Origin, transport, and retention of fluvial sedimentary organic matter in South Africa's largest freshwater wetland, Mkhuze Wetland System

Sedimentary organic matter (OM) analyses along a 130 km-long transect of the Mkhuze River from the Lebombo Mountains to its outlet into Lake St. Lucia, Africa’s most extensive estuarine system, revealed the present active trapping function of a terminal freshwater wetland. A combination of organic bulk parameters, thermal analyses, and determination of plant waxes, and their corresponding stable carbon (δ13C) and hydrogen (δD) isotopic signatures in surface sediments and local plant species enabled characterization and comparison of sedimentary OM in terms of stability, degradation status, sources, and sinks within and among the respective sub-environments of the Mkhuze Wetland System. This approach showed that fluvial sedimentary OM originating from inland areas is mainly deposited on the floodplain and Mkhuze Swamps. In contrast to samples from upstream areas, a distinctly less degraded signature characterizes the sedimentary OM in the northern section of Lake St. Lucia. Although lake sedimentary plant waxes are similar in the observed wax distribution pattern and δ13C values, they exhibit considerably higher δD values. This offset in δD indicates that lakeshore vegetation dominates plant-derived sedimentary OM in the lake, elucidating the effective capturing of OM and its fate in a sub-tropical coastal freshwater wetland. These findings raise important constraints for environmental studies assuming watershed-integrated signals in sedimentary archives retrieved from downstream lakes or offshore.

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.

Direct numerical simulation of droplet-mediated exchange fluxes in the marine atmospheric boundary layer

<p>Now it is a common knowledge that at sufficiently strong winds, sea-spray droplets interfere with  turbulent exchange processes occurring between atmosphere and hydrosphere. The results of field and laboratory experiments show that mass fraction of air-borne spume water droplets increases with the wind speed and their impact on the marine atmospheric boundary layer may become significant. The contribution of droplets to the momentum and sensible and latent heat fluxes may be crucial for our understanding of conditions favorable for the development of anomalous weather phenomena such as tropical hurricanes and polar lows. Phenomenological models and bulk algorithms are mostly based on hypothetical assumptions concerning the properties of droplet-air interaction which strongly influence the accuracy of their forecast. Lagrangian stochastic modeling also requires an adhoc knowledge of the properties of turbulent fields ‘seen’ by the droplets along their trajectories. These details of droplet-air interaction are difficult to measure in lab conditions and can be gleaned via direct numerical simulation (DNS). DNS solves primitive equations for the carrier air in the Eulerian frame and of droplets motion in a Lagrangian frame and accounts for the two-way coupling of momentum, heat and moisture between the carrier and dispersed phases, and allows us to investigate the droplet contribution to the exchange fluxes under different injection conditions and flow bulk parameters. The results obtained for different conditions show us that droplets dynamics and their contribution to the momentum and heat fluxes are controlled by many factors including droplets velocity at injection, the gravitational settling velocity, surface wave slope, bulk relative humidity and temperature of the atmospheric boundary layer as compared to the sea surface conditions.</p><p>This work is supported by the Ministry of Education and Science of the Russian Federation (Task No. 0030-2019-0020). Numerical algorithms were developed under the support of RFBR (20-05-00322, 21-55-52005, 18-05-60299). Postprocessing was performed under the support of the Russian Science Foundation (No. 19-17-00209).</p>

Characteristics of DSD Bulk Parameters: Implication for Radar Rain Retrieval

With the use of 213,456 one-minute measured data of droplet-size distribution (DSD) of rain collected during several National Aeronautics and Space Administration (NASA)-sponsored field campaigns, the relationships between rainfall rate R, mass-weighted diameter Dm and normalized intercept parameter Nw of the gamma DSD are studied. It is found, based on the simulations of the gamma DSD model, that R, Dm and Nw are closely interrelated, and that the ratio of R to Nw is solely a function of Dm, independent of the shape factor μ of the gamma distribution. Furthermore, the model-produced ratio agrees well with those from the DSD data. When a power-law equation is applied to fit the model data, we have: R = aN w D m b , where a = 1.588 × 10 − 4 , b = 4.706 . Analysis of two-parameter relationships such as R–Dm, Nw–R and Nw–Dm reveals that R and Dm are moderately correlated while Nw and Dm are negatively correlated. Nw and R, however, are uncorrelated. The gamma DSD model also reveals that variation of R–Dm relation is caused primarily by Nw. For the application of the Ku- and Ka-band dual-frequency radar for the retrieval of the DSD bulk parameters as well as the specific radar attenuations, the study is carried out to relate the dual-frequency radar reflectivity factors to the DSD and attenuation parameters.

Statistical Uncertainties of Space Plasma Properties Described by Kappa Distributions

The velocities of space plasma particles often follow kappa distribution functions, which have characteristic high energy tails. The tails of these distributions are associated with low particle flux and, therefore, it is challenging to precisely resolve them in plasma measurements. On the other hand, the accurate determination of kappa distribution functions within a broad range of energies is crucial for the understanding of physical mechanisms. Standard analyses of the plasma observations determine the plasma bulk parameters from the statistical moments of the underlined distribution. It is important, however, to also quantify the uncertainties of the derived plasma bulk parameters, which determine the confidence level of scientific conclusions. We investigate the determination of the plasma bulk parameters from observations by an ideal electrostatic analyzer. We derive simple formulas to estimate the statistical uncertainties of the calculated bulk parameters. We then use the forward modelling method to simulate plasma observations by a typical top-hat electrostatic analyzer. We analyze the simulated observations in order to derive the plasma bulk parameters and their uncertainties. Our simulations validate our simplified formulas. We further examine the statistical errors of the plasma bulk parameters for several shapes of the plasma velocity distribution function.

