Predicting environmental extremes in a period of climate change

预测气候变化时期的极端环境

• 批准号: DP160100738
• 负责人: Prof Ian Young
• 金额: $ 31.74万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-04-03 至 2020-09-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160100738
• 关键词: Predicting; environmental; extremes; period; climate

This project has the potential to reduce the uncertainty in the predictions of extreme winds and waves used to design and operate coastal and offshore facilities. Predictions are typically achieved by extrapolating recorded data to predict probable extremes. The uncertainties associated with this approach are very large. This project aims to develop a new approach called ‘large ensemble aggregate’ analysis, which brings together data from alternative model predictions or alternative measurement locations to expand the effective data and avoid the necessity for statistical extrapolation. This approach may significantly reduce the uncertainty in estimating extreme values. This would reduce the cost of constructing coastal and offshore facilities and decrease the risk of catastrophic failure.

该项目有可能减少用于设计和操作沿海和海上设施的极端风和海浪预测的不确定性。预测通常是通过外推记录数据来预测可能的极端情况来实现的。与此方法相关的不确定性非常大。该项目旨在开发一种称为“大集合汇总”分析的新方法，该方法将来自不同模型预测或不同测量地点的数据汇集在一起，以扩大有效数据并避免统计外推的必要性。这种方法可以显著减少估计极值时的不确定性。这将降低建造海岸和近海设施的成本，并降低发生灾难性故障的风险。