Journal
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
Volume 67, Issue -, Pages -Publisher
CO-ACTION PUBLISHING
DOI: 10.3402/tellusa.v67.26928
Keywords
data assimilation; hybrid methods; flow dependence
Categories
Funding
- Natural Environment Research Council (NERC) via the National Centre for Earth Observations (NCEO)
- Natural Environment Research Council (NERC) via the DIAMET project [NE/I0051G6/1]
- NERC [NE/I0125484/1]
- Natural Environment Research Council [nceo020004, NE/I005196/1, 1138322, NE/H008853/1, NE/I025484/1] Funding Source: researchfish
- NERC [NE/I025484/1, nceo020004, NE/H008853/1, NE/I005196/1] Funding Source: UKRI
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We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity.
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