支持向量机方法在FY-4A地面太阳辐射产品订正中的应用

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Abstract In order to obtain high-precision and high spatiotemporal resolution solar radiation spatial distribution data,a correction model for FY-4A inversion of total radiation values based on support vector machine(SVM) method was established using daily solar radiation data from seven ground radiation stations in Guizhou from March 1,2018 to April 30,2020.The results indicate that there is a significant difference between the pre correction FY-4A inversion total radiation and ground solar radiation observations,and the FY-4A inversion radiation values are generally higher,indicating a significant correlation between the two.After using SVM regression correction model to correct the ground solar radiation of FY-4A,the correlation coefficient between the two increased by 0.047,the average absolute error decreased by 50.00%,the average error improved by 87.49%,and the root mean square error decreased by 63.03%.The degree of dispersion after correction has decreased,and the correlation has significantly enhanced.The research results aim to better apply FY-4A ground solar radiation data to industries such as agricultural zoning,photovoltaic power generation,and building design.

Key words： solar radiation support vector machine FY-4A-GSRP correction

Received: 17 June 2024

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	Articles by authors
	Lin Xuefei
	Yang Lin
	Tian Pengju
	Zhu Jun
	Li Guangyi

Cite this article:

Lin Xuefei,Yang Lin,Tian Pengju等. Application of support vector machine method in FY4A ground solar radiation product correction[J]. Meteorological Hydrological and Marine Instrument, 2025, 42(1): 111-114.

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http://www.qxswhy.com/EN/ OR http://www.qxswhy.com/EN/Y2025/V42/I1/111

[1]	DICKINSON W C,CHEREMISINOFF P N.Solar Energy Technology Handbook[M].Boca Raton:CRC Press,2018.
[2]	于瑛,贾晓宇,陈笑.基于气候突变年的太阳辐射模型统计时长选取方法[J].哈尔滨工业大学学报,2021,53(1):193-200.
[3]	夏静雯,高爱臻,王晶,等.基于LandSat的宁波城市化进程对气温的影响评估[J].气象水文海洋仪器,2022,39(1):20-23.
[4]	梁进秋,申彦波,胡丽琴,等.FY-4A地表太阳入射辐射产品在山西高原的适用性研究[J].气象,2020,46(12):1575-1585.
[5]	丁立国,申彦波,马勋丹,等.FY-4A地面太阳辐射产品在贵州高原山区的适用性研究[J].高原气象,2022,41(4):1041-1050.
[6]	徐丽娜,申彦波,李忠,等.FY-4A SSI产品在辐射观测稀疏位置的偏差订正方案[J].高原气象,2022,41(4):1051-1061.
[7]	杨帆,罗敬,张东海,等.FY-4A辐射产品在贵州山区的检验与订正[J].沙漠与绿洲气象,2024,18(1):134-140.
[8]	徐丽娜,申彦波,胡玥明,等.基于随机森林算法的FY-4A地表入射太阳辐射空间订正[J].太阳能学报,2024,45(1):109-115.
[9]	陈永义,俞小鼎,高学浩,等.处理非线性分类和回归问题的一种新方法(Ⅰ):支持向量机方法简介[J].应用气象学报,2004(3):345-354.
[10]	王革丽,杨培才,毛宇清.基于支持向量机方法对非平稳时间序列的预测[J].物理学报,2008(2):714-719.
[11]	杨光,谭言科,吴凡.支持向量机方法在南京太阳总辐射推算中的应用[J].大气科学学报,2017,40(5):708-714.
[12]	赵文婧,王小勇,李艳,等.RMAPS模式预报总辐射的3种订正方法应用[J].兰州大学学报(自然科学版),2024,60(3):304-312.