中低纬山地气象2024，Vol.48Issue(6) ：51-58.

BP神经网络算法在FY-4A-SSI产品订正中的应用研究

Research on the Application of BP Neural Network Algorithm in FY-4A-SSI Product Correction

林雪飞 朱军 田鹏举 李光一 黎凤丹 胡晶晶
中低纬山地气象2024，Vol.48Issue(6) ：51-58.
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BP神经网络算法在FY-4A-SSI产品订正中的应用研究

Research on the Application of BP Neural Network Algorithm in FY-4A-SSI Product Correction

林雪飞 1朱军 2田鹏举 3李光一 4黎凤丹 5胡晶晶6
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作者信息

  • 1. 贵州省遵义市气象局,贵州 遵义 563000;贵州风云气象技术有限公司,贵州 贵阳 550002
  • 2. 贵州风云气象技术有限公司,贵州 贵阳 550002;贵州省气候中心,贵州 贵阳 550002
  • 3. 贵州风云气象技术有限公司,贵州 贵阳 550002;贵州省生态与农业气象中心,贵州 贵阳 550002
  • 4. 贵州省生态与农业气象中心,贵州 贵阳 550002
  • 5. 贵州省遵义市气象局,贵州 遵义 563000
  • 6. 中国民用航空温州空中交通管理站,浙江 温州 325000
  • 折叠

摘要

[目的]为了弥补地面观测辐射数据的不足,获取高精度的太阳辐射空间分布资料.[方法]利用贵州2018 年3月 1 日—2020 年4 月30 日7 个地面辐射站逐日太阳辐射资料,分月建立基于BP神经网络的FY-4A SSI产品订正模型,利用该模型对FY-4A总辐射进行订正与分析,并把订正结果和采用一元线性回归模型所得到的结果进行对比.[结果](1)FY-4A反演总辐射值存在对"低值辐射高估、高值辐射低估"的偏差分布特征,整体上FY-4A总辐射值偏高;(2)FY-4A总辐射值与站点值的相关系数R在0.767～0.926 之间,平均绝对误差MAE在5.04～6.98 MJ·m-2之间,平均误差ME在4.43～6.68 MJ·m-2之间,均方根误差RMSE在5.70～7.44 MJ·m-2之间;(3)采用线性方法订正后两者的R不变,MAE在 1.45～3.09 MJ·m-2之间,ME在-0.25～0.42 MJ·m-2之间,RMSE在2.12～4.70 MJ·m-2之间;(4)采用BP神经网络方法订正后两者的R在0.856～0.962 之间,MAE在 1.02～2.43 MJ·m-2之间,ME在-0.48～0.23 MJ·m-2之间,RMSE在 1.36～3.77 MJ·m-2之间.[结论]基于BP神经网络模型订正后的FY-4A总辐射值与站点值之间的误差较小,订正精度高于线性订正,有较好的稳定性,可适用于贵州高原山区FY-4A地面太阳辐射产品的订正.

Abstract

In order to compensate for the shortcomings of ground observation radiation data and obtain high-precision solar radiation spatial distribution data,a monthly FY-4A SSI product correction model based on BP neural network is established based on daily solar radiation data from seven ground radiation stations in Guizhou from March 1,2018 to April 30,2020.The model is used to correct and analyze the total radiation of FY-4A,and the correction results are compared with those obtained using an unary linear regression model.The results show that:(1)The total radiation value of FY-4A retrieval has a biased distribution characteristic of overestimating low-value radiation and underestimating high-value radiation,and overall,the total radiation value of FY-4A is relatively high.(2)The correlation coefficient R between the total radiation value of FY-4A and the station value is between 0.767 and 0.926,the mean absolute error MAE is between 5.04 and 6.98 MJ·m-2,the mean error ME is between 4.43 and 6.68 MJ·m-2,and the root mean square error RMSE is between 5.70 and 7.44 MJ·m-2.(3)After the linear correction,the R of the two remains unchanged,with MAE between 1.45 and 3.09 MJ·m-2,ME between-0.25 and 0.42 MJ·m-2,and RMSE between 2.12 and 4.70 MJ·m-2.(4)After corrected by the BP neural network method,the R of the two is between 0.856 and 0.962 MJ·m-2,MAE between 1.02 and 2.43 MJ·m-2,ME between-0.48 and 0.23 MJ·m-2,and RMSE between 1.36 and 3.77 MJ·m-2.Therefore,the error between the total radiation value of FY-4A correction based on the BP neural network model and the station value is small.The correction accuracy of FY-4A correction based on the BP neural network model is higher than that by the linear correction,and its stability is good.Therefore,the FY-4A correction based on the BP neural network model can be applied to the correction of FY-4A surface solar irradiance products in mountainous areas of Guizhou Plateau.

关键词

太阳辐射/FY-4A-SSI/BP神经网络/一元线性回归/订正

Key words

Solar radiation/FY-4A-SSI/BP neural network/unary linear regression/correction

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出版年

2024
中低纬山地气象
贵州省气象学会

中低纬山地气象

影响因子：0.33
ISSN：2096-5389
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