(Table 1) Gravity and pressure within the Sleipner area relative to ones at the reference station in 2002 and 2005

At Sleipner, CO2 is being separated from natural gas and injected into an underground saline aquifer for environmental purposes. Uncertainty in the aquifer temperature leads to uncertainty in the in situ density of CO2. In this study, gravity measurements were made over the injection site in 2002 and 2005 on top of 30 concrete benchmarks on the seafloor in order to constrain the in situ CO2 density. The gravity measurements have a repeatability of 4.3 µGal for 2003 and 3.5 µGal for 2005. The resulting time-lapse uncertainty is 5.3 µGal. Unexpected benchmark motions due to local sediment scouring contribute to the uncertainty. Forward gravity models are calculated based on both 3D seismic data and reservoir simulation models. The time-lapse gravity observations best fit a high temperature forward model based on the time-lapse 3D seismics, suggesting that the average in situ CO2 density is about to 530kg/m**3. Uncertainty in determining the average density is estimated to be ±65 kg/m**3 (95% confidence), however, this does not include uncertainties in the modeling. Additional seismic surveys and future gravity measurements will put better constraints on the CO2 density and continue to map out the CO2 flow.

Basic measurements of radiation from the Baseline Surface Radiation Network (BSRN) in the years 1992 to 2012, reference list of 6378 datasets

In the framework of the global energy balance, the radiative energy exchanges between Sun, Earth and space are now accurately quantified from new satellite missions. Much less is known about the magnitude of the energy flows within the climate system and at the Earth surface, which cannot be directly measured by satellites. In addition to satellite observations, here we make extensive use of the growing number of surface observations to constrain the global energy balance not only from space, but also from the surface. We combine these observations with the latest modeling efforts performed for the 5th IPCC assessment report to infer best estimates for the global mean surface radiative components. Our analyses favor global mean downward surface solar and thermal radiation values near 185 and 342 Wm**-2, respectively, which are most compatible with surface observations. Combined with an estimated surface absorbed solar radiation and thermal emission of 161 Wm**-2 and 397 Wm**-2, respectively, this leaves 106 Wm**-2 of surface net radiation available for distribution amongst the non-radiative surface energy balance components. The climate models overestimate the downward solar and underestimate the downward thermal radiation, thereby simulating nevertheless an adequate global mean surface net radiation by error compensation. This also suggests that, globally, the simulated surface sensible and latent heat fluxes, around 20 and 85 Wm**-2 on average, state realistic values. The findings of this study are compiled into a new global energy balance diagram, which may be able to reconcile currently disputed inconsistencies between energy and water cycle estimates.

Geological reference standards (JLK-1,JSD-1, JSD-2, JSD-3,JMS-1, JMS-2) measured by Itrax-XRF core scanner in different exposure times

X-ray fluorescence (XRF) core-scanning is a fast and nondestructive technique to assess elemental variations of unprocessed sediments. However, although the exposure time of XRF-scanning directly affects the scanning counts and total measurement time, only a few studies have considered the influence of exposure time during the scan. How to select an optimal exposure time to achieve reliable results and reduce the total measurement time is an important issue. To address this question, six geological reference materials from the Geological Survey of Japan (JLK-1, JMS-1, JMS-2, JSD-1, JSD-2, and JSD-3) were scanned by the Itrax-XRF core scanner using the Mo- and the Cr-tube with different exposure times to allow a comparison of scanning counts with absolute concentrations. The regression lines and correlation coefficients of elements that are generally used in paleoenvironmental studies were examined for the different exposure times and X-ray tubes. The results show that for those elements with relatively high concentrations or high detectability, the correlation coefficients are higher than 0.90 for all exposure times. In contrast, for the low detectability or low concentration elements, the correlation coefficients are relatively low, and improve little with increased exposure time. Therefore, we suggest that the influence of different exposure times is insignificant for the accuracy of the measurements. Thus, caution must be taken when interpreting the results of elements with low detectability, even when the exposure times are long and scanning counts are reasonably high.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.