<strong>GRAPES-GEPS</strong>对西太平洋副热带高压和南亚高压的集合预报评估与集合方法研究

doi:10.11867/j.issn.1001-8166.2020.060

地球科学进展 ›› 2020, Vol. 35 ›› Issue (7): 715 -730. doi: 10.11867/j.issn.1001-8166.2020.060

GRAPES-GEPS对西太平洋副热带高压和南亚高压的集合预报评估与集合方法研究

高丽 ¹(

),任鹏飞 ²,周放 ³,郑嘉雯 ⁴,任宏利 ²(

)

^1.国家气象中心，中国气象局数值预报中心，北京 100081
^2.中国气象科学研究院，灾害天气国家重点实验室，北京 100081
^3.中国科学院大气物理研究所，竺可桢—南森国际研究中心，北京 100029
^4.广东省气象局，广州市气象台，广东广州 511430

收稿日期:2020-05-31 修回日期:2020-06-30 出版日期:2020-07-10
通讯作者: 任宏利 E-mail:gaol@cma.gov.cn;renhl@cma.gov.cn
基金资助:
国家重点研发计划项目“冬奥中短期精细数值天气预报技术应用研发”(2018YFF0300103);国家自然科学基金项目“我国极端温度事件的中期天气可预报性和集合概率预报方法研究”(41875138)

Evaluations and Ensemble Approaches of Western-Pacific Subtropical High and South-Asian High Ensemble Forecasting in GRAPES-GEPS

Li Gao ¹( ),Pengfei Ren ²,Fang Zhou ³,Jiawen Zheng ⁴,Hongli Ren ²( )

^1.Numerical Weather Prediction Center of CMA, National Meteorological Center, Beijing 100081，China
^2.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081，China
^3.Climate Change Research Center, Institute of Atmospheric Physics, and Nansen-Zhu International Research Centre, Chinese Academy of Sciences, Beijing 100029，China
^4.Guangzhou Meteorological Service, Guangdong Province Meteorological Bureau, Guangzhou 511430，China

Received:2020-05-31 Revised:2020-06-30 Online:2020-07-10 Published:2020-08-21
Contact: Hongli Ren E-mail:gaol@cma.gov.cn;renhl@cma.gov.cn
About author:Gao Li (1978-), female, Alashan Zuoqi, Inner Mongolia Autonomous Region, Senior engineer. Research areas include weather dynamics and ensemble forecast. E-mail: gaol@cma.gov.cn
Supported by:
the National Key Research and Development Program of China "Research and development of short-medium-range numerical weather forecasting techniques and their applications to the 2022 Olympic Winter Games"(2018YFF0300103);The National Natural Science Foundation of China "Medium-range weather predictability and ensemble-based probabilistic forecasting method of extreme temperature event in China"(41875138)

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西太平洋副热带高压和南亚高压对东亚区域天气气候影响显著，运用数值模式集合系统提升其预报准确率对我国天气预报意义重大。采用国家级气象业务规范指标，系统地评估了中国气象局数值预报中心自主研发的GRAPES全球集合预报业务模式系统对2019年西太平洋副热带高压和南亚高压的集合预报技巧，并考察了不同集合方法对预报效果的影响，从而为东亚天气特别是极端事件的预报提供参考。结果显示，GRAPES全球集合预报系统对西太平洋副热带高压脊线的预报技巧最高，强度和面积次之，表现为偏弱的估计，西伸脊点的预报效果相对较差，表现为较观测偏东；对南亚高压强度和中心纬度指数的预报技巧较高，而对中心经度指数预报技巧相对较低。采用最大（小）值法可以有效降低该模式对西太平洋副热带高压强度和面积（西伸脊点）指数的预报偏差。而在南亚高压预报中，集合平均法比最值法具有略高技巧。对于极端性预报，最大值法较集合平均法可以显著提升对西太平洋副热带高压和南亚高压指数极端情形的预报性能，这从个例分析中也得到证实。从而表明集合最值法比平均法可能更适用于该模式的极端事件预报，应在业务应用中加以重视。

关键词: GRAPES-GEPS, 西太平洋副热带高压, 南亚高压, 集合平均法, 最数法

Based on the operational standard indices, the prediction skills of the Western-Pacific Subtropical High (WPSH) and South-Asian High (SAH) using 2019 real-time forecasts derived from the Global Ensemble Prediction System of GRAPES (GRAPES-GEPS) in China Meteorological Administration (CMA) Numerical Prediction Center were evaluated and the effects of different ensemble approaches on the prediction skills of WPSH and SAH indices were further investigated in this study. The results show that for WPSH, the GRAPES-GEPS has its highest prediction skill for the ridge line index, considerably high skill for the intensity and area indices, but relatively low skill for the western boundary index, and for SAH, it has comparatively high skill for the intensity and center latitude indices, but relatively lower skill for the center longitude index. Prediction errors of the GRAPES-GEPS for the WPSH forecasts are featured by the weaker intensity and area and the more eastward center position, compared with the observation, which can be effectively reduced by employing the maximum/minimum approach from ensemble members, relative to the ensemble mean approach. By direct comparison, prediction errors of the GRAPES-GEPS for the SAH forecasts are featured by the weaker intensity and the more southward center position, which tends to be slightly reduced using the ensemble mean approach. Finally, for the extreme forecast, the maximum approach provides superior performance for both WPSH and SAH than the ensemble mean approach, which can be validated in terms of the two extreme cases. These results clearly indicate that the maximum approach could better improve the GRAPES-GEPS performance for the extreme forecasting of the two primary circulation patterns than the traditional ensemble mean approach.

