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: 0935538
• 负责人: Istvan Szunyogh
• 金额: $ 22.32万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0935538&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.

这项研究的重点是确定性预测的上部范围（长达两周）的大气可预测性。 该研究将基于局部集合转换Kalman滤波器方法使用数据同化，以检查观察结果对预测的影响，尤其是确定先验的特定测量所需的位置（“目标观察”）以改善预测。 这项研究将以前的工作扩展到完美的模型方案（将模型与模型进行比较）与更现实的应用程序（前播模型与现实世界）。 该研究还将检查不同循环方案的可预测性时间限制，研究季节性对可预测性的影响，并评估即将到来的索尔佩克斯太平洋亚洲地区运动（T-PARC）对预测的观察结果的影响。 该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。 研究的果实有可能通过确定最有用的其他观察结果来改善预测，从而优化可部署的资源。 运营预测模型的使用应减轻知识从基础研究阶段到操作的过渡。 研究生将得到支持并参与研究。