Forecast error correction using dynamic data assimilation / by Sivaramakrishnan Lakshmivarahan, John M. Lewis, Rafal Jabrzemski.
Series: Springer atmospheric sciencesPublisher: Cham, Switzerland : Springer International Publishing, 2017Copyright date: 2017Description: xvi, 270 pages : illustrations (some color) ; 24 cmContent type:- text
- unmediated
- volume
- 9783319399959
- 3319399950
- 006.312 23
- QA76.9.D343 .L35 2017
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
BOOK | NCAR Library Mesa Lab | QA76.9 .D343 .L35 2017 | 1 | Available | 50583020009860 |
Part I Theory -- Introduction -- Dynamics of evolution of first- and second-order forward sensitivity: discrete time and continuous time -- Estimation of control errors using forward sensitivities: FSM with single and multiple observations -- Relation to adjoint sensitivity and impact of observation -- Estimation of model errors using Pontryagin's Maximum Principle- its relation to 4-D VAR and hence FSM -- FSM and predictability -- Lyapunov index -- Part II Applications -- Mixed-layer model -- the Gulf of Mexico problem -- Lagrangian data assimilation -- Conclusions -- Appendix -- Index.
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.