Truncation Optimization for Global Prediction Models

全局预测模型的截断优化

• 批准号: 9300976
• 负责人: Ferdinand Baer
• 金额: $ 18.62万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9300976&HistoricalAwards=false
• 关键词: Truncation; Optimization; Global; Prediction; Models

9300976 Baer Dr. Baer's previous results indicate that improved forecast skill with atmospheric prediction models can be achieved with appropriate distribution of model levels in the vertical. The initial studies were done with a model beginning with a basic state with the atmosphere at rest. While this approximation makes the problem tractable numerically, it is not terribly realistic. Under this award, the PI will consider a model in which more realistic assumptions and explore optimum grid distributions with respect to latitude and height. The results of this research should lead to numerical representations for weather and climate prediction models which are more efficient and accurate and remove significant numerical truncation errors. ***

小行星9300976 Baer博士以前的研究结果表明，通过在垂直方向上适当分布模型水平，可以提高大气预测模型的预测能力。 最初的研究是用一个从大气静止的基本状态开始的模型进行的。 虽然这种近似使问题在数值上易于处理，但它并不十分现实。 根据该奖项，PI将考虑一个模型，其中更现实的假设和探索最佳的网格分布相对于纬度和高度。 这项研究的结果将导致天气和气候预测模式的数值表示，更有效和准确，并消除显着的数值截断误差。 ***