ECMWF地表太阳辐射数据在我国的误差及成因分析

doi:10.12082/dqxxkx.2018.170381

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (2): 254-267.doi: 10.12082/dqxxkx.2018.170381

所属专题：气候变化与地表过程

ECMWF地表太阳辐射数据在我国的误差及成因分析

张星星^1,²(), 吕宁^1,^3,^*(), 姚凌^1,³, 姜侯^1,²

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023

收稿日期:2017-08-17 修回日期:2017-10-17 出版日期:2018-03-02 发布日期:2018-03-02
通讯作者: 吕宁 E-mail:zhangxx.15s@igsnrr.ac.cn;ning.robin@gmail.com
作者简介:
作者简介：张星星（1992-）,男,安徽宿松人,硕士生,主要从事地表太阳辐射反演研究。E-mail: zhangxx.15s@igsnrr.ac.cn
基金资助:
国家自然科学基金项目(41371016、41771380);中国科学院地理科学与资源研究所优秀青年人才基金项目(2015RC203)

Error Analysis of ECMWF Surface Solar Radiation Data in China

ZHANG Xingxing^1,²(), LV Ning^1,^3,^*(), YAO Ling^1,³, JIANG Hou^1,²

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2017-08-17 Revised:2017-10-17 Online:2018-03-02 Published:2018-03-02
Contact: LV Ning E-mail:zhangxx.15s@igsnrr.ac.cn;ning.robin@gmail.com
Supported by:
National Natural Science Foundation of China, No.41371016, 41301380;The CAS Youth Innovation Promotion Association, and the Yong Talent Fund of Institute of Geographic Sciences and Natural Resources Research, No.2015RC203

摘要/Abstract

摘要：

利用2000-2009年中国气象局（CMA）地表太阳辐射台站资料,对欧洲中期天气预报中心（ECMWF）地表太阳下行短波辐射产品进行多时间尺度的计算与分析,检验ECMWF地表辐射产品对于中国地区太阳辐射特征的表现。本文通过聚类分析将中国地区分为8个区域,考虑到ECMWF大气因素对ECMWF地表辐射的影响和大气因子分布的空间异质性,引入地理探测器对ECMWF再分析辐射产品的时空误差进行定量分析,来判明影响ECMWF辐射精度的主要大气因子。结果表明：总体上看,ECMWF地表太阳辐射要高于地面观测数据,月均偏差为18.28W/m²;ECMWF地表太阳辐射表现出季节性差异,夏秋季节明显好于春冬季节,相对偏差较大的数据集中分布在12、1、2和3月,相对偏差较小的数据集中分布在6、7、8和9月;不同区域在冬季和夏季的主导大气影响因子不同,夏季中国西北（1区）、高原（3区）、西南（4区）和四川盆地（5区）地区主导影响因子都是气溶胶,东南（6区）地区的主导影响因子是地表反照率和气溶胶,中东部地区（7区）的主导影响因子是云覆盖率和气溶胶,但是因子解释较小,分别为0.0228和0.0202,东北地区（8区）4个因子均未通过显著性系数检验,因子对相对偏差的变化影响不显著;冬季中国西北（1区）、高原（3区）、中东（7区）、东北（8区）和四川盆地（5区）地区的主导影响因子都是云覆盖率,西南（5区）和东南（6区）地区的辐射主要受到气溶胶的影响。

关键词: 地表太阳下行辐射, ECMWF再分析资料, CMA站点, 地理探测器, 影响因子

Abstract:

Comparison of surface radiation data of ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data and data from station observation (China Meteorological Administration) is conducted at different time scales to check whether reanalysis data can reflect the characteristics of surface solar radiation over China. Based on the cluster analysis method, China is divided into 8 regions in order to study the regional differences of the surface radiation products of the ECMWF reanalysis data in China. Taking into account the influence of atmospheric factors on the earth's surface radiation and the spatial stratified heterogeneity of the atmospheric distribution, the geographical detector is used to find the causes of errors in different sites of reanalysis data. Overall, ECMWF is higher than the ground observation station data and the monthly deviation is 18.2835W/m². ECMWF shows seasonal difference, greater deviation in spring and winter, less deviation in summer and autumn. Large relative deviation of the data mainly distributed in December, January, February and March while minor relative deviation of the data mainly concentrated in July, June, August and September. The dominant atmospheric factors in different regions are different in winter and summer. In summer, from zone 1 to 5 the dominant factors are aerosols and the power of determinant is larger. The dominant factors of the zone 6 are albedo and aerosol. The dominant factors of the zone 7 are cloud cover and aerosol but the power of determinant is small, merely 0.0228 and 0.0202, respectively. Failing significance test indicates that the four factors had no significant effect on the relative deviation in the zone 8. In winter, the dominant factors of zone 4, 6, 8 and zone 1, 3, 5, 7 are aerosol and cloud coverage, respectively.

Key words: surface solar radiation, ECMWF reanalysis data, CMA stations, Geo-Detector software, influence factors

张星星, 吕宁, 姚凌, 姜侯. 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 Xingxing,LV Ning,YAO Ling,JIANG Hou. Error Analysis of ECMWF Surface Solar Radiation Data in China[J]. Journal of Geo-information Science, 2018, 20(2): 254-267.DOI:10.12082/dqxxkx.2018.170381

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数据源	数据时期	时间分辨率	空间分辨率	引用
ERA-Interim	1979年-至今	3 h	0.75°×0.75°	文献[16]
CMA	1957年-至今	天	N/A(站点)	http：//cdc.nmic.cn/home.do
CERES	2000年-至今	月	1°×1°	文献[17]

	月份												总数
	1		2	3	4	5	6	7	8	9	10	11	12
超过上限	75	60	216	307	477	330	276	171	123	138	100	55	2328
低于下限	695	516	397	305	323	298	201	259	364	436	510	539	4843
通过测试	29 920	27 569	30 077	29 088	29 890	29 072	30 213	30 260	29 213	30 116	29 090	30 096	354 604
错误百分率/%	2.5090	2.2896	1.9974	2.0606	2.6067	2.1145	1.5543	1.4011	1.6397	1.8703	2.0539	1.9355	1.9800

分区	夏季（6、7、8和9月）					冬季（12、1、2和3月）
分区	气溶胶		云覆盖率	水汽含量	地表反照率	气溶胶	云覆盖率	水汽含量	地表反照率
1	0.3405	0.0516（N）	0.0460	0.0302（N）		0.0196（N）	0.2085	0.0124（N）	0.0184（N）
2	0.4953	0.2546	0.2210	0.0497（N）		0.4457	0.0587（N）	0.4952	0.1080（N）
3	0.3405	0.0202（N）	0.0432（N）	0.1789		0.0843（N）	0.2396	0.0899	0.0477（N）
4	0.3753	0.2221	0.2507	0.5970（N）		0.4899	0.1088	0.1173	0.0266（N）
5	0.3258	0.0426（N）	0.1047	0.1941		0.1128	0.1174	0.0933	0.0504（N）
6	0.2278	0.0550（N）	0.0920	0.2556		0.2074	0.0284（N）	0.0091（N）	0.0211（N）
7	0.0202	0.0228	0.0125	0.0173		0.0626	0.1225	0.0961	0.0072（N）
8	0.0502（N）	0.0522（N）	0.0355（N）	0.0965（N）		0.1113	0.2828	0.0058（N）	0.0304（N）

ECMWF地表太阳辐射数据在我国的误差及成因分析

Error Analysis of ECMWF Surface Solar Radiation Data in China

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