Extreme waves induced by cyclone Nargis at Myanmar coast: numerical modeling versus satellite observations

Natural Hazards - The deltaic coast of Myanmar was severely hit by tropical cyclone Nargis in May 2008. In the present study, a top-down numerical simulation approach using the Weather Research and...     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

     

Phase center modeling for LEO GPS receiver antennas and its impact on precise orbit determination

Most satellites in a low-Earth orbit (LEO) with demanding requirements on precise orbit determination (POD) are equipped with on-board receivers to collect the observations from Global Navigation Satellite systems (GNSS), such as the Global Positioning System (GPS). Limiting factors for LEO POD are nowadays mainly encountered with the modeling of the carrier phase observations, where a precise knowledge of the phase center location of the GNSS antennas is a prerequisite for high-precision orbit analyses. Since 5 November 2006 (GPS week 1400), absolute instead of relative values for the phase center location of GNSS receiver and transmitter antennas are adopted in the processing standards of the International GNSS Service (IGS). The absolute phase center modeling is based on robot calibrations for a number of terrestrial receiver antennas, whereas compatible antenna models were subsequently derived for the remaining terrestrial receiver antennas by conversion (from relative corrections), and for the GNSS transmitter antennas by estimation. However, consistent receiver antenna models for space missions such as GRACE and TerraSAR-X, which are equipped with non-geodetic receiver antennas, are only available since a short time from robot calibrations. We use GPS data of the aforementioned LEOs of the year 2007 together with the absolute antenna modeling to assess the presently achieved accuracy from state-of-the-art reduced-dynamic LEO POD strategies for absolute and relative navigation. Near-field multipath and cross-talk with active GPS occultation antennas turn out to be important and significant sources for systematic carrier phase measurement errors that are encountered in the actual spacecraft environments. We assess different methodologies for the in-flight determination of empirical phase pattern corrections for LEO receiver antennas and discuss their impact on POD. By means of independent K-band measurements, we show that zero-difference GRACE orbits can be significantly improved from about 10 to 6 mm K-band standard deviation when taking empirical phase corrections into account, and assess the impact of the corrections on precise baseline estimates and further applications such as gravity field recovery from kinematic LEO positions.     

Close-range constrained image sequences

This paper deals with a novel imaging arrangement for special modeling purpose. The method introduced here will take care of the geometrical consistence of photography. It is based on image ray bundles and is meant for conditions where imaging is accomplished inside the object space (e.g. in case of interior model reconstruction). Imaging is designed to be constrained in terms of camera position and orientation and to consist of two image sequences. The image orientations will be resolved by use of LSQ-estimation in heavily overdetermined conditions. The benefits of overdetermination are discussed and the accuracy assessment in a real experiment is published. The accuracy assessment is accomplished with help of exterior reference and stochastic error propagation.     

Experimental impact cratering: A summary of the major results of the MEMIN research unit

This paper reviews major findings of the Multidisciplinary Experimental and Modeling Impact Crater Research Network (MEMIN). MEMIN is a consortium, funded from 2009 till 2017 by the German Research Foundation, and is aimed at investigating impact cratering processes by experimental and modeling approaches. The vision of this network has been to comprehensively quantify impact processes by conducting a strictly controlled experimental campaign at the laboratory scale, together with a multidisciplinary analytical approach. Central to MEMIN has been the use of powerful two-stage light-gas accelerators capable of producing impact craters in the decimeter size range in solid rocks that allowed detailed spatial analyses of petrophysical, structural, and geochemical changes in target rocks and ejecta. In addition, explosive setups, membrane-driven diamond anvil cells, as well as laser irradiation and split Hopkinson pressure bar technologies have been used to study the response of minerals and rocks to shock and dynamic loading as well as high-temperature conditions. We used Seeberger sandstone, Taunus quartzite, Carrara marble, and Weibern tuff as major target rock types. In concert with the experiments we conducted mesoscale numerical simulations of shock wave propagation in heterogeneous rocks resolving the complex response of grains and pores to compressive, shear, and tensile loading and macroscale modeling of crater formation and fracturing. Major results comprise (1) projectile–target interaction, (2) various aspects of shock metamorphism with special focus on low shock pressures and effects of target porosity and water saturation, (3) crater morphologies and cratering efficiencies in various nonporous and porous lithologies, (4) in situ target damage, (5) ejecta dynamics, and (6) geophysical survey of experimental craters.     

