风暴尺度集合预报中的混合初始扰动方法及其在北京2012年“7.21”暴雨预报中的应用

庄潇然; 闵锦忠; 王世璋; 周凯; 蔡沅辰

doi:10.3878/j.issn.1006-9895.1605.15233

风暴尺度集合预报中的混合初始扰动方法及其在北京2012年“7.21”暴雨预报中的应用

doi: 10.3878/j.issn.1006-9895.1605.15233

庄潇然^{1, 2,},
闵锦忠^{1, 2, ,},
王世璋^{1, 2},
周凯^{1, 2},
蔡沅辰^{1, 2}

1.
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044
2.
南京信息工程大学气象灾害教育部重点实验室, 南京 210044

基金项目:

国家自然科学基金项目 Grants 41430427 and 40975068

详细信息

作者简介:
庄潇然,男,1991年出生,博士研究生,主要从事风暴尺度集合预报技术研究。E-mail:zxrxz3212009@163.com

通讯作者:
闵锦忠,E-mail:minjz@nuist.edu.cn

中图分类号: P456.7
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出版历程
- 收稿日期: 2015-07-26
- 网络出版日期: 2016-05-12
- 刊出日期: 2017-01-15

A Blending Method for Storm-Scale Ensemble Forecast and Its Application to Beijing Extreme Precipitation Event on July 21, 2012

Xiaoran ZHUANG^{1, 2
,},
Jingzhong MIN^{1, 2
, ,},
Shizhang WANG^{1, 2},
Kai ZHOU^{1, 2},
Yuanchen Cai^{1, 2}

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044
2.
Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044

Funds:

National Natural Science Foundation of China Grants 41430427 and 40975068

摘要

摘要: 风暴尺度集合预报系统（Storm-Scale Ensemble Forecast system，简称SSEFs）中集合成员之间发散度不足一直都是研究的难点。本文尝试了将Barnes空间滤波融入到集合转换卡尔曼滤波（ETKF）更新预报系统中的混合初值扰动法。该方案将ETKF方法的小尺度信息与来自于侧边界条件扰动的大尺度信息相结合，缓解了扰动在侧边界不匹配的问题。通过2012年北京“7.21”暴雨并使用邻位方法对比分析了不同初值扰动方案在不同时间尺度与空间尺度上的特征，在此基础上进一步探讨了构造混合初始扰动法的可行性。结果表明：ETKF试验所构造的初始扰动无法与侧边界条件扰动相匹配，混合后的初始扰动可以有效缓解SSEFs中由于初始扰动与侧边界扰动不匹配产生的虚假波动，其中大尺度信息保留较多的混合试验（ETKF80）和动力降尺度方案（Down）在减少虚假波动方面的效果最优；从集合离散度来看，在前期暖区降水阶段ETKF的离散度在小尺度上最大，随着锋面降水的开始，Down的离散度逐渐超过ETKF，而使用各滤波波段构造的混合试验同时具备ETKF与Down二者的特征。选择合理的滤波波段可以获得最为合理的离散度表现（ETKF180），说明仅考虑侧边界匹配（Down和ETKF80）并不能获得最合理的集合离散度，应综合考虑其他因素。从降水概率预报结果来看，选取合适的滤波波段所构造的混合扰动试验同样获得了较好的效果。
- 风暴尺度集合预报系统 /
- 集合转换卡尔曼滤波 /
- 混合初始扰动 /
- 离散度 /
- 概率预报
Abstract: In order to overcome the under-dispersive problem in the storm-scale ensemble forecast system (SSEFs), a new blending method to generate initial perturbation is designed and tested for the WRF SSEFs. This new scheme is based on the combination of the ETKF (Ensemble Transform Kalman Filter) and the Barnes filter for scale decomposition. This scheme is applied to the simulation of the Beijing extreme precipitation event on July 21, 2012and the neighborhood methods is employed to verify the performance of this new scheme. Results indicate that the blending method can effectively solve the scale mismatch problem in the lateral boundary in storm-scale ensemble prediction system, in which ETKF80 (with wavelength scale of 180 km) and Down (Dynamical downscaling) show the best overall performance. The Dispersion Fractions Skill Score (DFSS) shows that the ETKF has a larger spread in small scales during the period of warm area precipitation while Down produces a larger spread at large scales during period of frontal precipitation. The experiments with initial perturbations generated by the blending method take advantage of both the ETKF and Down. The ETKF180 (with wavelength scale of 180 km) generates the most reasonable ensemble spreads. Results also indicate that in order to get better ensemble spread in SSEFs, not only the lateral scale mismatch but also some other elements (such as the interaction between different scales of initial perturbation) should be considered. The blending method ETKF180 also improves the precipitation probability forecast.
- Storm-scale ensemble forecast system /
- Ensemble transform Kalman filter /
- Blending initial perturbation /
- Spread /
- Probability prediction

