Independent component regression for predicting the responses of biaxial base‐isolated buildings

This paper presents a regression model to predict the base displacement responses of biaxial base‐isolated buildings using independent component analysis. The model proposed utilizes multiple ground motion intensity measures from North American and Japanese earthquakes as inputs, and transforms them into an independent component space using independent component regression (ICR). Unlike other latent variable methods, such as principal component regression, ICR does not readily allow for dimensionality reduction of the components that do not contribute significantly to the explained variance of the original data set. Hence, a whitening‐step to transform the correlated variables into uncorrelated ones is introduced prior to performing ICR. Prediction results are presented and compared with the simulation results for two building models with increasing degree of complexity. The results show that the model based on ICR results in good estimates for the base displacement responses, and the standard errors remain relatively small and constant across a range of isolation periods. Copyright © 2009 John Wiley & Sons, Ltd.     

Numerical Investigation of the Influence of Mineral Dust on the Tropospheric Chemistry of East Asia

An investigation of the influence of mineral dust ontrace gas cycles in the troposphere is carried out inthis study. A 3D regional scale atmospheric chemistrymodel (STEM-III) which includes aerosol processes isused for the numerical simulations for May 1987.Heterogeneous interactions between gaseous species(SO₂, N₂O₅, HNO₃, HO₂andH₂O₂) and the dust particles are considered.Emissions of dust behind convective cold fronts aremodeled. The transport and distribution of mineraldust predicted from the model is compared withsatellite measurements (aerosol index from TOMS). Themodel is shown to capture the synoptic variability inthe observed aerosol index. Calculations show twomajor dust events in May 1987, during which thedust levels close to the source reach more than500 g/m³. The transport of dust is mostlyrestricted towards the north, with the net continentaloutflow of 6 Tg for the entire month. Results showthat the presence of mineral aerosol can greatlyimpact sulfate and nitrate distributions. Averagedover the month of May, the presence of dust isestimated to increase particulate sulfate and nitratelevels in east Asia by 40%. Furthermore, the sulfateand nitrate on the dust particles are predicted to beassociated with the coarse mode (3–5 m particlediameter), consistent with observations over Japan.The influence of mineral dust on the photochemicaloxidant cycle is also investigated. For the entiremonth, a5–10% decrease in boundary layer ozone ispredicted by the model closer to regions of higherdust levels. The ratio of nitric acid to NO_x overmarine regions is reduced by a factor between 1 and 2in the boundary layer to more than 2 in the freetroposphere as a result of aerosol processes.     

Mineralogical and geochemical patterns of urban surface soils, the example of Pforzheim, Germany

This study presents a combined geochemical and mineralogical survey of urban surface soils. Many studies on urban soils are restricted to purely chemical surveys in order to investigate soil pollution caused by anthropogenic activities such as traffic, heating, industrial processing, waste disposal and many more. In environmental studies, chemical elements are often distinguished as lithogenic and anthropogenic elements. As a novel contribution to those studies, the authors combined the analysis of a broad set of chemical elements with the analysis of the main mineralogical phases. The semi-quantification of mineralogical phases supported the assignment of groups of chemical elements to lithogenic or anthropogenic origin. Minerals are important sinks for toxic elements. Thus, knowledge about their distribution in soils is crucial for the assessment of the environmental hazards due to pollution of urban soils. In Pforzheim, surface soils (0–5 cm depth) from various land use types (forest, agriculture, urban green space, settlement areas of various site densities) overlying different geological units (clastic and chemical sediments) were investigated. Urban surface soils of Pforzheim reflect to a considerable degree the mineral and chemical composition of parent rocks. Irrespective of the parent rocks, elevated concentrations of heavy metals (Zn, Cu, Pb, Sn, Ag) were found in soils throughout the whole inner urban settlement area of Pforzheim indicating pollution. These pollutants will tend to accumulate in inner urban surface soils according to the available adsorption capacity, which is normally higher in soils overlying limestone than in soils overlying sandstone. However, inner urban surface soils overlying sandstone show elevated concentrations of carbonates, phyllo-silicates and Fe and elevated pH values compared with forest soils overlying sandstone. Thus, in comparison to forest soils overlying sandstones, inner urban soils overlying sandstone affected by pollution concurrently possess elevated concentrations of mineral phases typically providing relatively high adsorption capacities for heavy metals.     

