Atmospheric stability index using radio occultation refractivity profiles

A new stability index based on atmospheric refractivity at ~500 hPa level and surface measurements of temperature, pressure and humidity is formulated. The new index named here as refractivity based lifted index (RLI) is designed to give similar results as traditionally used lifted index derived from radiosonde profiles of temperature, pressure and humidity. The formulation of the stability index and its comparison with the traditional temperature profile based lifted index (LI) is discussed. The index is tested on COSMIC radio occultation derived refractivity profiles over Indian region. The forecast potential of the new index for rainfall on 2°×2° latitude–longitude spatial scale with lead time of 3–24 hours indicate that the refractivity based lifted index works better than the traditional temperature based lifted index for the Indian monsoon region. Decreasing values of RLI tend to give increasing rainfall probabilities.     

Climate Change Studies Using Coupled Model: Land Surface Perspective

Community Climate System Model (CCSM3), a coupled model developed by National Center for Atmospheric Research (NCAR) containing atmosphere, ocean, sea-ice and land processes, simulation have been analysed for suitability in the Indian Monsoon region. A long control run of CCSM3 with constant forcing at every year has been done and the model climatology has been generated using 20 years of simulation. Atmospheric component of the model has been able to capture the large scale phenomena, however, regional monsoon variability not fully captured by the model simulation. A suitable modification in the flux coupler and the convective parameterization process in regional scale certainly improve the atmospheric part of the climate system. Another major component of the climate model is the representation of Land Surface Processes (LSP). A successful inclusion of LSP in climate model must address the issues related to the regional scale variation of the properties of LSP. A proper understanding of land surface processes is very crucial for climate simulations using numerical models. To understand the LSP-monsoon coupling, the offline Community Land Model (CLM), taken from CCSM3 land component, simulation forced with three hourly atmospheric boundary conditions have also been analyzed and compared with the CLM version of coupled CCSM mode. The distribution of surface heat flux in CCSM coupled mode shows some discrepancies compared to the offline CLM. Both the simulation results are compared with existing climatological features and assessment to improve CCSM3 for the regional climate change studies is made.     

Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones

Weather Research and Forecasting (WRF-ARW) model and its three-dimensional variational data assimilation (3D-Var) system are used to investigate the impact of the Quick Scatterometer (QuikSCAT) near surface winds, Special Sensor Microwave/Imager (SSM/I)-derived Total Precipitable Water (TPW), and Meteosat-7-derived Atmospheric Motion Vectors (AMVs) on the track and intensity prediction of tropical cyclones over the North Indian Ocean. The case of tropical cyclone, Gonu (June 2007; Arabian Sea), is first tested and the results show significant improvements particularly due to the assimilation of QuikSCAT winds. Three other cases, cyclone Mala (April 2006; Bay of Bengal), Orissa super cyclone (October 1999; Bay of Bengal), and Very Severe Cyclonic storm (October 1999; Bay of Bengal), are then examined. The prediction of cyclone tracks improved significantly with the assimilation of QuikSCAT winds. The track improvement resulted from the relocation of the initial cyclonic vortices after the assimilation of QuikSCAT wind vectors. After the assimilation of QuikSCAT winds, the mean (for four cyclone cases) track errors for first, second, and third day forecasts are reduced to 72, 101, and 166?km, respectively, from 190, 250, and 381?km of control (without QuikSCAT winds) runs. The assimilation of QuikSCAT winds also shows positive impact on the intensity (in terms of maximum surface level winds) prediction particularly for those cyclones, which are at their initial stages of the developments at the time of data assimilation. The assimilation of SSM/I TPW has significant influence (negative and positive) on the cyclone track. In three of the four cases, the assimilation of the SSM/I TPW resulted in drying of lower troposphere over cyclonic region. This decrease of moisture in TPW assimilation experiment resulted in reduction of cyclonic intensity. In three of the four cyclones, the assimilation of Meteosat-7 AMVs shows negative impact on the track prediction.     

