Forecast Error Correction using Dynamic Data Assimilation

• Κύριοι συγγραφείς: Lakshmivarahan, Sivaramakrishnan (Συγγραφέας), Lewis, John M. (Συγγραφέας), Jabrzemski, Rafal (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Springer Atmospheric Sciences,
• Θέματα: Computer science.; Geology > Statistical methods.; Atmospheric sciences.; Computers.; Data mining.; Computer simulation.; Computer Science.; Data Mining and Knowledge Discovery.; Simulation and Modeling.; Models and Principles.; Atmospheric Sciences.; Quantitative Geology.
• Φυσική περιγραφή: XVI, 270 p. 125 illus., 104 illus. in color. online resource.
• ISBN: 9783319399973
• ISSN: 2194-5217

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. .