Irregularly structured ionospheric regions may cause amplitude and phase fluctuations of radio signals. Such distortion is called ionospheric scintillation. These ionospheric irregularities occur as part of depleted plasma density regions that are generated at the magnetic equator after sunset by equatorial ionospheric plasma instability mechanism. Also known as ionospheric bubbles, they drift upward to high altitudes at the equator and extend/expand to low latitudes along the Earth magnetic field lines. Ionospheric irregularities affect the space weather since they present large variations with the solar cycle and during solar flares and coronal mass ejections. In general, navigation systems such as the Global Positioning System and telecommunications systems are also affected by the scintillation. The aim of this work is to apply data mining for the prediction of ionospheric scintillation. Data mining can be divided into two categories: descriptive or predictive. The first one describes a data set in a concise and summarized way, while the second one, used in this work, analyzes the data to build a model and tries to predict the behavior of a new data set. In this study we employed data series of ionospheric scintillation and other parameters such as the level of solar activity, vertical drift velocity of the plasma at the magnetic equator, and magnetic activity. The results show that prediction of the ionospheric scintillation occurrence during the analyzed period was possible regardless of the high variability of the ionospheric parameters that affect the generation of such irregularities.

Electron density irregularity structures, often associated with ionospheric plasma bubbles, drive amplitude and phase fluctuations in radio signals that, in turn, create a phenomenon known as ionospheric scintillation. The phenomenon occurs frequently around the magnetic equator where plasma instability mechanisms generate postsunset plasma bubbles and density depletions. A previous correlation study suggested that scintillation at the magnetic equator may provide a forecast of subsequent scintillation at the equatorial ionization anomaly southern peak. In this work, it is proposed to predict the level of scintillation over São Luís (2.52°S, 44.3°W; dip latitude:~2.5°S) near the magnetic equator with lead time of hours but without specifying the moment at which the scintillation starts or ends. A collection of extended databases relating scintillation to ionospheric variables for São Luís is employed to perform the training of an artificial neural network with a new architecture. Two classes are considered, not strong (null/weak/moderate) and strong scintillation. An innovative scheme preprocesses the data taking into account similarities of the values of the variables for the same class. A formerly proposed resampling heuristic is employed to provide a balanced number of tuples of each class in the training set. Tests were performed showing that the proposed neural network is able to predict the level of scintillation over the station on the evening ahead of the data sample considered between 17:30 and 19:00 LT.

Ionospheric scintillation refers to amplitude and phase fluctuations in radio signals due to electron density irregularities associated to structures named ionospheric plasma bubbles. The phenomenon is more pronounced around the magnetic equator where, after sunset, plasma bubbles of varying sizes and density depletions are generated by plasma instability mechanisms. The bubble depletions are aligned along Earth's magnetic field lines, and they develop vertically upward over the magnetic equator so that their extremities extend in latitude to north and south of the dip equator. Over Brazil, developing bubbles can extend to the southern peak of the Equatorial Ionization Anomaly, where high levels of ionospheric scintillation are common. Scintillation may seriously affect satellite navigation systems, such as the Global Navigation Satellite Systems. However, its effects may be mitigated by using a predictive model derived from a collection of extended databases on scintillation and its associated variables. This work proposes the use of a classification and regression decision tree to perform a study on the correlation between the occurrence of scintillation at the magnetic equator and that at the southern peak of the equatorial anomaly. Due to limited size of the original database, a novel resampling heuristic was applied to generate new training instances from the original ones in order to improve the accuracy of the decision tree. The correlation analysis presented in this work may serve as a starting point for the eventual development of a predictive model suitable for operational use.

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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.

Abstract.We investigate the relevance of chaotic saddles and unstable periodic orbits at the onset of intermittent chaos in the phase dynamics of nonlinear Alfvén waves by using the Kuramoto-Sivashinsky (KS) equation as a model for phase dynamics. We focus on the role of nonattracting chaotic solutions of the KS equation, known as chaotic saddles, in the transition from weak chaos to strong chaos via an interior crisis and show how two of these unstable chaotic saddles can interact to produce the plasma intermittency observed in the strongly chaotic regimes. The dynamical systems approach discussed in this work can lead to a better understanding of the mechanisms responsible for the phenomena of intermittency in space plasmas.

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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.