Assessing Atmospheric Predictability with a Global Analysis-Forecast System

这项研究的重点是在确定性预报的上限范围内(最长约两周)的大气可预报性。这项研究将使用基于局部集合变换卡尔曼滤波法的数据同化方法，以检查观测对预报的影响，特别是先验地确定特定测量(“目标观测”)最需要的位置，以改进预报。这项研究将以前在完美模型场景中的工作(将模型与模型进行比较)扩展到更现实的应用(预测模型与现实世界)。这项研究还将检查不同环流机制的可预报时限，调查季节性对可预报性的影响，并评估即将到来的THORPEX亚太区域运动(T-PARC)收集的观测对预报的影响。这项研究将使用国家环境预测中心最近运行的全球预测系统模型。研究成果有可能通过精确确定哪些额外的观测最有用来改进预测，从而优化可部署的资源。业务预测模型的使用应便于将知识从基础研究阶段转移到业务阶段。一名研究生将得到支持并参与这项研究。

The focus of this research is on atmospheric predictability at the upper ranges of deterministic forecasting (up to about two weeks). The study will use data assimilation based on the Local Ensemble Transform Kalman Filter method to examine the impact of observations on forecasts, and in particular to determine a priori the most needed locations for specific measurements ("targeted observations") to improve forecasts. This research extends previous work in perfect model scenarios (comparing models to models) to more realistic applications (forcast models versus real world). The study will also examine the predictability time limits for different circulation regimes, investigate seasonal effects on predictability, and assess the impacts of observations collected in the upcoming THORPEX Pacific Asian Regional Campaign (T-PARC) on forecasts. The study will use a recent operational version of the Global Forecast System model of the National Centers for Environmental Prediction. The fruits of the research have the possibility to improve forecasting by pinpointing where additional observations are most useful, thereby optimizing deployable resources. The use of an operational forecast model should ease the transition of knowledge from the basic research phase to operations. A graduate student will be supported and involved in the research.