Fredrick H. M. Semazzi

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An explicit finite-volume solver is proposed for numerical simulation of non-hydrostatic atmospheric dynamics with promise for efficiency on massively parallel machines via low communication needs and large time steps. Solving the governing equations with a single stage lowers communication, and using the method of characteristics to follow information as(More)
Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of pressure and circulation anomalies that span distant geographical areas (teleconnec-tions). In this paper, we present a novel graph based approach called shared reciprocal nearest neighbors that considers only reciprocal positive and negative edges in the(More)
—The application of complex networks to study complex phenomena, including the Internet, social networks, food networks, and others, has seen a growing interest in recent years. In particular, the use of complex networks and network theory to analyze the behavior of the climate system is an emerging topic. This newfound interest is due to the difficulty of(More)
First-principles based predictive understanding of complex , dynamic physical phenomena, such as regional precipitation or hurricane intensity and frequency, is quite limited due to the lack of complete phenomeno-logical models underlying their physics. To address this gap, hypothesis-driven, manually-constructed, conceptual hurricane models and models for(More)
Understanding extreme events, such as hurricanes or forest fires, is of paramount importance because of their adverse impacts on human beings. Such events often propagate in space and time. Predicting—even a few days in advance—what locations will get affected by the event tracks could benefit our society in many ways. Arguably, simulations from first(More)
—A dynamic physical system often undergoes phase transitions in response to fluctuations induced on system parameters. For example, hurricane activity is the climate system's response initiated by a liquid-vapor phase transition associated with non-linearly coupled fluctuations in the ocean and the atmosphere. Because our quantitative knowledge about highly(More)
Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a " first-class citizen " of machine learning algorithms is largely absent from the literature. Furthermore, traditional data(More)
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