Extreme Learning Machine Based Modeling of Resilient Modulus of Subgrade Soils

This paper investigates the potential of two variants of extreme learning machine based regression approaches in predicting the resilient modulus of cohesive soils. Support vector regression was used to compare the performance of the proposed extreme learning machine based regression approaches. The dataset used in this study was derived from literature and consists of 9 input parameters with a total of 891 cases. For testing, two methods i.e. train/test and tenfold cross validation was used. In case of train and test methods, a total of 594 randomly selected cases were used to train different algorithms and the remaining 297 data were used to test the created models. Correlation coefficient value of 0.991 (root mean square error = 3.47 MPa) was achieved by polynomial kernel based extreme learning machine in comparison to 0.990 and 0.990 (root mean square error = 4.790 and 4.290 MPa) by simple extreme learning machine and radial basis kernel function based support vector regression respectively with test dataset. Comparisons of results with tenfold cross validation also suggest that polynomial kernel based extreme learning machine works well in terms of root mean square error and computational cost with the used dataset. Sensitivity analysis suggests the importance of confining stress and deviator stress in predicting the resilient modulus when using with polynomial kernel based extreme learning machine modeling approach.     

Ground water quality classification by decision tree method in Ardebil region,Iran

A decision tree-based approach is proposed to predict ground water quality based on the United States Salinity Laboratory (USSL) diagram using the data from aquifers in agricultural lands of Ardebil province, northwest of Iran. Several combinations of hydro chemical parameters of groundwater and monthly precipitation with different lag time were considered to find an accurate and economical alternative for groundwater quality classification. The performance evaluation was based on the number of correctly classified instances (CCI) and kappa statistics. The results suggested the suitability of decision tree-based classification approach for the used data sets. The overall average of CCI and kappa statistic for the prediction of groundwater quality classes based on the USSL diagram was 0.88 and 0.83 %, respectively. Principal component analysis (PCA) was also used to determine the important parameters for groundwater quality classification. The results showed that groundwater quality classification by decision tree is more precise and efficient in comparison with PCA. The best alternative could evaluate groundwater quality class with only two parameters: electrical conductivity and cumulative precipitation of 11 months earlier. The developed model is able to predict water quality class by only two variables and this lead to a reduction in the number of variables analyzed on a routine basis, resulting in a significant reduction in laboratory costs and latency times between the sampling moment and the outcome of the laboratory analyses.     

Texture, microstructure and geochemistry of magnetite from the Banduhurang uranium mine, Singhbhum Shear Zone, India — implications for physico-chemical evolution of magnetite mineralization

The Singhbhum Shear Zone in eastern India is one of the largest repositories of uranium and copper in India. Besides uranium and copper, apatite-magnetite mineralization is widespread in this shear zone. This study aims at deciphering the physico-chemical evolution of magnetite mineralization in relation to progressive shearing integrating field relations, micro-textures, structures and compositions of magnetite in the Banduhurang uranium mine. Apatite-magnetite ores occur as discrete patches, tongues, and veins in the strongly deformed, fine grained quartzchlorite schist. Textures and microstructures of magnetite indicate at least three stages of magnetite formation. Coarsegrained magnetite (magnetite-1) with long, rotational, and complex strain fringes, defined by fibrous and elongate quartz, is assigned to a stage of pre-/early-shearing magnetite formation. Medium grained magnetite (magnetite-2), characterized by single non-rotational strain fringe equivalent to the youngest fringe of magnetite-1, grew likely at the mid-/late-stage of shearing. Fine grained magnetite (magnetite-3) is generally devoid of any pressure shadow. This indicates even a much later stage of formation of this magnetite, presumably towards the closing stage of shearing. Some of the magnetite-1 grains are optically heterogeneous with a dark, pitted Cr-Ti-bearing core overgrown by lighter, fresh rim locally containing pyrite, chalcopyrite, and chlorite inclusions. The cores are also locally characterized by high Al and Si content. Homogeneous magnetite-1 is optically and compositionally similar to the overgrowth of heterogeneous magnetite-1. This homogeneous magnetite-1 that grew as separate phase is contemporaneous with the overgrowth on pitted core of heterogeneous magnetite-1. Magnetite-2 is compositionally very similar to homogeneous magnetite-1, but is devoid of sulfide inclusion. Magnetite-3 is generally devoid of any silicate or sulfide inclusion and is most pure with least concentrations of trace/minor elements. The high Al and Si content in some magnetite can be explained by coupled substitution that involves substitution of Si⁴⁺ for Fe³⁺ in the tetrahedral sites and Fe²⁺ for Fe³⁺ in the octahedral sites, with a simple substitution of Al³⁺ for Fe³⁺ in the octahedral sites. The mode of occurrences of apatite-magnetite ores indicates a predominantly hydrothermal origin of most magnetite. However, the Cr-Ti-bearing magnetite-1 cores and inferred mafic nature of the original protolith indicates that some magnetite was inherited from the original igneous rock. We propose that the pre-/early-shearing hydrothermal event of magnetite formation was associated with sulfide mineralization and alteration of existing magmatic magnetite. The second stage of magnetite formation at the mid-/late-stage of shearing was not associated with sulfide formation. Finally, fine-grained compositionally pure magnetite formed at the closing stage of shearing likely due to metamorphism of Fe-rich protolith.     

