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Volume 10, issue 12 | Copyright
Geosci. Model Dev., 10, 4419-4441, 2017
https://doi.org/10.5194/gmd-10-4419-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 05 Dec 2017

Development and technical paper | 05 Dec 2017

The ABC model: a non-hydrostatic toy model for use in convective-scale data assimilation investigations

Ruth Elizabeth Petrie et al.
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Cited articles
Ames, W. F.: Numerical Methods for Partial Differential Equations, Nelson, London, 1958.
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteorol. Soc., 134, 1971–1996, 2008.
Bannister, R. N.: How is the Balance of a Forecast Ensemble Affected by Adaptive and Nonadaptive Localization Schemes?, Mon. Weather Rev., 143, 3680–3699, 2015.
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteorol. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017.
Bannister, R. N., Migliorini, S., and Dixon, M.: Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics, Tellus A, 63, 497–512, 2011.
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Short summary
The model and experiments in this paper are to study atmospheric flows on small (kilometre) scales. Compared to larger-scale flows, kilometre-scale motion is more difficult to predict, and geophysical balances are less valid. For these reasons, data assimilation (or DA, the task of using observations to initialise models) is more difficult, as the character of forecast errors (which have to be corrected by DA) is more difficult to represent. This model will be used to study small-scale DA.
The model and experiments in this paper are to study atmospheric flows on small (kilometre)...
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