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Discussion papers
https://doi.org/10.5194/gmd-2019-318
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-318
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 03 Feb 2020

Submitted as: model description paper | 03 Feb 2020

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This preprint is currently under review for the journal GMD.

The “ABC-DA system” (v1.4): a variational data assimilation system for convective scale assimilation research with a study of the impact of a balance constraint

Ross Noel Bannister Ross Noel Bannister
  • National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, RG6 6BB, UK

Abstract. Following the development of the simplified atmospheric convective-scale "toy" model (the ABC model, named after its three key parameters: the pure gravity wave frequency, A, the controller of the acoustic wave speed, B, and the constant of proportionality between pressure and density perturbations, C), this paper introduces its associated variational data assimilation system, ABC-DA. The purpose of ABC-DA is to permit quick and efficient research into data assimilation methods suitable for convective scale systems. The system can also be used as an aid to teach and demonstrate data assimilation principles.

ABC-DA is flexible, configurable and is efficient enough to be run on a personal computer. The system can run a number of assimilation methods (currently 3DVar and 3DFGAT have been implemented), with user configurable observation networks. Observation operators for direct observations and wind speeds are part of the system, although these can be expanded relatively easily. A key feature of any data assimilation system is how it specifies the background error covariance matrix. ABC-DA uses a control variable transform method to allow this to be done efficiently. This version of ABC-DA mirrors many operational configurations, by modelling multivariate error covariances with uncorrelated control parameters, and spatial error covariances with special uncorrelated spatial patterns separately for each parameter.

The software developed (amongst other things) does model runs, calibration tasks associated with the background error covariance matrix, testing and diagnostic tasks, single data assimilation runs, multi-cycle assimilation/forecast experiments, and has associated visualisation software.

As a demonstration, the system is used to tackle a scientific question concerning the role of geostrophic balance (GB) to model background error covariances between mass and wind fields. This question arises because, although GB is a very useful mechanism that is successfully exploited in larger scale assimilation systems, its use is questionable at convective scales due to the typically larger Rossby numbers where GB is not so relevant. A series of identical twin experiments is done in cycled assimilation configurations. One experiment exploits GB to represent mass-wind covariances in a mirror of an operational set-up (with use of an additional vertical regression (VR) step, as used operationally). This experiment performs badly where assimilation error accumulates over time. Two further experiments are done: one that does not use GB, and another that does but without the VR step. Turning off GB impairs the performance, and turning off VR improves the performance in general. It is concluded that there is scope to further improve the way that the background error covariance matrices are calibrated, with some directions discussed.

Ross Noel Bannister

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Status: open (until 04 Apr 2020)
Status: open (until 04 Apr 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Ross Noel Bannister

Data sets

Sample data R. Bannister https://doi.org/10.5281/zenodo.3531926

Model code and software

User guide, model code, plotting software, and scripts R. Bannister https://doi.org/10.5281/zenodo.3531926

Ross Noel Bannister

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Latest update: 28 Feb 2020
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Short summary
Forecasting models start from initial conditions, and data assimilation (DA) is the way that initial conditions are found from a combination of previous model data and latest observations. The "ABC" model is a simplified convective-scale model developed previously, and "ABC-DA" is the version of this system that includes the DA capability. This system is described in the present paper and its performance is demonstrated with a range of options that control how the data assimilation is done.
Forecasting models start from initial conditions, and data assimilation (DA) is the way that...
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