A Numerical Study on the Screening of Blast-Induced Waves for Reducing Ground Vibration

Blasting is often a necessary part of mining and construction operations, and is the most cost-effective way to break rock, but blasting generates both noise and ground vibration. In urban areas, noise and vibration have an environmental impact, and cause structural damage to nearby structures. Various wave-screening methods have been used for many years to reduce blast-induced ground vibration. However, these methods have not been quantitatively studied for their reduction effect of ground vibration. The present study focused on the quantitative assessment of the effectiveness in vibration reduction of line-drilling as a screening method using a numerical method. Two numerical methods were used to analyze the reduction effect toward ground vibration, namely, the “distinct element method” and the “non-linear hydrocode.” The distinct element method, by particle flow code in two dimensions (PFC 2D), was used for two-dimensional parametric analyses, and some cases of two-dimensional analyses were analyzed three-dimensionally using AUTODYN 3D, the program of the non-linear hydrocode. To analyze the screening effectiveness of line-drilling, parametric analyses were carried out under various conditions, with the spacing, diameter of drill holes, distance between the blasthole and line-drilling, and the number of rows of drill holes, including their arrangement, used as parameters. The screening effectiveness was assessed via a comparison of the vibration amplitude between cases both with and without screening. Also, the frequency distribution of ground motion of the two cases was investigated through fast Fourier transform (FFT), with the differences also examined. From our study, it was concluded that line-drilling as a screening method of blast-induced waves was considerably effective under certain design conditions. The design details for field application have also been proposed.     

Direct and semi-direct radiative effects of anthropogenic aerosols in the Western United States: Seasonal and geographical variations according to regional climate characteristics

The direct and semi-direct radiative effects of anthropogenic aerosols on the radiative transfer and cloud fields in the Western United States (WUS) according to seasonal aerosol optical depth (AOD) and regional climate are examined using a regional climate model (RCM) in conjunction with the aerosol fields from a GEOS-Chem chemical-transport model (CTM) simulation. The two radiative effects cannot be separated within the experimental design in this study, thus the combined direct- and semi-direct effects are called radiative effects hereafter. The CTM shows that the AOD associated with the anthropogenic aerosols is chiefly due to sulfates with minor contributions from black carbon (BC) and that the AOD of the anthropogenic aerosol varies according to local emissions and the seasonal low-level winds. The RCM-simulated anthropogenic aerosol radiative effects vary according to the characteristics of regional climate, in addition to the AOD. The effects on the top of the atmosphere (TOA) outgoing shortwave radiation (OSRT) range from ?0.2?Wm^?2 to ?1?Wm^?2. In Northwestern US (NWUS), the maximum and minimum impact of anthropogenic aerosols on OSRT occurs in summer and winter, respectively, following the seasonal AOD. In Arizona-New Mexico (AZNM), the effect of anthropogenic sulfates on OSRT shows a bimodal distribution with winter/summer minima and spring/fall maxima, while the effect of anthropogenic BC shows a single peak in summer. The anthropogenic aerosols affect surface insolation range from ?0.6?Wm^?2 to ?2.4?Wm^?2, with similar variations found for the effects on OSRT except that the radiative effects of anthropogenic BC over AZNM show a bimodal distribution with spring/fall maxima and summer/winter minima. The radiative effects of anthropogenic sulfates on TOA outgoing longwave radiation (OLR) and the surface downward longwave radiation (DLRS) are notable only in summer and are characterized by strong geographical contrasts; the summer OLR in NWUS (AZNM) is reduced (enhanced) by 0.52?Wm^?2 (1.14?Wm^?2). The anthropogenic sulfates enhance (reduce) summer DLRS by 0.2?Wm^?2 (0.65?Wm^?2) in NWUS (AZNM). The anthropogenic BC affect DLRS noticeably only in AZNM during summer. The anthropogenic aerosols affect the cloud water path (CWP) and the radiative transfer noticeably only in summer when convective clouds are dominant. Primarily shortwave-reflecting anthropogenic sulfates decrease and increase CWP in AZNM and NWUS, respectively, however, the shortwave-absorbing anthropogenic BC reduces CWP in both regions. Due to strong feedback via convective clouds, the radiative effects of anthropogenic aerosols on the summer radiation field are more closely correlated with the changes in CWP than the AOD. The radiative effect of the total anthropogenic aerosols is dominated by the anthropogenic sulfates that contribute more than 80% of the total AOD associated with the anthropogenic aerosols.     

