中国大陆地区ERA5下行短波辐射数据适用性评估与对比

doi:10.12082/dqxxkx.2021.180357

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (12): 2261-2274.doi: 10.12082/dqxxkx.2021.180357

中国大陆地区ERA5下行短波辐射数据适用性评估与对比

张俊兵¹(), 沈润平¹^,^*(), 师春香², 白磊³^,⁴, 刘军建⁵, 孙帅²

1.南京信息工程大学地理科学学院,南京 210044
2.国家气象信息中心,北京 100081
3.中国科学院大学,北京 100046
4.中国科学院新疆生态与地理研究所荒漠与绿洲国家重点实验室,乌鲁木齐 830011
5.中国气象局乌鲁木齐沙漠气象研究所,乌鲁木齐 830002

收稿日期:2018-07-03 修回日期:2021-09-27 出版日期:2021-12-25 发布日期:2022-02-25
通讯作者: *沈润平（1963— ）,男,江西湖口人,教授,博士生导师,主要从事陆面过程遥感与模拟研究. E-mail: rpshen@nuist.edu.cn
作者简介:张俊兵（1992— ）,男,山西朔州人,硕士生,研究方向为陆面过程模拟与遥感。E-mail: zhangjob_921@163.com
基金资助:
国家重点研发计划项目(2018YFC1506602);国家自然科学基金重点项目(91437220)

Evaluation and Comparison of Downward Solar Radiation from New Generation Atmospheric Reanalysis ERA5 across Mainland China

ZHANG Junbing¹(), SHEN Runping¹^,^*(), SHI Chunxiang², BAI Lei³^,⁴, LIU Junjian⁵, SUN Shuai²

1. School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
2. National Meteorological Information Center, Beijing 100081, China
3. University of Chinese Academy of Sciences, Beijing 100046, China
4. State Key Laboratory of Desert and Oasis, Institute of Ecology and Geography of the Chinese Academy of Sciences, Urumqi 830011, China
5. Urumqi Desert Meteorological Institute, China Meteorological Administration, Urumqi 830002, China

Received:2018-07-03 Revised:2021-09-27 Online:2021-12-25 Published:2022-02-25
Supported by:
National Key Research and Development Program of China(2018YFC1506602);National Natural Science Foundation of China(91437220)

摘要/Abstract

摘要：

ERA5地表下行太阳短波辐射数据是欧洲中期天气预报中心（ECMWF）最新的,具有高时空分辨率的再分析产品,该短波辐射产品可作为陆面模式大气强迫数据之一,并在区域气候评估、农业以及太阳能资源等方面具有重要应用。本文利用中国区域2011—2018年经过质控的91个国家级地面辐射站点观测数据,对其在中国大陆地区的适用性进行多时空尺度的评估,并与ERA-Interim、CFSR、MERRA2共3套全球大气再分析产品和1套CERES卫星反演SYN1deg的产品进行了比较。结果表明：① 在月均值尺度上,与其他再分析产品比较,ERA5产品与站点数据的Corr最高（0.939）,RMSE最小（28.309 W/m²）,Bias（15.4 W/m²）略大于ERA-Interim产品（13.2 W/m²）;CERES卫星反演产品与站点数据的Corr为0.955,RMSE为20.042 W/m²,Bias为5.3 W/m²;② 5套产品的辐射值均高于地面观测数据,存在高估现象,总体上,ERA5产品在中国大陆地区的整体精度高于其他再分析产品,但与CERES卫星反演产品还存在一定差距,日均值比较结论亦具有相似规律。③ 分区评估结果表明在再分析产品中,ERA5产品在4个区域与观测数据都有更好的一致性,但5套产品均在南部区域表现不佳。并且与东北和北部区域相比,ERA5产品和CERES卫星反演产品在西部区域和观测数据相比的RMSE和Bias也相对偏大。