Variability patterns of the annual frequency and timing of low streamflow days across the United States and their linkage to regional and large‐scale climate

Low‐flow events can cause significant impacts to river ecosystems and water‐use sectors; as such, it is important to understand their variability and drivers. In this study, we characterise the variability and timing of annual total frequency of low‐streamflow days across a range of headwater streams within the continental United States. To quantify this, we use a metric that counts the annual number of low‐flow days below a given threshold, defined as the cumulative dry days occurrence (CDO). First, we identify three large clusters of stream gauge locations using a Partitioning Around Medoids (PAM) clustering algorithm. In terms of timing, results reveal that for most clusters, the majority of low‐streamflow days occur from the middle of summer until early fall, although several locations in Central and Western United States also experience low‐flow days in cold seasons. Further, we aim to identify the regional climate and larger scale drivers for these low‐streamflow days. Regionally, we find that precipitation deficits largely associate with low‐streamflow days in the Western United States, whereas within the Central and Eastern U.S. clusters, high temperature indicators are also linked to low‐streamflow days. In terms of larger scale, we examine sea surface temperature (SST) anomalies, finding that extreme dry years exhibit a high degree of co‐occurrence with different patterns of warmer SST anomalies across the Pacific and Northern Atlantic Oceans. The linkages identified with regional climate and SSTs offer promise towards regional prediction of changing conditions of low‐streamflow events.     

Modelling pile capacity using Gaussian process regression

This paper investigates the potential of a Gaussian process (GP) regression approach to predict the load-bearing capacity of piles. Support vector machines (SVM) and empirical relations were used to compare the performance of the GP regression approach. The first dataset used in this study was derived from actual pile-driving records in cohesion-less soil. Out of a total of 94 pieces of data, 59 were used to train and the remaining 35 data were used to test the created models. A radial basis function and Pearson VII function kernels were used with both GP and SVM. The results from this dataset indicate improved performance by GP regression in comparison to SVM and empirical relations. To validate the performance of the GP regression approach, another dataset consisting of 38 pieces of data was considered. The results from this dataset also suggest improved performance by the Pearson VII function kernel-based GP regression modelling approach in comparison to SVM.     

Measurements of drifter cluster dispersion

Abstract

The motion of clusters of drifters in stratified coastal waters was studied. Wave‐like motion dominated the single‐particle statistics and had length scales larger than the cluster dimensions; consequently it was largely filtered out of motion relative to the cluster centroid. But the stochastic motion causing eddy diffusion seemed to be equally present in both single‐particle motion and motion relative to the cluster centroid. The single‐particle kinetic energy was 10 and 2 times the kinetic energy of motion relative to the centroid in summer and winter, respectively. The relative motion had longer Lagrangian integral time‐scales and smaller Eulerian spatial correlation scales than the single‐particle motion. Integral length scales of relative motion were of 0.1 and 0.2 times the standard deviation of drifter positions about the centroid for summer and winter ensembles, respectively. The y component of ensemble‐averaged relative eddy diffusivity in summer (0.21 m² s^?1) was much larger than that in winter (0.036 m² s^?1) whereas the summer x component (0.066 m² s^?1) was similar to that in winter (0.087 m² s^?1). The dispersion of individual clusters can vary considerably from that expected from the ensemble‐averaged eddy diffusivity. The cluster dispersion was intermittent, with long quiescent periods of gradual cluster deformation and short events causing rapid cluster deformation. In quiescent periods the centroid motion and velocity gradients were consistent with the kinematics of internal waves.