Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Sensitivity of Data Assimilation Configuration in WAVEWATCH III applying Ensemble Optimal Interpolation

  • Hye Min Lim (Pukyong National University) ;
  • Kyeong Ok Kim (Korea Institute of Ocean Science and Technology) ;
  • Hanna Kim (Korea Institute of Ocean Science and Technology) ;
  • Sang Myeong Oh (National Institute of Meteorological Sciences, KMA) ;
  • Young Ho Kim (Pukyong National University)
  • Received : 2024.08.14
  • Accepted : 2024.08.29
  • Published : 2024.08.31
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Abstract

We aimed to evaluate the effectiveness of ensemble optimal interpolation (EnOI) in improving the analysis of significant wave height (SWH) within wave models using satellite-derived SWH data. Satellite observations revealed higher SWH in mid-latitude regions (30° to 60° in both hemispheres) due to stronger winds, whereas equatorial and coastal areas exhibited lower wave heights, attributed to calmer winds and land interactions. Root mean square error (RMSE) analysis of the control experiment without data assimilation revealed significant discrepancies in high-latitude areas, underscoring the need for enhanced analysis techniques. Data assimilation experiments demonstrated substantial RMSE reductions, particularly in high-latitude regions, underscoring the effectiveness of the technique in enhancing the quality of analysis fields. Sensitivity experiments with varying ensemble sizes showed modest global improvements in analysis fields with larger ensembles. Sensitivity experiments based on different decorrelation length scales demonstrated significant RMSE improvements at larger scales, particularly in the Southern Ocean and Northwest Pacific. However, some areas exhibited slight RMSE increases, suggesting the need for region-specific tuning of assimilation parameters. Reducing the observation error covariance improved analysis quality in certain regions, including the equator, but generally degraded it in others. Rescaling background error covariance (BEC) resulted in overall improvements in analysis fields, though sensitivity to regional variability persisted. These findings underscore the importance of data assimilation, parameter tuning, and BEC rescaling in enhancing the quality and reliability of wave analysis fields, emphasizing the necessity of region-specific adjustments to optimize assimilation performance. These insights are valuable for understanding ocean dynamics, improving navigation, and supporting coastal management practices.

Keywords

Acknowledgement

This research was funded by the Korea Meteorological Administration Research and Development Program "Development of Marine Meteorology Monitoring and Next-generation Ocean Forecasting System" under Grant (KMA2018-00420).

