An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0
نویسندگان
چکیده
Abstract. This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system in which the local transform (LETKF) is implemented with version 1.0 of Stony Brook Parallel Ocean Model (sbPOM) to assimilate satellite and situ observations at a daily frequency. A series sensitivity experiments are performed various settings incremental analysis update (IAU) covariance inflation methods, for relaxation-to-prior perturbations spread (RTPP RTPS, respectively) multiplicative (MULT) considered. We evaluate geostrophic balance accuracy compared control experiment IAU not applied. The results show that improves balance, degrades accuracy, reduces spread, RTPP RTPS have opposite effect. using combination significant improvement both when parameter 0.8–0.9. ≤0.8 increases values between 1.1, but improved significantly same time. MULT inflating forecast by 5 % do demonstrate sufficient skill maintaining reproducing surface flow field regardless whether applied or not. 11 d consistent results. Therefore, value 0.8–0.9 found be best setting EnKF-based system.
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ژورنال
عنوان ژورنال: Geoscientific Model Development
سال: 2022
ISSN: ['1991-9603', '1991-959X']
DOI: https://doi.org/10.5194/gmd-15-8395-2022