Diagnostic Studies of Numerical Long-Range Predictions

Colucci建议用数值模式研究扩展的(30天)预报的准确性，并确定初始和演变状态的动力学特征，这些特征在模型表现出低技能的阶段和高技能的阶段之间区分开来，而在这些阶段中预测技能的变化在初始定义的状态中有很小的变化。所使用的技术是“案例合成”，其中气候记录根据相关属性(在这种情况下是预报技能)的某种客观度量被细分，并为每个子类别构造合成模式。成功是通过在复合材料之间找到物理上有意义的和可解释的差异来显示的，然后这些差异可以被用来识别特定的模型缺陷。它高度依赖于从业者的流星学和气候学洞察力。科鲁奇过去在NSF支持下取得的成就表明，他在这方面很有资格。这项研究将有助于美国全球变化研究计划的一个目标，即在1996-2000年期间实施的季节性气候的可操作的数值预报模式。这一模型将作为预测多年气候变化(由自然和/或人为原因引起)的后续模型的垫脚石，具有足够的可信度，可作为制定私营、国家和国际部门资源管理政策的有用指南。

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.