QSO Lyman-Alpha Absorbers and Galaxy Halos

We review the current status of our survey of galaxies in fields of HST target QSOs, which has allowed us to identify the galaxies responsible for a number of Lyman-α absorption systems. We emphasize the use of QSO absorption lines to study the structure and kinematics of the large gaseous halos that virtually all galaxies appear to possess. This revised version was published online in July 2006 with corrections to the Cover Date.     

A comparative study of barium peculiarity indices

From DDO photometry an indicator of barium stars have been elaborated. This indicator is closely related to the barium line intensities derived by means of narrow-band photometry and spectroscopic observations. A better discrimination appears when our indicator is used instead of the qualitative Warner index.     

GPS Monitoring in the N-W Part of the Volcanic Island of Tenerife,Canaries, Spain: Strategy and Results

This paper describes design, observation methodology, results and interpretation of the GPS surveys conducted in the areas of the N-W of Tenerife where deformation was detected using InSAR. To avoid undesirable antenna positioning errors in the stations built using nails, we designed and used calibrated, fixed-length metal poles, allowing us to guarantee that the GPS antenna was stationed with a height repeatability of the order of 1 mm and of less than 3 millimeters on the horizontal plane. The results demonstrate that this system is ideal for field observation, especially to detect small displacements that might be masked by accidental errors in height measurements or centering when observed with a tripod. When observations were processed, we found that using different antenna models in the same session sometimes causes errors that can lead to rather inaccurate results. We also found that it is advisable to observe one or two stations in all the sessions. The results have reconfirmed the displacement in the Chío deformation zone for the period 1995–2000 and indicate a vertical rebound from 2000 to 2002. They also confirm that the subsidence detected by InSAR to the south of the Garachico village has continued since 2000, although the magnitude of the vertical deformation has increased from around 1 cm to more than 3 cm a year. Detected displacements could be due to groundwater level variation throughout the island. A first attempt of modelling has been made using a simple model. The results indicate that the observed deformation and the groundwater level variation are related in some way. The obtained results are very important because they might affect the design of the geodetic monitoring of volcanic reactivation on the island, which will only be actually useful if it is capable of distinguishing between displacements that might be linked to volcanic activity and those produced by other causes. Even though the study was limited to a given area of Tenerife, in the Canary Islands, some conclusions apply to, and are of general interest in similar geodynamic studies.     

Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator

Forecast combination has been studied in econometrics for a long time, and the literature has shown the superior performance of forecast combination over individual predictions. However, there is still controversy on which is the best procedure to specify the forecast weights. This paper explores the possibility of using a procedure based on Entropy Econometrics, which allows setting the weights for the individual forecasts as a mixture of different alternatives. In particular, we examine the ability of the Data-Weighted Prior Estimator proposed by Golan (J Econom 101(1):165–193, 2001) to combine forecasting models in a context of small sample sizes, a relative common scenario when dealing with time series for regional economies. We test the validity of the proposed approach using a simulation exercise and a real-world example that aims at predicting gross regional product growth rates for a regional economy. The forecasting performance of the Data-Weighted Prior Estimator proposed is compared with other combining methods. The simulation results indicate that in scenarios of heavily ill-conditioned datasets the approach suggested dominates other forecast combination strategies. The empirical results are consistent with the conclusions found in the numerical experiment.     

Regional maximum rainfall analysis using L-moments at the Titicaca Lake drainage,Peru

Theoretical and Applied Climatology - The present study investigates the application of the index flood L-moments-based regional frequency analysis procedure (RFA-LM) to the annual maximum 24-h...     

