Blended wind fields for wave modeling of tropical cyclones in the South China Sea and East China Sea

Shao, Zhuxiao; Liang, Bingchen; Li, Huajun; Wu, Guoxiang; Wu, Zhaohui

doi:10.1016/j.apor.2017.11.012

Cited by 77 publications

(25 citation statements)

References 76 publications

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“…Combining the reanalysis or dynamic and parametric typhoon winds is believed to generate a better wind field in both the near-field and far-field regions of typhoons for storm wave and surge modeling [1,4,5,15,[46][47][48]. Hence, two methods were applied to construct the hybrid winds from the CFSV2 and MRV and from the ERA5 and MRV.…”

Section: Hybrid Typhoon Wind Fieldmentioning

confidence: 99%

“…Hence, two methods were applied to construct the hybrid winds from the CFSV2 and MRV and from the ERA5 and MRV. Method number 1 (H1) blends CFSV2 (or ERA5) and MRV directly through the following formula [1]:…”

Section: Hybrid Typhoon Wind Fieldmentioning

confidence: 99%

“…Typhoons are usually associated with extreme wind waves and storm surges, impacting navigational safety, infrastructure in nearshore and offshore waters (harbors, seawalls, lighthouses, etc. ), and coastal habitats [1][2][3][4][5]. The oceanic and coastal hazards caused by typhoon-driven storm waves pose a greater threat to human life, property, and infrastructure than storm surges in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Simulation of Large Wave Heights from Super Typhoon Nepartak (2016) in the Eastern Waters of Taiwan

Hsiao

Chen

et al. 2020

JMSE

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Super Typhoon Nepartak (2016) was used for this case study because it is the most intense typhoon that made landfall in Taiwan in the past decade. Winds extracted from the Climate Forecast System version 2 (CFSV2) and ERA5 datasets and merged with a parametric typhoon model using two hybrid techniques served as the meteorological conditions for driving a coupled wave-circulation model. The computed significant wave heights were compared with the observations recorded at three wave buoys in the eastern waters of Taiwan. Model performance in terms of significant wave height was also investigated by employing the CFSV2 winds under varying spatial and temporal resolutions. The results of the numerical experiments reveal that the simulated storm wave heights tended to decrease significantly due to the lower spatial resolution of the hourly winds from the CFSV2 dataset; however, the variations in the storm wave height simulations were less sensitive to the temporal resolution of the wind field. Introducing the combination of the CFSV2 and the parametric typhoon winds greatly improved the storm wave simulations, and similar phenomena can be found in the exploitation of the ERA5 dataset blended into the parametric wind field. The overall performance of the hybrid winds derived from ERA5 was better than that from the CFSV2, especially in the outer region of Super Typhoon Nepartak (2016).

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Section: Hybrid Typhoon Wind Fieldmentioning

confidence: 99%

Section: Hybrid Typhoon Wind Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Numerical Simulation of Large Wave Heights from Super Typhoon Nepartak (2016) in the Eastern Waters of Taiwan

Hsiao

Chen

et al. 2020

JMSE

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“…Liang et al (2016) investigated the wave climate of the Bohai Sea, Yellow Sea, and East China Sea for the period from 1990 to 2011 by using the SWAN. Shao et al (2018) analysed 29 tropical cyclones (TCs) in the South China Sea (SCS) and East China Sea (ECS) for a period of four years (2011)(2012)(2013)(2014) at 10 buoy locations. It was found that the ERA-Interim largely under predicted the wind speeds near the TC centre, although the Holland model performed generally satisfactorily for that location.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of Typhoon Waves Generated by Three Typhoons in the China Sea

Shi

Tang

Shen

et al. 2019

Preprint

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Abstract. The typhoon waves generated in the China Sea during the Chan-hom (1509), Linfa (1510) and Nangka (1511) typhoons that occurred in 2015 were numerically investigated. The wave model was based on the a third generation spectral wind-wave model SWAN, in which the wind fields for driving waves were derived from the ERA-interim (ECMWF), CFSv2 (The NCEP Climate Forecast System Version 2) and CCMP (Cross-Calibrated Multi-Platform) datasets. The numerical results were validated using buoy data and satellite observation data. The simulation results under the three types of wind fields were in good agreement with the observed data. The CCMP wind data was the best in simulating waves overall, and the wind speeds pertaining to ERA and CCMP were notably smaller than those observed near the typhoon centre. The Holland wind model was used to revise and optimize the wind speed pertaining to the CCMP near the typhoon centre, and the wind speed correction coefficient, correction formula and corresponding parameters were determined. Based on these findings, the CCMP and CCMP/Holland blended wind fields were used to simulate the typhoon waves generated during the Meranti (1614), Rai (1615) and Malakas (1616) typhoons that occurred in September 2016. A comparison between the simulated wave heights and those obtained from the Jason-2 altimeter data indicated that all correlation coefficients between the simulated values and the satellite observations were greater than 0.75. The blended wind field was better overall in simulating the wave heights. The simulated maximum wave heights were more similar to the satellite observations, and the root mean square error of the blended wind field was 0.223 m lower than that of the CCMP. The results demonstrated that the CCMP wind-driven SWAN model could appropriately simulate the typhoon waves generated by three typhoons in China Sea, and the use of the CCMP/Holland blended wind field could effectively improve the accuracy of typhoon wave simulations.

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“…[3] developed design values for significant wave height accounting for direction and seasonality by means of a non-stationary model. [4] compared the behavior of tropical cyclone data (extreme values), obtained through simulations, versus those recorded at buoys located in the Meridional Chinese Sea and the Oriental Chinese Sea, and proposed an approach combining the set of data. [5] used peak over threshold (POT) methods and block maxima for the assessment of return values for significant wave heights.…”

Section: Introductionmentioning

confidence: 99%

Single Site Extreme Wave Analysis in the Pacific Ocean Comparing Stationary and Non-stationary GEV Models

Calderón-Vega

Mösso

García–Soto

2019

CJAST

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The adequate knowledge of the weather behavior is very important for the design and management of socioeconomical, environmental and sustainability human interests in the coasts and oceans. In the present study an extreme value analysis of maximum significant waves recorded at a buoy located in the Pacific Ocean was carried out. The analysis was carried out from two perspectives, by considering a Generalized Extreme Value (GEV) model with stationary distribution (i.e., the time variations are not accounted for), and by considering a non-stationary GEV model, which incorporates the monthly seasonality of maximum observed values in time increments; the maximum significant wave behavior was parameterized using harmonic functions for the distribution measures. Both approaches were compared for a single buoy. In the study a seasonality effect was found, which was also present at the Gulf of Mexico in previous studies, and which cannot be captured by a stationary model.

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Blended wind fields for wave modeling of tropical cyclones in the South China Sea and East China Sea

Cited by 77 publications

References 76 publications

Numerical Simulation of Large Wave Heights from Super Typhoon Nepartak (2016) in the Eastern Waters of Taiwan

Numerical Simulation of Large Wave Heights from Super Typhoon Nepartak (2016) in the Eastern Waters of Taiwan

Numerical Investigation of Typhoon Waves Generated by Three Typhoons in the China Sea

Single Site Extreme Wave Analysis in the Pacific Ocean Comparing Stationary and Non-stationary GEV Models

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