Diagnostic Studies of Numerical Long-Range Predictions

数值长期预测的诊断研究

• 批准号: 9112922
• 负责人: Stephen Colucci
• 金额: --
• 依托单位: Cornell Univ - State: AWDS MADE PRIOR MAY 2010
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-01 至 1993-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9112922&HistoricalAwards=false
• 关键词: Diagnostic; Studies; Numerical; Long; Range

Colucci proposes to study the accuracy of extended (30-day) forecasts by a numerical model, and to identify those dynamical characteristics of initial and evolving states that distiguish between periods in which the model exhibits low skill, from those with high skill, and those in which forecast skill changes drasti- cally with minor variations in the initially defined state. The technique to be used is "case compositing", in which the climato- logical record is subdivided according to some objective measure of the relevant property (in this case, forecast skill) and composite patterns are constructed for each of the subcategories. Success is shown by finding physically meaningful and interpretable differ- ences between the composites, which may then be used to identify specific model deficiencies. It is highly dependent on the meteor- ological and climatological insights of the practitioner. Colucci's past accomplishments under prior NSF support show that he is well qualified in this respect. The study will contribute to one object- ive of the US Global Change Research Program, viz., an operational numerical forecast model for seasonal climate to be implemented during the 1996-2000 period. Such a model will serve as a stepping stone to subsequent models predicting multi-annual climate changes (induced by natural and/or man-made causes) with sufficient credi- bility to serve as useful guides in the framing of resource- management policy within the private, national, and international sectors.

Colucci建议研究延长（30天）的准确性 预测的数值模型，并确定这些动态 区分初始状态和演变状态的特征 在模型表现出低技能的时期之间， 高技能的人，预测技能变化的人， 在最初定义的状态下具有微小的变化。的 技术将被使用是“情况合成”，其中气候- 逻辑记录是根据某些客观的度量来细分的， 相关属性（在本例中为预测技能）和组合 为每个子类别构造模式。成功是 通过发现物理上有意义和可解释的不同- 复合材料之间的差异，然后可以用来识别 具体模型缺陷。它高度依赖于流星- 实践者的科学和气候学见解。科卢奇 过去在NSF的支持下取得的成就表明，他很好 在这方面有资格。这项研究将有助于实现一个目标- 美国全球变化研究计划，即，操作 季节性气候数值预报模式即将启用 在1996-2000年期间。这样的模式将作为一个步骤， 石头到后续模型预测多年气候变化 （由自然和/或人为原因引起的）有足够的信用， 在资源框架中充当有用指南的能力- 私人、国家和国际范围内的管理政策 板块