A Case Study of Wave Forecast Over South China Sea Using SWAN Model and Ensemble Kalman Filter Method

Thanh Nguyen Trung, Lars Robert Hole, Tien Du Duc and Huan Nguyen Minh
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Thanh Nguyen Trung: VNU University of Science, Vietnam
Tien Du Duc: Norwegian Meteorological Institute, Norway

Oceanography & Fisheries Open Access Journal, 2020, vol. 12, issue 4, 101-114

Abstract: The study uses the simulating waves nearshore (SWAN) model and ensemble Kalman filter (EnKF) method, which has been integrated in the Open Data Assimilation system, to assimilate wave observation data from the oil platform over the South China Sea - East Sea of Vietnam. A set of experiments with and without EnKF was established based on the different parameters, including ensemble size, standard deviation for the wind noise field, time correlation scale for boundaries updating processes, and the horizontal correlation scale in covariance derivation. Compared to observation and re-analysis European Centre for Medium-Range Weather Forecasts (ECMWF) data, results showed improved forecasts on the significant wave height and other SWAN model variables with data assimilation. The experiments have clearly updated error forecast information of the model (positive bias) to the 24 hour forecast results. The significant sensitivities to both magnitude and distribution for increments of model analysis fields and for forecast results on spatial and time correlation scales show the importance of investigating more suitable EnKF parameters for specific area and observation applications.

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Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jofoaj:v:12:y:2020:i:4:p:101-114

DOI: 10.19080/OFOAJ.2020.12.555842

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