João C. Carvalho

Novel approaches to entropic regularization

In this work, two new entropic regularization techniques are introduced. They represent a generalization of the standard MaxEnt regularization method, and allow for a greater flexibility for introducing any prior information about the expected structure of the true physical model, or its derivatives, into the inversion procedure. The first technique is based on the minimization of the entropy of the vector of first-differences of unknown parameters. Adopting standard terminology, it is known as the minimum first-order entropy method (MinEnt-1). To illustrate the essential feature of the method, MinEnt-1 is applied to the reconstruction of two-dimensional geoelectric conductivity distributions from magnetotelluric data. The second technique is based on the maximization of the entropy of the vector of second-differences of the unknown parameters, and is denoted as the MaxEnt-2 method. The MaxEnt-2 method is applied to the retrieval of vertical profiles of temperature in the atmosphere from remote sensing data.

Telemetric monitoring system for meteorological and limnological data acquisition

(2006). Telemetric monitoring system for meteorological and limnological data acquisition. SIL Proceedings, 1922-2010: Vol. 29, No. 4, pp. 1747-1750.

Inverse problems in space science and technology

Solutions for inverse problems appearing in space applications and space technology are described. The inverse problem is formulated as a nonlinear optimization problem. Usually some additional information must be added from our previous knowledge about the physical phenomenon. In general this a priori information means smoothness, in other words, regularized solutions are searched for. The methodology is applied to geophysics (magneto-telluric inversion), meteorology (temperature retrieval from satellite data), and oceanography (inverse hydrologic optics), as examples of space applications. The scheme is also employed for solving an inverse problem emerging from technology: the inverse design of a space radiator.

Neural network based models in the inversion of temperature vertical profiles from radiation data

In this article, vertical temperature profiles are inferred by a neural network based inverse procedure from satellite data, nonlinear function estimation. A multilayer perceptrons (MLP) network is trained using data provided by the direct model characterized by the radiative transfer equation (RTE). The neural network results are compared to the ones obtained from previous works. In addition, real radiation data from the high resolution infrared radiation sounder (HIRS/2) is used as input for the neural networks to generate temperature profiles that are compared to measured temperature profiles from radiosonde. Analysis of the neural network results reveals the generated profiles closely approximate to the results obtained with regularized inversions, thus showing adequacy of neural network based models in solving the inverse problem of temperature retrieval from satellite data. The advantages of using neural network based systems are related to their intrinsic features of parallelism; after trained, the networks are much faster than regularized approaches, and hardware implementation possibilities that may imply in very fast processing systems. §Selected paper from Inverse Problems, Design and Optimization Symposium, 2004.

New entropic regularization technique for temperature profiling

In this paper a new regularization technique is introduced and applied to the problem of retrieval of vertical temperature profiles in the atmosphere from remote sensing data. This is a key issue in Meteorology since it provides an important input for weather forecasting models, mainly in the Southern Hemisphere, where there are large areas uncovered by data collecting ground stations. The new regularization technique is derived from the well known Maximum Entropy method, and is based on the maximization of the entropy of the vector of second-differences of the unknown parameters. Simulations using real satellite data achieved a good agreement with radiosonde measurements. Numerical simulations have also shown that the temperature profiles retrieved with the new technique are relatively independent on the choice of the initial guess.

Localização de áreas de monitoramento telemétrico em ambientes aquáticos da Amazônia

The present work illustrates the application of remote sensing and image processing methods to define appropriate sites for installing buoy moored telemetric systems at the surface of Amazon floodplain lakes for long-term limnologic-micrometeorologic monitoring. The technique consists essentially of Boolean operations over Amazon plume maps and historic inundation of the Curuai Lake at distinct stages of the hydrologic cycle. The precise location for the long-term monitoring is vital to the development of models concerning air-water trace gas exchange in the Amazon floodplains.

Máscara de espalhamento e precipitação para os canais microondas do satélite NOAA

This work presents a methodology to identify precipitation and/or scattering pixels in the Advanced Microwave Sensor Unit (AMSU) channels. This procedure is useful for applications in atmospheric temperature and moisture retrievals over Brazil under cloudy sky conditions. A subjective analysis based on a case study involving comparisons with infrared, visible and microwave images was applied for validation purpose. The results show an excellent relationship of cloud tops with low brightness temperature affected by scattering due to water drops and ice and the areas identified by the algorithm as being influenced by precipitation and/or scattering effect.