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CMA Global Ensemble Prediction Using Singular Vectors from Background Field
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摘要: 目前中国气象局全球集合预报系统（China Meteorological Administration Global Ensemble Prediction System，CMA-GEPS）利用CMA全球数值预报系统分析场计算奇异向量（ANSV），欧洲中期天气预报中心采用同化背景场计算奇异向量（FCSV），在业务流程上先于计算ANSV，可优化集合预报系统运行时间。为此，在CMA-GEPS中探索采用FCSV进行集合预报的可行性，分析ANSV和FCSV的空间分布及相似指数，进而针对夏秋季节10个个例开展采用ANSV和FCSV的全球集合预报试验，从等压面要素集合预报技巧、中国地区24 h累积降水概率预报技巧、台风路径集合预报技巧、台风中心最低海平面气压预报技巧等方面对比二者结果。结果表明：ANSV和FCSV的主要结构特征相似，两组集合预报结果相当，表明在CMA-GEPS中使用FCSV可行，可作为未来高分辨率CMA-GEPS业务系统建设的选项。
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关键词:
- 奇异向量;
- CMA全球数值预报系统;
- 集合预报
Abstract: China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) adopts singular vector method to generate initial perturbations. CMA-GEPS currently uses the initial analysis field from CMA Global Forecast System (CMA-GFS) data assimilation to calculate singular vector (ANSV). With this dependency, in the operational running procedures of CMA numerical weather prediction systems, the singular vector calculation starts when CMA-GFS analysis job is finished. With the improvement of model resolution, especially the horizontal resolution, the computation time of data assimilation analysis and the ensemble forecasts would be lengthened. Given relatively limited high-performance computational resources, it would bring great challenge for delivering the ensemble forecast products on time. ECMWF uses the data assimilation background field to calculate singular vector (FCSV), which could be implemented earlier than the computation of ANSV in the operational flow and then optimize the computation time for ensemble prediction system (EPS), and it shows that the performance of FCSV ensemble is comparable to ANSV ensemble.Based on CMA-GFS background field and SV calculation module, the feasibility of applying FCSV in CMA-GEPS is investigated. First, the spatial structures of ANSV and FCSV and their similarity index are analyzed. And then, two ensembles based on ANSV and FCSV are conducted for 10 cases in summer and autumn. The forecasts from ANSV ensemble and FCSV ensemble are comprehensively evaluated in terms of the ensemble prediction skill of barometric surface variables, the probability prediction of 24 hours accumulated precipitation in China, tropical cyclone track ensemble prediction skill, and the forecast skill of the minimum sea level pressure at tropical cyclone center. The results show that for the dominant extra-tropical singular vector in CMA-GEPS, the ANSV and FCSV have similar horizontal and vertical structures, their general similarity index is 0.6-0.8, and two ensembles have the comparable forecast skill over extratropics. For tropical singular vector which are only calculated when tropical cyclones are observed, their similarity index between ANSV and FCSV is relatively lower than that in extratropics, and FCSV ensemble shows slightly smaller ensemble spread but comparable error for tropical cyclone tracks. For the precipitation forecast, two ensembles have similar forecast skills for moderate to heavy rain. For mean sea level pressure forecast of strong tropical cyclone case, two ensemble have members showing the skill in terms of structures and magnitude. Therefore, it is feasible to apply FCSV in CMA-GEPS, and it could be an option to construct singular vector-based initial perturbations for future high-resolution operational CMA-GEPS.-
