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基于GRAPES-GFS次季节预报的误差诊断和预报能力分析

齐倩倩 朱跃建 陈静 田华 佟华

齐倩倩, 朱跃建, 陈静, 等. 2021. 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, 45(6): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20157
引用本文: 齐倩倩, 朱跃建, 陈静, 等. 2021. 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, 45(6): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20157
QI Qianqian, ZHU Yuejian, CHEN Jing, et al. 2021. Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20157
Citation: QI Qianqian, ZHU Yuejian, CHEN Jing, et al. 2021. Error Diagnosis and Assessment of Sub-seasonal Forecast Using GRAPES-GFS Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1−19 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

doi: 10.3878/j.issn.1006-9895.2008.20157
基金项目: 国家重点研究发展计划项目——地球系统模式大数据平台与诊断评估系统研制2017YFA0604502、中国气象局数值预报中心青年基金项目400441、国家自然科学基金项目41906022
详细信息
    作者简介:

    齐倩倩,女,1989年出生,博士,工程师。主要从事数值天气预报和产品后处理开发研究。E-mail: qiqianqianhenu@163.com

    通讯作者:

    陈静,E-mail: chenj@cma.gov.cn

  • 中图分类号: P435

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

Funds: National Key Research and Development Program of China—Earth System Model Big Data Platform and Diagnostic Evaluation System Developing (Grant 2017YFA0604502), the Youth Fund of Numerical Weather Prediction Center of CMA (Grant 400441), National Natural Science Foundation of China (Grant 41906022)
  • 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。
  • 图  1  (a1–a3)2018年冬季(2018年11月、12月及2019年1月)和(b1–b3)2019年夏季(2019年6月、7月、8月)全球季节平均的2 m温度(单位:℃)分布:(a1、b1)GRAPES-GFS分析场;(a2、b2)NCEP/NCAR再分析资料;(a3、b3)ECMWF再分析资料

    Figure  1.  The distribution of the global seasonal average of daily 2-m temperature (units: ℃) from (a1, b1) GRAPES-GFS, (a2, b2) NCEP/NCAR reanalysis, and (a3, b3) ECMWF reanalysis during the winter (Nov–Dec–Jan) in 2018 and summer (Jun–Jul–Aug) in 2019, respectively

    图  2  2018年冬季(左列)和2019年夏季(右列)全球2 m温度的季节平均的偏差分布(单位:℃):(a1、a2)GRAPES-GFS与NCEP/NCAR之差;(b1、b2)GRAPES-GFS与ECMWF之差

    Figure  2.  The distribution of the global seasonal average error of daily 2-m temperatures (units: ℃): (a1, a2) Difference between GRAPES-GFS and NCEP/NCAR; (b1, b2) difference between GRAPES-GFS and ECMWF during the winter in 2018 (left column) and summer in 2019 (right column)

    图  3  (a)中国东南区及(b)非洲干旱区日平均2 m温度的时间序列(单位:℃)。其中,不同颜色的曲线代表不同分析资料的2 m温度序列,标签中的数字表示全部月份2 m温度的平均值

    Figure  3.  Time series of daily 2-m temperature over (a) South East China and (b) arid region of Africa (units: ℃). The different colour lines indicate the daily 2-m temperature series with different analysis data. The numbers in the legends indicate mean 2-m temperature of the whole reforecast period

    图  4  2018年冬季(左列)和2019年夏季(右列)季节平均的(a1、a2)GRAPES-GFS分析场以及(b1、b2)NCEP/NCAR再分析资料的500 hPa位势高度(单位:gpm)分布,(c1、c2)GRAPES-GFS与NCEP/NCAR500 hPa位势高度之差(单位:gpm)的分布

    Figure  4.  Seasonal means of 500-hPa geopotential heights from (a1, a2) GRAPES-GFS, (b1, b2) NCEP/NCAR reanalysis and (c1, c2) their corresponding errors (units: gpm) during the winter in 2018 and summer in 2019, respectively

    图  5  GRAPES-GFS模式2 m温度超前1~4周(第一行至第四行)次季节预报平均的相对误差(单位:℃):(a1–a4)参考态为GRAPES-GFS分析场;(b1–b4)参考态为NCEP/NCAR分析场;(c1–c4)参考态为ECMWF分析场

    Figure  5.  Relative errors (units: ℃) of daily 2-m temperature sub-seasonal predictions for weeks 1, 2, 3 and 4 (from top line to bottom line): (a1–a4) With the reference field from the GRAPE-GFS analysis, (b1–b4) with the reference field from NCEP/NCAR reanalysis, (c1–c4) with the reference field from ECMWF analysis

    图  6  2018年11月至2019年8月, 去除海洋区域后,GRAPES-GFS模式对全球、北半球和东亚陆地地区2 m温度分别超前预报(a)1周、(b)2周、(c)3周和(d)4周时均方根误差的时间序列图。不同颜色的曲线代表不同区域的均方根误差序列,标签中的数字表示全部月份均方根误差的平均值

    Figure  6.  RMSE of 2-m temperature forecasts over the global area (land only), Northern Hemisphere (NH) and East Asia (EA) for (a) leading 1 week, (b) leading 2 weeks, (c) leading 3 weeks and (d) leading 4 weeks during the period from November in 2018 to August in 2019. The different lines indicate the RMSE series over different regions and the numbers in the legends indicate the mean RMSE of the whole reforecast period

