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Comparison of extended and ensemble based Kalman filters with low and high resolution primitive equation ocean models

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Academic year: 2021

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Figure 1: A diagram summarizing the algorithms of the SEEK and SEIK filters: X a and X f denote the analysis and forecast states, M , M, H and H the transition and the observation operators and their linearized operators, Q and R the corresponding model an
Fig. 3. Basin average kinetic energy and norm of the second deriva- deriva-tive for both models.
Fig. 4. Evolution of the RRMS error as function of time for the SEEK and SEIK filters with the low resolution Mediterranean model using an error covariance matrix of rank 50 (analysis step is 5 days).
Fig. 5. Evolution of the RRMS error as function of time for the SEEK and SEIK filters with the low resolution Mediterranean model using different ranks for the error covariance matrices (anal-ysis step is 5 days).
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