Chalcides and pnictides of group VIII transition metals: Far-infrared spectroscopic studies on compounds MX 2, MXY,and MY 2 with pyrite,marcasite, and arsenopyrite structure

The far-infrared (FIR) spectra of pyrites, marcasites and loellingites, and arsenopyrites of the type MX ₂, MY ₂, and MXY with M=Fe, Co, Ru, Rh, Os, Ir X=S, Se, Te, and Y=P, As, and Sb have been studied, including group theoretical treatment of the phonon modes. The internal Y - Y and X - Y stretching modes, infrared (IR) allowed only in the case of the arsenopyrites, have been found to be in the range 440–490, 470–510, 450–490, 430–450 and 400–420 cm^?1 for MP ₂, MPS, MPSe, MAsS, and MSbS type compounds, respectively. From the obtained spectra intensity weighted mean phonon frequencies, i.e. central frequencies as defined by Plendl (1961), and mass weighted ones have been calculated and interpreted in terms of the strength of the MX and MY bonds, especially comparing 3d, 4d, and 5d transition metal compounds. Method of preparation and X-ray data of the chalcides and pnictides studied are also given.     

Looking for a needle in a haystack: Probability density based classification and reconstruction of dormers from 3D point clouds

Accurate reconstruction of roofs with dormers is challenging. Without careful separation of the dormer points from the points on the roof surface, the estimation of the roof areas is distorted. The characteristic distortion of the density distribution in comparison to the expected normal distribution is the starting point of our method. We propose a hierarchical method which improves roof reconstruction from LiDAR point clouds in a model‐based manner, separating dormer points from roof points using classification methods. The key idea is to exploit probability density functions to reveal roof properties and to skilfully design the features for a supervised learning method using support vector machines. The approach is tested based on real data as well as simulated point clouds.     

Water in the Martian interior—The geodynamical perspective

Petrological analysis of the Martian meteorites suggests that rheologically significant amounts of water are present in the Martian mantle. A bulk mantle water content of at least a few tens of ppm is thus expected to be present despite the potentially efficient degassing during accretion, magma ocean solidification, and subsequent volcanism. We examine the dynamical consequences of different thermochemical evolution scenarios testing whether they can lead to the formation and preservation of mantle reservoirs, and compare model predictions with available data. First, the simplest scenario of a homogenous mantle that emerges when ignoring density changes caused by the extraction of partial melt is found to be inconsistent with the isotopic evidence for distinct reservoirs provided by the analysis of the Martian meteorites. In a second scenario, reservoirs can form as a result of partial melting that induces a density change in the depleted mantle with respect to its primordial composition. However, efficient mantle mixing prevents these reservoirs from being preserved until present unless they are located in the stagnant lid. Finally, reservoirs could be formed during fractional crystallization of a magma ocean. In this case, however, the mantle would likely end up being stably stratified as a result of the global overturn expected to accompany the fractional crystallization. Depending on the assumed density contrast, little secondary crust would be produced and the lithosphere would be extremely cool and dry, in contrast to observations. In summary, it is very challenging to obtain a self‐consistent evolution scenario that satisfies all available constraints.     

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models.     

Climate change impacts on runoff in the Ferghana Valley (Central Asia)

The main freshwater source of arid/semi-arid Central Asia is stored in its high mountain glaciers. Water for the downstream countries is mainly supplied through the Syrdarya River that originates at the confluence of the Naryn and Karadarya rivers in the Ferghana Valley. Runoff generation from glaciers plays a crucial role, although a considerable number of small tributaries supply the river with additional runoff from snowmelt and rain in the mountains surrounding the Ferghana Valley. Observations of rising air temperature and accelerated glacier shrinkage make it most likely that the relative contributions of the smaller tributaries will increase. Hitherto, assessments of climate change effects on the water resource availability have largely neglected the growing importance of the runoff from smaller tributaries. We used a dynamically downscaled A1B SRES scenario for climate change effects for the period 2071–2100 in relation to the reference period of 1971–2000 and a version of the conceptual hydrological Hydrologiska Byråns Vattenavdelning model (HBV-light) to estimate runoff contributions with particular respect to the small tributaries. The simulations showed a 12–42% decrease in summer runoff; and a 44–107% increase in winter-spring runoff. This indicates the hydrological regime is shifting towards a runoff from snowmelt earlier in the year. The study suggests that actions for climate change adaptation should be complemented by land management configured to secure optimal runoff supplement from the smaller catchments.     

