Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8395–8410, 2022
https://doi.org/10.5194/gmd-15-8395-2022
Geosci. Model Dev., 15, 8395–8410, 2022
https://doi.org/10.5194/gmd-15-8395-2022
Development and technical paper
18 Nov 2022
Development and technical paper | 18 Nov 2022

An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0

Shun Ohishi et al.

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Cited articles

Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS NGDC-24, National Geophysical Data Center, NOAA, https://doi.org/10.7289/V5C8276M, 2009. 
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Baduru, B., Paul, B., Banerjee, D. S., Sanikommu, S., and Paul, A.: Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation, Ocean Model., 143, 101470, https://doi.org/10.1016/j.ocemod.2019.101470, 2019. 
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An introduction to Himawari-8/9 – Japan's new-generation geostationary meteorological satellites, J. Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. 
Short summary
We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.