@article{777615,
      recid = {777615},
      author = {Lakshmivarahan, S., and Lewis, John M., and Jabrzemski,  Rafal,},
      title = {Forecast error correction using dynamic data assimilation  /},
      pages = {1 online resource (xvi, 270 pages)},
      abstract = {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. .},
      url = {http://library.usi.edu/record/777615},
}