Time-frequency analysis based on curvelet transforms with time skewing

Oil and gas exploration gradually changes to the deep and complex areas. The quality of seismic data restricts the effective application of conventional time-frequency analysis technology, especially in the case of low signal-to-noise ratio. To address this problem, we propose a curvelet-based time-frequency analysis method, which is suitable for seismic data, and takes into account the lateral variation of seismic data. We first construct a kind of curvelet adapted to seismic data. By adjusting the rotation mode of the curvelet in the form of time skewing, the scale parameter can be directly related to the frequency of the seismic data. Therefore, the curvelet coefficients at different scales can reflect the time-frequency information of the seismic data. Then, the curvelet coefficients, which represent the dominant azimuthal pattern, are converted to the time-frequency domain. Since the curvelet transform is a kind of sparse representation for the signal, the screening process of the dominant coefficient masks most of the random noise, which enables the method to adapt for the low signal-to-noise ratio data. Results of synthetic and field data experiments using the proposed method demonstrate that it is a good approach to identify weak signals from strong noise in the time-frequency domain.     

Dependence of DOLP on Coronal Electron Temperature,Speed, and Structure

We study the predictive capabilities of magnetic-feature properties (MF) generated by the Solar Monitor Active Region Tracker (SMART: Higgins et al. in Adv. Space Res. 47, 2105, 2011) for solar-flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 which has been previously studied by Ahmed et al. (Solar Phys. 283, 157, 2013) and a subset of that dataset that only includes detections that are NOAA active regions (ARs). The main contributions of this work are: we use marginal relevance as a filter feature selection method to identify the most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive classification rules to predict future observations. For comparison, we employ a Random Forest, Support Vector Machine, and a set of Deep Neural Network models, as well as lasso for feature selection. Using the linear model with three features we obtain significantly better results (True Skill Score: TSS = 0.84) than those reported by Ahmed et al. (Solar Phys. 283, 157, 2013) for the full dataset of SMART detections. The same model produced competitive results (TSS = 0.67) for the dataset of SMART detections that are NOAA ARs, which can be compared to a broader section of flare-forecasting literature. We show that more complex models are not required for this data.

     

Petrogenesis of main group pallasite meteorites based on relationships among texture,mineralogy, and geochemistry

Main group pallasite meteorites are samples of a single early magmatic planetesimal, dominated by metal and olivine but containing accessory chromite, sulfide, phosphide, phosphates, and rare phosphoran olivine. They represent mixtures of core and mantle materials, but the environment of formation is poorly understood, with a quiescent core–mantle boundary, violent core–mantle mixture, or surface mixture all recently suggested. Here, we review main group pallasite data sets and petrologic characteristics, and present new observations on the low‐MnO pallasite Brahin that contains abundant fragmental olivine, but also rounded and angular olivine and potential evidence of sulfide–phosphide liquid immiscibility. A reassessment of the literature shows that low‐MnO and high‐FeO subgroups preferentially host rounded olivine and low‐temperature P₂O₅‐rich phases such as the Mg‐phosphate farringtonite and phosphoran olivine. These phases form after metal and silicate reservoirs back‐react during decreasing temperature after initial separation, resulting in oxidation of phosphorus and chromium. Farringtonite and phosphoran olivine have not been found in the common subgroup PMG, which are mechanical mixtures of olivine, chromite with moderate Al₂O₃ contents, primitive solid metal, and evolved liquid metal. Lower concentrations of Mn in olivine of the low‐MnO PMG subgroup, and high concentrations of Mn in low‐Al₂O₃ chromites, trace the development and escape of sulfide‐rich melt in pallasites and the partially chalcophile behavior for Mn in this environment. Pallasites with rounded olivine indicate that the core–mantle boundary of their planetesimal may not be a simple interface but rather a volume in which interactions between metal, silicate, and other components occur.     

Semiempirical band structure calculations on skutterudite-type compounds

Semiempirical band structure calculations were performed on several skutterudite-type compounds by using the extended Hückel method. Starting with the molecular orbital calculations on isolated P₄ and As₄ rings, the reason for the band dispersions of the skutterudites was found to be the interactions between the nonmetal atoms. Both the intermolecular and the intramolecular interactions between the phosphorus atoms are stronger than those between the arsenic atoms. Hence, the dispersion of the bands in CoP₃ is larger than that in CoAs₃. The COOP (crystal orbital overlap population) integrals of the intramolecular P-P bonds reveal the relation between the valence electron count and the observed bond lengths. The P-P bonds in the skutterudite-type compounds like TP₃ (T = Co, Rh, Ir) become stronger by reduction as in NiP₃ and weaker by oxidation as in RT₄X₁₂ (X = P, As, Sb; R = alkaline earth or rare earth metals) because the bands near the Fermi level are bonding. The electronic reason for the geometric distortion of the Ge₂Y₂ (Y = S, Se) units of mixed skutterudites TGe_1.5Y_1.5 is caused by an electron pair gap on germanium, which corresponds to low electron density perpendicular to the ring plane on the germanium atoms. Received: 6 October 1998 / Revised, accepted: 18 June 1999