Analysis of Reliability of Weather Fields for Typhoon Sanba (1216)

Kwon, Kab Keun; Jho, Myeong Hwan; Ryu, Kyong Ho; Yoon, Sung Bum

doi:10.9765/kscoe.2020.32.6.465

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The JMA-MSM (Saito et al, 2006) wind data are the product of a meso-scale weather forecast model operated by JMA and have a high spatial-temporal resolution. As such, these data have been used as input data in previous studies on numerical simulation of waves in the waters around Korea (Kim et al, 2020;Kwon et al, 2020;Yoon et al, 2020). Table 2 lists the spatial-temporal resolution, area, and data provision period for the 3 models used as input wind data in this study.…”

Section: Sea Surface Wind Datamentioning

confidence: 99%

Optimization of SWAN Wave Model to Improve the Accuracy of Winter Storm Wave Prediction in the East Sea

Son

2021

J. Ocean Eng. Technol.

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In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen’s formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA’s) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts’ newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA’s) meso-scale forecasting data. We analyzed the accuracy of each model’s results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

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