Impact of satellite observed microwave SST on the simulation of tropical cyclones

The impact of realistic representation of sea surface temperature (SST) on the numerical simulation of track and intensity of tropical cyclones formed over the north Indian Ocean is studied using the Weather Research and Forecast (WRF) model. We have selected two intense tropical cyclones formed over the Bay of Bengal for studying the SST impact. Two different sets of SSTs were used in this study: one from TRMM Microwave Imager (TMI) satellite and other is the weekly averaged Reynold's SST analysis from National Center for Environmental Prediction (NCEP). WRF simulations were conducted using the Reynold's and TMI SST as model boundary condition for the two cyclone cases selected. The TMI SST which has a better temporal and spatial resolution showed sharper gradient when compared to the Reynold's SST. The use of TMI SST improved the WRF cyclone intensity prediction when compared to that using Reynold's SST for both the cases studied. The improvements in intensity were mainly due to the improved prediction of surface latent and sensible heat fluxes. The use of TMI SST in place of Reynold's SST improved cyclone track prediction for Orissa super cyclone but slightly degraded track prediction for cyclone Mala. The present modeling study supports the well established notion that the horizontal SST gradient is one of the major driving forces for the intensification and movement of tropical cyclones over the Indian Ocean.

Multi-spectral satellite image classification using Glowworm Swarm Optimization

This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.

Minor mergers and their impact on the kinematics of old and young stellar populations in disk galaxies

By means of N-body simulations we investigate the impact of minor mergers on the angular momentum and dynamical properties of the merger remnant. Our simulations cover a range of initial orbital characteristics and gas-to-stellar mass fractions (from 0 to 20%), and include star formation and supernova feedback. We confirm and extend previous results by showing that the specific angular momentum of the stellar component always decreases independently of the orbital parameters or morphology of the satellite, and that the decrease in the rotation velocity of the primary galaxy is accompanied by a change in the anisotropy of the orbits. However, the decrease affects only the old stellar population, and not the new population formed from gas during the merging process. This means that the merging process induces an increasing difference in the rotational support of the old and young stellar components, with the old one lagging with respect to the new. Even if our models are not intended specifically to reproduce the Milky Way and its accretion history, we find that, under certain conditions, the modeled rotational lag found is compatible with that observed in the Milky Way disk, thus indicating that minor mergers can be a viable way to produce it. The lag can increase with the vertical distance from the disk midplane, but only if the satellite is accreted along a direct orbit, and in all cases the main contribution to the lag comes from stars originally in the primary disk rather than from stars in the satellite galaxy. We also discuss the possibility of creating counter-rotating stars in the remnant disk, their fraction as a function of the vertical distance from the galaxy midplane, and the cumulative effect of multiple mergers on their creation.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.