Optimization of multi-model ensemble forecasting of typhoon waves

Pan, Shunqi ORCID: https://orcid.org/0000-0001-8252-5991, Fan, Yang-ming, Chen, Jia-ming and Kao, Chia-chuen 2016. Optimization of multi-model ensemble forecasting of typhoon waves. Water Science and Engineering 9 (1) , pp. 52-57. 10.1016/j.wse.2016.02.001

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Abstract

Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles). The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TC Hydraulic engineering. Ocean engineering
Uncontrolled Keywords:	Wave modeling; Optimization; Forecasting; Typhoon waves; WAVEWATCH III; Locally weighted learning algorithm
Publisher:	Elsevier
ISSN:	1674-2370
Date of First Compliant Deposit:	12 April 2016
Date of Acceptance:	30 November 2015
Last Modified:	05 May 2023 16:49
URI:	https://orca.cardiff.ac.uk/id/eprint/89046

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