Roberto P. Souto

Fuzzy ant colony optimization for estimating chlorophyll concentration profile in offshore sea water

The determination of some inherent optical properties can be addressed by estimating the ocean chlorophyll concentration, if bio-optical models can be applied – such as for the offshore sea water. This inverse problem can be formulated as an optimization problem and iteratively solved, where the radiative transfer equation is the direct model. An objective function is given by the square difference between computed and in situ experimental radiances at every iteration. In the standard ant colony optimization (ACO), the pheromone is reinforced only on the best ant of the population. The fuzzy strategy consists in including additional pheromone quantity on the best ant, but a small pheromone quantity is also spread over the other solutions close to the best one. Test results show that the fuzzy-ACO produces better inverse solutions.

Processing Mesoscale Climatology in a Grid Environment

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.

Reconstruction of vertical profiles of the absorption and scattering coefficients from multispectral radiances

An inverse hydrologic optics problem is solved using a recent intrinsic regularization scheme that is coupled to a standard Ant Colony System (ACS). The regularization scheme pre-selects candidate solutions based on their smoothness, quantified by a Tikhonov norm. The Chlorophyll profile is reconstructed from upwelling radiance experimental measurements in the ocean water using 10 wavelengths (multispectral approach) and upward polar directions. Vertical profiles of the absorption and scattering coefficients are estimated from the Chlorophyll profile by means of bio-optical models. The inverse problem is formulated as an optimization problem and iteratively solved by an ACS using the radiative transfer equation as direct model. An objective function is given by the square difference between computed and experimental radiances at every iteration. Each candidate solution corresponds to a discrete Chlorophyll profile. The radiative transfer equation is solved using the Laplace transform discrete ordinate (LTSN) method. A parallel implementation of the Ant Colony System is used and executed in a distributed memory machine.

A parallel implementation of the LTSn method for a radiative transfer problem

A radiative transfer solver that implements the LTSn method was optimized and parallelized using the MPI message passing communication library. Timing and profiling information was obtained for the sequential code in order to identify performance bottlenecks. Performance tests were executed in a distributed memory parallel machine, a multicomputer based on IA-32 architecture. The radiative transfer equation was solved for a cloud test case to evaluate the parallel performance of the LTSn method. The LTSn code includes spatial discretization of the domain and Fourier decomposition of the radiances leading to independent azimuthal modes. This yields an independent radiative transfer equation for each mode that can be executed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.

Parallel implementation of a Lagrangian stochastic model for pollution dispersion

Pollutant dispersion models in the atmosphere can describe by Eulerian or Lagrangian approaches. Lagrangian models belong to the class of Monte Carlo methods. This type of method is very flexible, solving more complex problems, however this computational cost is greater than Eulerian models, as it is well established in the atmospheric pollutant and nuclear engineering communities. A parallel version of the 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 are considered particles emitted from a pollutant source, evolving under stochastic forcing. This yields independent evolution equations for each particle of the model that can be executed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.

Performance Analysis of Radiative Transfer Algorithms for Inverse Hydrologic Optics in a Parallel Environment

Abstract Three algorithms for solving the radiative transfer equation (RTE) were studied: Hydrolight, PEESNA, and LTSN. These algorithms correspond, respectively, to implementations of the invariant imbedding, analytical discrete‐ordinates, ant LTS N methods. As a first step, the performance of each algorithm was evaluated running in the same sequential machine. The related codes were used in a hydrologic‐optics test case for coastal ocean type of water, in order to calculate the surface‐emergent radiation intensities (radiances) given the incident radiances and inherent optical properties such as the absorption and the scattering coefficients. Timing and profiling of the three codes was performed in order to evaluate processing times and identify performance bottlenecks. Next, each algorithm was studied concerning the feasibility of its parallelization using the Message Passing Interface (MPI) message‐passing communication library and execution in a distributed‐memory machine, a multicomputer based on IA‐32 architecture. The three codes perform spatial discretization of the domain and Fourier decomposition of the radiances obtaining independent azimuthal modes. Therefore, an independent RTE can be written for each azimuthal mode and can be assigned to a different processor, in a parallel implementation. The speed‐up that can be achieved increases with the fraction of time spent in the azimuthal mode, but total execution time is also an important issue. Results are discussed and further strategies are proposed.

Chlorophyll concentration profiles from in situ radiances by ant colony optimization

A methodology for the reconstruction of vertical profiles of the absorption (a) and scattering (b) coefficients in natural waters is presented. Reconstruction is performed using single-wavelength in situ radiance measurements at several depths. The depth is discretized by a multi-region approach assuming that absorption and scattering coefficients are constant in each region. The inverse problem is iteratively computed employing the radiative transfer equation as direct model, and bio-optical models to correlate the chlorophyll concentration to these coefficients. At every iteration, the inverse solver generates a candidate solution that is a set of discrete chlorophyll concentration values. For each region, the concentration is mapped to the values of absorption and scattering coefficients. The radiative transfer equation is then solved by a parallel version of the Laplace transform discrete ordinate (LTSN) method considering polar and azimuthal scattering angles. An objective function is given by the square difference between reconstructed and experimental radiances. In order to compensate the nearly exponential radiance decay with depth, that unbalances the influence of the radiance at different depths, a depth correction factor is applied to weight radiance values at each level. This objective function is minimized by an Ant Colony System (ACS) implementation. A new regularization scheme pre-selects candidate solutions based on their smoothness quantified by the Tikhonovs norm. A new chlorophyll candidate profile is then generated and iterations proceed. Synthetic and real data show the suitability of the proposed method.

Parallel implementation of a Lagrangian stochastic model for pollutant dispersion

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.

ANALYSIS FOR PRECIPITATION CLIMATE PREDICTION ON SOUTH OF BRAZIL

An evaluation for the precipitation climatology for the Rio Grande do Sul state (Brazil) is performed. The BRAMS code is feeded with boundary conditions from the CPTEC-INPE global circulation model (GCM). The simulated precipitation for this region in Brazil presented a climate standard diferent from the observations. An spectral analysis on the wind was carried out to explain the desagreement.

Parallel Performance Analysis of a Regional Numerical Weather Prediction Model in a Petascale Machine

This paper presents the parallel performance achieved by a regional model of numerical weather prediction (NWP), running on thousands of computing cores in a petascale supercomputing system. It was obtained good scalability, running with up to 13440 cores, distributed in 670 nodes. These results enables this application to solve large computational challenges, such as perform weather forecast at very high spatial resolution.