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高分辨率模式对中国地表短波辐射季节预测

刘波 马利斌 容新尧 苏京志 鄢钰函 华莉娟 唐彦丽

刘波, 马利斌, 容新尧, 等. 高分辨率模式对中国地表短波辐射季节预测. 应用气象学报, 2022, 33(3): 341-352. DOI:  10.11898/1001-7313.20220308..
引用本文: 刘波, 马利斌, 容新尧, 等. 高分辨率模式对中国地表短波辐射季节预测. 应用气象学报, 2022, 33(3): 341-352. DOI:  10.11898/1001-7313.20220308.
Liu Bo, Ma Libin, Rong Xinyao, et al. High-resolution model for seasonal prediction of surface shortwave radiation in China. J Appl Meteor Sci, 2022, 33(3): 341-352. DOI:  10.11898/1001-7313.20220308.
Citation: Liu Bo, Ma Libin, Rong Xinyao, et al. High-resolution model for seasonal prediction of surface shortwave radiation in China. J Appl Meteor Sci, 2022, 33(3): 341-352. DOI:  10.11898/1001-7313.20220308.

高分辨率模式对中国地表短波辐射季节预测

DOI: 10.11898/1001-7313.20220308
资助项目: 

国家重点研发计划 2019YFC1510001

中国气象科学研究院基本科研业务费 2020Y005

中国气象科学研究院基本科研业务费 2021Z005

详细信息
    通信作者:

    刘波, 邮箱:boliu@cma.gov.cn

High-resolution Model for Seasonal Prediction of Surface Shortwave Radiation in China

  • 摘要: 基于中国气象科学研究院T255全球高分辨率气候系统模式(CAMS-CSM)2011—2020年多样本集合回报试验,评估模式在中国及3个典型区域地表短波辐射(downward short-wave radiation flux,DSWRF)的季节预测能力。结果表明:CAMS-CSM模式能较好预测DSWRF的季节变化特征,但春季、夏季预测强度偏弱,秋季、冬季偏强。不同季节、不同地区DSWRF异常场的预报技巧差异明显。由DSWRF异常的空间相关系数和时间相关系数可以看到,内蒙古和西北地区秋季、冬季预报技巧较高,京津冀部分地区夏季、秋季节预报技巧较低。从趋势异常综合评分指数看,中国区域超前1个月预报各季节评分均超过70分,对西北地区夏季、秋季的评分指数最高,超过80分。整体而言,高分辨率气候模式对中国区域DSWRF超前0~1个月预报有一定预测能力,尤其是太阳能资源丰富的西北地区,可为未来DSWRF短期预测及太阳能清洁能源选址等提供参考。除模式系统性偏差外,春季、夏季DSWRF预报偏差主要来源于总云量预报的模拟偏差,改进模式云微物理过程是提高DSWRF季节预测能力的重要途径。
  • 图  1  重点区域

    Fig. 1  Key areas

    图  2  各季节DSWRF观测气候态

    Fig. 2  Seasonal distribution of the observed DSWRF

    图  3  各季节DSWRF LM0预报与观测气候态差异

    Fig. 3  Difference of seasonal DSWRF between the prediction at LM0 and the observation

    图  4  各季节DSWRF观测标准差

    Fig. 4  easonal standard deviation of the observed DSWRF

    图  5  各季节DSWRF LM0预报与观测标准差的差异

    Fig. 5  Differences of seasonal DSWRF standard deviation between the prediction at LM0 and the observation

    图  6  DSWRF LM0预报异常与观测时间相关系数

    (黑色打点表示达到0.1显著性水平)

    Fig. 6  TCC of DSWRF between the prediction at LM0 and the observation

    (black dots denote passing the test of 0.1 level)

    图  7  各季节DSWRF不同超前时间预报区域平均与观测的时间相关系数

    Fig. 7  TCC of DSWRF between regional averaged prediction at different lead months and the observation in different seasons

    图  8  各季节DSWRF不同超前时间预报区域平均与观测的距平相关系数

    Fig. 8  ACC of DSWRF between regional averaged prediction at different lead months and observation in different seasons

    图  9  各季节DSWRF不同超前时间预报区域平均的P指数

    Fig. 9  Regional averaged P index of DSWRF predicted at different lead months in different seasons

    图  10  模式LM0预报的不同季节DSWRF预报偏差与总云量预报偏差相关分布

    (黑色打点表示达到0.1显著性水平)

    Fig. 10  Correlation coefficients between DSWRF biases and total cloud cover biases at LM0

    (black dots denote passing the test of 0.1 level)

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  • 收稿日期:  2022-01-19
  • 修回日期:  2022-04-07
  • 刊出日期:  2022-05-31

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