Assessing Atmospheric Predictability with a Global Analysis-Forecast System

本研究的重点是在确定性预报的较高范围内（最多约两周）的大气可预测性。该研究将使用基于局部集合变换卡尔曼滤波方法的数据同化来检查观测对预测的影响，特别是先验地确定最需要的特定测量位置（“目标观测”）以改进预测。这项研究将之前在完美模型场景（将模型与模型进行比较）中的工作扩展到更现实的应用（预测模型与现实世界）。该研究还将审查不同环流制度的可预测时限，调查季节对可预测性的影响，并评估即将到来的THORPEX太平洋亚洲区域运动（T-PARC）收集的观测资料对预报的影响。这项研究将使用国家环境预测中心的全球预测系统模型的最新操作版本。研究的成果有可能通过精确地指出在哪些地方额外的观测最有用来改进预测，从而优化可部署的资源。使用业务预测模型可以简化从基础研究阶段到业务阶段的知识过渡。一名研究生将被支持并参与研究。

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.