HTML全文

图 1 ETKF-blending 集合更新预报系统框架示意图

Figure 1. Schematic diagrams of the ETKF (Ensemble Transform Kalman Filter)-blending ensemble update system

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图 2 4 种滤波波段的Barnes 低通滤波响应函数

Figure 2. Response functions of Barnes low-pass filtering for the four blending schemes

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图 3 扰动成员1 在850 hPa 的温度扰动（单位：K）：（a）Down；（b）ETKF；（c）ETKF80；（d）ETKF180；（e）ETKF350；（f）ETKF500

Figure 3. 3 850-hPa temperature perturbations (units: K) for Member 1: (a) Expt Down; (b) Expt ETKF; (c) Expt ETKF80; (d) Expt ETKF180; (e) Expt ETKF350; (f) Expt ETKF500

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图 4 各组集合预报试验内区域与外区域地面气压离散度标准化偏差：（a）ETKF；（b）ETKF80；（c）ETKF180；（d）ETKF350；（e）ETKF500；（f）Down

Figure 4. Normalized differences in surface pressure spread between inner domain and outer domain for different ensemble forecast experiments: (a) ETKF, (b) ETKF80, (c) ETKF180, (d) ETKF350, (e) ETKF500, and (f) Down

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图 5 2012年7月21日03 时至7月22日00 时北京地区（39.4°～41°N，115.4°～117.5°E）观测与各组集合试验的集合成员、控制试验区域平均的逐小时累积降水量（单位：mm h^-1）时间序列：（a）ETKF；（b）ETKF80；（c）ETKF180；（d）ETKF350；（e）ETKF500；（f）Down。虚线为控制试验，黑色实线为观测，灰色实线为集合成员

Figure 5. Spatially averaged hourly accumulated precipitation over Beijing area (39.4°-41°N, 115.4°-117.5°E) as a function of lead time from initialization time 0300 UTC 21 Jul for observations (OBS), ensemble members of different ensemble experiments, and control experiments (CTL): (a) ETKF; (b) ETKF80; (c) ETKF180; (d) ETKF350; (e) ETKF500; (f) Down

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图 6 （a-e）离散分区预报技巧评分与（f-g）误差分区预报技巧评分（×10）：（a、f）ETKF80 与ETKF 的差；（b、g） ETKF180 与ETKF 的差；（c、h）ETKF350 与ETKF 的差；（d、i）ETKF500 与ETKF 的差；（e、j）Down 与ETKF 的差

Figure 6. (a-e) Dispersion and (f-g) error fractions skill scores (dFSS and eFSS) for (a, f) difference between the ETKF80 and ETKF, (b, g) difference between the ETKF180 and ETKF, (c, h) difference between the ETKF350 and ETKF, (d, i) difference between the ETKF500 and ETKF, (e, j) difference between the Down and ETKF

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图 7 各组集合预报试验时间平均的逐三小时累积降水量BSS

Figure 7. Temporally averaged BSS (Brier skill score) of different ensemble experiments with reference scheme of ETKF for 3-h accumulated precipitation

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图 8 阈值为（a-e）5 mm (3 h)^-1 和（b-f）10 mm (3 h)^-1 的各组集合试验的BSS：（a、f）ETKF80；（b、g）ETKF180；（c、h）ETKF350；（d、i） ETKF500；（e、j）Down

Figure 8. BSS for different ensemble experiments with thresholds of (a-e) 5 mm (3 h)^-1 and (b-f) 10 mm (3 h)^-1: (a, f) ETKF80; (b, g) ETKF180; (c, h) ETKF350; (d, i) ETKF500; (e, j) Down

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表 1 集合预报试验方案

Table 1. Schemes of the different ensemble forecast experiments

试验名称	内区域初始扰动	外区域初始扰动(为内区域提供侧边界条件扰动)
ETKF	ETKF方法	动力降尺度方法
ETKF80	混合扰动方法(48～120 km)	动力降尺度方法
ETKF180	混合扰动方法(80～180 km)	动力降尺度方法
ETKF350	混合扰动方法(140～560 km)	动力降尺度方法
ETKF500	混合扰动方法(240～840 km)	动力降尺度方法
Down	动力降尺度方法	动力降尺度方法

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