ENSEMBLE KALMAN FILTER EXPERIMENTS WITH A PRIMITIVE-EQUATION GLOBAL MODEL

Simple item page
dc.contributor.advisorKalnay, Eugeniaen_US
dc.contributor.authorMiyoshi, Takemasaen_US
dc.contributor.departmentMeteorologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2006-02-04T06:31:58Z
dc.date.available2006-02-04T06:31:58Z
dc.date.issued2005-06-30en_US
dc.description.abstractThe ultimate goal is to develop a path towards an operational ensemble Kalman filtering (EnKF) system. Several approaches to EnKF for atmospheric systems have been proposed but not systematically compared. The sensitivity of EnKF to the imperfections of forecast models is unclear. This research explores two questions: 1. What are the relative advantages and disadvantages of two promising EnKF methods? 2. How large are the effects of model errors on data assimilation, and can they be reduced by model bias correction? Chapter 2 contains a theoretical review, followed by the FORTRAN development and testing of two EnKF methods: a serial ensemble square root filter (serial EnSRF, Whitaker and Hamill 2002) and a local EnKF (LEKF, Ott et al. 2002; 2004). We reproduced the results obtained by Whitaker and Hamill (2002) and Ott et al. (2004) on the Lorenz (1996) model. If we localize the LEKF error covariance, LEKF outperforms serial EnSRF. We also introduce a method to objectively estimate the optimal covariance inflation. In Chapter 3 we apply the two EnKF methods and the three-dimensional variational method (3DVAR) to the SPEEDY primitive-equation global model (Molteni 2003), a fast but relatively realistic model. Perfect model experiments show that EnKF greatly outperforms 3DVAR. The 2-day forecast "errors of the day" are very similar to the analysis errors, but they are not similar among different methods except in low ensemble dimensional regions. Overall, serial EnSRF outperforms LEKF, but their difference is substantially reduced if we localize the LEKF error covariance or increase the ensemble size. Since LEKF is much more efficient than serial EnSRF when using parallel computers and many observations, LEKF would be the only feasible choice in operations. In Chapter 4 we remove the perfect model assumption using the NCEP/NCAR reanalysis as the "nature" run. The advantage of EnKF to 3DVAR is reduced. When we apply the model bias estimation proposed by Dee and da Silva (1998), we find that the full dimensional model bias estimation fails. However, if instead we assume that the bias is low dimensional, we obtain a substantial improvement in the EnKF analysis.en_US
dc.format.extent9416251 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/3046
dc.language.isoen_US
dc.subject.pqcontrolledGeophysicsen_US
dc.subject.pquncontrolledAtmospheric scienceen_US
dc.subject.pquncontrolledMeteorologyen_US
dc.subject.pquncontrolledNumerical Weather Predictionen_US
dc.subject.pquncontrolledData assimilationen_US
dc.subject.pquncontrolledKalman filteringen_US
dc.subject.pquncontrolledEnsemble Kalman filteringen_US
dc.titleENSEMBLE KALMAN FILTER EXPERIMENTS WITH A PRIMITIVE-EQUATION GLOBAL MODELen_US
dc.typeDissertationen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
umi-umd-2620.pdf
Size:
8.98 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Atmospheric & Oceanic Science Theses and Dissertations