| 基于GRAPES-GFS次季节预报的误差诊断和预报能力分析 |
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| 引用本文： | 齐倩倩,朱跃建,陈静,田华,佟华.基于GRAPES-GFS次季节预报的误差诊断和预报能力分析[J].大气科学,2022,46(2):327-345. |
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| 作者姓名： | 齐倩倩 朱跃建 陈静 田华 佟华 |
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| 作者单位： | 1.中国气象局地球系统数值预报中心，北京 100081 |
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| 基金项目： | 国家自然科学基金;中国气象局数值预报中心青年基金项目;国家重点研究发展计划项目 |
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| 摘 要： | 基于GRAPES(Global and Regional Assimilation Prediction System)全球预报系统(GRAPES-GFS)的2018年9月至2019年8月的分析场和35天预报的试验数据,对该系统延伸期次季节预报进行误差诊断和预报能力分析.结果表明,该系统可描述2018冬季及2019年夏...
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| 关 键 词： | 2 m温度 500 hPa位势高度 MJO GRAPES-GFS模式 次季节预报 |
| 收稿时间： | 2020-05-21 |
Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model |
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| QI Qianqian,ZHU Yuejian,CHEN Jing,TIAN Hua,TONG Hua.Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model[J].Chinese Journal of Atmospheric Sciences,2022,46(2):327-345. |
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| Authors: | QI Qianqian ZHU Yuejian CHEN Jing TIAN Hua TONG Hua |
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| Affiliation: | 1.Center for Earth System Modeling and Prediction of China Meteorological Administration (CMA), Beijing 1000812.National Meteorological Center, Beijing 1000813.National Oceanic and Atmospheric Administration (NOAA)/National Weather Service (NWS)/National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC), College Park, Maryland, USA 20742 |
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| Abstract: | Using the analyses and leading 35-day predictions with the Global and Regional Assimilation Prediction System-Global Forecast System (GRAPES-GFS) during the period from September 2018 to August 2019, we diagnosed the prediction errors and evaluated the extended forecast capability to provide a numerical weather guidance for the prediction at a sub-seasonal timescale. Results showed that, GRAPES-GFS could capture the spatial distribution characteristics of 2-m temperatures and 500 hPa geopotential heights during the winter in 2018 and summer in 2019, however there existed large system bias related to 2-m temperature analysis in the desert plateau areas where there was significant thermal forcing effect, especially in arid areas of Africa. Related to the 2-m temperature, the Root-Mean-Square Errors (RMSE) of the leading 1- to 3-week predictions approximated to the linear growth. GRAPES-GFS possessed a high prediction skill in the East Asia and Austria but had relatively low prediction skills in the ocean areas compared with that of the land areas. For the leading 1- to 3-week predictions related to the 500 hPa geopotential height, the prediction skills were higher at the low latitudes than at the high latitudes of East Asia. Also, the prediction skills for the tropics were much lower than for the other regions, of which the northern hemisphere was higher than that of the southern hemisphere. Regarding to the related Madden-Julian Oscillation (MJO), it is found that GRAPES-GFS could reproduce the propagation characteristics of spatial-temporal variations related to the upper and lower zonal wind and could capture the location of strong convective activity signals. However, the positive anomaly of the Outgoing Long Wave Radiation (OLR) was much weaker and the negative anomaly was much stronger. GRAPES-GFS could skillfully forecast MJO with 11 leading days from the view of Anomaly Correlation Coefficient (ACC), which was about the same level as the results from other forecasting models. For the selected two strong MJO cases, GRAPES-GFS could describe the MJO propagation process exactly but had a stronger signal during the MJO developing and decaying periods. |
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