rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation
  name:
    name part: Meng, Zhiyong
    name part: 1969-
    role:
      role term: creator
  name:
    name part: Zhang, Fuqing
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
  abstract: This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a mesoscale model in increasingly realistic contexts from under a perfect model assumption and in the presence of significant model error with synthetic observations to real-world data assimilation in comparison to the three-dimensional variational (3DVar) method via both case study and month-long experiments. The EnKF is shown to be promising for future application in operational data assimilation practice. The EnKF with synthetic observations, which is implemented in the mesoscale model MM5, is very effective in keeping the analysis close to the truth under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the presence of significant model errors from physical parameterization schemes, the EnKF performs reasonably well though sometimes it can be significantly degraded compared to its performance under the perfect model assumption. Using a combination of different physical parameterization schemes in the ensemble (the so-called "multi-scheme" ensemble) can significantly improve filter performance due to the resulting better background error covariance and a smaller ensemble bias. The EnKF performs differently for different flow regimes possibly due to scale- and flow-dependent error growth dynamics and predictability. Real-data (including soundings, profilers and surface observations) are assimilated by directly comparing the EnKF and 3DVar and both are implemented in the Weather Research and Forecasting model. A case study and month-long experiments show that the EnKF is efficient in tracking observations in terms of both prior forecast and posterior analysis. The EnKF performs consistently better than 3DVar for the time period of interest due to the benefit of the EnKF from both using ensemble mean for state estimation and using a flow-dependent backg…
  note: by Zhiyong Meng.
  note: "Major Subject: Atmospheric Sciences"
  note: Title from author supplied metadata (automated record created on Nov. 2, 2007.)
  note: Vita.
  note: Abstract.
  note: Electronic resource.
  note: Mode of access: World Wide Web.
  note: System requirements: World Wide Web access and Adobe Acrobat Reader.
  note: Ph. D. Texas A&M University 2007.
  note: Includes bibliographical references.
  note: Text (Dissertation).
  subject:
    topic: Major atmospheric sciences
  subject:
    topic: EnKF Mesoscale
  identifier: http://hdl.handle.net/1969.1/5870
  record info:
    record content source: TXA
    record creation date: 080214
    record change date: 20151108081333.0
    record identifier: in00002353270