| GRAPES全球集合预报初始条件及模式物理过程不确定性方法研究 |
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| 引用本文： | 李晓莉,陈静,刘永柱,彭飞,霍振华.GRAPES全球集合预报初始条件及模式物理过程不确定性方法研究[J].大气科学学报,2019,42(3):348-359. |
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| 作者姓名： | 李晓莉 陈静 刘永柱 彭飞 霍振华 |
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| 作者单位： | 国家气象中心;中国气象局数值预报中心 |
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| 基金项目： | 国家重点研发计划项目（2018YFC1506703）；国家科技支撑技术项目（2015BAC03B01）；公益性行业（气象）科研专项（GYHY201506005） |
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| 摘 要： | 为描述GRAPES全球模式初始条件的不确定性,基于适合集合预报应用的GRAPES全球奇异向量技术,依据大气初始误差符合正态分布的特征,采用高斯取样奇异向量来构造全球集合预报初始扰动,在此基础上建立了GRAPES全球集合预报系统(GRAPES-GEPS)。利用GRAPES全球同化分析场,对采用初始扰动的GRAPES-GEPS连续试验预报结果进行检验和分析。结果表明:GRAPES-GEPS中高度场、风场及温度场预报的集合离散度能有效快速增加,集合平均均方根误差与集合离散度的关系合理;相对控制预报的均方根误差,集合平均的预报优势在预报中期非常显著。为进一步体现GRAPES-GEPS中模式物理过程的不确定性,发展了模式物理过程倾向随机扰动技术(SPPT)。试验结果表明:SPPT方案的应用有效提高了GRAPES-GEPS在南、北半球和热带地区等压面要素预报的集合离散度,同时一定程度减小了集合平均误差,进而改进了集合平均误差与集合离散度的关系,其中SPPT方案在热带地区的改进最为显著。本文发展的基于奇异向量的初始扰动方法和模式扰动SPPT方案在中国气象局2018年12月业务化运行的GRAPES-GEPS中得到了应用。
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| 关 键 词： | GRAPES奇异向量 初始扰动 模式扰动SPPT方案 GRAPES全球集合预报系统 |
| 收稿时间： | 2019/3/18 0:00:00 |
| 修稿时间： | 2019/4/20 0:00:00 |
Representations of initial uncertainty and model uncertainty of GRAPES global ensemble forecasting |
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| LI Xiaoli,CHEN Jing,LIU Yongzhu,PENG Fei and HUO Zhenghua.Representations of initial uncertainty and model uncertainty of GRAPES global ensemble forecasting[J].大气科学学报,2019,42(3):348-359. |
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| Authors: | LI Xiaoli CHEN Jing LIU Yongzhu PENG Fei and HUO Zhenghua |
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| Institution: | National Metrological Center, Beijing 100081, China;Numerical Prediction Center, China Meteorological Administration, Beijing 100081, China,National Metrological Center, Beijing 100081, China;Numerical Prediction Center, China Meteorological Administration, Beijing 100081, China,National Metrological Center, Beijing 100081, China;Numerical Prediction Center, China Meteorological Administration, Beijing 100081, China,National Metrological Center, Beijing 100081, China;Numerical Prediction Center, China Meteorological Administration, Beijing 100081, China and National Metrological Center, Beijing 100081, China;Numerical Prediction Center, China Meteorological Administration, Beijing 100081, China |
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| Abstract: | According to the normal distribution characteristics of initial atmospheric errors,in order to describe the uncertainties in the initial conditions of the GRAPES global model,this paper used the Gausssian sampling method to construct initial perturbations for GRAPES global ensemble prediction system (GRAPE-GEPS) from GRAPES singular vectors (SVs),which are specifically designed for ensemble forecasting.Using the global assimilation analysis field of GRAPES,this paper carried out the ensemble experiments of GRAPES-GEPS with SV-based initial perturbations.Results show that the growths of ensemble spreads of geopotential height,wind speed and temperature increase quickly,and the growth rate remains stable during the forecast range.The small distance between ensemble mean RMSE and ensemble spread indicates that there are reasonable spread-skill relations in the GRAPES-GEPS.Compared with RMSE of unperturbed control forecast,there are clear advantages observed in RMSE of ensemble mean,particularly in the middle range forecasts.The stochastically perturbed parametrization tendency (SPPT) scheme is used to represent the model uncertainties in the GRAPES-GEPS.It can be seen that the inclusion of the SPPT scheme improves the ensemble spreads of geopotential height,temperature and wind speed at each forecast lead time in the Northern (Southern) Hemispheric extra-tropics and the tropics in particular,and improves the spread-skill relations as well.The SV-based initial perturbations and SPPT scheme have been used in the operational GRAPES-GEPS at China Meteorological Administration since December 2018. |
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| Keywords: | singular vector of GRAPES initial perturbation stochastically perturbed parametrization tendency scheme GRAPES global ensemble prediction system |
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