Abstract
A topic of current interest in the analysis of sea-level states is to investigate the occurrence of future rare events which is essential for the prediction of flooding risks, coastal management and in the design of coastal defences and offshore structures. Nowadays, it is widely believed that the frequency of such rare events is increasing as a result of climatic and other changes, although they are hard to predict and their effects are, yet, poorly understood. Recent developments in multivariate statistical techniques for discrimination, clustering and dimension reduction for time series, have the potential to aid on the construction of new tools and models for forecasting the occurrence and impact of such future rare events. In studies of regional sea-level variability, tidal measurements are often analyzed individually for characterizing sea-level variability at each location. Marginal analysis, however, is in itself insufficient to come with an accurate description of regional sea-level variability. An alternative approach is to consider simultaneously the whole data set of sea-level records from a given region, and characterize regional variability in terms of locations exhibiting similar behavior through clustering techniques. Cluster analysis is a useful approach for characterizing regional variability of locations exhibiting similar behavior in terms of, for example, short-term or long-term predictions of extreme values. In this work, time series clustering is applied to the analysis of long tide gauge records from the Baltic Sea. In order to describe the regional variability of Baltic sea-level, tide gauge measurements are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-years return values. This is relevant for the design of marine systems and coastal structures, which requires a good knowledge of the most severe sea-level conditions that they need to withstand during their lifetime, and also for describing and understanding the variability of extreme sea heights in a climate change context.

Abstract
The analysis of trends in air temperature observations is one of the most common activities in climate change studies. This work examines the changes in daily mean air temperature over Central Europe using quantile regression, which allows the estimation of trends, not only in the mean but in all parts of the data distribution. A bootstrap procedure is applied for assessing uncertainty on the derived slopes and the resulting distributions are summarised via clustering. The results show considerable spatial diversity over the central European region. A distinct behaviour is found for lower (5%) and upper (95%) quantiles, with higher trends around 0.15 degrees C decade(-1) at the 5% quantile and around 0.20 degrees C decade(-1) at the 95% quantile, the largest trends (>0.2 degrees C decade(-1)) occurring in the Alps.

Abstract
Seven soil-gas radon monitoring stations were placed along the active front of a granite quarry in Canas de senhorim, Central Portugal, recording continuously for 81 days. Important differences in the radon concentration were found between stations, with average values comprised between 102 and 2982 Bq m(-3), which can be explained by the local presence of uranium anomalies in the regional late-orogenic Hercynian granite, usually associated with faults. One of the boreholes exhibits large radon anomalies lasting for several days, and two, contrary to the others, show a clear daily periodic behaviour, with minima around 19:00 LT and maxima around 07:00 LT. The different patterns observed in stations placed at such a short distance (< 100 m) has no clear explanation and deserves further investigation. Data analysis shows no evidence of soil-gas radon concentration changes during explosions carried out at the quarry. This is likely to result from the absence of a progressive stress field affecting the rock, as typically occurs before an earthquake.

Abstract
The behavior of alpha silicon diodes, gamma crystal scintillators and ionization chamber detectors employed for long-term radon monitoring in geological media was studied and a comparison of the efficiency and sensitivity, the capability to resolve signal to noise, background, stability, and reliability of their long-term measurements is presented. An understanding of the qualities of monitoring techniques is necessary for determining suitability to the characteristics of the individual monitoring site and what exactly they will measure: radon in an air cavity, in porous media or in water. The experimental layout was located inside the Amram Mountain research tunnel near Elat (Gulf of Aqaba), within a closed room in the tunnel core. This enabled monitoring natural temporal radon variations under fairly stable internal conditions, at a high-resolution sampling rate of once every several minutes. In an interval of several days, all the sensors responded simultaneously to the same eventual radon variations. An ionization chamber device, the AlphaGUARD designed with a long-time stable calibration factor and an inherent QA-System, was used as reference calibration of the different radon detectors. The results indicate that the higher sensitivity of 2-4 orders of magnitude exhibited by gamma sensors even with narrow dimensions (1 '' x 3 '' BGO detector) are preferred for long-term radon monitoring in comparison to the solid-state alpha detectors and ionization chambers.

Abstract

Abstract
We present and numerically characterize a surface-plasmon-resonance sensor based on an H-shaped optical fiber. In our design, the two U-shaped grooves of the H-fiber are first coated with a thin gold layer and then covered by a uniform titanium dioxide layer to facilitate spectral tuning of the device. A finite element method analysis of the sensor indicates that a refractive-index resolution of up to 5.10(3) nm/RIU can be obtained. (C) 2011 Optical Society of America