中国不同气候区逐日直散分离模型适用性分析及通用计算模型优化

利用中国5个气候区59个气象台站1981——2010年的日值气象数据,对比分析8个散总比和3个散射系数直散分离模型在中国不同气候区的适用性。采用判定系数（R2）、均方根误差（RMSE）、平均绝对误差（MABE）、平均误差（MBE）和全局性能系数（GPI）5个误差评价指标,确定各气候区最适宜的模型形式。以该模型为基础,建立适用于中国不同气候区的散总比和散射系数逐日直散分离通用模型。结果表明,除线性形式散射系数模型精度较差外,其他模型计算精度均较高,散总比和散射系数模型平均R2分别为0.85和0.62;基于晴空指数-日照百分率的二次多项式散总比模型和基于日照百分率三次多项式散射系数模型在不同气候区精度均最高;以该模型为基础建立中国不同气候区散总比和散射系数逐日直散分离通用模型,其平均R2分别为0.89和0.70。     

基于线性方法的内蒙古地区太阳总辐射月均值估算

探讨了内蒙古地区太阳总辐射月均值与日照百分率的关系,基于5个气象站1996—1998年连续3 a的月日照时数（n）和太阳总辐射值（R_s）。计算得到Angstrom方程的系数a和b,与和清华等拟合得到的中国西部太阳总辐射公式中的a=0.185,b=0.595,比较一致。同时,R_s和n之间的直接线性关系,R与月平均温度（T）之间的直接线性关系也能用来估算太阳总辐射月均值,总均方根误差约为80 MJ·m^-2/month,总百分比误差约为18%。     

计算水平地面月平均日总太阳辐射量的日照类模型

本文评述了基于日照对数，用于计算水平地面月平均总太阳辐射量的三类日照类模型，并对其进行了比较。     

基于地面观测资料的太阳辐射量统计反演法研究

为了在缺乏实测太阳辐射量的情况下精准评估光伏潜力,更好地规划和开发利用太阳辐射气候资源,促进地方经济发展以及助力国家碳中和目标的实现,研究提出一种基于地面观测资料的太阳辐射量计算方法.根据气候学原理,利用与太阳辐射相关的周边地面台站日照百分率和实测日太阳总辐射等观测数据,建立间接估算到达地面的月太阳总辐射量的计算模型;...     

复杂地形区晴空太阳辐射模型构建与应用

以昆仑山提孜那甫河流域为研究区,利用中分辨率成像光谱仪(MODIS)提供的大气数据基于Iqbal Model C模型估算晴空大气透射率空间分布,并引入地形开阔度(SVF)和遥感地表反照率数据分别用于估算散射辐射地形阻挡以及反射辐射反照率系数空间分布,最后结合Kumar模型的直接辐射地形阻挡模拟过程,实现对Kumar模型的改进,改进后模型综合考虑了大气以及地形对太阳辐射的影响。利用改进后模型对研究区地表太阳辐射时空分布进行模拟和分析,基于地面气象站点观测数据对模拟结果进行验证。结果表明：模型估算值与站点观测值存在很好的一致性,相关系数R²为0.96,平均绝对误差(MAE)为1.47 MJ/m²,平均绝对相对误差(MARE)为12.26%。春季、夏季以及秋季模型的模拟精度较高,冬季模型的模拟精度较低,可能的原因为冬季MODIS大气数据有所低估。     

江苏省太阳总辐射月经验系数计算方法的对比研究

基于江苏省淮安、吕泗、南京及周边省份莒县、郑州、固始、合肥、宝山、杭州、屯溪共计10个辐射代表站建站以来历年逐月太阳总辐射和日照百分率数据,拟合各辐射代表站月经验系数a、b值作为样本,采用反距离权重插值法及REOF分区法两种气候学方法计算江苏省各无辐射观测气象站的月经验系数a、b,并分析不同气候学方法计算的月经验系数估算太阳总辐射量与实测值的差异。结果表明,采用气候学计算方法估算太阳总辐射量实际可行;以两种不同气候学方法计算的月经验系数估算太阳总辐射量效果均较好,但采用REOF分区法所得的经验系数更适用于江苏省太阳总辐射量的气候学估算。     