关键词: ERA5, 地表下行短波辐射, 陆面模式, 站点观测, 中国大陆, 评估, 全球大气再分析, 卫星反演

Abstract:

The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed ERA5, a global atmospheric reanalysis product with high spatiotemporal resolution. The Shortwave Downward Radiation (SWDN) of ERA5 is an important atmospheric forcing dataset which has important applications in regional climate assessment, agriculture, and solar energy resource utilization. In this study, the observed SWDN dataset after quality control was collected from 91 official radiation monitoring stations across mainland China in 2011-2018 and was applied to evaluate the SWDN in ERA5 on different spatial and temporal scales, together with other three reference SWDN datasets from global atmospheric reanalysis products (i.e., ERA-Interim, CFSR, and MERRA2) and the CERES satellite inversion product (SYN1deg). Results show that: ① On the monthly mean scale, the ERA5 product had the highest correlation coefficient (Corr) with the station observation data (0.939) and the lowest Root Mean Square Error (RMSE) (28.309 W/m²), compared with other reanalysis products. The average bias of ERA5 (15.4 W/m²) was slightly higher than that of the ERA-Interim product (13.2W/m²). The Corr between CERES satellite inversion product and observation data was 0.955, the RMSE was 20.042W/m², and the Bias was 5.3W/m²; ② The radiation values of all these five SWDN products were overestimated against the observation data. In general, the overall accuracy of the ERA5 product in mainland China was higher than the other reanalysis products, but was lower than the CERES satellite inversion product. The comparison of daily mean values between products also showed similar results; ③ Regional evaluation results show that the SWDN in ERA5 had a good consistency with observation data in four regions across mainland China. All five SWDN products performed poorly in the southern region. Compared to the northeastern and northern regions, the RSME and the bias of the ERA5 product and the CERES satellite inversion product relative to observations were larger in the western region.

Key words: ERA5, surface downward shortwave radiation, land surface model, station observation, Chinese mainland, evaluate, global atmospheric reanalysis dataset, satellite retrieved algorithm.

张俊兵, 沈润平, 师春香, 白磊, 刘军建, 孙帅. 中国大陆地区ERA5下行短波辐射数据适用性评估与对比[J]. 地球信息科学学报, 2021, 23(12): 2261-2274.DOI:10.12082/dqxxkx.2021.180357

ZHANG Junbing, SHEN Runping, SHI Chunxiang, BAI Lei, LIU Junjian, SUN Shuai. Evaluation and Comparison of Downward Solar Radiation from New Generation Atmospheric Reanalysis ERA5 across Mainland China[J]. Journal of Geo-information Science, 2021, 23(12): 2261-2274.DOI:10.12082/dqxxkx.2021.180357

