Revision of solar radiation product ERA5 based on random forest algorithm
-
摘要: 为了进一步提高太阳辐射量空间分布资料的精度,利用2013年93个中国太阳辐射逐时资料,对欧洲中期天气预报中心(ECMWF)ERA5平均地表太阳下行短波辐射产品(0.25°x0.25°)进行多尺度的误差分析,并利用多种相关的气象、地理等要素训练随机森林模型,对ERA5总辐射产品进行订正与分析,最后利用该模型得到订正后的逐时辐射量空间分布,使得再分析资料更好地应用于农业、电力和城市建设等行业。研究结果表明: ①2013年ERA5太阳辐射量与站点观测量的MAE,RMSE和R分别为27.60 W/m2,29.87 W/m2和0.97,且ERA5值比站点实测值偏高; ②利用随机森林订正后精度得到提高,校正后ERA5太阳辐射量与站点实测值的MAE,RMSE,R分别为3.34 W/m2,3.85 W/m2,1.00,相关性明显提高; ③订正前后的辐射量的空间宏观分布规律一致,但是ERA5太阳辐射量在局部地区有明显的下降。Abstract: This study performed a multi-scale error analysis of the mean surface downward shortwave radiation flux product ERA5 (0.25° x 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.
-
Key words:
- ERA5 products /
- solar radiation /
- random forest revision
-
-
[1] 刘钊, 于学峰. 太阳驱动地球环境变化研究进展[J]. 自然杂志, 2012, 34(3):154-156,160.[1] 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. [3] 赵康, 桂雪晨, 葛坚. 高大空间中太阳辐射对热舒适的影响及室内参数设计[J]. 太阳能学报, 2019, 40(9):2655-2662.[3] 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.[4] 张欣欣, 景丽. 太阳能热水系统在建筑给排水设计中的应用[J]. 智能建筑与智慧城市, 2021(2):88-89.[4] 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.[5] Zhang J F. Estimation models of solar radiation at different time scales[D]. Chongqing: Xi‘nan University, 2013.[6] 吕宁. 近年来中国地表太阳辐射时空变化及影响因素分析[D]. 北京: 中国科学院地理科学与资源研究所, 2009.[6] 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.[7] 蔡子颖. 我国华东、中南地区地面太阳辐射变化规律及其原因分析[D]. 南京: 南京信息工程大学, 2009.[7] 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.[8] 张星星, 吕宁, 姚凌, 等. ECMWF地表太阳辐射数据在我国的误差及成因分析[J]. 地球信息科学学报, 2018, 20(2):254-267. [8] 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. [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.[11] 陈昱文, 黄小猛, 李熠, 等. 基于ECMWF产品的站点气温预报集成学习误差订正[J]. 应用气象学报, 2020, 31(4):494-503.[11] 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.[12] 李净, 温松楠. 基于3种机器学习法的太阳辐射模拟研究[J]. 遥感技术与应用, 2020, 35(3):615-622.[12] 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. [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. [15] 许立兵, 王安喜, 汪纯阳, 等. 基于机器学习的海洋环境预报订正方法研究[J]. 海洋通报, 2020, 39(6):695-704.[15] 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.[16] 陈有龙, 宁雨珂, 唐荣年, 等. 基于时空独立的随机森林模型对海南热带气温数值预报的订正[J]. 海南大学学报(自然科学版), 2020, 38(4):356-364.[16] 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.[17] 芦华, 谢旻, 吴钲, 等. 基于机器学习的成渝地区空气质量数值预报PM2.5订正方法研究[J]. 环境科学学报, 2020, 40(12):4419-4431.[17] 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, BostrOm T, et al. Random forest regression for improved mapping of solar irradiance at high latitudes[J]. Solar Energy, 2019, 198:81-92.
-
计量
- 文章访问数: 1354
- PDF下载数: 98
- 施引文献: 0
下载: