A large ensemble based data assimilation experiment with the coupled global atmosphere and aerosol transport models
Abstract
Data assimilation methods are applied to various researches not only for atmospheric models but also for aerosol transport models to estimate more accurate analysis. In the ensemble based data assimilation methods, the background error covariance is estimated from ensemble forecasts. Sampling errors due to a limited ensemble size influence to the analysis accuracy, and covariance localization techniques are generally applied to remove the sampling errors. Although the covariance localization plays an essential role in the ensemble based filters, the covariance localization may remove important information between distant grid points or between different variables. Therefore, in this study, we apply the local ensemble transform Kalman filter (LETKF) known as one of ensemble based filters to the coupled global atmosphere and aerosol transport model developed in Meteorological Research Institute and investigate the structure of covariance by increasing the ensemble size. This presentation will include the most recent results up to the time of the symposium.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A11F2266K
- Keywords:
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- 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 1910 Data assimilation;
- integration and fusion;
- INFORMATICSDE: 1986 Statistical methods: Inferential;
- INFORMATICSDE: 3260 Inverse theory;
- MATHEMATICAL GEOPHYSICS