图/表 13

图1

表1

图2

图3

图4

表2

图5

图6

表3

图7

表4

表5

图8

参考文献 33

[1]	王延慧, 史玉光, 何清, 等. 短波辐射研究概述[J]. 沙漠与绿洲气象, 2013, 7(2):68-73.
	[ Wang Y H, Shi Y G, He Q, et al. Research summary on shortwave solar radiation[J]. Desert and Oasis Meteorology, 2013, 7(2):68-73. ]
[2]	吕宁, 刘荣高, 刘纪远. 1998-2002年中国地表太阳辐射的时空变化分析[J]. 地球信息科学学报, 2009, 11(5):623-630.
	[ Lv N, Liu R G, Liu J Y. The spatiotemporal variation of downward shortwave radiation over China from 1998 to 2002[J]. Journal of Geo-information Science, 2009, 11(5):623-630. ]
[3]	Wang D D et al. A synergic study on estimating surface downward shortwave radiation from satellite data[J]. Remote Sensing of Environment, 2021, 264.
[4]	胡斯勒图, 施建成, 李明, 等. 基于卫星数据的地表下行短波辐射估算:方法,进展及问题[J]. 中国科学:地球科学, 2020, 50(7):27-42.
	[ Hu S L T, Shi J C, Li M, et al. A review of the estimation of downward surface shortwave radiation based on satellite data: Methods, progress and problems[J]. Science China Earth Sciences, 2020, 50(7):27-42. ]
[5]	马润, 胡斯勒图, 尚华哲, 等. 基于葵花-8卫星大气产品的地表下行短波辐射计算[J]. 遥感学报, 2018, 23(5):924-934.
	[ Ma R, Hu S L T, Shang H Z, et al. Estimation of downward surface shortwave radiation from Himawari-8 atmospheric products. Journal of Remote Sensing, 2018, 23(5):924-934. ]
[6]	吴其重, 王自发, 崔应杰. 我国近20年太阳辐射时空分布状况模式评估[J]. 应用气象学报, 2010, 21(3):343-351.
	[ Wu Q Z, Wang Z F, Cui Y J. Evaluating the solar radiation resources of China in recent 20 years by meteorological model[J]. Journal of Applied Meteorological Science, 2010, 21(3):343-351. ]
[7]	魏合理, 徐青山, 张天舒. 用GMS-5气象卫星遥测地面太阳总辐射[J]. 遥感学报, 2003, 7(6):303-312.
	[ Wei H L, Xu Q S, Zhang T S. Observation of solar irradiance at the surface from GMS-5[J]. Journal of Remote Sensing, 2003, 7(6):303-312. ]
[8]	Rossow W B. International Satellite Cloud Climatology Project (ISCCP) documentation of new cloud datasets[J]. Wmo/td737 World Climate Research Programme, 1996, 115(2):157-162.
[9]	Wang W, Xie P, Yoo S H, et al. An assessment of the surface climate in the NCEP climate forecast system reanalysis[J]. Climate Dynamics, 2011, 37(7-8):1601-1620. doi: 10.1007/s00382-010-0935-7
[10]	赵天保, 符淙斌, 柯宗建, 等. 全球大气再分析产品的研究现状与进展[J]. 地球科学进展, 2010, 25(3):242-254.
	[ Zhao T B, Fu Z B, Ke Z J, et al. Global atmosphere reanalysis datasets: current status and recent advances[J]. Advances in Earth Science, 2010, 25(3):242-254. ]
[11]	谢潇, 何金海, 祁莉. 4种再分析产品在中国区域的适用性研究进展[J]. 气象与环境学报, 2011, 27(5):58-65.
	[ Xie X, He J M, Qi L. A review on applicability evaluation of four reanalysis datasets in China[J]. Journal of Meteorology and Environment, 2011, 27(5):58-65. ]
[12]	邓小花, 翟盘茂, 袁春红. 国外几套再分析产品的对比与分析[J]. 气象科技, 2010, 38(1):1-8.
	[ Deng X H, Zhai P M, Yuan C H. Comparative analysis of NCEP/NCAR, ECMWF and JMA reanalysis[J]. Meteorological Science and Technology, 2010, 38(1):1-8. ]
[13]	Wang A, Zeng X. Evaluation of multi reanalysis products with in situ observations over the Tibetan Plateau[J]. Journal of Geophysical Research Atmospheres, 2012, 117(D5):214-221.
[14]	杨凤娟, 亢燕铭, 刘琼, 等. 新疆地面太阳辐射及其CERES/SSF卫星资料适用性研究[J]. 干旱区研究, 2019, 36(6):80-89.
	[ Yang F J, Kang Y M, Liu Q, et al. Xinjiang ground solar radiation and CERES/SSF satellite data applicability research[J]. Journal of research in arid areas, 2019, 36(6):80-89. ]
[15]	Xia X A, Wang P C, Chen H B, et al. Analysis of downwelling surface solar radiation in China from national centers for environmental prediction reanalysis, satellite estimates, and surface observations[J]. Journal of Geophysical Research Atmospheres, 2006, 111(D9):2105-2117.
[16]	刘军建, 师春香, 贾炳浩, 等. FY-2E地面太阳辐射反演及数据集评估[J]. 遥感信息, 2018, 33(1):104-110.
