Revision of solar radiation product ERA5 based on random forest algorithm
WANG Xuejie1(), SHI Guoping2(), ZHOU Ziqin1, ZHEN Yang1
1. Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China 2. School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
This study performed a multi-scale error analysis of the mean surface downward shortwave radiation flux product ERA5 (0.25° × 0.25°) of the European Centre for Medium-Range Weather Forecasts (ECMWF) using 93 pieces of solar radiation hourly data in 2013 of China. Subsequently, this study revised and analyzed the total radiation product ERA5 by training the random forest model using various relevant elements such as meteorological and geographic ones. Finally, the model was used to obtain the map of revised hourly radiation spatial distribution. As a result, the reanalyzed data can be better applied in industries such as agriculture, electric power, and urban construction. The results are as follows. ① The MAE, RMSE, and R values between the ERA5 solar radiation and the measured values of stations in 2013 were 27.60 W/m2, 29.87 W/m2, and 0.97 respectively. Moreover, the ERA5 values were higher than the measured values of stations. ② The accuracy was improved after the revision using the random forest model. After revision, the MAE, RMSE, and R values between the ERA5 solar radiation and the measured values of stations were 3.34 W/m2, 3.85 W/m2, and 1.00, respectively, indicating that correlation was significantly improved. ③ The spatial macroscopic distribution patterns of radiation before and after revision were consistent, but the ERA5 radiation value significantly decreased in local areas.
王雪洁, 施国萍, 周子钦, 甄洋. 基于随机森林算法对ERA5太阳辐射产品的订正[J]. 自然资源遥感, 2022, 34(2): 105-111.
WANG Xuejie, SHI Guoping, ZHOU Ziqin, ZHEN Yang. Revision of solar radiation product ERA5 based on random forest algorithm. Remote Sensing for Natural Resources, 2022, 34(2): 105-111.
Liu Z, Yu X F. The progress in study of the effects of the solar variation on the earth environment[J]. Chinese Journal of Nature, 2012, 34(3):154-156,160.
[2]
Roberto R, Renzo R. Distributed estimation of incoming direct solar radiation over a drainage basin[J]. Journal of Hydrology, 1995, 166(3):461-478.
doi: 10.1016/0022-1694(94)05099-J
Zhao K, Gui X C, Ge J. Influence of solar radiation on thermal comfort in large spaces and corresponding design of indoor parameters[J]. Acta Energiae Solaris Sinica, 2019, 40(9):2655-2662.
Zhang X X, Jing L. Application of solar water heating system in the design of building water supply and drainage[J]. Intelligent Building and City Information, 2021(2):88-89.
[5]
张佳飞. 多时间尺度太阳辐射估算模型[D]. 重庆: 西南大学, 2013.
Zhang J F. Estimation models of solar radiation at different time scales[D]. Chongqing: Xi’nan University, 2013.
Lyu N. Analysis of spatio-temporal variation of surface downward shortwave radiation and associated effecting factors over China in recent years[D]. Beijing: Institute of Geographic Sciences and Natural Resources Research, CAS, 2009.
Cai Z Y. Analysis on the variation of surface solar radiation in East China,Central South China and its causes[D]. Nanjing: Nanjing University of Information Science and Technology, 2009.
Zhang X X, Lyu N, Yao L, et al. Error analysis of ECMWF surface solar radiation data in China[J]. Journal of Geo-Information Science, 2018, 20(2):254-267.
[9]
Zhang X T, Liang S L, Wang G X, et al. Evaluation of the reanalysis surface incident shortwave radiation products from NCEP,ECMWF,GSFC,and JMA using satellite and surface observations[J]. Remote Sensing, 2016, 8(3):225-237.
doi: 10.3390/rs8030225
[10]
Wang A H, Zeng X B. Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau[J]. Journal of Geophysical Research: Atmospheres, 2012, 117:D05102.
Chen Y W, Huang X M, Li Y, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products[J]. Journal of Applied Meteorological Science, 2020, 31(4):494-503.
Li J, Wen S N. Simulation of solar radiation based on three machine learning methods[J]. Remote Sensing Technology and Application, 2020, 35(3):615-622.
[13]
Benali L, Notton G, Fouilloy A, et al. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam,horizontal diffuse and global components[J]. Renewable Energy, 2019, 132:871-884.
doi: 10.1016/j.renene.2018.08.044
[14]
Yu W, Zhang X T, Hou N, et al. Estimation of surface downward shortwave radiation over China from AVHRR data based on four machine learning methods[J]. Solar Energy, 2019, 177:32-46.
doi: 10.1016/j.solener.2018.11.008
Xu L B, Wang A X, Wang C Y, et al. Research on correction method of marine environment prediction based on machine learning[J]. Marine Science Bulletin, 2020, 39(6):695-704.
Chen Y L, Ning Y K, Tang R N, et al. Tropical temperature correction for numerical forecast in Hainan based on spatiotemporal independence random forest model[J]. Natural Science Journal of Hainan University, 2020, 38(4):356-364.
Lu H, Xie M, Wu Z, et al. Adjusting PM2.5 prediction of the numerical air quality forecast model based on machine learning methods in Chengyu region[J]. Acta Scientiae Circumstantiae, 2020, 40(12):4419-4431.
[18]
Babar B, Luppino L T, BostrÖm T, et al. Random forest regression for improved mapping of solar irradiance at high latitudes[J]. Solar Energy, 2019, 198:81-92.
doi: 10.1016/j.solener.2020.01.034
2022, Vol. 34 
), 施国萍2(
