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报告题目:
全球变化科学紫荆论坛(第259期): Research and Development of GSI-based Ensemble-Variational (EnVar) Hybrid Data Assimilation for Global, Hurricane and Convective-scale Severe Weather Prediction
 报告人:
Prof. Xuguang Wang
Multiscale data Assimilation and Predictability (MAP) Laboratory
School of Meteorology University of Oklahoma, Norman,OK
报告时间:
2018-07-05 10:30
报告地点:
蒙民伟科技大楼南楼S818
主办单位:
地球系统科学系
  简介:
GSI-based 3D and 4D ensemble-variational (EnVar) hybrid data assimilation (DA) system improved US NWS NOAA global forecast significantly since it was implemented operationally in 2012and 2016 respectively.  This seminar will first discuss two new developments including the cost effective valid time shifting method to increase ensemble size and the multi-resolution ensemble 4DEnVar.
Research and development have also been made to further develop the GSI-based hybrid DA system for convection-allowing regional modeling systems.  In the second part of the seminar, the hybrid DA system is extended with the operational Hurricane Weather Research and Forecast (HWRF) modeling system to improve high-resolutiontropical cyclone prediction. Experiments have demonstrated that the new DA system was able to improve the hurricane intensity forecasts.  The fully cycled and self-consistent hybrid DA system for HWRF was operationally implemented beginning summer 2017. In addition, recent research using the HWRF hybrid DA system to identify and diagnose a model error associated with the PBL physics scheme,and to best assimilate inner core data will be discussed.
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