Key words:
- singular vectors;
- CMA-GFS;
- ensemble forecasts
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图 1 初始时刻为2020年9月7日12：00 ANSV试验和FCSV试验北半球中高纬度目标区第1奇异向量、第2奇异向量和第5奇异向量(放大500倍)在第28层的位温扰动分量(填色，单位：K)和求解奇异向量使用的500 hPa位势高度初始场(等值线，单位：gpm)
Fig. 1 Potential temperature perturbation component(the shaded, unit:K) of the first singular vector, the second singular vector and the fifth singular vector(multiplied by 500) at 28 model level and the initial 500 hPa geopotential height(the contour, unit:gpm) in the Northern Hemisphere used to compute singular vectors corresponding to ANSV experiment and FCSV experiment with initial time of 1200 UTC 7 Sep 2020
图 2 初始时刻为2020年9月7日12：00 ANSV试验和FCSV试验北半球中高纬度目标区第1奇异向量、第2奇异向量和第5奇异向量(放大500倍)的位温扰动分量(单位：K)在50°N的垂直结构
Fig. 2 Vertical structures of the potential temperature perturbation component(unit:K) of the first singular vector, the second singular vector and the fifth singular vector(multiplied by 500) at 50°N in the Northern Hemisphere corresponding to ANSV experiment and FCSV experiment with initial time of 1200 UTC 7 Sep 2020
图 3 北半球等压面要素的控制预报均方根误差、集合平均均方根误差和集合离散度随预报时效演变
Fig. 3 Evolutions of root mean square error of the control forecast, root mean square error of ensemble mean and ensemble spread isobaric variables in the Northern Hemisphere
图 4 北半球等压面要素连续分级概率评分(CRPS)随预报时效演变
Fig. 4 Evolutions of the continuous ranked probability score(CRPS) for isobaric variables in the Northern Hemisphere
图 5 台风路径预报误差和路径集合离散度随预报时效演变
(a)所有台风个例的路径预报误差和集合离散度平均值，(b)台风预报路径误差箱线图，(c)台风路径集合离散度箱线图
Fig. 5 Evolutions of the typhoon track forecast error and ensemble spread
(a)averaged typhoon track forecast error and ensemble spread for all typhoon cases, (b)boxplot for the track forecast error, (c)boxplot for the track ensemble spread
图 6 中国地区24 h累积降水相对作用特征技巧(AROC)评分随预报时效演变
Fig. 6 Evolution of the area under relative operating characteristic curve(AROC) score for 24 h accumulated precipitation over China
图 7 2020年8月31日12：00分析场、控制预报、ANSV和FCSV集合平均预报的72 h平均海平面气压(单位：hPa)
(等值线间隔为4 hPa)
Fig. 7 Mean sea level pressure of the operational assimilation analysis field, mean sea level pressure of 72 h forecasts from the control forecast, the ensemble mean for ANSV and FCSV at 1200 UTC 31 Aug 2020(unit:hPa)
(the isoline interval is 4 hPa)
图 8 2020年8月31日12：00 ANSV试验和FCSV试验5个集合成员预报的72 h平均海平面气压(单位：hPa)
(等值线间隔为4 hPa)
Fig. 8 Mean sea level pressure of 72 h forecasts from 5 ensemble members in ANSV experiment and FCSV experiment at 1200 UTC 31 Aug 2020(unit:hPa)
(the isoline interval is 4 hPa)
表 1 ANSV和FCSV相似指数在不同区间对应的奇异向量集合数量比例
Table 1 Fraction of singular vector ensemble number based on similarity index between ANSV and FCSV at different intervals
区域 奇异向量数量 奇异向量集合数量 相似指数在不同区间对应的奇异向量集合数量比例 [0.0, 0.5) [0.5, 0.6) [0.6, 0.7) [0.7, 0.8) [0.8, 0.9) [0.9, 1.0] 南北半球中高纬度目标区 30 38 0.000 0.000 0.526 0.474 0.000 0.000 5 38 0.000 0.105 0.395 0.263 0.237 0.000 热带气旋目标区 5 16 0.687 0.125 0.063 0.125 0.00 0.00 注：南北半球中高纬度目标区指30°~80°N和30°~80°S地区，奇异集合数量为个例数量的2倍；热带气旋目标区指以热带气旋中心位置为中心的10°×10°区域，奇异向量集合数量为个例数量。 
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