摘要
利用CMA全球集合预报(CMA-GEPS)业务系统2020年6月1日至2021年5月31日一整年的500 hPa位势高度场(H_(500))预报数据,诊断评估了CMA-GEPS在北半球地区误差增长及预报性能的尺度依赖特征。使用谱滤波方法实现H_(500)不同尺度(包括行星尺度、天气尺度与次天气尺度)分量的分离。从集合平均均方根误差(简称集合平均误差)-离散度关系来看,在预报前期(108 h之前),CMA-GEPS集合平均误差小于集合离散度,存在过度发散的问题,主要是由天气尺度分量离散度过大导致;在预报后期(108 h之后),CMA-GEPS集合平均误差大于集合离散度,离散度偏小,是由行星尺度与天气尺度分量离散度不足共同引起。采用Dalcher等1987年修订的误差增长模型对H_(500)集合平均预报误差增长特征进行诊断分析,发现CMA-GEPS误差增长过程合理,初始误差在次天气尺度上增长最快,行星尺度上增长最慢;就绝对(相对)误差而言,模式误差对预报误差的影响随空间尺度的增大而增大(减小)。此外,将使用1989至2018年共计30 a的ERA-Interim再分析逐日数据得到的气候态分布作为参考预报,通过连续分级概率预报技巧评分(Continuously Ranked Probability Skill Score,CRPSS)检验了CMA-GEPS H_(500)及其不同尺度分量的概率预报技巧。结果表明,行星尺度分量概率预报技巧最高,次天气尺度分量最小,未经滤波的H_(500)预报技巧位于行星尺度与天气尺度分量预报技巧之间。上述诊断结果可为CMA-GEPS改进方向提供一定的客观依据。
Using the _(500) hPa geopotential height(H_(500))forecast data of the CMA global ensemble prediction operational system(CMA-GEPS)for a 1-year period from 1 June 2020 to 31 May 2021,the scale-dependent characteristics of error growth and forecast performance of the CMA-GEPS in the Northern Hemisphere are evaluated.The H_(500) field is decomposed into different scales(including the planetary scale,the synoptic scale and the sub-synoptic scale)by using the spectral filtering method.The relationship between the Root Mean Square Error of the ensemble mean(RMSE)and ensemble spread(SPD)indicates that the CMA-GEPS is over-dispersive(RMSE is smaller than SPD)in the early forecast stage(before 108 h),which is mainly attributed to the excessive dispersion on the synoptic scale.In the subsequent forecast period(beyond 108 h),the CMA-GEPS is under-dispersive(RMSE is greater than SPD),which is caused by insufficient spread on both the planetary scale and the synoptic scale.The error growth model modified by Dalcher et al.in 1987 is applied to diagnose the characteristics of the H_(500) forecast error growth.It is found that the error growth processes of CMA-GEPS are reasonable,and the initial error grows fastest on the sub-synoptic scale and slowest on the planetary scale.In terms of absolute(relative)errors,impacts of model errors on forecast errors increase(decrease)with increasing spatial scale.In addition,taking the climatological distribution derived from the daily dataset of the ERA-Interim reanalysis for 30 years from 1989 to 2018 as the reference forecast,the Continuously Ranked Probability Skill Score(CRPSS)is computed to verify the probabilistic forecast skills of H_(500) within CMA-GEPS together with its components on different scales.Results reveal that the forecast skills for the planetary scale are the highest,and those for the sub-synoptic scale are the lowest.Moreover,the probabilistic skills of the unfiltered H_(500) lie between skills of the planetary and synoptic scales.Above diagnostic results can provide an objective basis for further improvement of the CMA-GEPS.
作者
彭飞
李晓莉
陈静
赵滨
PENG Fei;LI Xiaoli;CHEN Jing;ZHAO Bin(CMA Earth System Modeling and Prediction Centre,Beijing 100081,China;State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China;Key Laboratory of Earth System Modeling and Prediction,China Meteorological Administration,Beijing 100081,China)
出处
《气象学报》
CAS
CSCD
北大核心
2023年第4期605-618,共14页
Acta Meteorologica Sinica
基金
国家重点研发计划项目(2021YFC3000902)
国家自然科学基金项目(41905090、U2242213)。
关键词
尺度依赖
谱滤波
离散度-误差关系
误差增长模型
概率预报技巧
Scale-dependence
Spectral filtering
Spread-error relationship
Error growth model
Probabilistic forecast skills
