Classification of Weather Indices on Secured Computational Grid

Abstract

Grid promises significant advances in support of e-science application, whilst much of the predicted infrastructure is still under development. Deployment of these applications across the grid continues requiring a high level of expertise and a significant amount of efforts, mainly due to overall complexity of the grid. Large scale parallel scientific applications often require computational and data grids to obtain large compute and data resources necessary for execution regardless of heterogeneity, occurrences of faults and complexity. This research paper reveals the grid enabled computing capabilities for climate impact modeling and change detection facilitating for rainfall prediction. The large dataset utilized in computations are Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) data. It presents the basic field of air-sea interaction parameters and covers the period July 1987 to December 2005. In order to assess the valuable user friendliness of grid computing to a large community of researchers who need to perform large scale computation, we deployed an application developed for rainfall prediction on NirmaGrid Testbed.