Shape Classification of AltiKa 40-Hz Waveforms using Linear Discriminant Analysis and Bayes Decision Rule in the Gujarat Coastal Region

Shape classification of the 40-Hz waveforms obtained by the recently launched AltiKa satellite has been attempted in the paper. Since retracking algorithms suitable for altimeter return echoes based on Brown model are not applicable for the echoes from coastal ocean, specific algorithms are to be devised for such echoes. In the coastal ocean, waveforms display a wide variety of shapes due to varying coastline geometry, and topography. Hence, a proper classification strategy is required for classifying the waveforms into various categories so that suitable retracker could be applied to each category for retrieving the oceanic parameters. The algorithm consists of three steps: feature selection, linear discriminant analysis, and Bayesian classifier. The classification algorithm has been applied to the waveforms in the close proximity of Gujarat coast. Independent validation has been done near the eastern coast of India. Confusion matrices obtained for both the coasts are quite encouraging. Individual examples of classification have been provided for the purpose of illustration.     

Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models

Changes in climate are expected to lead to changes in the characteristics extreme rainfall frequency and intensity. In this study, we propose an integrated approach to explore potential changes in intensity-duration-frequency (IDF) relationships. The approach incorporates uncertainties due to both the short simulation periods of regional climate models (RCMs) and the differences in IDF curves derived from multiple RCMs in the North American Regional Climate Change Assessment Program (NARCCAP). The approach combines the likelihood of individual RCMs according to the goodness of fit between the extreme rainfall intensities from the RCMs’ historic runs and those from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data set and Bayesian model averaging (BMA) to assess uncertainty in IDF predictions. We also partition overall uncertainties into within-model uncertainty and among-model uncertainty. Results illustrate that among-model uncertainty is the dominant source of the overall uncertainty in simulating extreme rainfall for multiple locations in the U.S., pointing to the difficulty of predicting future climate, especially extreme rainfall regimes. For all locations a more intense extreme rainfall occurs in future climate; however the rate of increase varies among locations.     

Climatology of the Eastern Arabian Sea during the last glacial cycle reconstructed from paired measurement of foraminiferal δO and Mg/Ca

Paired measurements of Mg/Ca and δ¹⁸O of Globigerenoides sacculifer from an Eastern Arabian Sea (EAS) sediment core indicate that sea-surface temperature (SST) varied within 2°C and sea-surface salinity within 2 psu during the last 100 ka. SST was coldest (∼ 27°C) during Marine Isotope Stage (MIS) 4 and 2. Sea-surface salinity was highest (∼ 37.5 psu) during most of the last glacial period (∼ 60–18 ka), concurrent with increased δ¹⁸O_G.sacculifer and C/N ratios of organic matter and indicative of sustained intense winter monsoons. SST time series are influenced by both Greenland and Antarctic climates. However, the sea-surface salinity time series and the deglacial warming in the SST record (beginning at ∼ 18 ka) compare well with the LR04 benthic δ¹⁸O-stack and Antarctic temperatures. This suggests a teleconnection between the climate in the Southern Hemisphere and the EAS. Therefore, the last 100-ka variability in EAS climatology appears to have evolved in response to a combination of global climatic forcings and regional monsoons. The most intense summer monsoons within the Holocene occurred at ∼ 8 ka and are marked by SST cooling of ∼ 1°C, sea-surface salinity decrease of 0.5 psu, and δ¹⁸O_G.sacculifer decrease of 0.2‰.     

Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data

In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean.