A Diffusion-Based Processor Reallocation Strategy for Tracking Multiple Dynamically Varying Weather Phenomena

Many meteorological phenomena occur at different locations simultaneously. These phenomena vary temporally and spatially. It is essential to track these multiple phenomena for accurate weather prediction. Efficient analysis require high-resolution simulations which can be conducted by introducing finer resolution nested simulations, nests at the locations of these phenomena. Simultaneous tracking of these multiple weather phenomena requires simultaneous execution of the nests on different subsets of the maximum number of processors for the main weather simulation. Dynamic variation in the number of these nests require efficient processor reallocation strategies. In this paper, we have developed strategies for efficient partitioning and repartitioning of the nests among the processors. As a case study, we consider an application of tracking multiple organized cloud clusters in tropical weather systems. We first present a parallel data analysis algorithm to detect such clouds. We have developed a tree-based hierarchical diffusion method which reallocates processors for the nests such that the redistribution cost is less. We achieve this by a novel tree reorganization approach. We show that our approach exhibits up to 25% lower redistribution cost and 53% lesser hop-bytes than the processor reallocation strategy that does not consider the existing processor allocation.

On Selection of Features for Regional Hydrologic Studies

Feature selection is an important first step in regional hydrologic studies (RHYS). Over the past few decades, advances in data collection facilities have resulted in development of data archives on a variety of hydro-meteorological variables that may be used as features in RHYS. Currently there are no established procedures for selecting features from such archives. Therefore, hydrologists often use subjective methods to arrive at a set of features. This may lead to misleading results. To alleviate this problem, a probabilistic clustering method for regionalization is presented to determine appropriate features from the available dataset. The effectiveness of the method is demonstrated by application to regionalization of watersheds in conterminous United States for low flow frequency analysis. Plausible homogeneous regions that are formed by using the proposed clustering method are compared with those from conventional methods of regionalization using L-moment based homogeneity tests. Results show that the proposed methodology is promising for RHYS.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

Remote sensing applications in water resources

With the introduction of the earth observing satellites, remote sensing has become an important tool in analyzing the Earth's surface characteristics, and hence in supplying valuable information necessary for the hydrologic analysis. Due to their capability to capture the spatial variations in the hydro-meteorological variables and frequent temporal resolution sufficient to represent the dynamics of the hydrologic processes, remote sensing techniques have significantly changed the water resources assessment and management methodologies. Remote sensing techniques have been widely used to delineate the surface water bodies, estimate meteorological variables like temperature and precipitation, estimate hydrological state variables like soil moisture and land surface characteristics, and to estimate fluxes such as evapotranspiration. Today, near-real time monitoring of flood, drought events, and irrigation management are possible with the help of high resolution satellite data. This paper gives a brief overview of the potential applications of remote sensing in water resources.

Coupled polynomial field approach for elimination of flexure and torsion locking phenomena in the Timoshenko and Euler-Bernoulli curved beam elements

The curvature related locking phenomena in the out-of-plane deformation of Timoshenko and Euler-Bernoulli curved beam elements are demonstrated and a novel approach is proposed to circumvent them. Both flexure and Torsion locking phenomena are noticed in Timoshenko beam and torsion locking phenomenon alone in Euler-Bernoulli beam. Two locking-free curved beam finite element models are developed using coupled polynomial displacement field interpolations to eliminate these locking effects. The coupled polynomial interpolation fields are derived independently for Timoshenko and Euler-Bernoulli beam elements using the governing equations. The presented of penalty terms in the couple displacement fields incorporates the flexure-torsion coupling and flexure-shear coupling effects in an approximate manner and produce no spurious constraints in the extreme geometric limits of flexure, torsion and shear stiffness. the proposed couple polynomial finite element models, as special cases, reduce to the conventional Timoshenko beam element and Euler-Bernoulli beam element, respectively. These models are shown to perform consistently over a wide range of flexure-to-shear (EI/GA) and flexure-to-torsion (EI/GJ) stiffness ratios and are inherently devoid of flexure, torsion and shear locking phenomena. The efficacy, accuracy and reliability of the proposed models to straight and curved beam applications are demonstrated through numerical examples. (C) 2012 Elsevier B.V. All rights reserved.

Regionalization of extreme rainfall in India

Regionalization of extreme rainfall is useful for various applications in hydro-meteorology. There is dearth of regionalization studies on extreme rainfall in India. In this perspective, a set of 25 regions that are homogeneous in 1-, 2-, 3-, 4- and 5-day extreme rainfall is delineated based on seasonality measure of extreme rainfall and location indicators (latitude, longitude and altitude) by using global fuzzy c-means (GFCM) cluster analysis. The regions are validated for homogeneity in L-moment framework. One of the applications of the regions is in arriving at quantile estimates of extreme rainfall at sparsely gauged/ungauged locations using options such as regional frequency analysis (RFA). The RFA involves use of rainfall-related information from gauged sites in a region as the basis to estimate quantiles of extreme rainfall for target locations that resemble the region in terms of rainfall characteristics. A procedure for RFA based on GFCM-delineated regions is presented and its effectiveness is evaluated by leave-one-out cross validation. Error in quantile estimates for ungauged sites is compared with that resulting from the use of region-of-influence (ROI) approach that forms site-specific regions exclusively for quantile estimation. Results indicate that error in quantile estimates based on GFCM regions and ROI are fairly close, and neither of them is consistent in yielding the least error over all the sites. The cluster analysis approach was effective in reducing the number of regions to be delineated for RFA.

