Ensemble-based data assimilation for thermally forced circulations

Altuǧ Aksoy, Fuqing Zhang, John W. Nielsen-Gammon, Craig C. Epifanio

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

The effectiveness of the ensemble Kalman filter (EnKF) for thermally forced circulations is investigated with simulated observations. A two-dimensional, nonlinear, hydrostatic, non-rotating, and incompressible sea breeze model is developed for this purpose with buoyancy and vorticity as the prognostic variables. Model resolution is 4 km horizontally and 50 m vertically. Forcing is maintained through an explicit heating function with additive stochastic noise. Pure forecast experiments reveal that the model exhibits moderate nonlinearity. The strongest nonlinearity occurs along the sea breeze front at the time of peak sea breeze phase. Considerable small-scale error growth occurs at this phase for vorticity, while buoyancy is dominated by large-scale error as the direct result of the initial condition uncertainty. In the EnKF experiments, simulated buoyancy observations (with assumed error of 10-3 ms-2) on land surface with 40-km spacing are assimilated every 3 hours. As a result of their resolution, the observations naturally sample the larger-scale flow structure. At the first analysis step, the filter is found to remove most of the large-scale error resulting from the initial conditions and the domain-averaged error of buoyancy and vorticity is reduced by about 83% and 42%, respectively. Subsequent analyses continue to remove error at a progressively slower rate and the error ultimately stabilizes within about 24 hours for both variables. At later model times, while mostly large-scale buoyancy errors due to the stochastic heating uncertainty are effectively removed, the filter also performs well at reducing smaller-scale vorticity errors associated with the sea breeze front. This is an indication that observations also contain useful small-scale information relevant at the scales of frontal convergence.

Original languageEnglish (US)
Article numberD16105
Pages (from-to)1-15
Number of pages15
JournalJournal of Geophysical Research D: Atmospheres
Volume110
Issue number16
DOIs
StatePublished - Aug 27 2005

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assimilation
data assimilation
sea breeze
buoyancy
vorticity
Buoyancy
Vorticity
Kalman filter
uncertainty
nonlinearity
heat
filter
heating
Kalman filters
flow structure
hydrostatics
spatial distribution
land surface
spacing
experiment

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Cite this

Aksoy, A., Zhang, F., Nielsen-Gammon, J. W., & Epifanio, C. C. (2005). Ensemble-based data assimilation for thermally forced circulations. Journal of Geophysical Research D: Atmospheres, 110(16), 1-15. [D16105]. https://doi.org/10.1029/2004JD005718
Aksoy, Altuǧ ; Zhang, Fuqing ; Nielsen-Gammon, John W. ; Epifanio, Craig C. / Ensemble-based data assimilation for thermally forced circulations. In: Journal of Geophysical Research D: Atmospheres. 2005 ; Vol. 110, No. 16. pp. 1-15.
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Aksoy, A, Zhang, F, Nielsen-Gammon, JW & Epifanio, CC 2005, 'Ensemble-based data assimilation for thermally forced circulations', Journal of Geophysical Research D: Atmospheres, vol. 110, no. 16, D16105, pp. 1-15. https://doi.org/10.1029/2004JD005718

Ensemble-based data assimilation for thermally forced circulations. / Aksoy, Altuǧ; Zhang, Fuqing; Nielsen-Gammon, John W.; Epifanio, Craig C.

In: Journal of Geophysical Research D: Atmospheres, Vol. 110, No. 16, D16105, 27.08.2005, p. 1-15.

Research output: Contribution to journalArticle

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AB - The effectiveness of the ensemble Kalman filter (EnKF) for thermally forced circulations is investigated with simulated observations. A two-dimensional, nonlinear, hydrostatic, non-rotating, and incompressible sea breeze model is developed for this purpose with buoyancy and vorticity as the prognostic variables. Model resolution is 4 km horizontally and 50 m vertically. Forcing is maintained through an explicit heating function with additive stochastic noise. Pure forecast experiments reveal that the model exhibits moderate nonlinearity. The strongest nonlinearity occurs along the sea breeze front at the time of peak sea breeze phase. Considerable small-scale error growth occurs at this phase for vorticity, while buoyancy is dominated by large-scale error as the direct result of the initial condition uncertainty. In the EnKF experiments, simulated buoyancy observations (with assumed error of 10-3 ms-2) on land surface with 40-km spacing are assimilated every 3 hours. As a result of their resolution, the observations naturally sample the larger-scale flow structure. At the first analysis step, the filter is found to remove most of the large-scale error resulting from the initial conditions and the domain-averaged error of buoyancy and vorticity is reduced by about 83% and 42%, respectively. Subsequent analyses continue to remove error at a progressively slower rate and the error ultimately stabilizes within about 24 hours for both variables. At later model times, while mostly large-scale buoyancy errors due to the stochastic heating uncertainty are effectively removed, the filter also performs well at reducing smaller-scale vorticity errors associated with the sea breeze front. This is an indication that observations also contain useful small-scale information relevant at the scales of frontal convergence.

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