Key words: GRAPES-GEPS, Western-Pacific Subtropical High, South-Asian High, Ensemble mean approach, Maximum/minimum approach

中图分类号:

P424

图1 GRAPES-GEPS对 500 hPa位势高度场预报的相关系数空间分布

Fig.1 Correlation coefficients of 500 hPa geopotential height between GRAPES-GEPS and observations

图2 GRAPES-GEPS对西太副高强度( a)、面积( b)、脊线位置( c)和西伸脊点( d)指数的预报评分 Taylor图
数字1~15代表提前预报的天数

Fig.2 Taylor diagram for forecasting skills of GRAPES-GEPS for the intensity（ a）， area（ b）， ridge point（ c） and ridge line（ d） index of the western Pacific subtropical high
The numbers 1~15 represent the days forecasted in advance

图3 GRAPES-GEPS对 200 hPa位势高度场预报的相关系数空间分布

Fig.3 Correlation coefficients of 200 hPa geopotential height between GRAPES-GEPS and observations

图4 GRAPES-GEPS对南亚高压的强度( a)、中心纬度( b)、中心经度( c)指数的预报评分 Taylor图
数字1~15代表提前预报的天数

Fig.4 Taylor diagram for forecasting skills of GRAPES-GEPS for the intensity（ a）， center latitude（ b）， and center longitude（ c） index of the southAsia high
The numbers 1~15 represent the days forecasted in advance

图5 GRAPES-GEPS对西太副高强度( a)、面积( b)、脊线位置( c)和西伸脊点( d)指数预报的 Talagrand分布

Fig.5 Talagrand distribution of the intensity (a), area (b), ridge point (c), and the ridge line (d) index of western Pacific subtropical high forecasted by GRAPES-GEPS

图6 GRAPES-GEPS所有集合成员对西太副高指数预报的命中率( a)和离散度( b)对预报时效的演变

Fig.6 The evolution of the hit rate (a) and dispersion (b) of all GRAPES-GEPS ensemble members to the forecast of western Pacific subtropical high index

图7 GRAPES-GEPS对南亚高压强度( a)、中心纬度( b)和中心经度( c)指数预报的 Talagrand分布

Fig.7 Talagrand distribution of the intensity (a), center latitude (b), and center longitude (c) index of south Asia high forecasted by GRAPES-GEPS

图8 GRAPES-GEPS所有集合成员对南亚高压指数预报的命中率( a)，离散度( b)对预报时效的演变

Fig.8 The evolution of the hit rate (a) and dispersion (b) of all GRAPES-GEPS ensemble members to the forecast of south Asia high index

图9 两种集合方法预报的西太副高强度( a)、面积( b)和西伸脊点( c)指数的均方根误差

Fig.9 Root mean square error of the intensity (a), area (b) and ridge point (c) indices of western Pacific subtropical high forecasted by the two ensemble methods

图10 两种集合方法预报的西太副高指数的极端个例（ 90百分位以上）的均方根误差

Fig.10 Root mean square error of extreme cases (above 90th percentile) of the sub-high index predicted by the two ensemble methods

图11 两种集合方法预报的南亚高压指数的极端个例（ 90百分位以上）的均方根误差

Fig.11 Root mean square error of extreme cases (above 90th percentile) of the SAH index predicted by the two ensemble methods

图12 2019年 9月 23~26日平均的 500 hPa高度场的实况与预报
(a)观测；(b)~(d)使用集合平均法提取的GRAPES-GEPS提前5天(b)、10天(c)和15天(d)的预报场；(e)~(g) 采用最大值法提取GRAPES-GEPS提前5天(e)、10天(f)和15天(g)的预报场；黑色粗线代表5 880 gpm，等值线间隔为40 gpm

Fig.12 Distribution of the 500 hPa geopotential height from observation and GRAPES-GEPS averaged over the period from 23 to 26 September 2019
(a) Observation； (b)~(d) The forecasts of GRAPES-GEPS extracted by the ensemble mean method for 5 days(b), 10 days(c) and 15 days(d) in advance； (e)~(g) The forecasts of GRAPES-GEPS extracted by the Maximum method for 5 days(b), 10 days(c) and 15 days(d) in advance. The lack thick line represents 5 880 gpm, the contour line interval is 40 gpm

图13 2019年 7月 29日至 8月 1日平均的 200 hPa高度场的实况与预报
(a)观测；(b）~（d)为使用集合平均法提取的GRAPES-GEPS提前5天(b)、10天(c)和15天(d)的预报场，(e）~（g)采用最大值法提取GRAPES-GEPS提前5天(e)、10天(f)和15天(g)的预报场；黑色粗线代表5 880 gpm，等值线间隔为40 gpm

Fig.13 Distribution of the 200 hPa geopotential height from observation and GRAPES-GEPS averaged over the period from 29 July to 1 August 2019
(a) Observation； (b）~（d) The forecasts of GRAPES-GEPS extracted by the ensemble mean method for 5 days(b), 10 days(c) and 15 days(d) days in advance, (e）~（g) The forecasts of GRAPES-GEPS extracted by the Maximum method for 5 days(b), 10 days(c) and 15 days(d) days in advance. The black thick line represents 12 500 gpm, the contour line interval is 50 gpm

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