Statistical Atmospheric Predictability at Varying Time Scales

传统上，大气研究中的可预报性方面一直关注其对天气预报的初始条件和边界条件的依赖性。 预测.研究人员将研究中间范围，其中重要的可预测性领域可能会发现在每周的时间尺度。两个足够复杂但计算效率高的数值模型将用于探索统计预测。将研究初始和边界条件，系综大小和最佳的时间平均策略的影响。能够对集合预报的技能和该技能的来源做出合理的先验估计将在预报界引起相当大的兴趣。该项目将涉及一名研究生的参与。

Traditionally the aspect of predictability in atmospheric research has been concerned with its dependence on initial conditions for weather prediction and on boundary conditions for prediction. The investigators will study the intermediate range where important areas of predictability may be found at weekly timescales. Two numerical models that are sufficiently complex but computationally efficient will be used to explore statistical prediction. The influence of initial and boundary conditions, ensemble size and the optimal temporal averaging strategies will be studied. Being able to make a reasonable a-priori estimate of an ensemble forecast's skill and the sources for that skill would be of considerable interest in the forecasting community. The project will involve the participation of a graduate student.