The number of required observations in data assimilation for a shallow-water flow

TitleThe number of required observations in data assimilation for a shallow-water flow
Publication TypeJournal Article
Year of Publication2013
AuthorsWhartenby W.G, Quinn J.C, Abarbanel H.DI
JournalMonthly Weather Review
Volume141
Pagination2502-2518
Date Published2013/07
Type of ArticleArticle
ISBN Number0027-0644
Accession NumberWOS:000330516300021
Keywordsdynamics; state; systems
Abstract

The authors consider statistical ensemble data assimilation for a one-layer shallow-water equation in a twin experiment: data are generated by an N x N enstrophy-conserving grid integration scheme along with an Ekman vertical velocity at the bottom of an Ekman layer driving the flow and Rayleigh and eddy viscosity dissipation damping the flow. Data are generated for N = 16 and the chaotic flow that results is analyzed. This analysis is performed in a path-integral formulation of the data assimilation problem. These path integrals are estimated by a Monte Carlo method using a Metropolis Hastings algorithm. The authors' concentration is on the number of measurements L-c that must be assimilated by the model to allow accurate estimation of the full state of the model at the end of an observation window. It is found that for this shallow-water flow approximately 70% of the full set of state variables must be observed to accomplish either goal. The number of required observations is determined by examining the number needed to synchronize the observed data L-c and the model output when L data streams are assimilated by the model. Synchronization occurs when L >= L-c and the correct selection of which L-c data are observed is made. If the number of observations is too small, so synchronization does not occur, or the selection of observations does not lead to synchronization of the data with the model output, state estimates during and at the end of the observation window and predictions beyond the observation window are inaccurate.

DOI10.1175/mwr-d-12-00103.1
Short TitleMon. Weather Rev.
Student Publication: 
No