Feasibility Analysis of the Use of Binary Genetic Algorithms as Importance Samplers Application to a 1-D DC Resistivity Inverse Problem

DC resistivity inverse problems are ill-posed. For the Vertical Electrical Sounding method the acceptable solutions lie in very narrow elongated-shape regions in the model space. To characterize this ensemble of solutions is a central question. In a Bayesian framework this issue is solved adopting as solution the so-called model posterior probability distribution. However, due to the nonlinearity of the problem, this distribution is not explicitly known, or it is difficult to calculate. Therefore, algorithms that efficiently sample the model space according to it (importance sampling) are very desirable. The main goal of this paper is to numerically explore the performance of binary genetic algorithms as posterior importance sampling strategies. Their behavior will be firstly analyzed using 2D synthetic posterior test functions bearing the relevant properties of the real geo-electrical inverse problem. The conclusions will be again checked through the histogram reconstruction of parameters in a synthetic VES case, and eventually, in a real, higher dimensional, sea-water coastal intrusion problem, by comparing the results with those obtained with a theoretically correct Metropolis-Hasting importance sampler (simulated annealing without cooling). Percentile curves are introduced as an effective tool for risk assessment. We show that binary genetic algorithms perform well under very general assumptions. When the roulette wheel is the selection method used, mutation is over 10%, and the algorithm does not incorporate elitism. The results do not depend on the values of the remaining tuning parameters. Finally, to improve the efficiency of the sampling strategy, we introduce a binary genetic algorithm with oriented search space. This is done with the help of linearization of the forward operator and singular value decomposition around the maximum posterior estimate. It is shown, also, that the logarithmic model parameterization is adequate for this task.     

Extreme maximum temperature events and their relationships with large-scale modes: potential hazard on the Iberian Peninsula

Theoretical and Applied Climatology - The aim of this paper is to analyze spatiotemporal distribution of maximum temperatures in the Iberian Peninsula (IP) by using various extreme maximum...     

Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool

The use and development of bias correction (BC) methods has grown fast in recent years, due to the increased demand of unbiased projections by many sectoral climate change impact applications. Case studies are frequently based on multi-variate climate indices (CIs) combining two or more essential climate variables that are frequently individually corrected prior to CI calculation. This poses the question of whether the BC method modifies the inter-variable dependencies and eventually the climate change signal. The direct bias correction of the multi-variate CI stands as a usual alternative, since it preserves the physical and temporal coherence among the primary variables as represented in the dynamical model output, at the expense of incorporating the individual biases on the CI with an effect difficult to foresee, particularly in the case of complex CIs bearing in their formulation non-linear relationships between components. Such is the case of the Fire Weather Index (FWI), a meteorological fire danger indicator frequently used in forest fire prevention and research. In the present work, we test the suitability of the direct BC approach on FWI as a representative multi-variate CI, assessing its performance in present climate conditions and its effect on the climate change signal when applied to future projections. Moreover, the results are compared with the common approach of correcting the input variables separately. To this aim, we apply the widely used empirical quantile mapping method (QM), adjusting the 99 empirical percentiles. The analysis of the percentile adjustment function (PAF) provides insight into the effect of the QM on the climate change signal. Although both approaches present similar results in the present climate, the direct correction introduces a greater modification of the original change signal. These results warn against the blind use of QM, even in the case of essential climate variables or uni-variate CIs.     

The role of constitutive models in MPM simulations of granular column collapses

The granular column collapse is a well-established experiment which consists of having a vertical column of granular material on a flat surface and letting it collapse by gravity. Despite its simplicity in execution, the numerical modelling of a column collapse remains challenging. So far, much attention has been dedicated in assessing the ability of various numerical methods in modelling the large deformation and little to the role of the constitutive model on both the triggering mechanism and the flow behaviour. Furthermore, the influence of the initial density, and its associated dilatancy and strength characteristics, have never been included in the analyses. Most past numerical investigations had relied on simple constitutive relations which do not consider the softening behaviours. The aim of this study is to illustrate the influence of the constitutive model on the on-set of failure, the flow behaviour and the deposition profile using the material point method. Three constitutive models were used to simulate the collapse of two granular columns with different geometries and for two densities. The results of the simulations showed that the constitutive model had a twofold influence on the collapse behaviour. It defined the volume of the mobilised mass which spread along the flat surface and controlled the dissipation of its energy. The initial density was found to enhance the failure angle and flow behaviours and was more significant for small columns than for larger ones. The analysis of the potential energy of the mobilised mass explained the existence of two collapse regimes.