Local dimension and finite time prediction in spatiotemporal chaotic systems

Predictability is related to the uncertainty in the outcome of future events during the evolution of the state of a system. The cluster weighted modeling (CWM) is interpreted as a tool to detect such an uncertainty and used it in spatially distributed systems. As such, the simple prediction algorithm in conjunction with the CWM forms a powerful set of methods to relate predictability and dimension.

Local dimension and finite time prediction in coupled map lattices

Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour. In this paper, we discuss the use of the bred vector (BV) dimension, a recently introduced statistics, to identify the regimes where a finite time forecast is feasible. Using the tools from dynamical systems theory and Bayesian modelling, we show the finite time predictability in two-dimensional coupled map lattices in the regions of low BV dimension. © Indian Academy of Sciences.

Human Accessibility Modelling Applied to Protected Areas Management

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A radar reflectivity-runoff model for use in flood warning

This model connects directly the radar reflectivity data and hydrological variable runoff. The catchment is discretized in pixels (4 Km × 4 Km) with the same resolution of the CAPPI. Careful discretization is made so that every grid catchment pixel corresponds precisely to CAPPI grid cell. The basin is assumed a linear system and also time invariant. The forecast technique takes advantage of spatial and temporal resolutions obtained by the radar. The method uses only the measurements of the factor reflectivity distribution observed over the catchment area without using the reflectivity - rainfall rate transformation by the conventional Z-R relationships. The reflectivity values in each catchment pixel are translated to a gauging station by using a transfer function. This transfer function represents the travel time of the superficial water flowing through pixels in the drainage direction ending at the gauging station. The parameters used to compute the transfer function are concentration time and the physiographic catchment characteristics. -from Authors

Uso de flutuadores como ferramenta de avaliação da influência do vento na circulação de uma lagoa de estabilização facultativa primária

Pós-graduação em Engenharia Civil - FEIS

Análise de cenários para auxílio à tomada de decisão no plantio mecanizado de cana-de-açúcar

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.