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: Linear diagnostics to assess the performance of an ensemble forecast system
  name:
    name part: Satterfield, Elizabeth A.
    role:
      role term: creator
  name:
    name part: Szunyogh, Istvan
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: The performance of an ensemble prediction system is inherently flow dependent. This dissertation investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model grid point location ℓ. A local error covariance matrix Pℓ. is defined for each local region and the diagnostics are applied to the linear space Sℓ. defined by the range of the ensemble based estimate of Pℓ. The particular diagnostics are chosen to help investigate the ability of Sℓ. to efficiently capture the space of true forecast or analysis uncertainties, accurately predict the magnitude of forecast or analysis uncertainties, and to distinguish between the importance of different state space directions. Additionally, we aim to better understand the roots of the underestimation of the magnitude of uncertainty by the ensemble at longer forecast lead times. Numerical experiments are carried out with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on a reduced (T62L28) resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere are used in these experiments. It is found that (i) paradoxically, the linear space Sℓ. provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time, (ii) Sℓ. provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth, (iii) the E-dimension is a reliable predictor of the performance of Sℓ. in predicting the space of forecast uncertainties, (iv) the ensemble grossly underestimates the forecast error variance in Sℓ., (v) when realistic observation coverage is used, the ensemble typically overest…
  note: by Elizabeth A. Satterfield.
  note: "Major Subject: Atmospheric Sciences"
  note: Title from author supplied metadata (automated record created 2011-11-01 09:04:20).
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2010 http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8258
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major atmospheric sciences
  subject:
    topic: Ensemble Forecasting
  subject:
    topic: Predictability
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8258
  record info:
    record content source: TXA
    record creation date: 111115
    record change date: 20151202144302.0
    record identifier: in00002732681