一种计算无辐射资料地区最佳倾角的研究

构建基于分布较广的常规气象台站的日照百分率数据来计算水平面太阳辐射的模型,拟合结果表明模型精度较高。在此基础上使用空间插值技术得到全国范围内水平面太阳辐射,使用MODIS反照率产品获取地形反射辐射,之后依据各向异性辐射模型计算光伏阵列面太阳辐射,循环寻优生成最佳倾角的全国空间连续分布图。这套计算方法与结果可为包括无辐射观测记录地区在内的光伏阵列最佳倾角的确定提供科学参考。     

城市大气污染对到达地表的太阳辐射的影响研究

在晴空（无云）的条件下,大气污染是影响到达地球表面太阳辐射的重要因素之一。选择中国6个典型城市（北京、沈阳、上海、武汉、广州和成都）,利用2014年1月—2020年12月的空气质量日监测数据以及地面太阳辐射、日照时数等逐日观测数据,定量分析晴空条件下大气污染指数（AQI）与地表太阳总辐射、散射辐射的关系。结果表明：1）大气污染会降低清晰度指数,增加散射系数,对于地表太阳总辐射有衰减作用,对于散射辐射有增强作用。2）2014—2020年,大气污染（AQI>100）使得晴天地表太阳总辐射的年衰减总量和相对衰减量（共7 a）较大的是北京（212.40 MJ/m²,4.01%）、沈阳（184.16 MJ/m²,3.00%）、上海（123.80 MJ/m²,4.37%）和武汉（106.36 MJ/m²,3.04%）,而成都（58.03 MJ/m²,3.82%）和广州（18.76MJ/m²,0.96%）的衰减总量较小。3）大气污染（AQI>100）使得晴天散射辐射的年增加总量和相对衰减量分别是北京256.64 MJ/m²（12.96%）、沈阳134.45 MJ/m²（7.10%）、武汉22.62 MJ/m²（1.36%）、成都43.40 MJ/m²（9.71%）、上海94.74 MJ/m²（8.25%）和广州37.79 MJ/m²（5.90%）。     

基于机器学习模型的关中地区GPMIMERG降水数据订正方法

针对GPM_IMERG降水数据存在系统误差的问题,以关中地区为例,在筛选海陆位置、地形、植被指数（NDVI）变量的基础上,运用支持向量机（SVM）、随机森林（RF）、高斯过程回归（GPR）模型对IMERG月降水数据进行订正,并通过34个站点数据验证订正模型。结果表明,关中地区IMERG数据具有良好的可替代性,其决定系数R²达0.76,平均绝对误差M_MAE、均方根误差R_RMSE分别为6.94、9.77 mm;经机器学习模型订正后星地数据之间的R²提升了2.05%～58.33%,R_RMSE、M_MAE分别降低了0.85%～71.23%、0.10%～73.47%;与GPR、RF模型相比,SVM模型的R_RMSE、M_MAE分别减小16.76%、9.76%和24.73%、14.11%,对IMERG数据订正具有更好的适用性。     

基于天气分型的水平面总辐射及散射辐射建模分析

为获得无太阳辐射观测地区的水平面总辐射及散射辐射量提出一种组合模型。首先通过日照百分率将天气情况分成3类,在此基础上,探究总云量、气溶胶等气象环境因子对水平面总辐射的影响,构建水平面总辐射线性模型;其次,考虑不同天气类型下气象环境因子的特征,建立基于高斯过程回归（GPR）的散射比和散射系数模型,进而获得散射辐射量;最后,得到每种天气类型下最优水平面总辐射模型与散射辐射模型构成的组合模型。结果表明,所提组合模型可有效提高水平面总辐射与散射辐射的预测精度。     

Estimation of monthly mean daily diffuse radiation in China

In this study, several equations are employed to estimate monthly mean daily diffuse solar radiation for eight typical meteorological stations in China. Estimated values are compared with measured values in terms of statistical error tests such as mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE). All the models fit the data adequately and can be used to estimate monthly mean daily diffuse solar radiation from global solar radiation and sunshine hours. This study finds that the quadratic model performed better than the other models:     

Empirical model development and comparison with existing correlations

This study utilized monthly mean daily values of global solar-radiation and sunshine duration at 41 locations in Saudi Arabia and developed an empirical correlation for the estimation of global solar radiation at locations where it is not measured. The paper also presents the comparison between the present correlation and other models developed under different geographical and varied meteorological conditions. The comparisons are made using standard statistical tests, namely mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute bias error (MABE) tests. The errors are calculated using monthly mean daily measured and estimated values of global solar radiation at all 41 locations. The study found that the present correlation produced the best estimates of global solar radiation.     

