利用国防科技大学全球中期数值天气预报模式(YinHe Global Spetral model,YHGS)产品驱动WRF对2018年7月4日华中地区暴雨过程进行模拟,并与ERA-interim资料作初始场模拟结果对比,评估YHGS模式产品在此次暴雨过程预报中的应用能力。结果表...利用国防科技大学全球中期数值天气预报模式(YinHe Global Spetral model,YHGS)产品驱动WRF对2018年7月4日华中地区暴雨过程进行模拟,并与ERA-interim资料作初始场模拟结果对比,评估YHGS模式产品在此次暴雨过程预报中的应用能力。结果表明:(1)WRF-YHGS对2018年7月4日华中地区暴雨过程有一定的预报能力,其模拟的大尺度环流形势、水汽收支量变化趋势与WRF-ERA有着很好的一致性,YHGS模式产品驱动中尺度数值预报是可行的。(2)WRF-YHGS模拟效果较WRF-ERA差,但大雨量级WRF-ERA湿偏差较大,两组试验各物理量模拟结果存在一定差距,且随着积分时间的增加差异逐渐增大。(3)WRF-YHGS、WRF-ERA模拟结果的差异主要来自YHGS与ERA初始场中差异较大的次天气尺度运动和YHGS全球模式预报场误差两个方面。展开更多
A more efficiem noise filtering technique is needed in ensemble data assimilation, to improve traditional spectral filtering methods that cannot reflect the local characteristics of spatial scales. In this paper, we p...A more efficiem noise filtering technique is needed in ensemble data assimilation, to improve traditional spectral filtering methods that cannot reflect the local characteristics of spatial scales. In this paper, we present the design of a novel constrained wavelet threshold denoising method （CWTDNM） by introducing an improved threshold value and a new constraining parameter. The proposed method aims to filter noise swamped over different scales. We prepared an ideal experiment object based on the two-dimensional barotropic vorticity equation. A suitable wavelet basis function （i.e., Dbl 1） and the optimal number of decomposition levels （i.e., five） were first selected. The results show that, given the wavelet coefficients are constrained by the parameter, the CWTDNM can produce better filtering results with the smallest root mean square error （RMSE） compared to similar methods. In addition, the filtering accuracy of 10 ensemble sample variances using the CWTDNM is equivalent to that estimated directly from 80 ensemble samples, but with the runtime reduced to approximately one-seventh. Furthermore, a large peak signal-to-noise ratio, which implies a low RMSE, suggests that the proposed method suitably preserves most of the information after denoising.展开更多
文摘利用国防科技大学全球中期数值天气预报模式(YinHe Global Spetral model,YHGS)产品驱动WRF对2018年7月4日华中地区暴雨过程进行模拟,并与ERA-interim资料作初始场模拟结果对比,评估YHGS模式产品在此次暴雨过程预报中的应用能力。结果表明:(1)WRF-YHGS对2018年7月4日华中地区暴雨过程有一定的预报能力,其模拟的大尺度环流形势、水汽收支量变化趋势与WRF-ERA有着很好的一致性,YHGS模式产品驱动中尺度数值预报是可行的。(2)WRF-YHGS模拟效果较WRF-ERA差,但大雨量级WRF-ERA湿偏差较大,两组试验各物理量模拟结果存在一定差距,且随着积分时间的增加差异逐渐增大。(3)WRF-YHGS、WRF-ERA模拟结果的差异主要来自YHGS与ERA初始场中差异较大的次天气尺度运动和YHGS全球模式预报场误差两个方面。
基金supported by the National Natural Science Foundation of China(Grant Nos.41375113,41475094,41305101&41605070)
文摘A more efficiem noise filtering technique is needed in ensemble data assimilation, to improve traditional spectral filtering methods that cannot reflect the local characteristics of spatial scales. In this paper, we present the design of a novel constrained wavelet threshold denoising method （CWTDNM） by introducing an improved threshold value and a new constraining parameter. The proposed method aims to filter noise swamped over different scales. We prepared an ideal experiment object based on the two-dimensional barotropic vorticity equation. A suitable wavelet basis function （i.e., Dbl 1） and the optimal number of decomposition levels （i.e., five） were first selected. The results show that, given the wavelet coefficients are constrained by the parameter, the CWTDNM can produce better filtering results with the smallest root mean square error （RMSE） compared to similar methods. In addition, the filtering accuracy of 10 ensemble sample variances using the CWTDNM is equivalent to that estimated directly from 80 ensemble samples, but with the runtime reduced to approximately one-seventh. Furthermore, a large peak signal-to-noise ratio, which implies a low RMSE, suggests that the proposed method suitably preserves most of the information after denoising.
