power incision model (SPIM) which parametrizes erosional efficiency by the bulk parameter K whose value is largely determined by bedrock erodibility. Inversion of the SPIM using longitudinal river profiles enables resolving values of K if histories of rock-uplift or base level change are known. Here, we present an approach to estimate K-values for the Wutach catchment, southern Germany. The catchment is a prominent example of river piracy that occurred ~18 ka ago as response to headward erosion of a tributary to the Rhine. Base level fall of up to 170 m triggered a wave of upstream migrating knickpoints that represent markers for the transient response of the landscape. Knickpoint migration along the main trunk stream and its tributaries passed different lithological settings, which allows us to estimate K for crystalline and sedimentary bedrock units of variable erodibility.</p>

Constants in Discrete Poincaré and Friedrichs Inequalities and Discrete Quasi-Interpolation

This paper provides a discrete Poincaré inequality innspace dimensions on a simplexKwith explicit constants. This inequality bounds the norm of the piecewise derivative of functions with integral mean zero onKand all integrals of jumps zero along all interior sides by its Lebesgue norm times{C(n)\operatorname{diam}(K)}. The explicit constant{C(n)}depends only on the dimension{n=2,3}in case of an adaptive triangulation with the newest vertex bisection. The second part of this paper proves the stability of an enrichment operator, which leads to the stability and approximation of a (discrete) quasi-interpolator applied in the proofs of the discrete Friedrichs inequality and discrete reliability estimate with explicit bounds on the constants in terms of the minimal angle{\omega_{0}}in the triangulation. The analysis allows the bound of two constants{\Lambda_{1}}and{\Lambda_{3}}in the axioms of adaptivity for the practical choice of the bulk parameter with guaranteed optimal convergence rates.

Predicting Large Ocean Wave Events Characterized by Bimodal Energy Spectra in the Presence of a Low-Level Southerly Wind Feature

Three large wave events are simulated with WaveWatch III using different wind inputs and physics packages. The modeled output, including spectral shape and bulk parameter time series, are compared with National Data Buoy Center buoy observations offshore of Newport, Oregon. The atmospheric conditions that generate these large waves include a strong southerly wind along with a distant cyclone. The energetic contributions of these simultaneously occurring atmospheric features result in a wave field characterized by bimodal energy spectra for two events and unimodal energy spectra for the third event. The analysis of model output evaluates bulk parameter time series of significant wave height, mean period, and mean wave direction derived from partitioned energy spectra. A consistent underestimation in wave energy approaching from the southwestern direction is found for the output associated with all model configurations. This wave energy is generated by the southerly wind. An overestimation in swell energy approaching from the northwest is also found for all model configurations. The model configuration that most accurately reproduces the southerly wave energy results in the best performance for the overall bulk parameters.

Turbulent Erosion of Persistent Cold-Air Pools: Numerical Simulations*

High-resolution idealized numerical simulations are used to examine the turbulent removal of cold-air pools commonly observed in mountain valleys and basins. A control simulation with winds aloft increasing from 0.5 to 20 m s−1 over 20 h combined with typical cold-air pool stratification illustrates the interplay over time of lowering of the top of the cold-air pool, spillover downstream of the valley from the upper reaches of the cold-air pool, wavelike undulations affecting the cold-air pool’s depth and stratification across the valley, and smaller temporal- and spatial-scale Kelvin–Helmholtz waves within the uppermost layers of the cold-air pool. The heat budget within the cold-air pool demonstrates the nearly compensating effects of vertical and horizontal advection combined with turbulent heating of the upper portion of the cold-air pool and cooling in the layers immediately above the cold-air pool. Sensitivities of turbulent mixing in cold-air pools to stratification and upstream terrain are examined. Although the characteristics of the turbulent mixing differ as the stratification and topography are modified, a bulk parameter [the cold-air pool Froude number (Fr)] characterizes the onset and amplification of turbulent mixing and the time of cold-air pool removal. When Fr > 1, Kelvin–Helmholtz waves and turbulent heat fluxes commence. Turbulent heat flux and wave activity increase until Fr = 2, after which the cold-air pool breaks down and is removed from the valley. The rate of cold-air pool removal is proportional to its strength; that is, a strong inversion generates larger heat fluxes once turbulent erosion is underway.