	[ Liu J J, Shi C X, Jia B H, et al. Retrievals and evaluation of downward surface solar radiation derived from FY-2E[J]. Remote sensing information, 2018, 33(1):104-110. ]
[17]	刘军建, 师春香, 韩帅, 等. 多源地面短波辐射数据融合与评估[J]. 遥感技术与应用, 2018, 33(5):850-856.
	[ Liu J J, Shi C X, Han S, et al. The data fusion of multi-source downward surface solar radiation and evaluation[J]. Remote sensing technology and application, 2018, 33(5):850-856. ]
[18]	You Q, Sanchez-Lorenzo A, Wild M, et al. Decadal variation of surface solar radiation in the Tibetan Plateau from observations, reanalysis and model simulations[J]. Climate Dynamics, 2013, 40(7-8):2073-2086. doi: 10.1007/s00382-012-1383-3
[19]	Yang R, Ek M, Meng J. Surface water and energy budgets for the mississippi river basin in three NCEP reanalysis[J]. Journal of Hydrometeorology, 2015, 16(2):857-873. doi: 10.1175/JHM-D-14-0056.1
[20]	傅良, 卞林根, 效存德, 等. 四种再分析辐射产品在东南极高原适用性评价[J]. 极地研究, 2015, 27(1):56-64.
	[ Fu L, Bian L G, Xiao C D, et al. Evaluation of four reanalysis radiation datasets from the east antarctic plateau[J]. Chinese Journal of Polar Research, 2015, 27(1):56-64. ]
[21]	王丹, 盛立芳, 石广玉, 等. 中国地表太阳辐射再分析数据与观测的比较[J]. 应用气象学报, 2012, 23(6):729-738.
	[ Wang D, Sheng L F, Shi G Y, et al. Comparison of surface solar radiation reanalysis data and observations over China[J]. Journal of Applied Meteorological Science, 2012, 23(6):729-738. ]
[22]	Jia B H, Xie Z H, Dai A G, et al. Evaluation of satellite and reanalysis products of downward surface solar radiation over East Asia: Spatial and seasonal variations[J]. Journal of Geophysical Research Atmospheres, 2013, 118(9):3431-3446. doi: 10.1002/jgrd.50353
[23]	Doelling D R, Loeb N G, Keyes D F, et al. Geostationary enhanced temporal interpolation for CERES flux products[J]. Journal of Atmospheric & Oceanic Technology, 2013, 30(6):1072-1090.
[24]	Kato S, Loeb N G. Twilight irradiance reflected by the Earth estimated from Clouds and the Earth's Radiant Energy System (CERES) measurements[J]. Journal of Climate, 2003, 16(15):2646-2650.
[25]	Saha S, Moorthi S, Wu X, et al. The NCEP climate forecast system version 2[J]. Journal of Climate, 2014, 27(6):2185-2208. doi: 10.1175/JCLI-D-12-00823.1
[26]	马志泉, 陈钦明, 高德政. 用中国地区ERA-Interim产品计算ZTD和ZWD的精度分析[J]. 大地测量与地球动力学, 2012, 32(2):100-104.
	[ Ma Z Q, Chen Q M, Gao D Z. Study on accuracy of ZTD and ZWD calculated from ERA-Interim data over China[J]. Journal of Geodesy and Geodynamics, 2012, 32(2):100-104. ]
[27]	Hersbach H, Dee D. ERA5 reanalysis is in production[J]. ECMWF Newsletter, 2016, 147(7):1201-1209.
[28]	Lawrence C, Krzysztof W, Andrea M, et al. Structure and dynamics of the Quasi-Biennial oscillation in MERRA-2[J]. Journal of Climate, 2016, 29(14):7703-7706. doi: 10.1175/JCLI-D-16-0089.1
[29]	中国气象局. 地面气象观测规范[M]. 北京: 气象出版社, 2005.
	[ China Meteorological Administration. Ground meteorological observation specification[M]. Beijing: Meteorological publishing, 2005. ]
[30]	中国气象局. 地面气象辐射观测产品质量控制(QX/T117-2010)[M]. 北京: 气象出版社, 2010.
	[ China Meteorological Administration. Quality control of ground meteorological radiation observation data (QX/T117-2010)[M]. Beijing: Meteorological publishing, 2010. ]
[31]	张彦丽, 赵军. 复杂地形区太阳短波辐射空间变异分析[J]. 地理与地理信息科学, 2017, 33(4):99-106.
	[ Zhang Y L, Zhao J. Spatial variation analysis of solar shortwave radiation in complex terrain[J]. Geography and Geo-information Science, 2017, 33(4):99-106. ]
[32]	Si Y W et al. Effects of single-layer low clouds on the surface solar radiation in East Asia[J]. Solar Energy, 2021, 224:1099-1106. doi: 10.1016/j.solener.2021.06.047
[33]	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. doi: 10.12082/dqxxkx.2018.170381
	[ Zhang X X, Lv 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. ]

