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<p>Wave hindcasting is pivotal for offshore operations and still poses challenges to modelers. Enhancing the accuracy of wave prediction involves either leveraging wind inputs from atmospheric models or assimilating wave observations. The present study uses an ensemble-based wave data assimilation method to refine wave parameter predictions. Focusing on the Bay of Bengal in the Indian Ocean. The wind-wave model, SWAN has been set up with forcing from six-hourly ECMWF ERA5 wind datasets with a resolution of 0.25°x0.25°. To generate an ensemble of wave fields, the wind vector has been perturbed from its initial states. Subsequently, the ensemble-based data assimilation scheme was developed to enhance the accuracy of significant wave height and mean wave period derived from the wind-wave model. The proposed scheme strategically distributes the errors across the model domain through a gain matrix. The study further highlights gain contours at various observation locations to illustrate the efficacy of the assimilation process. Based on the results, significant improvements in wave height prediction with the assimilation scheme demonstrate an effectiveness of 30% to 50% in reducing root mean square errors at validation locations.</p>

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Ensemble-based data assimilation to improve wind-wave field in the Bay of Bengal

  • 期刊论文

  • 发表日期：

    2025年10月30日

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  • Tharani A.,
  • Sannasiraj S. A.,
  • Balaji C.,
  • Seemanth M.,
  • Suchandra Aich Bhowmick

摘要

Wave hindcasting is pivotal for offshore operations and still poses challenges to modelers. Enhancing the accuracy of wave prediction involves either leveraging wind inputs from atmospheric models or assimilating wave observations. The present study uses an ensemble-based wave data assimilation method to refine wave parameter predictions. Focusing on the Bay of Bengal in the Indian Ocean. The wind-wave model, SWAN has been set up with forcing from six-hourly ECMWF ERA5 wind datasets with a resolution of 0.25°x0.25°. To generate an ensemble of wave fields, the wind vector has been perturbed from its initial states. Subsequently, the ensemble-based data assimilation scheme was developed to enhance the accuracy of significant wave height and mean wave period derived from the wind-wave model. The proposed scheme strategically distributes the errors across the model domain through a gain matrix. The study further highlights gain contours at various observation locations to illustrate the efficacy of the assimilation process. Based on the results, significant improvements in wave height prediction with the assimilation scheme demonstrate an effectiveness of 30% to 50% in reducing root mean square errors at validation locations.