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 ...
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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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Add this copy of Forecast Error Correction Using Dynamic Data to cart. $70.48, very good condition, Sold by Dotcom liquidators / dc1 rated 4.0 out of 5 stars, ships from Fort Worth, TX, UNITED STATES, published 2016 by Springer.
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