References

  1. Booij, N., Ris, R.C., and Holthuijsen, L.H., 1999, A third-generation wave model for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans, 104(C4), 7649-7666.
  2. Bouttier, F., and Courtier, P., 2002, Data assimilation concepts and methods. Meteorological Training Course Lecture Series, ECMWF, 59 p.
  3. Burgers, G., van Leeuwen, P.J., and Evensen, G., 1998, Analysis scheme in the ensemble Kalman filter. Monthly Weather Review, 126(6), 1719-1724.
  4. Counillon, F., and Bertino, L., 2009, Ensemble optimal interpolation: multivariate properties in the Gulf of Mexico. Tellus A: Dynamic Meteorology and Oceanography, 61(2), 296-308.
  5. Cummings, J.A., and Smedstad, O.M., 2009, Variational data assimilation for the global ocean. Ocean Dynamics, 59(6), 991-1008.
  6. Daniel, T., Manley, J., and Trenaman, N., 2011, The Wave Glider: Enabling a new approach to persistent ocean observation and research. Ocean Dynamics, 61(10), 1509-1520.
  7. Evensen, G., 1994, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143-10162.
  8. Evensen, G., 2003, The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367.
  9. Gaspari, G., and Cohn, S.E., 1999, Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125(554), 723-757.
  10. Gommenginger, C.P., Strokosz, M.A., Challenor, P.G., and Cotton, P.D., 2003, Measuring ocean wave period with satellite altimeters: A simple empirical model. Geophysical Research Letters, 30(22), 2150.
  11. Hamill, T.M., Whitaker, J.S., and Snyder, C., 2001, Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Monthly Weather Review, 129(11), 2776-2790.
  12. Hisaki, Y., 2005, Ocean wave directional spectra estimation from an HF ocean radar with a single antenna array: Observation. Journal of Geophysical Research, 110(C11). https://doi.org/10.1029/2005JC002881
  13. Houtekamer, P.L., and Mitchell, H.L., 1998, Data assimilation using an ensemble Kalman filter technique. Monthly Weather Review, 126(3), 796-811.
  14. Houtekamer, P.L., and Mitchell, H.L., 2001, A sequential ensemble Kalman filter for atmospheric data assimilation. Monthly Weather Review, 129(1), 123-137.
  15. Kim, Y.H., Hwang, C., and Choi, B.J., 2015, An assessment of ocean climate reanalysis by the data assimilation system of KIOST from 1947 to 2012. Ocean Modelling, 91, 1-22. doi: 10.1016/j.ocemod.2015.02.006
  16. Komen, G.J., Cavaleri, L., Donelan, M., Hasselmann, S., Hasselmann, K., and Janssen, P.A.E.M., 1994, Dynamics and Modelling of Ocean Waves. Cambridge University Press, 532 p.
  17. Li, J., and Zhang, S., 2020, Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5. 16 and SWAN v41. 20. Geoscientific Model Development, 13(3), 1035-1054.
  18. Liu, J.C., Xu, B., and Wang, J.C., 2023, Ensemble-based assimilation of wave model predictions: contrasting the impact of assimilation in nearshore and offshore forecasting at different distances from assimilated data. Applied Ocean Research, 140, 103726.
  19. Lzaguirre, C., Mendez, F.J., Menendez, M., and Losada, I.J., 2011, Global extreme wave height variability based on satellite data. Geophysical Research Letters, 38(10), 415-421.
  20. Mitsuyasu, H., Tasai, F., Suhara, T., Mizuno, S., Ohkusu, M., Honda, T., and Rikiishi, K., 1980, Observation of the power spectrum of ocean waves using a cloverleaf buoy. Journal of Physical Oceanography, 10(2), 286-296.
  21. Miyoshi, T., 2011, The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Monthly Weather Review, 139(5), 1519-1535.
  22. Oke, P.R., Allen, J.S., Miller, R.N., and Egbert, G.D., 2002, Assimilation of surface velocity data into a primitive equation coastal ocean model. Journal of Geophysical Research, 107(C9), 5-1-5-25.
  23. Oke, P.R., Sakov, P., and Corney, S.P., 2007, Impacts of localisation in the EnKF and EnOI: Experiments with a small model. Ocean Dynamics, 57(1), 32-45.
  24. Penny, S.G., Behringer, D.W., Carton, J.A., and Kalnay, E., 2013, A hybrid global ocean data assimilation system at NCEP. Monthly Weather Review, 141(8), 2740-2760.
  25. Qi, P., and Cao, L., 2015, The assimilation of Jason-2 significant wave height data in the north Indian ocean using the ensemble optimal interpolation. IEEE Transactions on Geoscience and Remote Sensing, 54 (1), 287-297.
  26. Queffeulou, P., 2004, Long-term validation of wave height measurements from altimeters. Marine Geodesy, 27, 495-510.
  27. Rapizo, H., Babanin, A.V., Schulz, E., Hemer, M.A., and Durrant, T.H., 2015, Observation of wind-waves from a moored buoy in the Southern Ocean. Ocean Dynamics, 65, 1275-1288.
  28. Sakov, P., Counillon, F., Bertino, L., Lisaeter, K.A., Oke, P.R., and Korablev, A., 2012, TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Science, 8(4), 633-656.
  29. Saulter, A.N., Bunney, C., King, R.R., and Waters, J., 2020, An application of NEMOVAR for regional wave model data assimilation. Frontiers in Marine Science, 7, 579834.
  30. The WAVEWATCH III® Development Group, 2019, User manual and system documentation of WAVEWATCH III® version 6.07. NOAA/NWS/NCEP/MMAB Technical Note 333, 465 pp.
  31. WAMDI Group, 1988, The WAM model - A third generation ocean wave prediction model. Journal of Physical Oceanography, 18(12), 1775-1810.
  32. Walsh, E.J., Hancock, D.W., and Hines, D.E., 1989, An observation of the Directional Wave Spectrum Evolution from shoreline to fully developed. Journal of Physical Oceanography, 19(5), 670-690.
  33. Waters, J., Wyatt, L.R., Wolf, J., and Hines, A., 2013, Data assimilation of partitioned HF radar wave data into Wavewatch III. Ocean Modelling, 72, 17-31.
  34. Wittmann, P., and Cummings, J., 2005, Assimilation of Altimeter Wave Measurements into Wavewatch III. 12 p.
  35. Xie, J., Counillon, F., Zhu, J., and Bertino, L., 2011, An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI. Ocean Science, 7(5), 609-627.
  36. Xie, J., and Zhu, J., 2010, Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model. Ocean Modelling, 33(3), 283-298.
  37. Yu, H., Li, J., Wu, K., Wang, Z., Yu, H., Zhang, S., Hou, Y., and Kelly R.M., 2018, A global high-resolution ocean wave model improved by assimilating the satellite altimeter significant wave height. International Journal of Applied Earth Observation and Geoinformation, 70, 43-50.