Improved prediction of local significant wave height by considering the memory of past winds
Journal article
Zhang, Shaotong, Yang, Zhen, Zhang, Yaqi, Zhao, Shangrui, Wu, Jinran, Wang, Chenghao, Wang, You-Gan, Jeng, Dong-Sheng, Nielsen, Peter, Li, Guangxue and Li, Sanzhong. (2023). Improved prediction of local significant wave height by considering the memory of past winds. Water Resources Research. 59(8), p. Article e2023WR034974. https://doi.org/10.1029/2023WR034974
Authors | Zhang, Shaotong, Yang, Zhen, Zhang, Yaqi, Zhao, Shangrui, Wu, Jinran, Wang, Chenghao, Wang, You-Gan, Jeng, Dong-Sheng, Nielsen, Peter, Li, Guangxue and Li, Sanzhong |
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Abstract | Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high-precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data-driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind-decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves. |
Keywords | wind waves; wind speed; wind direction; in situ observation; support vector machine regression; Yellow River Delta |
Year | 2023 |
Journal | Water Resources Research |
Journal citation | 59 (8), p. Article e2023WR034974 |
Publisher | Wiley-Blackwell Publishing, Inc. |
ISSN | 0043-1397 |
Digital Object Identifier (DOI) | https://doi.org/10.1029/2023WR034974 |
Scopus EID | 2-s2.0-85167867407 |
Page range | 1-17 |
Funder | National Natural Science Foundation of China (NSFC) |
Chunhui Program Collaborative Scientific Research Project | |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 11 Aug 2023 |
Publication process dates | |
Accepted | 28 Jul 2023 |
Deposited | 29 Nov 2023 |
Grant ID | 42276215 |
42121005 | |
202202004 |
https://acuresearchbank.acu.edu.au/item/8zzx7/improved-prediction-of-local-significant-wave-height-by-considering-the-memory-of-past-winds
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