Four Dimensional Model Assimilation Of Data

This volume explores and evaluates the development, multiple applications, and usefulness of four-dimensional (space and time) model assimilations of data in the atmospheric and oceanographic sciences and projects their applicability to the earth sciences as a whole. Using the predictive power of geophysical laws incorporated in the general circulation model to produce a background field for comparison with incoming raw observations, the model assimilation process synthesizes diverse, temporarily inconsistent, and spatially incomplete observations from worldwide land, sea, and space data acquisition systems into a coherent representation of an evolving earth system. The book concludes that this subdiscipline is fundamental to the geophysical sciences and presents a basic strategy to extend the application of this subdiscipline to the earth sciences as a whole.

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 is the final technical report for the project entitled: "High-Performance Computing and Four-Dimensional Data Assimilation: The Impact on Future and Current Problems," funded at NPAC by the DAO at NASA/GSFC. First, the motivation for the project is given in the introductory section, followed by the executive summary of major accomplishments and the list of project-related publications. Detailed analysis and description of research results is given in subsequent chapters and in the Appendix. Makivic, Miloje S. Goddard Space Flight Center

This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are active research scientists, and many of them are internationally known leading experts in their areas, ensuring that the text is authoritative.This book comprises four parts that are logically connected from data, modeling, data assimilation integrating data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation.Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today''s earth science and modeling communities.

Data evaluation and data combination require the use of a wide range of probability theory concepts and tools, from deductive statistics mainly concerning frequencies and sample tallies to inductive inference for assimilating non-frequency data and a priori knowledge. Computational Methods for Data Evaluation and Assimilation presents interdisciplinary methods for integrating experimental and computational information. This self-contained book shows how the methods can be applied in many scientific and engineering areas. After presenting the fundamentals underlying the evaluation of experimental data, the book explains how to estimate covariances and confidence intervals from experimental data. It then describes algorithms for both unconstrained and constrained minimization of large-scale systems, such as time-dependent variational data assimilation in weather prediction and similar applications in the geophysical sciences. The book also discusses several basic principles of four-dimensional variational assimilation (4D VAR) and highlights specific difficulties in applying 4D VAR to large-scale operational numerical weather prediction models.

Data assimilation is the combination of information from observations and models of a particular physical system in order to get the best possible estimate of the state of that system. The technique has wide applications across a range of earth sciences, a major application being the production of operational weather forecasts. Others include oceanography, atmospheric chemistry, climate studies, and hydrology. Data Assimilation for the Earth System is a comprehensive survey of both the theory of data assimilation and its application in a range of earth system sciences. Data assimilation is a key technique in the analysis of remote sensing observations and is thus particularly useful for those analysing the wealth of measurements from recent research satellites. This book is suitable for postgraduate students and those working on the application of data assimilation in meteorology, oceanography and other earth sciences.