Improving atmosphere-ocean prediction on time scales ranging from days tro seasons: assimilation of Argo data and the construction of ensemble predictions

改进从季节到天数的时间尺度上的大气-海洋预测：Argo数据的同化和集合预测的构建

• 批准号: 327695-2011
• 负责人: Tang, Youmin
• 金额: $ 2.04万
• 依托单位: University of Northern British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=527075
• 关键词: Improving; atmosphere; ocean; prediction; time

This research proposal aims to improve the prediction of the atmosphere-ocean coupled system from days to seasons by: 1) improving oceanic data assimilation techniques; and 2) developing an optimal perturbation technique for multiple predictions (i.e., ensemble prediction). Emphasis will be placed on the assimilation of ocean data generated by Argo buoys and the construction of ensemble climate predictions, as well as the verification of the ensemble predictions. The effort will be focused on assimilation experiments and ensemble predictions using the NCAR (National Center for Atmospheric Research) CCSM4 (Community Climate System Model version 4). Through the proposed program, we expect to improve our ability to predict weather (i.e., short-term forecasts) and climate (long-term forecasts) at multiple time scales, especially in seasonal climate prediction. The expected findings and conclusions may also lead to important theoretical contributions and a deeper understanding of ocean data assimilation and the predictability study of multiple time scales. Thus this research has both strong theoretical components and very practical applications for Canada.

这项研究计划旨在通过以下方式改善从白天到季节的大气-海洋耦合系统的预测：1）改善海洋数据同化技术; 2）开发用于多种预测的最佳扰动技术（即，集合预测）。重点将放在Argo浮标产生的海洋数据的同化和建立集合气候预测，以及验证集合预测。这项工作将集中在同化实验和集合预测使用NCAR（国家大气研究中心）CCSM 4（社区气候系统模型版本4）。通过拟议的计划，我们希望提高我们预测天气的能力（即，短期预报）和气候（长期预报），特别是季节性气候预报。预期的发现和结论也可能导致重要的理论贡献和更深入地了解海洋数据同化和多个时间尺度的可预测性研究。因此，这项研究既有很强的理论成分和加拿大非常实际的应用。