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齐倩倩, 朱跃建, 陈静, 等. 2022. 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, 46(2): 327−345. doi: 10.3878/j.issn.1006-9895.2008.20157
引用本文: 齐倩倩, 朱跃建, 陈静, 等. 2022. 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, 46(2): 327−345. doi: 10.3878/j.issn.1006-9895.2008.20157
QI Qianqian, ZHU Yuejian, CHEN Jing, et al. 2022. Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(2): 327−345. doi: 10.3878/j.issn.1006-9895.2008.20157
Citation: QI Qianqian, ZHU Yuejian, CHEN Jing, et al. 2022. Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(2): 327−345. doi: 10.3878/j.issn.1006-9895.2008.20157

基于GRAPES-GFS次季节预报的误差诊断和预报能力分析

Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model

  • 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年夏季2 m温度和500 hPa位势高度的空间分布特征,但在热力强迫作用显著的高原沙漠地区,尤其是非洲干旱区,GRAPES-GFS的2 m温度分析场存在较大的系统偏差。GRAPES-GFS模式的2 m温度在超前1~3周预报的均方根误差近似线型增长,最终趋于稳定。海洋区域2 m温度的预报技巧较陆地低,东亚及澳大利亚预报技巧较高。关于500 hPa位势高度,在超前1~3周预报时,东亚中低纬度预报技巧明显高于中高纬度地区,热带地区的远低于其它地区,北半球的高于南半球。关于MJO,GRAPES-GFS可描述高层和低层纬向风场的传播和模态特征,可抓住较强对流活动信号的具体位置,但地球向外长波辐射(OLR)在赤道地区正距平信号偏弱,负距平信号偏强。GRAPES-GFS模式对MJO的距平相关系数(ACC)有效预报技巧达到11天左右,与一般大气模式预报水平接近。对于选取的两次强MJO事件个例,在超前6天的预报上,GRAPES-GFS可准确地描述2次事件的传播过程,但MJO信号在发展和衰亡阶段强度偏强。

     

    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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