Thermophysical properties of Almahata Sitta meteorites (asteroid 2008 TC3) for high‐fidelity entry modeling

Asteroid 2008 TC₃ was characterized in a unique manner prior to impacting Earth's atmosphere, making its October 7, 2008, impact a suitable field test for or validating the application of high‐fidelity re‐entry modeling to asteroid entry. The accurate modeling of the behavior of 2008 TC₃ during its entry in Earth's atmosphere requires detailed information about the thermophysical properties of the asteroid's meteoritic materials at temperatures ranging from room temperature up to the point of ablation (T ~ 1400 K). Here, we present measurements of the thermophysical properties up to these temperatures (in a 1 atm. pressure of argon) for two samples of the Almahata Sitta meteorites from asteroid 2008 TC₃: a thick flat‐faced ureilite suitably shaped for emissivity measurements and a thin flat‐faced EL6 enstatite chondrite suitable for diffusivity measurements. Heat capacity was determined from the elemental composition and density from a 3‐D laser scan of the sample. We find that the thermal conductivity of the enstatite chondrite material decreases more gradually as a function of temperature than expected, while the emissivity of the ureilitic material decreases at a rate of 9.5 × 10^?5 K^?1 above 770 K. The entry scenario is the result of the actual flight path being the boundary to the load the meteorite will be affected with when entering. An accurate heat load prediction depends on the thermophysical properties. Finally, based on these data, the breakup can be calculated accurately leading to a risk assessment for ground damage.     

On the occurrence and origin of anthropogenic radionuclides found in a fragment of the Chelyabinsk (LL5) meteorite

A piece of the 2013 Chelyabinsk meteorite was investigated for its content of anthropogenic radionuclides. In addition to traces of cesium‐137 that had been previously reported for this particular fragment, we found an unusually high amount of strontium‐90, which indicates that the source of this contamination was the Kyshtym accident (1957). A high Sr‐90/Cs‐137 activity ratio is characteristic for Kyshtym‐derived contaminations. Based on the cesium‐137 content in the soil from the finding site, it is estimated that the fragment was contaminated with soil particles in the milligram range upon impact. Investigation of the soil revealed very unusual ferromagnetic characteristics and an iron‐rich chemical composition. Mössbauer spectroscopy indicated the presence of steel components in this soil, suggesting that the investigated meteorite fragment was found in an industrial dumping site rather than natural soil.     

Nonequilibrium Processes in the Solar Corona,Transition Region,Flares, and Solar Wind <Emphasis Type="Italic">(Invited Review)</Emphasis>

We review the presence and signatures of the non-equilibrium processes, both non-Maxwellian distributions and non-equilibrium ionization, in the solar transition region, corona, solar wind, and flares. Basic properties of the non-Maxwellian distributions are described together with their influence on the heat flux as well as on the rates of individual collisional processes and the resulting optically thin synthetic spectra. Constraints on the presence of high-energy electrons from observations are reviewed, including positive detection of non-Maxwellian distributions in the solar corona, transition region, flares, and wind. Occurrence of non-equilibrium ionization is reviewed as well, especially in connection to hydrodynamic and generalized collisional-radiative modeling. Predicted spectroscopic signatures of non-equilibrium ionization depending on the assumed plasma conditions are summarized. Finally, we discuss the future remote-sensing instrumentation that can be used for the detection of these non-equilibrium phenomena in various spectral ranges.     

Hydraulic roughness over simple subaqueous dunes

Detailed studies of flow over subaqueous dunes in laboratory flumes were used to suggest a virtual near-bed layer of twice the dune height in which the mean velocity is accelerated towards the crest by contraction. The mean flow velocity in this layer above the crest, transformed into friction velocity by means of the surface skin roughness, is shown to give values consistent with measured values. The resulting dimensionless shear stress due to skin friction is depth-independent, in contrast to that derived by means of often cited traditional methods. As a result of the relationship between dune height and the thickness of the near-bed layer, an expression for the expansion loss behind dunes was formulated and used to relate form resistance directly to dune height.