Collaborative Research: Enabling Petascale Ensemble-Based Data Assimilation for the Numerical Analysis and Prediction of High-Impact Weather

合作研究：实现基于千万亿次集合的数据同化，以进行高影响天气的数值分析和预测

• 批准号: 0905040
• 负责人: Ming Xue
• 金额: $ 90.19万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905040&HistoricalAwards=false
• 关键词: Collaborative; Research; Enabling; Petascale; Ensemble

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This proposal addresses the most challenging problems of very-high-resolution Numerical Weather Prediction, obtaining the optimal state estimations for initializing ensembles of predictions by assimilating the highest volume of weather observations available, and addressing problem sizes and scales that are only attainable on petascale computing platforms. The project is focused on the very hard problem of thunderstorm initiation, propagation and evolution. The effect of this investment in more accurate and timely forecast of thunderstorms can have a potentially transformative impact on hazard analysis and management.

该奖项是根据2009年美国复苏和再投资法案资助的（公法111-5）。该提案解决了极高分辨率数值天气预报的最具挑战性的问题，通过同化可用的最大量的天气观测来获得用于初始化预测集合的最佳状态估计，并解决只有在千万亿次计算平台上才能达到的问题大小和规模。该项目的重点是雷暴的产生、传播和演变这一非常困难的问题。在更准确和及时地预报雷暴方面的这种投资的效果可能对灾害分析和管理产生潜在的变革性影响。