双月刊

ISSN 1006-9895

CN 11-1768/O4

公里尺度区域变分同化中引入大尺度约束的影响研究
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1.国家气象中心;2.中国气象局数值预报中心

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国家自然科学基金41705085,国家重点研发计划项目2017YFC1502000,中国气象局数值预报(GRAPES)发展专项


Impact Studies of Introducing the Large-Scale Constraint into Km-Scale Regional Variational Data Assimilation
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    摘要:

    公里尺度资料同化系统的框架设计和资料选择均侧重于中小尺度分析,常存在大尺度分析能力不足的问题。本研究在GRAPES(Global/Regional Assimilation and Prediction System)区域3km三维变分同化目标泛函中增加大尺度约束,将全球系统的大尺度信息引入到分析框架中去,研究其对公里尺度同化预报的影响。一个月的数值试验结果表明,引入大尺度约束可以显著改进大尺度形势场的分析和预报,提高降水预报评分,减少2m温度和10m风场的分析预报误差。进一步的,定量降水敏感性试验结果表明,大尺度湿度场和温度场约束对于改进降水评分十分重要。这其中,湿度场约束对于减少降水空报以及提高短时临近降水的TS评分重要,而温度场约束对于改进较长时效的TS降水评分重要。此外,在均引入大尺度约束的条件下,采用完全循环(一个月中间无冷启)方案运行的试验获得了与局部循环(每日冷启)相当的分析预报结果。这为GRAPES区域公里尺度系统采用完全循环方案,进一步简化流程,减少计算消耗奠定了很好的基础。

    Abstract:

    Since the framework design and observation selection are mainly for meso- and small-scale analysis, kilometer-scale data assimilation (DA) systems often suffer from insufficient large-scale analysis capabilities. This work adds an extra large-scale constraint to the cost-function of the GRAPES (Global/Regional Assimilation and Prediction System) regional 3km variational DA framework, to study the impacts of introducing the large-scale information of the global system on the kilometer-scale DA and forecast. The results of numerical experiments in one month show that, the introduction of large-scale constraint can significantly improve the analysis and forecast capabilities of the synoptic situation field, increase the precipitation forecast scores, and reduce analysis and forecast error of 2m temperature and 10m wind. Furthermore, results of the quantitative precipitation sensitivity tests show that the large-scale constraint of the temperature and humidity field is a very important factor for improving the precipitation scores. The results also indicate that the humidity field constraint is important for reducing precipitation false alarm and improving the TS scores for short-term precipitation forecast, while the temperature field constraint is important for improving the TS scores for longer forecast ranges. In addition, under the condition of introducing the large-scale constraint, the analysis and prediction results of the experiment with the full cycling scheme (no cold start during one month cycling) are equivalent to that of the experiment with partial cycle (daily cold start). This laid a good foundation for the GRAPES kilometer-scale system to adopt the full cycling scheme to further simplify the cycle process and reduce the calculation consumption.

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历史
  • 收稿日期:2020-07-06
  • 最后修改日期:2020-09-10
  • 录用日期:2021-04-08
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