CMA Global Ensemble Prediction Using Singular Vectors from Background Field

Huo Zhenhua; Li Xiaoli; Chen Jing; Liu Yongzhu

doi:10.11898/1001-7313.20220602

Vol.33, NO.6, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(6): 655-667

Huo Zhenhua, Li Xiaoli, Chen Jing, et al. CMA global ensemble prediction using singular vectors from background field. J Appl Meteor Sci, 2022, 33(6): 655-667. DOI: 10.11898/1001-7313.20220602.

Citation:

Huo Zhenhua, Li Xiaoli, Chen Jing, et al. CMA global ensemble prediction using singular vectors from background field. J Appl Meteor Sci, 2022, 33(6): 655-667. DOI: 10.11898/1001-7313.20220602.

Huo Zhenhua, Li Xiaoli, Chen Jing, et al. CMA global ensemble prediction using singular vectors from background field. J Appl Meteor Sci, 2022, 33(6): 655-667. DOI: 10.11898/1001-7313.20220602.

Citation:

Huo Zhenhua, Li Xiaoli, Chen Jing, et al. CMA global ensemble prediction using singular vectors from background field. J Appl Meteor Sci, 2022, 33(6): 655-667. DOI: 10.11898/1001-7313.20220602.

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CMA Global Ensemble Prediction Using Singular Vectors from Background Field

DOI: 10.11898/1001-7313.20220602

Huo Zhenhua^{1, 2},
Li Xiaoli^{1, 2
,
,},
Chen Jing^{1, 2},
Liu Yongzhu^{1, 2}

1.
CMA Earth System Modeling and Prediction Center, Beijing 100081
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2022-05-30
Rev Recd Date: 2022-07-29

Available Online: 2022-11-21

Publish Date: 2022-11-17

Abstract

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.
- singular vectors;
- CMA-GFS;
- ensemble forecasts

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References(32)

Figures and Tables

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

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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

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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

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Fig. 4 Evolutions of the continuous ranked probability score(CRPS) for isobaric variables in the Northern Hemisphere

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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

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Fig. 6 Evolution of the area under relative operating characteristic curve(AROC) score for 24 h accumulated precipitation over China

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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)

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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)

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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
区域	奇异向量数量	奇异向量集合数量	[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
南北半球中高纬度目标区	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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