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

Liu Bo; Ma Libin; Rong Xinyao; Su Jingzhi; Yan Yuhan; Hua Lijuan; Tang Yanli

doi:10.11898/1001-7313.20220308

Vol.33, NO.3, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(3): 341-352

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.

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:

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

DOI: 10.11898/1001-7313.20220308

Liu Bo^{1, 2
,
,},
Ma Libin^{1, 2},
Rong Xinyao^{1, 2},
Su Jingzhi^{1, 2},
Yan Yuhan²,
Hua Lijuan^{1, 2},
Tang Yanli^{1, 2}

1.
CMA Earth System Modeling and Prediction Centre, Beijing 100081
2.
State Key Laboratory of Severe Weather, Beijing 100081

Received Date: 2022-01-19
Rev Recd Date: 2022-04-07
Publish Date: 2022-05-31

Abstract

Abstract

Based on the global high-resolution climate model CAMS-CSM developed by Chinese Academy of Meteorological Sciences, the seasonal prediction skill of downward short-wave radiation flux (DSWRF) in China and three key regions is evaluated during the period of 2011-2020. The results show that the high-resolution version of CAMS-CSM can well predict the seasonal and interannual variability of DSWRF, but the predicted intensity is relatively weaker in spring and summer, while slightly stronger in autumn and winter compared to the observation. The prediction of the climate mean state doesn't change much with the lead time, indicating the systematic bias of the DSWRF is formed steadily in the early stage of model integration. However, there are obvious diversities in the prediction skill of the DSWRF anomalies in different seasons and different regions. From the anomalous spatial and temporal correlation coefficients, it can be noted that the prediction skill is higher in Inner Mongolia and Northwest China in autumn and winter, while lower in some areas of Beijing-Tianjin-Hebei in summer and autumn. From the perspective of comprehensive assessment of trend anomalies (P index), the model can score more than 70 points for all seasons in China at 0-month lead time, and the best performance can be close to 80 points for summer and autumn in Northwest China. Overall, the high-resolution version of CAMS-CSM climate model has certain prediction capability for DSWRF at 0-1 month ahead in China, especially in northwest regions where the solar-radiation is rich all year, which can provide specific scientific guidance for the future DSWRF short-term prediction and the solar energy site selection. In addition to the systematic bias of the model, there is a significant negative correlation between the predicted DSWRF bias and the total cloud cover bias, indicating that the bias of DSWRF prediction mainly comes from the simulation bias of total cloud cover, especially in spring and summer, as well as in autumn and winter in South China. In order to improve the prediction accuracy of DSWRF, it is an effective way to reduce the uncertainty of the model cloud microphysical processes. However, it is difficult to meet the demand of practical application with only high-resolution climate model, and its results still need to be processed with methods such as dynamic downscaling and bias revision to further improve the prediction skills.
- downward short-wave radiation flux;
- high-resolution;
- climate model;
- CAMS-CSM;
- seasonal prediction

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References(40)

Figures and Tables

图 1 重点区域

Fig. 1 Key areas

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图 2 各季节DSWRF观测气候态

Fig. 2 Seasonal distribution of the observed DSWRF

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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

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图 4 各季节DSWRF观测标准差

Fig. 4 easonal standard deviation of the observed DSWRF

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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

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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)

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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

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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

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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

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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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