针对WRF模式中行星边界层参数化过程倾向项的扰动方法
投稿时间:2019-12-23  修订日期:2020-01-21  点此下载全文
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作者单位E-mail
武天杰 南京信息工程大学 tianjie@nuist.edu.cn 
闵锦忠 南京信息工程大学 minjz@nuist.edu.cn 
基金项目:国家重点研发计划(2017YFC1502100)
中文摘要:针对WRF模式中行星边界层参数化过程中的不确定性,发展了一种针对行星边界层参数化过程的随机物理扰动方案(SPPBLPT),该方案针对行星边界层计算的温度、风场、水汽倾向项进行扰动。使用该方案、多行星边界层参数化方案、多参数扰动方案及针对WRF模式总倾向的随机物理过程扰动(SPPT)方案对2014年7月进行对比实验,发现使用较大格点方差的SPPBLPT方案能有效降低地面温度与风场的误差,也能降低降水的预报误差,而其他方案对预报改善不明显。针对地面温度和风场的BS评分显示,SPPBLPT方案通过降低可靠性评分(提高可靠性),显著改善了集合预报对温度与风场的可能性预报,同时该方案能显著提高降水的GSS评分,在所有实验中,较大格点方差的SPPBLPT方案表现最好。虽然SPPBLPT方案降低了FSS评分,但是该评分的降低并不显著。针对行星边界层参数化过程的随机物理扰动方案(SPPBLPT)能显著提升集合预报系统性能,但是该方案的扰动参数的设置还需要进一步研究。
中文关键词:行星边界层  随机物理过程扰动  集合预报  WRF模式
 
Stochastic Perturbation on Planetary Boundary Layer Parameterization Tendencies in WRF model
Abstract:Targeting at the uncertainty in the parameterization process of the planetary boundary layer in the WRF model, a Stochastic Perturbation on Planetary Boundary Layer Parameterization Tendency scheme (SPPBLPT) was developed. This scheme is perturbing the temperature, wind and water vapor tendencies. A comparative experiment was conducted in July 2014 using SPPBLPT scheme, the multi-planet boundary layer parameterization scheme, the multi-parameter perturbation scheme, and the Stochastic Physical Process Perturbation (SPPT) scheme, and it was found that the SPPBLPT scheme with a large grid-point variance can effectively reduce the error of 2m temperature and wind field, and it can also reduce precipitation error, while other schemes do not improve the forecast significantly. The BS scores for 2m temperature and wind field show that the SPPBLPT scheme significantly improves the possibility of ensemble forecasting for temperature and wind field by reducing the reliability score (increasing performance). At the same time, the scheme can significantly improve the GSS score of precipitation. In all experiments, the SPPBLPT scheme with the larger grid-point variance performed best. Although the SPPBLPT scheme reduced the FSS score, the reduction is not significant. The SPPBLPT scheme can significantly improve the performance of ensemble forecasting systems, but the setting of perturbation parameters for this scheme needs further investigation.
keywords:PBL  stochastic perturbation  ensemble forecast  WRF model
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