Waxing and waning of observed extreme annual tropical rainfall

We begin by providing observational evidence that the probability of encountering very high and very low annual tropical rainfall has increased significantly in the most recent decade (1998-present) compared with the preceding warming era (1979-1997). These changes over land and ocean are spatially coherent and comprise a rearrangement of very wet regions and a systematic expansion of dry zones. While the increased likelihood of extremes is consistent with a higher average temperature during the pause (compared with 1979-1997), it is important to note that the periods considered are also characterized by a transition from a relatively warm to a cold phase of the El Nino Southern Oscillation (ENSO). To probe the relation between contrasting phases of ENSO and extremes in accumulation further, a similar comparison is performed between 1960 and 1978 (another extended cold phase of ENSO) and the aforementioned warming era. Though limited by land-only observations, in this cold-to-warm transition, remarkably, a near-exact reversal of extremes is noted both statistically and geographically. This is despite the average temperature being higher in 1979-1997 compared with 1960-1978. Taking this evidence together, we propose that there is a fundamental mode of natural variability, involving the waxing and waning of extremes in accumulation of global tropical rainfall with different phases of ENSO.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Optimal scheduling of hydro-thermal power systems under shortage

The problem of optimal scheduling of the generation of a hydro-thermal power system that is faced with a shortage of energy is studied. The deterministic version of the problem is first analyzed, and the results are then extended to cases where the loads and the hydro inflows are random variables.

Quantum-Ohmic Resistance Fluctuation In Disordered Conductors - An Invariant Imbedding Approach

It is now well known that in extreme quantum limit, dominated by the elastic impurity scattering and the concomitant quantum interference, the zero-temperature d.c. resistance of a strictly one-dimensional disordered system is non-additive and non-self-averaging. While these statistical fluctuations may persist in the case of a physically thin wire, they are implicitly and questionably ignored in higher dimensions. In this work, we have re-examined this question. Following an invariant imbedding formulation, we first derive a stochastic differential equation for the complex amplitude reflection coefficient and hence obtain a Fokker-Planck equation for the full probability distribution of resistance for a one-dimensional continuum with a Gaussian white-noise random potential. We then employ the Migdal-Kadanoff type bond moving procedure and derive the d-dimensional generalization of the above probability distribution, or rather the associated cumulant function –‘the free energy’. For d=3, our analysis shows that the dispersion dominates the mobilitly edge phenomena in that (i) a one-parameter B-function depending on the mean conductance only does not exist, (ii) an approximate treatment gives a diffusion-correction involving the second cumulant. It is, however, not clear whether the fluctuations can render the transition at the mobility edge ‘first-order’. We also report some analytical results for the case of the one dimensional system in the presence of a finite electric fiekl. We find a cross-over from the exponential to the power-low length dependence of resistance as the field increases from zero. Also, the distribution of resistance saturates asymptotically to a poissonian form. Most of our analytical results are supported by the recent numerical simulation work reported by some authors.

Fluctuation phenomena in electrochemistry part I. The formalism

A unified approach to the problem of electrochemical fluctuations is presented. On the basis of the Langevin procedure, primary noise sources are introduced in the basic phenomenological equations and a discussion of the secondary noise sources arising in the expressions for the power spectra of currents is given.

Fluctuation phenomena in electrochemistry part II. Modelling the noise sources

A discussion of the modelling of the primary and secondary noise sources introduced in the formalism of fluctuation phenomena in a previous report is presented. It is illustrated that the generalisation of the modelling of noise sources in mass transport as given by Tyagai is limited in its applicability. A general procedure for the same is discussed in detail.

Mesospheric influences on linkages between solar activity and lower atmospheric phenomena

Analyses of rocket data at mid- and high-latitude locations over the American Continent show a solar activity-dependent mesospheric heating effect in the 60 to 90 km altitude region. A study of the altitude dependence of the effect shows that the heating and associated processes propagating downwards through the mesosphere do not cause discernible effects, below the 50 to 60 km layer. At Thumba, a significant short-term heating effect attributable to varying solar ultraviolet fluxes causing variable heating of atmospheric ozone is observed. This effect does not seem to propagate downwards into the upper stratosphere.

A hydro-wood net-energy approach to hydro project design

An integrated approach to energy planning, when applied to large hydroelectric projects, requires that the energy-opportunity cost of the land submerged under the reservoir be incorporated into the planning methodology. Biomass energy lost from the submerged land has to be compared to the electrical energy generated, for which we develop four alternative formulations of the net-energy function. The design problem is posed as an LP problem and is solved for two sites in India. Our results show that the proposed designs may not be viable in net-energy terms, whereas a marginal reduction in the generation capacity could lead to an optimal design that gives substantial savings in the submerged area. Allowing seasonal variations in the hydroelectric generation capacity also reduces the reservoir size. A mixed hydro-wood generation system is then examined and is found to be viable.

Sequence generator for periodic sampling of transient phenomena

A simple instrument that can provide a sequence of timed pulses for first initiating a transient process and then enabling sampling and recording periodically during the course of the transient event is described. The time delay between the first of these sampling pulses and the start of the transient event is adjustable. This sequence generator has additional features that make it ideal for use in acquiring the waveforms when a storage oscilloscope is used as the recording device. For avoiding the clutter caused by many waveforms being recorded at the same place on an oscilloscope screen such features as displacements of waveforms in the X and Y directions and trace blanking at places where the waveform is not required, have been incorporated. This sequence generator has been employed to acquire a sequence of Raman scattered radiation signals from an adiabatically expanding saturated vapour probed by a flashlamp-pumped dye laser.