Assessing Atmospheric Predictability with a Global Analysis-Forecast System

使用全球分析预报系统评估大气可预测性

• 批准号: 0722721
• 负责人: Istvan Szunyogh
• 金额: $ 38.98万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0722721&HistoricalAwards=false
• 关键词: Assessing; Atmospheric; Predictability; Global; Analysis

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.

这项研究的重点是在确定性预报的上限范围（最多约两周）的大气可预报性。 这项研究将使用基于局部包络变换卡尔曼滤波方法的数据同化，以审查观测对预报的影响，特别是事先确定最需要进行具体测量的地点（“目标观测”），以改进预报。 这项研究扩展了以前的工作，在完美的模型场景（比较模型的模型）更现实的应用程序（预测模型与真实的世界）。 研究亦会探讨不同环流系统的可预测性时限，调查季节性对可预测性的影响，以及评估即将进行的太平洋亚洲区域活动（T-PARC）所收集的观测资料对预测的影响。 这项研究将使用国家环境预报中心全球预报系统模型的最新运行版本。 研究的成果有可能通过精确定位额外观测最有用的地方来改进预测，从而优化可部署的资源。 业务预测模型的使用应便于知识从基础研究阶段向业务的过渡。 一名研究生将得到支持并参与研究。