彭飞,李晓莉,陈静. 2020. GRAPES全球集合预报系统不同随机物理扰动方案影响分析[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.074
GRAPES全球集合预报系统不同随机物理扰动方案影响分析
The impacts of different stochastic physics perturbation schemes on the GRAPES Global Ensemble Prediction System
投稿时间:2019-10-23  修订日期:2020-07-17
DOI:10.11676/qxxb2020.074
中文关键词:  随机物理倾向扰动方案,随机动能补偿方案,模式扰动,扰动特征,集合预报
英文关键词:the Stochastically Perturbed Parameterization Tendencies (SPPT) scheme, the Stochastic Kinetic Energy Backscatter (SKEB) scheme, model perturbation, features of perturbations, ensemble prediction
基金项目:国家自然科学基金(41905090)
作者单位E-mail
彭飞 国家气象中心 pf_spring@163.com 
李晓莉 国家气象中心 lixl@cma.gov.cn 
陈静 国家气象中心 chenj@cma.cn 
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中文摘要:
      为了更好的理解不同随机物理扰动方案对全球中期集合预报的影响差异,本文基于GRAPES全球集合预报系统(GRAPES-GEPS)对比分析了随机物理倾向扰动(Stochastically Perturbed Parameterization Tendencies,SPPT)方案、随机动能补偿(Stochastic Kinetic Energy Backscatter,SKEB)方案和联合使用SPPT与SKEB这三种模式扰动方案所产生的扰动特征及其对集合预报的影响。为避免初值扰动的影响,考察随机物理方案所产生的扰动特征时,不使用初值扰动。通过扰动与误差相关性分析(PECA)发现,不同随机物理扰动方案所产生的扰动对预报误差均具有一定的描述能力,而且同时使用SPPT与SKEB两种方案时,扰动对误差的描述能力最好。对所有扰动方案来说,扰动总能量最初主要集中在热带地区对流层中高层以及平流层低层。随着预报时效的增加,扰动总能量不断增加,其大值区不断向热带外地区转移。从扰动总能量的谱结构来看,扰动能量均呈现升尺度发展的特征。在基于奇异向量初值扰动的GRAPES-GEPS中,随机物理扰动方案的使用均能够显著增加不同地区等压面要素的集合离散度,并在一定程度上改善集合平均误差。由于集合离散度的增加,预报失误率显著减小。连续分级概率评分(CRPS)也有所减小,尤其是在热带地区,改进更为显著。此外,中国地区不同量级(小雨、中雨、大雨和暴雨)降水概率预报技巧在一定程度上得到改善。上述改进均在联合使用SPPT与SKEB方案时最好,这与扰动总能量、扰动与误差相关性分析结果一致。
英文摘要:
      For better understanding the impacts of different stochastic physics perturbation schemes on global medium ensemble forecasts, this research conducts a comparative analysis about the features of perturbations yielded by the Stochastically Perturbed Parameterization Tendencies (SPPT) scheme, the Stochastic Kinetic Energy Backscatter (SKEB) scheme, and the combination of the SPPT and SKEB schemes as well as the impacts of these three model perturbation methods on ensemble forecasts based on the GRAPES Global Ensemble Prediction System (GRAPES-GEPS).To avoid the impacts from initial perturbations, initial perturbations are disabled when the features of perturbations produced by stochastic physics schemes are explored. Via the perturbation versus error correlation analysis (PECA), it is found that perturbations yielded by different stochastic physics perturbation schemes have the ability to capture forecast errors. Furthermore, when the combination of the SPPT and SKEB schemes is applied, the produced perturbations simulate forecast errors best. For all stochastic perturbation schemes, total energy of perturbations is initially concentrated in the middle and upper troposphere and the lower stratosphere of the tropics. In addition, the total energy of perturbations is increased with the forecast lead time, for which the maxima propagate towards the extratropical regions. From the spectra of total energy of perturbations, it is observed that the perturbation energy evolves upscale. In the GRAPES-GEPS built on the initial perturbations derived from singular vectors, the applications of stochastic physics perturbation schemes increase the ensemble spreads for fields at different isobaric surfaces in different regions and improve the root-mean-square errors of the ensemble means to some extent. Due to the increased ensemble spreads, outliers are significantly decreased. The continuous rank probability scores (CRPS) are also reduced, which is more pronounced in the tropics. Furthermore, the probabilistic forecast skills of rainfall in China for light rain (>=0.1mm/24h), moderate rain (>=10mm/24h), heavy rain (>=25mm/24h), and rainstorm (>=50mm/24h) are improved to some extent. Above-mentioned improvements perform best when the combination of the SPPT and SKEB schemes is employed. This is consistent with the results from the analyses on total energy of perturbations and PECA.
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