Forecast Error Correction Using Dynamic Data Assimilation

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.

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

Publisher description

This book constitutes the refereed proceedings of the First International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in November 2014.The 24 revised full papers and 7 short papers were carefully reviewed and selected from 62 submissions and cover topics on sensing, imaging and retrieval for the oceans, atmosphere, space, land, earth and planets that is informed by the environmental context; algorithms for modeling and simulation, downscaling, model reduction, data assimilation, uncertainty quantification and statistical learning; methodologies for planning and control, sampling and adaptive observation, and efficient coupling of these algorithms into information-gathering and observing system designs; and applications of methodology to environmental estimation, analysis and prediction including climate, natural hazards, oceans, cryosphere, atmosphere, land, space, earth and planets.