Recent and future sea surface temperature trends in tropical pacific warm pool and cold tongue regions

Using coral data, sea surface temperature (SST) reanalysis data, and Climate Model Intercomparison Project III (CMIP3) data, we analyze 20th-century and future warm pool and cold tongue SST trends. For the last 100?years, a broad La Nina-like SST trend, in which the warming trend of the warm pool SST is greater than that of the cold tongue SST, has appeared in reanalysis SST data sets, 20C scenario experiments of the CMIP3 data and less significantly in coral records. However, most Coupled General Circulation Models subjected to scenarios of future high greenhouse gas concentrations produce larger SST warming trends in cold tongues than in warm pools, resembling El Nino-like SST patterns. In other words, warmer tropical climate conditions correspond to stronger El Nino-like response. Heat budget analyses further verify that warmer tropical climates diminish the role of the ocean’s dynamic thermostat, which currently regulates cold tongue temperatures. Therefore, the thermodynamic thermostat, whose efficiency depends on the mean temperature, becomes the main regulator (particularly via evaporative cooling) of both warm pool and cold tongue temperatures in future warm climate conditions. Thus, the warming tendency of the cold tongue SST may lead that of the warm pool SST in near future.     

Regional adjustment of emission strengths via four dimensional data assimilation

The Four-Dimensional Data Assimilation was performed to evaluate source emission strengths over the United States. The USEPA Models-3 system (CMAQ/MM5/SMOKE) and ridge regression are used as the forward and inverse models, respectively. The continental US is divided into six regions, and data assimilation is performed for each region in July 2001 and January 2002. In addition, two separate scaling factors are calculated for weekdays and weekends. Results show that base emissions for CO and SO₂ sources are relatively accurate. Base emissions for PEC source are overestimated 100%, but those for POA source are underestimated up to 70% when compared with the adjusted emissions. Emissions for NH₃, NO _x , and PMFINE sources are relatively accurate in July 2001, but those in January 2002 are around 100% higher than the adjusted emissions. Base VOC emissions in July 2001 are similar to the adjusted emissions but those in January 2002 are underestimated up to 70% when compared with the adjusted emissions. Though the emission adjustment itself improves the overall air quality model performance, a better improvement is expected with the modification of speciation profiles and temporal allocations in the Models-3 system, as well.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

Retrieval of outgoing longwave radiation from COMS narrowband infrared imagery

Hourly outgoing longwave radiation(OLR) from the geostationary satellite Communication Oceanography Meteorological Satellite(COMS) has been retrieved since June 2010. The COMS OLR retrieval algorithms are based on regression analyses of radiative transfer simulations for spectral functions of COMS infrared channels. This study documents the accuracies of OLRs for future climate applications by making an intercomparison of four OLRs from one single-channel algorithm(OLR12.0using the 12.0 μm channel) and three multiple-channel algorithms(OLR10.8+12.0using the 10.8 and 12.0 μm channels; OLR6.7+10.8using the 6.7 and 10.8 μm channels; and OLR All using the 6.7, 10.8, and 12.0 μm channels). The COMS OLRs from these algorithms were validated with direct measurements of OLR from a broadband radiometer of the Clouds and Earth's Radiant Energy System(CERES) over the full COMS field of view [roughly(50°S–50°N, 70°–170°E)] during April 2011.Validation results show that the root-mean-square errors of COMS OLRs are 5–7 W m-2, which indicates good agreement with CERES OLR over the vast domain. OLR6.7+10.8and OLR All have much smaller errors(～ 6 W m-2) than OLR12.0and OLR10.8+12.0(～ 8 W m-2). Moreover, the small errors of OLR6.7+10.8and OLR All are systematic and can be readily reduced through additional mean bias correction and/or radiance calibration. These results indicate a noteworthy role of the6.7 μm water vapor absorption channel in improving the accuracy of the OLRs. The dependence of the accuracy of COMS OLRs on various surface, atmospheric, and observational conditions is also discussed.     

Projections of high resolution climate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 1: surface air temperature

We projected surface air temperature changes over South Korea during the mid (2026-2050) and late (2076-2100) 21st century against the current climate (1981-2005) using the simulation results from five regional climate models (RCMs) driven by Hadley Centre Global Environmental Model, version 2, coupled with the Atmosphere- Ocean (HadGEM2-AO), and two ensemble methods (equal weighted averaging, weighted averaging based on Taylor’s skill score) under four Representative Concentration Pathways (RCP) scenarios. In general, the five RCM ensembles captured the spatial and seasonal variations, and probability distribution of temperature over South Korea reasonably compared to observation. They particularly showed a good performance in simulating annual temperature range compared to HadGEM2-AO. In future simulation, the temperature over South Korea will increase significantly for all scenarios and seasons. Stronger warming trends are projected in the late 21st century than in the mid-21st century, in particular under RCP8.5. The five RCM ensembles projected that temperature changes for the mid/late 21st century relative to the current climate are +1.54°C/+1.92°C for RCP2.6, +1.68°C/+2.91°C for RCP4.5, +1.17°C/+3.11°C for RCP6.0, and +1.75°C/+4.73°C for RCP8.5. Compared to the temperature projection of HadGEM2-AO, the five RCM ensembles projected smaller increases in temperature for all RCP scenarios and seasons. The inter-RCM spread is proportional to the simulation period (i.e., larger in the late-21st than mid-21st century) and significantly greater (about four times) in winter than summer for all RCP scenarios. Therefore, the modeled predictions of temperature increases during the late 21st century, particularly for winter temperatures, should be used with caution.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.