No abstract

No abstract

a b s t r a c tThis work presents a regularization technique applied to an inverse radiative transfer problem formulated as a finite dimensional optimization problem and solved by a hybridization of the ant colony optimization (ACO) with the Levenberg-Marquardt method. It is considered a one-dimensional isotropically-scattering medium with finite optical thickness, space dependent scattering albedo and plane-parallel geometry. The direct radiative transfer problem models transmission of radiation through this medium by the linear version of the Boltzmann equation considering polar angle discretization and azimuthal symmetry. A discrete ordinates method combined with the finite difference method is employed to solve it. Reconstruction of the albedo profile is performed from the intensities of the polar-discretized emergent radiation acquired with external detectors, using a recently proposed regularization technique. Since smooth albedo profiles are expected, such information is used in a new generation of ants in order to perform a pre-selection of the ants. This scheme can be viewed as a kind of pre-regularization in face of the reconstructed profiles that are smooth and show good agreement with the exact solution. In addition, this scheme saves processing time as fewer candidate solutions (ants) are evaluated. Noiseless and noisy data of the emergent radiation intensities were employed in the reconstructions.

Enhancing the quality of weather and climate forecasts are central scientific research objectives worldwide. However, simulations of the atmosphere, usually demand high processing power and large storage resources. In this context, we present the GBRAMS project, that applies grid computing to speed up the generation of a regional model climatology for Brazil. A grid infrastructure was built to perform long-term integrations of a mesoscale numerical model (BRAMS), managing a queue of up to nine independent jobs submitted to three clusters spread over Brazil. Three distinct middlewares, Globus Toolkit, OurGrid and OAR/CIGRI, were compared in their ability to manage these jobs, and results on the usage of each node of the grid are provided. We analyze the impact of the resulted climatology in the accuracy of climate forecast, showing model bias removal which indicates correctness of the generated climatology. Our central contribution are how to use grid computing to speed-up climatology generation and the middleware impact on this enterprise.

No abstract

No abstract

No abstract

Lagrangian dispersion models have shown to be effective and reliable tools for simulating the airborne pollutant dispersion. However, the main drawback for their use as regulatory models is the associated high computational costs. Consequently, in this paper a parallel version of a Lagrangian particle model-LAMBDA-is developed using the MPI message passing communication library. Performance tests were executed in a distributed memory parallel machine, a multicomputer based on IA-32 architecture. Portions of the pollutant in the air emitted from its source are simulated as fictitious particles whose trajectories evolve under stochastic forcing. This yields independent evolution equations for each particle of the model that can be computed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.