Assessment of Ku- and Ka-band Dual-Frequency Radar for Snow Estimates

<p>Dual-frequency radars have been increasingly used for detecting and retrieving cloud and precipitation, such as the Ku- and Ka-band Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite. The objective of this study is to evaluate performance of the standard dual-frequency technique, which uses the differential frequency ratio (DFR), defined as the difference of radar reflectivities between two wavelengths, for the estimation of snow microphysical properties and the associated bulk parameters from Ku- and Ka-band as well as Ka- and W-band dual-frequency radars. Although the DFR-based technique is effective in obtaining snow properties, its retrieval accuracy depends on the model assumptions, which include parameterization of particle size distribution (PSD), empirical mass-size relation that links the observed geometrical size of particle to its mass, and the radar scattering model. The complex nature of snowflakes regarding shape, structure, and the inability of the modeled PSD to represent actual snow spectra, lead to errors in the estimates of snow parameters. Additionally, uncertainties associated with scattering computations of snowflakes also affect the accuracy of the dual-wavelength radar retrieval of snow. Therefore, understanding the uncertainties in snow precipitation estimation that depend on PSD parameterizations and scattering models of individual particles is important in evaluating the overall performance of dual-frequency retrieval techniques. Furthermore, separation of the uncertainties associated with the PSD models and the scattering models and their respective contributions to overall uncertainties are useful for gaining insight into ways to improve the retrieval methods.</p><p>Snow PSD is usually modelled as a gamma distribution with 2 or 3 free parameters depending on whether its shape factor is fixed or taken as a function of D<sub>m</sub>. In this study, our focus is on an assessment of the uncertainties in snow estimates arising from the PSD parameterization and the mass-size relation. To do this, measured PSD data are employed. The snow mass spectra, which can be converted from measured PSD using an empirical mass-size relation, are used to obtain PSD parameters, e.g., the liquid-equivalent mass-weighted diameter (D<sub>m</sub>) and the normalized intercept of a gamma PSD (N<sub>w</sub>), and the snow bulk parameters, such as snow water content (SWC) and liquid-equivalent snowfall rate (R) if a measured fall velocity-size relationship is utilized. Coupling measured PSD with particle scattering model, measured radar parameters can be computed, which are subsequently used as inputs to the standard dual-frequency algorithm. An evaluation of the retrieval accuracy is conducted by comparing the radar estimates of D<sub>m</sub>, N<sub>w</sub>, SWC and R with the same quantities directly computed from the PSD spectra (or truth). In this study, measurements of the snow PSD and fall velocity acquired from the Snow Video Imager/Particle Image Probe (SVI/PIP) at the NASA Wallops flight facility site in Virginia are employed. There are several scattering databases available that provide the scattering properties of snow aggregates in accordance with various snow and ice crystal growth models. Variability of the snow estimates caused by the differences of various scattering tables will be analyzed to explore the uncertainties associated with the scattering tables.</p>

On the Determination of Kappa Distribution Functions from Space Plasma Observations

The velocities of space plasma particles, often follow kappa distribution functions. The kappa index, which labels and governs these distributions, is an important parameter in understanding the plasma dynamics. Space science missions often carry plasma instruments on board which observe the plasma particles and construct their velocity distribution functions. A proper analysis of the velocity distribution functions derives the plasma bulk parameters, such as the plasma density, speed, temperature, and kappa index. Commonly, the plasma bulk density, velocity, and temperature are determined from the velocity moments of the observed distribution function. Interestingly, recent studies demonstrated the calculation of the kappa index from the speed (kinetic energy) moments of the distribution function. Such a novel calculation could be very useful in future analyses and applications. This study examines the accuracy of the specific method using synthetic plasma proton observations by a typical electrostatic analyzer. We analyze the modeled observations in order to derive the plasma bulk parameters, which we compare with the parameters we used to model the observations in the first place. Through this comparison, we quantify the systematic and statistical errors in the derived moments, and we discuss their possible sources.

Determining the Bulk Parameters of Plasma Electrons from Pitch-Angle Distribution Measurements

Electrostatic analysers measure the flux of plasma particles in velocity space and determine their velocity distribution function. There are occasions when science objectives require high time-resolution measurements, and the instrument operates in short measurement cycles, sampling only a portion of the velocity distribution function. One such high-resolution measurement strategy consists of sampling the two-dimensional pitch-angle distributions of the plasma particles, which describes the velocities of the particles with respect to the local magnetic field direction. Here, we investigate the accuracy of plasma bulk parameters from such high-resolution measurements. We simulate electron observations from the Solar Wind Analyser’s (SWA) Electron Analyser System (EAS) on board Solar Orbiter. We show that fitting analysis of the synthetic datasets determines the plasma temperature and kappa index of the distribution within 10% of their actual values, even at large heliocentric distances where the expected solar wind flux is very low. Interestingly, we show that although measurement points with zero counts are not statistically significant, they provide information about the particle distribution function which becomes important when the particle flux is low. We also examine the convergence of the fitting algorithm for expected plasma conditions and discuss the sources of statistical and systematic uncertainties.

Analytical Relationships Between Bulk Microphysical Parameters of Nucleation, Aitken, Accumulation and Coarse Mode Particles and Extinction and Backscatter Coefficients Measured with Lidar

We derive analytical relationships between bulk microphysical parameters of nucleation, Aitken, accumulation and coarse mode particles and extinction and backscatter coefficients measured at wavelengths 355, 532 and 1064 nm. The bulk parameters are represented by number concentration, mean (effective) radius, variance and complex refractive index. Analytical relationships hold true for arbitrarily shaped particles and complex refractive indices m=mR-imI, where mR>>mI. The accuracy is above 60%.