    图  7  GRAPES-GFS模式对2 m温度超前1至4周预报的时间相关系数。(a) 超前预报1周;(b) 超前预报2周;(c) 超前预报3周;(d) 超前预报4周。其中,右标题的数字表示全球平均的TCC技巧

    Figure  7.  TCC of 2-m temperature forecasts during weeks 1, 2, 3 and 4 for (a) leading 1 week, (b) leading 2 weeks, (c) leading 3 weeks and (d) leading 4 weeks. The numbers in the right title indicate the mean TCC over the global areas

    图  8  2018年11月至2019年8月, GRAPES-GFS模式对全球、北半球和东亚地区500 hPa位势高度场分别超前预报(a)1周、(b)2周、(c)3周和(d)4周PAC技巧的时间序列。其中,不同颜色的曲线代表不同区域的空间距平相关PAC预报技巧序列,标签中的数字表示全部月份PAC预报技巧的平均值

    Figure  8.  The PAC (Pattern Anomaly Correlation) time series of 500-hPa geopotential height forecasts during weeks 1, 2, 3 and 4 over the global area, Northern Hemisphere (NH) and East Asia (EA) for (a) leading 1 week, (b) leading 2 weeks, (c) leading 3 weeks and (d) leading 4 weeks during the period from November in 2018 to August in 2019. The different lines indicate the PAC series over different regions and the numbers in the legends indicate the mean PAC skill of the whole reforecast period

    图  9  GRAPES-GFS模式对500 hPa位势高度场超前预报(a)1周、(b)2周、(c)3周和(d)4周的时间相关系数(TCC)。其中,右标题的数字表示全球平均的TCC技巧

    Figure  9.  TCC (temporal correlation coefficient) of 500-hPa geopotential height forecasts during weeks 1, 2, 3 and 4 for (a) leading 1 week, (b) leading 2 weeks, (c) leading 3 weeks and (d) leading 4 weeks. The numbers in the right title indicate the mean TCC over the global areas

    图  10  GRAPES-GFS(第一行)、NCEP/NCAR(第二行)分析场中MJO基本要素距平场在2018年9月至2019年8月在热带地区15°S~15°N平均的发展:(a1、a2)850 hPa纬向风;(b1、b2)200 hPa纬向风;(c1、c2)OLR

    Figure  10.  Composite evolution of the basic elements related to MJO averaged over 15°S–15°N as derived from GRAPES-GFS (top line) and NCEP/NCAR (bottom line) reanalysis: (a1, a2) 850-hPa zonal wind anomaly, (b1, b2) 200-hPa zonal wind anomaly and (c1, c2) the OLR anomaly

    图  11  2019年夏季平均与2018年冬季平均的(a1、b1)GRAPES-GFS分析场和(a2、b2)NCEP/NCAR再分析资料中OLR距平场(单位:W m−2)季节平均的空间分布

    Figure  11.  The seasonal average of OLR anomaly (units: W m−2) from (a1, b1) GRAPES-GFS and (a2, b2) NCEP/NCAR reanalysis during the summer in 2019 and winter in 2018, respectively.

    图  12  赤道地区(15°S~15°N)平均的MJO基本要素距平场的距平相关系数

    Figure  12.  Anomaly correlation coefficients by lead days for the basic elements related to MJO averaged over 15°S–15°N

    图  13  由赤道(15°S~15°N)平均的 OLR距平、U850距平和 U200距平的联合EOF获得的第一、第二模态纬向结构。其中,第一行是关于GRAPES-GFS的,第二行是关于NCEP/NCAR的。所有变量均进行标准化和带通滤波处理

    Figure  13.  The first mode (EOF1) and second mode (EOF2) of EOF of averaged over (15°S–15°N) OLR anomaly, U850 anomaly and U200 anomaly from GRAPES-GFS analysis data (top line) and NCEP/NCAR analysis data (bottom line). All variables are normalized and are 20-d band-pass filtered

    图  14  2018年9月至2019年8月RMM1+RMM2的距平相关系数

    Figure  14.  the anomaly correlation coefficient of RMM1 plus RMM2 for the period of September in 2018 to August in 2019

    图  15  (a)2018年11月13日至2019年1月20日和(b)2019年4月15日至6月12日澳大利亚气象局(ABOM)的实况、NCEP/NCAR再分析资料以及GRAPES-GFS超前6天预报的MJO空间位相传播对比

    Figure  15.  The RMM phase space diagrams as derived from MJO products of Australian Bureau of Meteorology (ABOM), NCEP/NCAR reanalysis data and leading 6-day predictions of GRAPES-GFS, respectively, for (a) the period of November 13, 2018 to January 20, 2019 and (b) the period of April 15, 2019 to June 12, 2019

    表  1  GRAPES-GFS与其他分析资料的2 m温度在不同季节的相似系数

    Table  1.   Similarity coefficients of seasonal-mean 2-m temperatures between three groups of analysis/reanalysis data

    GRAPES-GFS与其他分析资料的相似系数
    NCEP/NCAR再分析资料ECMWF再分析资料
    夏季0.950.87
    冬季0.960.89
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出版历程
  • 收稿日期:  2019-05-21
  • 录用日期:  2020-12-23
  • 网络出版日期:  2021-03-03

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