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dc.creatorHam, Joy Len_US
dc.date.accessioned2012-06-07T23:14:24Z
dc.date.available2012-06-07T23:14:24Z
dc.date.created2002en_US
dc.date.issued2002
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2002-THESIS-H33en_US
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.en_US
dc.descriptionIncludes bibliographical references (leaves 103-106).en_US
dc.descriptionIssued also on microfiche from Lange Micrographics.en_US
dc.description.abstractOver the past decade, ensemble forecasting has emerged as a powerful tool for numerical weather prediction. Not only does it produce the best estimate of the state of the atmosphere, it also could quantify the uncertainties associated with the best estimate and the predictability of an event. This additional information could be used to provide a more accurate estimate of the background error covariance matrix for use in data assimilation. This preliminary study explores the use of ensemble forecasts to gain information on predictability and the background error covariance matrix using data from the 24-25 January 2000 snowstorm that occurred along the East Coast of the United States. Adding randomly distributed uncorrelated initial perturbations to this data produced four sets of ensemble forecasts. The error growth characteristics estimated from these ensemble forecasts were found to be quantitatively similar to previous estimates that were achieved by perturbing the individual observations or by adding monochromatic wave disturbances. Analyses show that the isotropic random perturbations initially dissipated everywhere except for in the conditionally unstable regions. Error from the ensemble forecasts quickly developed horizontal and vertical structure that was associated with the cyclogenesis of the system. This error growth was a nonlinear process that was dictated by moist dynamics but not by the initial magnitude of the perturbations added to create the ensemble. The ensemble forecasts were also used to estimate the background error covariance matrix. These results show that initially uncorrelated errors develop a strong covariance or relationship between and among variables especially in the regions of cyclogenesis and convection. The covariances' development was controlled by the underlying governing dynamics, among which moist processes play a fundamental role. These covariances (correlations) indicate that observation/estimation of one state variable could provide valuable information on the state estimation of another state variable which is key to data assimilation. This paper demonstrates that further study on using ensemble forecasts to estimate the flow-dependent background error covariance matrix is warranted.en_US
dc.format.mediumelectronicen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.publisherTexas A&M Universityen_US
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en_US
dc.subjectatmospheric sciences.en_US
dc.subjectMajor atmospheric sciences.en_US
dc.titleMesoscale predictability and background error convariance estimation through ensemble forecastingen_US
dc.typeThesisen_US
thesis.degree.disciplineatmospheric sciencesen_US
thesis.degree.nameM.S.en_US
thesis.degree.levelMastersen_US
dc.type.genrethesis
dc.type.materialtexten_US
dc.format.digitalOriginreformatted digitalen_US


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