4.7 Article

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

期刊

APPLIED OCEAN RESEARCH
卷 71, 期 -, 页码 20-33

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apor.2017.11.012

关键词

Tropical cyclone; ERA-Interim; Holland model; Blended wind field; South China Sea; East China Sea; Wave modeling

资金

  1. National Science Fund [51739010, 51679223]
  2. Shandong Provincial Natural Science Key Basic Program [ZR2017ZA0202]
  3. Science and Technology Project of Ministry of Industry and Information Technology of China [E-0815C003]

向作者/读者索取更多资源

Accurate tropical cyclone (TC) wind fields are crucial for modeling TC waves. Usually, reanalysis wind data, such as the ERA-Interim dataset, and parametric TC models, such as the Holland model, are widely used to generate TC wind fields. In the, present work, 29 tropical cyclones (TCs) in the South China Sea (SCS) and East China Sea (ECS) of 4 years (2011-2014) are analyzed at 10 buoy locations. Among them, 9 TCs are selected as study cases due to buoys experience their whole processes of the TC passing. For the 9 selected TCs, data of the ERA-Interim and Holland model are compared with observation data of 10 buoys in the SCS and ECS. Results show that the ERA-Interim largely under-predicts wind speeds near the TC center, where the Holland model performs generally well. However, the Holland model fails to reproduce wind speeds in outer-region of the TC. After analyzing character of two sets of data, applicable ranges for the ERA-Interim and Holland model are identified with critical boundary limits, which are associated with the TC size. A formula for blended TC wind fields combining two datasets is proposed, which shows good capacity of the TC wind simulation. Then, the blended wind model is applied in TC wave simulations in the SCS and ECS, and shows a better performance than both the ERA-Interim and the Holland model. Thus, the proposed blending formula can be used to generate more accurate TC wind fields which are required in TC wave hindcasts. (C) 2017 Elsevier Ltd. All rights reserved.

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