产品	空间分辨率	时间分辨率	时段	数据制作方法	研究机构	获取地址
CERES_SYN1deg	1°×1°	日平均小时累积	2000—2021	辐射传输模型	NASA	http://ceres.larc.nasa.gov/order _data.php
CFSv2	0.2°×0.2°	6小时累积	2011—2021	3DVar	NCEP	https://rda.ucar.edu/datasets/ds094.0/#description
ERA-Interim	0.75°×0.75°	日累积	1979—2019	4DVar	ECMWF	http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/
ERA5	0.3°×0.3°	逐小时累积	1950—2021	4DVar	ECMWF	http://apps.ecmwf.int/data-catalogues/era5/?class=ea
MERRA2	0.5°×0.625°	逐小时累积	1980—2021	3DVar	NASA	https://disc.gsfc.nasa.gov/datasets?keywords="MERRA-2"&page=1&source=Models%2FAnalyses%20MERRA-2

产品	Corr	RMSE/(W/m²)	Bias/(W/m²)
CERES_SYN1deg	0.96±0.04	20.0±5.7	5.3±8.9
CFSR	0.91±0.06	39.0±11.9	26.9±15.8
ERA-Interim	0.90±0.10	31.6±9.2	13.2±17.0
ERA5	0.94±0.06	28.3±9.1	15.4±13.1
MERRA2	0.91±0.13	41.1±13.3	29.9±15.9

产品	Corr	RMSE/（W/m²）	Bias/（W/m²）
CERES_SYN1deg	0.95±0.05	31.1±8.1	5.7±9.1
CFSR	0.86±0.09	55.1±12.4	28.3±17.2
ERA-Interim	0.86±0.09	48.2±9.7	14.1±16.8
ERA5	0.90±0.07	42.8±9.2	16.2±13.4
MERRA2	0.84±0.12	57.6±14.9	31.5±16.6

空间相关性	CERES_SYN1deg	CFSR	ERA-Interim	ERA5	MERRA2
CERES_SYN1deg	-	-	-	-	-
CFSR	0.897	-	-	-	-
ERA-Interim	0.899	0.895	-	-	-
ERA5	0.937	0.922	0.945	-	-
MERRA2	0.879	0.849	0.857	0.869	-

产品	CERES_SYN1deg			ERA5			ERA-Interim			CFSR			MERRA2
区域	Corr	RMSE	Bias	Corr	RMSE	Bias	Corr	RMSE	Bias	Corr	RMSE	Bias	Corr	RMSE	Bias
西部	0.98	18.1	7.4	0.96	32.8	23.8	0.95	33.3	20.1	0.95	34.7	24.4	0.97	32.1	27.0
北部	0.98	13.9	3.8	0.96	25.0	16.5	0.94	27.0	13.9	0.93	38.2	31.1	0.94	36.8	28.3
东北	0.99	12.7	6.2	0.98	15.7	7.2	0.96	20.1	-4.1	0.97	26.2	19.2	0.98	25.3	16.9
南部	0.96	18.1	4.7	0.91	26.1	12.9	0.86	32.9	14.6	0.86	41.1	27.5	0.88	53.2	44.0

