庄照荣,李兴良. 2021. 尺度叠加高斯相关模型在GRAPES-RAFS中的应用[J]. 气象学报, 79(1):79-93, doi:10.11676/qxxb2021.005
尺度叠加高斯相关模型在GRAPES-RAFS中的应用
The application of superposition of Gaussian components in GRAPES-RAFS
投稿时间:2020-05-06  修订日期:2020-09-22
DOI:10.11676/qxxb2021.005
中文关键词:  水平相关模型  高斯函数  尺度叠加的高斯函数  三维变分  GRAPES-RAFS
英文关键词:Horizontal correlation model  Gaussian function  Superposition of Gaussian components  3DVar  GRAPES-RAFS
基金项目:国家重点研发计划项目(2017YFA0603901、2017YFC1502001)
作者单位E-mail
庄照荣 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
 
李兴良 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
lixliang@cma.gov.cn 
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中文摘要:
      背景误差水平相关模型影响着分析增量的结构,同时也决定着不同尺度上分析增量信息的多少。为了提高中小尺度系统的分析质量,研究尺度叠加高斯相关模型的特征及其在三维变分同化系统中的应用效果。通过分析高斯模型和尺度叠加高斯模型的空间特征,以及它们的拉普拉斯算子和谱响应函数的特征,同时依据统计的背景误差特征来改进背景误差水平相关模型。通过采用不同相关模型的GRAPES-RAFS快速分析预报循环批量试验表明:改进的尺度叠加高斯相关模型不仅能增加分析场的中小尺度信息,同时尺度叠加高斯函数描述的风场背景误差相关特征更符合实际统计结果,缓解了原高斯函数方案风场负相关偏大的问题。因而尺度叠加高斯相关模型的应用大幅度提高了分析质量,特别是风场分析质量,同时12 h内风场预报质量也略有提高,也显著提高了前24 h各量级降水的ETS评分,明显缓解了原高斯模型方案的虚警现象。尺度叠加高斯相关模型能明显增加分析的中小尺度信息和更合理地描述风场负相关结构,在三维变分分析中的应用能显著改进分析和降水预报质量。
英文摘要:
      The background error horizontal correlation model decides the structure of analysis increments affected by observations, and the analysis increments information changes with different scales in spectral space. The characteristics of Gaussian function (Gauss) and superposition of Gaussian components (SupG) are studied. Meanwhile, according to the structures of background error horizontal correlations estimated by NMC method, the correlation model is improved based on statistical results and characteristics of functions. The application of SupG correlation model can not only increase the meso- and small-scale information of analysis increments, but also mitigate the inappropriate large negative correlation because the correlation structure of wind with SupG is closer to actual weather. The analysis and forecast qualities between the control experiments (Gaussion function) and SupG experiments run with GRAPES-RAFS system are compared. The numerical results indicate that the convergence of the objective function is accelerated, and meso- and small-scale power spectra of analysis increments are increased so there are more meso- and small-scale information in analysis increments. The analysis qualities are obviously improved especially in wind analysis. Meanwhile the forecast qualities of 12 h wind are improved too. In addition, the precipitation forecast score ETS is higher in SupG experiments than in control experiments.
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