Itinerant Nature of Atom-Magnetization Excitation by Tunneling Electrons

We have performed single-atom magnetization curve (SAMC) measurements and inelastic scanning tunneling spectroscopy (ISTS) on individual Fe atoms on a Cu(111) surface. The SAMCs show a broad distribution of magnetic moments with \unit[3.5]{\mu_{\rm B}} being the mean value. ISTS reveals a magnetization excitation with a lifetime of \unit[200]{fsec} which decreases by a factor of two upon application of a magnetic field of \unit[12]{T}. The experimental observations are quantitatively explained by the decay of the magnetization excitation into Stoner modes of the itinerant electron system as shown by newly developed theoretical modeling.Comment: 3 Figures, Supplement not included, updated version after revisio

CB17: Inferring the dynamical history of a prestellar core with chemo-dynamical models

We present a detailed theoretical study of the isolated Bok globule CB17 (L1389) based on spectral maps of CS, HCO$^+$, C$^{18}$O, C$^{34}$S, and H$^{13}$CO$^+$ lines. A phenomenological model of prestellar core evolution, a time-dependent chemical model, and a radiative transfer simulation for molecular lines are combined to reconstruct the chemical and kinematical structure of this core. We developed a general criterion that allows to quantify the difference between observed and simulated spectral maps. By minimizing this difference, we find that very high and very low values of the effective sticking probability $S$ are not appropriate for the studied prestellar core. The most probable $S$ value for CB17 is 0.3--0.5. The spatial distribution of the intensities and self-absorption features of optically thick lines is indicative of UV irradiation of the core. By fitting simultaneously optically thin and optically thick transitions, we isolate the model that reproduces all the available spectral maps to a reasonable accuracy. The line asymmetry pattern in CB17 is reproduced by a combination of infall, rotation, and turbulent motions with velocities $\sim0.05$ km s$^{-1}$, $\sim0.1$ km s$^{-1}$, and $\sim0.1$ km s$^{-1}$, respectively. These parameters corresponds to energy ratios $E_{\rm rot}/E_{\rm grav}\approx0.03$, $E_{\rm therm}/E_{\rm grav}\approx0.8$, and $E_{\rm turb}/E_{\rm grav}\approx0.05$ (the rotation parameters are determined for $i=90^\circ$). The chemical age of the core is about 2 Myrs. In particular, this is indicated by the central depletion of CO, CS, and HCO$^+$. Based on the angular momentum value, we argue that the core is going to fragment, i.e., to form a binary (multiple) star. (abridged)Comment: ApJ, in pres

Comparison of uncertainty quantification methods for cloud simulation

Quantification of evolving uncertainties is required for both probabilistic forecasting and data assimilation in weather prediction. In current practice, the ensemble of model simulations is often used as a primary tool to describe the required uncertainties. In this work, we explore an alternative approach, the so-called stochastic Galerkin (SG) method, which integrates uncertainties forward in time using a spectral approximation in stochastic space. In an idealized two-dimensional model that couples nonhydrostatic weakly compressible Navier–Stokes equations to cloud variables, we first investigate the propagation of initial uncertainty. Propagation of initial perturbations is followed through time for all model variables during two types of forecast: the ensemble forecast and the SG forecast. Series of experiments indicate that differences in performance of the two methods depend on the system state and truncations used. For example, in more stable conditions, the SG method outperforms the ensemble of simulations for every truncation and every variable. However, in unstable conditions, the ensemble of simulations would need more than 100 members (depending on the model variable) and the SG method more than a truncation at five to produce comparable but not identical results. As estimates of the uncertainty are crucial for data assimilation, secondly we instigate the use of these two methods with the stochastic ensemble Kalman filter. The use of the SG method avoids evolution of a large ensemble, which is usually the most expensive component of the data assimilation system, and provides results comparable with the ensemble Kalman filter in the cases investigated