中国大陆地区ERA5下行短波辐射数据适用性评估与对比

Evaluation and Comparison of Downward Solar Radiation from New Generation Atmospheric Reanalysis ERA5 across Mainland China

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 33

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	彭振华, 李艳忠, 余文君, 星寅聪, 冯爱青, 杜深文. 遥感降水产品在中国不同气候区的适用性研究[J]. 地球信息科学学报, 2021, 23(7): 1296-1311.
[2]	方秀琴, 郭晓萌, 袁玲, 杨露露, 任立良, 朱求安. 随机森林算法在全球干旱评估中的应用[J]. 地球信息科学学报, 2021, 23(6): 1040-1049.
[3]	毕佳, 王贤敏, 胡跃译, 罗孟涵, 张俊华, 胡凤昌, 丁子洋. 一种基于改进SEIR模型的突发公共卫生事件风险动态评估与预测方法——以欧洲十国COVID-19为例[J]. 地球信息科学学报, 2021, 23(2): 259-273.
[4]	裴韬, 王席, 宋辞, 刘亚溪, 黄强, 舒华, 陈晓, 郭思慧, 周成虎. COVID-19疫情时空分析与建模研究进展[J]. 地球信息科学学报, 2021, 23(2): 188-210.
[5]	李照, 高惠瑛, 代晓奕, 孙海. 一种耦合LSTM算法和云模型的疫情传播风险预测模型[J]. 地球信息科学学报, 2021, 23(11): 1924-1925.
[6]	柳林, 吴雨菡, 宋广文, 肖露子. 犯罪防控警务策略及其时空效益评估研究进展[J]. 地球信息科学学报, 2021, 23(1): 29-42.
[7]	高吉喜, 赵少华, 侯鹏. 中国生态环境遥感四十年[J]. 地球信息科学学报, 2020, 22(4): 705-719.
[8]	李文梁, 汪驰升, 朱武. 中国大陆地区TanDEM-X 90 m DEM误差空间分布特征[J]. 地球信息科学学报, 2020, 22(12): 2277-2288.
[9]	曾婷婷, 宫阿都, 陈艳玲, 杨雨晴. 基于历史相似案例空间推演的地震伤亡人口评估方法研究[J]. 地球信息科学学报, 2020, 22(11): 2166-2176.
[10]	马晓辉,周洁萍,龚建华,黄琳,李文航,邹宇玲. 面向室内应急疏散标识的VR眼动感知实验与布局评估[J]. 地球信息科学学报, 2019, 21(8): 1170-1182.
[11]	邹雨轩, 吴志峰, 曹峥. 耦合土地利用回归与人口加权模型的PM_2.5暴露风险评估[J]. 地球信息科学学报, 2019, 21(7): 1018-1028.
[12]	武夕琳, 刘庆生, 刘高焕, 黄翀, 李贺. 高温热浪风险评估研究综述[J]. 地球信息科学学报, 2019, 21(7): 1029-1039.
[13]	方颖, 李连发. 基于机器学习的高精度高分辨率气象因子时空估计[J]. 地球信息科学学报, 2019, 21(6): 799-813.
[14]	杨腾飞, 解吉波, 李振宇, 李国庆. 微博中蕴含台风灾害损失信息识别和分类方法[J]. 地球信息科学学报, 2018, 20(7): 906-917.
[15]	鹿强, 吴琳, 陈昭, 王琪, 徐勇军, 阚荣才. 海上目标多源轨迹数据关联综述[J]. 地球信息科学学报, 2018, 20(5): 571-581.