基于GA-BP神经网络的逐时总辐射分组模型研究

通过分析影响太阳辐射的主要因素,提出以太阳高度角、季节和天气（晴空指数）作为数据划分依据的分组模型建立方法。以拉萨和西安地区的逐时气象数据和辐射数据为例,基于遗传算法（genetic algorithm,GA）优化的BP神经网络,建立太阳高度角、季节和天气类型的逐时总辐射分组模型。该研究揭示分组模型误差变化的规律,并将其估算误差与AllData模型比较。结果显示,相较于AllData模型,分组模型的估算误差均有降低。其中,天气分组模型误差最小,且西安的天气分组模型结果优于拉萨。西安天气分组模型平均绝对百分比误差（MAPE）和相对均方根误差（rRMSE）相较AllData模型结果分别下降3.96%和4.18%。研究结果表明分组模型能够降低逐时总辐射估算误差,可为估算逐时总辐射提供方法借鉴。     

Estimation of daily diffuse solar radiation in China

Applying the measured global and diffuse solar radiation data from 78 meteorological stations in China, a countrywide general correlation model for calculating the daily diffuse radiation was derived on the basis of Liu and Jordan method. Two widely used statistics: root mean square error and mean bias error were used to assess the performance of the correlation. And the correlation shows good behavior when applied to most of the stations. Subsequently, with the measured data from the 78 stations, an analysis of geographical distribution of solar energy resource in China was also presented in the form of clearness index (the ratio of global solar radiation to extraterrestrial radiation) percentage frequency, and results show that the solar energy resource in western and northern China is relatively abundant.     

Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey

In this study, seven different empirical equations are employed to estimate the monthly average daily global solar radiation on a horizontal surface for provinces in the different regions of Turkey, using only the relative duration of sunshine. Daily global solar radiation and sunshine measurement data collected for the provinces of Turkey are obtained from the Turkish State Meteorological Service. The regression constants of the new models developed in this study are found for the provinces of Turkey, as well as that of some models given in the literature. In order to indicate the performance of the models, the statistical test methods of the mean bias error (MBE), mean absolute bias error (MABE), mean relative error (MRE), root mean square error (RMSE) and correlation coefficient (r) are used.     

天气类型对逐时太阳总辐射模型适应性的影响

将分解模型（decomposition model）按照输入参数、构造方法及特性分为3类,选择拉萨、西安、上海2016年辐射观测值作为实验数据,引入精确度分值（accuracy score, AS）作为模型适应性评判标准,研究晴、多云、阴3种天气类型下分解模型适应性规律以及针对不同天气类型适应模型形式。结果表明天气类型对模型的适应性有显著影响,即由晴到阴模型整体适应性呈下降趋势;当天气特别晴朗时第Ⅱ类模型适应性明显优于其他两类模型,而对于其他天气类型第Ⅰ类的C.P.R模型适应性最好。利用天气类型适应模型,3个地区全年逐时总辐射误差平均可减小3%,平均绝对误差百分率（PMAE）在20%~25%之间,均方根误差百分率（PRMSE）在26%~32%之间。可见利用天气适应模型能够有效减小估算误差,逐时总辐射数据精度能够满足建筑节能设计需求。     

Assessment of diffuse solar energy under general sky condition using artificial neural network

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R²) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.     

Solar radiation over Saudi Arabia and comparisons with empirical models

We present a comparison between models developed by the present authors and 16 other models for different geographical and varied meteorological conditions. The comparisons are made using the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute bias error (MABE). These errors are calculated using monthly-mean, measured daily and estimated values of total solar radiation for 41 locations in Saudi Arabia. We find that our latitude, longitude, altitude, and sunshine-duration-dependent model given in Eq. (1)produced the best estimates for global solar radiation. The second- and third-best estimates were obtained from our linear model and other models given in Eq. (2)and Eq. (11), respectively.