东亚区域大气再分析技术研究及资料集建设

Development of Atmospheric Data Assimilation Techniques and Regional Reanalysis Datasets in the East Asia

  • 摘要: 从资料搜集、同化系统、数值模式、检验评估四个方面介绍了东亚区域大气再分析技术研究及资料建设取得的进展。就观测资料而言,搜集了常规地面和探空、飞机观测、GPS、雷达、风廓线、卫星导风、野外试验等观测资料。其中未参加国际交换的地面、探空资料、雷达等观测资料是本再分析资料构建的特色之一。针对Gridpoint Statistical Interpolation(GSI)同化系统,开展了该系统的本地化移植,并改进了GSI雷达径向风同化算子。对于Weather Research and Forecasting Model(WRF-ARW)模式,采用多组物理过程参数化方案组合,开展了批量个例和长时间模拟试验,并基于试验结果优化了模式的配置。检验评估系统采用NCAR模式评估系统MET。目前,再分析系统搭建及优化已完成,试验结果表明再分析系统初步具有在全球再分析的基础上提高区域再分析资料性能的能力,可将本再分析系统用于今后再分析数据的研制。

     

    Abstract: The atmospheric reanalysis in the East Asian region is the fifth sub-project of the key mission of China Meteorological Administration—"Meteorological Data Quality Control and Multi-source Data Fusion and Reanalysis". In this paper, the progress of the development of atmospheric reanalysis in East Asia is documented from four aspects of data collection, assimilation system, numerical model, and veri fications. In terms of observational data, of conventional ground-based observations and sounding, aircraft, GPS, radar, wind pro file, satellite observations have been collected, as well as data from field experiments. One of the characteristics of the regional reanalysis is that lots of observational data such as some conventional ground-based and sounding observations, and radar datasets are used, which are not shared with the Global Communications System (GTS). As for the data assimilation system, the Grids Statistical Interpolation (GSI) assimilation system is introduced and localized, especially with the radial wind radar assimilation operator improved. For the Weather Research and Forecasting Model (WRF-ARW) model, experiments of real cases and long-term simulations have been completed with multiple physical parameterization options, and the model con figurations have been optimized based on the experimental results. Besides, the NCAR Model Evaluation Tools (MET) has been introduced to build the veri fication system. At present, the reanalysis system has been completed and optimized. The experimental results show that the reanalysis system has a potential performance of improving the regional reanalysis based on a global reanalysis data. Therefore, the reanalysis system can be used to generate regional reanalysis datasets over East Asia in the future.

     

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