Abstract
To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System—Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1–30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%–10% and 30%–80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.
摘 要
为了更全面地表示模式不确定性, 我们在GRAPES区域集合预报系统中引入了随机扰动云微物理、 积云对流、 边界层和地表层参数化方案中的参数的随机参数扰动方案(SPP), 以及物理参数化倾向随机扰动方案(SPPT)和随机动能后向散射方案(SKEB), 以评估和比较多种随机扰动方案组合的总体性能. 我们共进行了六组试验, 试验时段为2015年6月1-30日, 试验区域为中国。试验结果显示: (1) 所有随机扰动试验均优于控制试验, 并且随机扰动方案的组合均比单个SPP方案表现更优, 这表明模式随机扰动方案可以有效地提高预测技能, 并且多个模式随机扰动方案的组合可以更好地表示模式不确定性. (2) 在降水预报和地面及高空要素检验中, 三种模式随机扰动方案(SPP,SPPT及SKEB)的组合优于任何两种模式随机扰动方案的组合, 其能更好地表示模型的不确定性、 提高预报技巧. (3) 将SKEB与SPP和/或SPPT结合使用, 可显著增加集合离散度并减少高空纬向风的离群率. 这主要是由于SKEB直接扰动风场, 它会极大地影响高空风场, 并且对改善风的集合离散度和离群率产生较大的贡献. (4) SPP的引入对整体概率技巧评分具有积极的影响, 且SPP不会导致任何波长下的动能谱的演化发生较大变化. (5) SPPT和SKEB的引入将使中尺度系统的动能发生5%-10%和30%-80%的变化, 并且所有三种随机扰动方案(SPP, SPPT和SKEB)主要影响中尺度系统的动能. 本研究表明了结合多种模式随机扰动方案的潜力, 并且为未来区域和全球集合预报的发展和设计奠定了基础
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Acknowledgements
We are grateful to four anonymous reviewers for their positive and constructive suggestions and comments. This work was sponsored by National Key Research and Development (R & D) Program of China, (Grant No. 2018YFC15 07405).
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Article Highlights:
• All stochastic experiments outperform the CTL experiment, and all combinations of stochastic schemes perform better than the single SPP scheme.
• The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes.
• All three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the kinetic energy (KE) of mesoscale systems.
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Xu, Z., Chen, J., Jin, Z. et al. Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble. Adv. Atmos. Sci. 37, 328–346 (2020). https://doi.org/10.1007/s00376-020-9171-1
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DOI: https://doi.org/10.1007/s00376-020-9171-1
Key words
- ensemble prediction
- model uncertainty
- stochastically perturbed parameterization
- multi-stochastic physics approaches