Graded-Density Reservoirs for Accessing High Stress Low Temperature Material States

In recently developed laser-driven shockless compression experiments an ablatively driven shock in a primary target is transformed into a ramp compression wave in a secondary target via unloading followed by stagnation across an intermediate vacuum gap. Current limitations on the achievable peak longitudinal stresses are limited by the ability of shaping the temporal profile of the ramp compression pulse. We report on new techniques using graded density reservoirs for shaping the loading profile and extending these techniques to high peak pressures.     

Aquifer Characterization Using Fiber Bragg Grating Multi-Level Monitoring System

Groundwater responses measured from multiple wells at different depths are essential for delineating the aquifer heterogeneity using hydraulic tomography (HT). In general, conducting HT requires many wells because traditional well monitoring is usually partially open at a specific depth interval or is fully penetrating. Accordingly, conducting an HT survey is typically costly and time-consuming. To tackle these issues, a new multi-level monitoring system (MLMS) for the HT survey was developed using the fiber Bragg grating (FBG) technique. This FBG MLMS could collect the depth-discrete groundwater observations from a fully penetrated 2-inch well. Three field campaigns were conducted to validate the capability of the FBG MLMS for HT surveys. The results show that the accuracy and stability of this MLMS are reliable and that FBG MLMS is beneficial for conducting an HT survey. Specifically, compared to the traditional monitoring well in an injection event, this FBG MLMS can concurrently cause an increase in the number of cross-hole tests several times and collect many more head observations than the standard methods, resulting in the observed flow fields efficiently reaching ergodic conditions and effectively improving the accuracy of the estimated hydraulic heterogeneity. Therefore, the FBG MLMS could be an alternative MLMS for efficiently and economically conducting an HT survey.     

Lateral bearing performance and mechanism of piles in the transition zone due to pit-in-pit excavation

Acta Geotechnica - Excavation types of foundation pits for large-scale or special-purpose urban construction have been more and more complex, and the environmental safety issues caused by pit...     

On a novel approach to forecast sparse rare events: applications to Parkfield earthquake prediction

Natural Hazards - Rare events are plentiful in nature and most of them have devastating consequences on human lives and property. Modeling such events is intrinsically challenging due to their very...     

Numerical simulation and evaluation of Asian dust events observed in Mongolia in spring 2011

The Asian dust forecasting model, Mongolian Asian Dust Aerosol Model (MGLADAM), has been operated by the National Agency for Meteorology and Environmental Monitoring of Mongolia since 2010, for the forecast of Asian dust storms. In order to evaluate the performance of the dust prediction model, we simulated Asian dust events for the period of spring 2011. Simulated features were compared with observations from two sites in the dust source region of the Gobi desert in Mongolia, and in the downstream region in Korea. It was found that the simulated wind speed and friction velocity showed a good correlation with observations at the Erdene site (one of the sites in the Gobi desert). The results show that the model is proficient in the simulation of dust concentrations that are within the same order of magnitude and have similar start and end times, compared with PM₁₀ observed at two monitoring sites in the Gobi regions. Root Mean Square Error (RMSE) of the dust simulation ranges up to 200 μg m^?3 because of the high concentrations in source regions, which is three times higher than that in the downstream region. However, the spatial pattern of dust concentration matches well with dust reports from synoptic observation. In the downwind regions, it was found that the model simluated all reported dust cases successfully. It was also found that the RMSE in the downwind region increased when the model integration time increased, but that in the source regions did not show consistent change. It suggests that MGLADAM has the potential to be used as an operational dust forecasting model for predicting major dust events over the dust source regions as well as predicting transported dust concentrations over the downstream region. However, it is thought that further improvement in the emission estimation is necessary, including accurate predictions in surface and boundary layer meteorology. In the downwind regions, background PM₁₀ concentration is considerably affected by other aerosol species, suggesting that a consideration of anthropogenic pollutants will be required for accurate dust forecasting.     

Potential impacts of northeastern Eurasian snow cover on generation of dust storms in northwestern China during spring

The effects of the northeastern Eurasian snow cover on the frequency of spring dust storms in northwestern China have been examined for the period 1979–2007. Averaged over all 43 stations in northwestern China, a statistically significant relationship has been found between dust-storm frequency (DSF) and Eurasian snow-water equivalent (SWE) during spring: mean DSF of 7.4 and 3.3 days for years of high- and low SWE, respectively. Further analyses reveal that positive SWE anomalies enhance the meridional gradients of the lower tropospheric temperatures and geopotential heights, thereby strengthening westerly jets and zonal wind shear over northwestern China and western Inner Mongolia of China, the regions of major dust sources. The anomalous atmospheric circulation corresponding to the Eurasian SWE anomalies either reinforces or weakens atmospheric baroclinicity and cyclogenesis, according to the sign of the anomaly, to affect the spring DSF. This study suggests that Eurasian SWE anomalies can be an influential factor of spring DSF in northwestern China and western Inner Mongolia of China.     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.     

Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.     

Statistical approach to determine the salinized ground water flow path and hydrogeochemical features around the underground LPG cavern,Korea

The ground water flow path of the coastal area in the Yellow Sea, Korea, was interpreted using both the cross‐correlation analysis of hydraulic properties and the principal component analysis (PCA) of ground water chemistry. Data was obtained from observation wells in the underground liquefied petroleum gas (LPG) cavern constructed in the coastal area of Pyeongtaek. Cross‐correlation results showed that the operating pressure became more influenced on artificial factors for the variation of the groundwater level of the study area (45–66% of correlation coefficient) even though its affecting area was limited to the region with fractures or faults, and also showed that the delay time from the variation of operating pressure to the fluctuation of ground water level were relatively long periods (28–31 days). Three hydrogeochemical events (encrusted cement dissolution, host rock dissolution, and seawater intrusion), which were dominantly influenced on ground water quality, could be induced from the result of PCA. Quantitative evaluation for these events using the mixed equation with principal component scores suggest that the dissolution of encrusted cement materials was the predominant factor (39·0% of the total mixed proportion) to change the chemical composition of the seepage water during the ground water flow from the observation wells to the cavern. Integration of the statistical results also imply that ground water flow and hydrogeochemistry were predominantly affected by artificial factors such as cavern operation pressure and dissolution of encrusted cement materials, which were interdependent factors on the observation wells with high cross‐correlation coefficients and pH. Copyright © 2007 John Wiley & Sons, Ltd.