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首页> 外文会议>ASME International Conference on Ocean, Offshore and Arctic Engineering >EXTREME WIND AND WAVE PREDICTABILITY FROM OPERATIONAL FORECASTS AT THE DRAKE PASSAGE
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EXTREME WIND AND WAVE PREDICTABILITY FROM OPERATIONAL FORECASTS AT THE DRAKE PASSAGE

机译：极高的风速和波峰可预测性

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The Drake Passage is an ocean area between the South America and Antarctic with very extreme wind and wave climates. The forecast accuracy of surface winds and significant wave heights is analyzed in the present paper, in order to study the behavior and distribution of model uncertainties as a function of: forecast range, severity, location, and numerical model. The operational forecast considered is run twice a day by the Brazilian Navy, and a period of one year (2017) is selected for the assessment. Observations consist of four satellite missions: JASON2, JASON3, CRYOSAT, and SARAL. The numerical atmospheric models with 10-m winds are GFS (Global Forecast System) and ICON (Icosahedral Nonhydrostatic Model). They force wave simulations using the model WAVEWATCH III with a mosaic of two grids. The forecast horizon analyzed is five days, which is made publicly available by the Brazilian Navy. Results show that under calm to moderate conditions, within the first two days of forecast, the wind and wave model skill is very high. However, above the 90th percentile and beyond the third forecast day, the predictability drops significantly. It highlights specific contours of forecast range versus percentiles where the wind and wave modelers should focus, in order to anticipate and to improve the predictability of extreme events at the Drake Passage.
机译：德雷克海峡（Drake Passage）是介于南美和南极之间的海洋地区，风和浪气候极为极端。本文分析了地表风和重要波高的预测准确性，以便研究模型不确定性的行为和分布，这些不确定性是以下因素的函数：预测范围，严重性，位置和数值模型。所考虑的作战预报每天由巴西海军执行两次，并选择一年（2017年）进行评估。观测包括四个卫星任务：JASON2，JASON3，CRYOSAT和SARAL。风速为10米的数值大气模型是GFS（全球预报系统）和ICON（二十面体非静水模型）。他们使用带有两个网格的WAVEWATCH III模型强制进行波仿真。分析的预测期限为五天，由巴西海军公开提供。结果表明，在平静到中等的条件下，在预测的前两天内，风浪模型技巧非常高。但是，在第90个百分位数以上并且在第三个预测日之后，可预测性会大大下降。它突出显示了风场和波浪建模人员应关注的预测范围与百分位数的特定等值线，以预测并提高Drake Passage极端事件的可预测性。

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