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This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints)—an optimal assimilation strategy called Forecast Sensitivity Method (FSM), as an alternative to the well-known four-dimensional variational (4D-Var) data assimilation method. 4D-Var works with a forward in time prediction model and a backward in time tangent linear model (TLM). The equivalence of data assimilation via 4D-Var and FSM is proven and problems using low-order dynamics clarify the process of data assimilation by the two methods. The problem of return flow over the Gulf of Mexico that includes upper-air observations and realistic dynamical constraints gives the reader a good idea of how the FSM can be implemented in a real-world situation.
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This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including adaptive observations, sensitivity analysis, parameter estimation and AI applications. The book is useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Celebrate the 50th anniversary of the metaphorical butterfly effect, born from Edward Lorenz's 1963 work on initial condition sensitivity. In 1972, it became a metaphor for illustrating how minor changes could yield an organized system. Lorenz Models: Chaos & Regime Changes Explore Lorenz models' 1960-2008 evolution, chaos theory, and attractors. Unraveling High-dimensional Instability Challenge norms in "Butterfly Effect without Chaos?" as non-chaotic elements contribute uniquely. Modeling Atmospheric Dynamics Delve into atmospheric dynamics via "Storm Sensitivity Study." Navigating Data Assimilation Explore data assimilation's dance in chaotic and nonchaotic settings via the observability Gramian. Chaos, Instability, Sensitivities Explore chaos, instability, and sensitivities with Lorenz 1963 & 1969 models. Unraveling Tropical Mysteries Investigate tropical atmospheric instability, uncovering oscillation origins and cloud-radiation interactions. Chaos and Order Enter atmospheric regimes, exploring attractor coexistence and predictability. The Art of Prediction Peer into predictability realms, tracing the "butterfly effect's" impact on predictions. Navigating Typhoons Journey through typhoons, exploring rainfall and typhoon trajectory prediction. Analyzing Sea Surface Temperature Examine nonlinear analysis for classification. Computational Fluid Dynamics Immerse in geophysical fluid dynamics progress, simulating atmospheric phenomena.
This book is a printed edition of the Special Issue "Selected Papers from the 14th Estuarine and Coastal Modeling Conference" that was published in JMSE
This third of three volumes includes papers from the second series of NODYCON, which was held virtually in February of 2021. The conference papers reflect a broad coverage of topics in nonlinear dynamics, ranging from traditional topics from established streams of research to those from relatively unexplored and emerging venues of research. These include · Complex dynamics of COVID-19: modeling, prediction and control · Nonlinear phenomena in bio-systems and eco-systems · Energy harvesting · MEMS/NEMS · Multifunctional structures, materials and metamaterials · Nonlinear waves · Chaotic systems, stochasticity, and uncertainty
Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing why and not just how. Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.