Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique

dIn this work, a perceptron neural-network technique is applied to estimate hourly values of the diffuse solar-radiation at the surface in São Paulo City, Brazil, using as input the global solar-radiation and other meteorological parameters measured from 1998 to 2001. The neural-network verification was performed using the hourly measurements of diffuse solar-radiation obtained during the year 2002. The neural network was developed based on both feature determination and pattern selection techniques. It was found that the inclusion of the atmospheric long-wave radiation as input improves the neural-network performance. on the other hand traditional meteorological parameters, like air temperature and atmospheric pressure, are not as important as long-wave radiation which acts as a surrogate for cloud-cover information on the regional scale. An objective evaluation has shown that the diffuse solar-radiation is better reproduced by neural network synthetic series than by a correlation model. (C) 2004 Elsevier Ltd. All rights reserved

A decision support system to evaluate the competitiveness of nations

The aim of this chapter is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations’ competitiveness. For this purpose, a methodology composed of three steps is used. To start with, a combined clustering analysis methodology is used to assign countries to appropriate clusters. Unlike the current methods that use a single criterion, the proposed methodology uses 135 criteria for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, the countries are ranked based on weights generated in the previous step. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated

Project MCITY Brazil: assessing urban climate features of São Paulo and Rio de Janeiro

The project MCITY Brazil aims to characterize the micrometeorological features of urban climate of two major urban areas of Brazil: The metropolitan regions of São Paulo and Rio de Janeiro Cities. They were chosen as the starting point in this project because they are the largest conurbations of Brazil. Altogether, they occupy approximately 13,733 km2 with a population of 31.6 million inhabitants and a fleet of 10.6 million vehicles. These two metropolitan areas contribute to almost 45% of the Brazilian gross domestic product. Three micrometeorological platforms were set up in São Paulo (rural, suburban and urban) and one in Rio de Janeiro (suburban) measuring major components of surface energy balance (net radiation, soil heat flux, sensible and latent heats). Four field campaigns were carried during 2013 where radiosondes were released every 3 hours during 10 days in Summer (February 9-28 in São Paulo, 12-21 March in Rio de Janeiro) and Winter (July 9-18 in Rio de Janeiro and August 6-15 in São Paulo). During these field campaigns a LIDAR was used to obtain the PBL temporal evolution in both Cities. This work describes the major features of energy balance at the surface in São Paulo and Rio de Janeiro. Special attention will be given to the methodology used to estimate the turbulent fluxes and the vertical extent of the PBL. The mean thermodynamic and moisture structures of the PBL are presented and their relation with the observed turbulent fluxes of sensible and latent heat is explored. The mean dynamic structure of the PBL is analysed including the presence of low level jet and the sea breeze. The time evolution of the São Paulo UHI mean intensity is described and its nature is reviewed considering as reference the available literature