不同日照情况下太阳日辐射估算方法研究——以江苏为例  

Estimation method of daily global radiation under different sunshine conditions:A case study of Jiangsu Province

作　　者：张佩[1] 高苹[1] 谢小萍[1] 拉巴 江旭 陈诗瑶 吴洪颜[1] ZHANG Pei;GAO Ping;XIE Xiaoping;LA Ba;JIANG Xu;CHEN Shiyao;WU Hongyan(Jiangsu Meteorological Bureau,Nanjing 210008,China;Lahsa Meteorological Bureau,Lahsa 850000,China;Tufts University,Boston 02155,U.S.A;Longyan Company of Fujian Provincial Tobacco Corporation,Longyan 364000,China)

机构地区：[1]江苏省气象局,南京210008 [2]拉萨市气象局,拉萨850000 [3]美国塔夫茨大学,波士顿02155 [4]福建省烟草公司龙岩市公司,龙岩364000

出　　处：《中国生态农业学报（中英文）》2022年第2期314-324,共11页Chinese Journal of Eco-Agriculture

基　　金：江苏省“333工程”高层次人才培养科研项目(BRA2019348);拉萨市科技计划项目(LSKJ202002);江苏省气象局科技项目(KM201905)资助。

摘　　要：太阳辐射是影响农田生态系统碳交换和能量收支等的关键因子。为了准确估算不同日照情况下的太阳日辐射量,更好地开展农田生态系统的相关研究,本文以江苏省为例,利用淮安、吕泗和南京3个辐射观测站2005—2020年逐日气象资料和辐射资料,以逐日日照时数是否为0,将研究样本划分为无日照和有日照两类,梳理了可观测得到的24个气象因子、3个地理因子,通过相关分析确定了不同日照情况下太阳日辐射的高度相关因子;选取3个站2005—2016年奇数年份的逐日资料样本作为建模集,采用基于最小二乘法的逐步回归方式,分别以太阳日辐射(GR)和日大气透明系数(太阳日辐射与天空辐射的比值,GR/SR)作为因变量建立了不同日照情况下的太阳日辐射估算模型;选取3个站2005—2016年偶数年份的逐日资料样本为组间验证集、2017—2020年的逐日资料样本为组外验证集。通过比较模型的拟合效果及其对建模集、组间验证集和组外验证集太阳日辐射的估算效果,最终确定太阳日辐射估算的最佳模型。结果表明:1)无论是在无日照情况还是有日照情况下,太阳日辐射都与各气象因子普遍呈现极显著相关(P<0.01)。其中在有日照情况下,太阳日辐射与日照因子呈最强的相关性,而在无日照情况下,太阳日辐射与日最高地表温度表现出最强的相关性,两者之间的相关系数高于其他气温类因子。2)无日照情况下应选择以太阳日辐射为因变量、以日最高地表温度和日露点温度为自变量的估算模型,模型的决定系数R;为0.650,对太阳日辐射的估算准确度接近75%;在有日照情况下选择以日大气透明系数为因变量、以日日照百分率和日日照时数为自变量集的估算模型,模型的决定系数R;可达0.769,对太阳日辐射的估算准确度平均为87.60%。基于该分段估算模型,江苏地区不同日照情况下的太阳日辐射估算准Global radiation is a key factor affecting carbon exchange and the surface energy budget of agroecosystems. To accurately estimate the daily global radiation(GR) under different sunshine conditions and to improve the research carried out on agroecosystems, this study used daily meteorological and radiation data collected between 2005 and 2020 at three radiation observation stations in Jiangsu Province, namely Huai’an, Lüsi, and Nanjing, to divide the research samples into two categories, namely with and without sunshine, according to whether the number of hours of sunshine per day was zero. In total, 24 observable meteorological factors and 3 geographical factors were identified, with the main factors influencing GR under different sunshine conditions being determined using correlation analysis. Daily data from the three stations collected during odd-numbered years between 2005 and 2016 were selected as the modeling dataset, and the least-squares stepwise regression method was adopted to establish the GR estimation models for conditions with and without sunshine, with GR and the daily atmospheric transparency coefficient(ratio of GR to sky radiation [SR],GR/SR) representing the dependent variables. Daily data samples from the three stations collected during even-numbered years between 2005 and 2016 were selected as the between-group verification set, while daily data samples collected from 2017 to 2020 were selected as outside-group verification sets. The optimal GR estimation model for Jiangsu Province was determined by comparing the model fits and the estimation effects of the original models with the between-group and the outside-group verification sets.The results showed that first, GR was significantly correlated with most of the meteorological factors(P<0.01) regardless of the presence of sunshine. GR under sunshine conditions had the strongest correlation with sunshine factors, while GR under without sunshine condition had the strongest correlation with the daily maximum ground temperature(TGMax). Furth

关 键 词：太阳日辐射 日照时数 逐步回归 日照情况 估算模型 

分 类 号：S161.1[农业科学—农业气象学]