A new simple method for estimating monthly and daily solar radiation. Performance and comparison with other methods at Lleida (NE Spain); a semiarid climate

Summary A simple new method is proposed for estimating relative sunshine duration and solar irradiance on both a monthly and daily basis. The method requires the occurrence of precipitation, and maximum and minimum air temperatures as input. Comparisons with two other published simple methods: Bristow and Campbell (1984); and Hargreaves et al. (1985), and a modified version of the method proposed by McCaskill (1990), were conducted for a weather station at Lleida (NE Spain), which has a continental semiarid climate. The new method performed slightly better than the others mentioned above, mainly as a result of a reduction in errors when estimating cases of high solar radiation. Received May 23, 2000/Revised February 14, 2001     

Parameterization and mapping of solar radiation in data sparse regions

Knowledge of temporal and spatial variation of solar radiation is essential for many applications. In this work, a simple and feasible procedure is conducted to map the daily solar radiation for Liaoning province, one of the most important agricultural areas in China, but with sparsely measured solar radiation data. The daily sunshine duration are interpolated to the whole area, subsequently, solar radiation are calculated by ?ngstr?m-Prescott model, the generic parameters of which are determined by least square to minimize the overall fitting residual between the ratio of actual to potential sunshine duration and the ratio of actual to extra-terrestrial solar radiation of the sites where solar radiation are available. In other local regions with sparse data, mapping of the solar radiation could be done following the simple procedure. In the present study area, using the interpolated daily sunshine duration data by ANUSPLIN, ?ngstr?m-Prescott model with the generic parameters (a = 0.505, and b = 0.204) returns reasonable results, with the overall RMSE of 2.255 MJ m^?2, and RRMSE of 16.54%. The daily solar radiation varies between 5.26 in December and 22.74 MJ m^?2 in May, and shows an obviously spatial variation which is mainly contributed to the climate and topography. The substitution of solar radiation from nearby station is preferred to estimation by ?ngstr?m-Prescott model if the distance between the stations falls below the threshold of 135 ± 15 km. The RMSE of such substitution increases by approximately 0.157 MJ m^?2 per 10 km.     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

Precise estimation of hourly global solar radiation for micrometeorological analysis by using data classification and hourly sunshine

We have developed a method for estimating hourly global solar radiation (GSR) from hourly sunshine duration data. This procedure requires only hourly sunshine duration as the input data and utilizes hourly precipitation and daily snow cover as auxiliary data to classify time intervals into six cases according to weather conditions. To obtain hourly GSR using a simple algebraic form, a quadratic function of the solar elevation angle and the sunshine duration ratio is used. Daily GSR is given by a sum of hourly GSRs. We evaluated the performance of the newly developed method using data obtained at 67 meteorological stations and found that the estimated GSR is highly consistent with that observed. Hourly and daily root-mean-square misfits are approximately 0.2 MJ/m²/h (~55 W/m²) and 1.4 to 1.5 MJ/m²/day (~16 to 17 W/m²), respectively. Our classification of weather conditions is effective for reducing estimation errors, especially under cloudy skies. Since the sunshine duration is observed at more meteorological stations than GSR, the proposed new method is a powerful tool for obtaining solar radiation with hourly resolution and a dense geographical distribution. One of the proposed methods, GSRgrn, can be applicable to hourly GSR estimations at different observation sites by setting local parameters (the precipitable water, surface albedo, and atmospheric turbidity) suitable to the sites. The hourly GSR can be applied for various micrometeorological studies, such as the heat budget of crop fields.     

Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables

Solar radiation is an essential and important variable to many models. However, it is measured at a very limited number of meteorological stations in the world. Developing method for accurate estimation of solar radiation from measured meteorological variables has been a focus and challenging task. This paper presents the method of solar radiation estimation using support vector machine (SVM). The main objective of this work is to examine the feasibility of SVM and explore its potential in solar radiation estimation. A total of 20 SVM models using different combinations of sunshine ratio, maximum and minimum air temperature, relative humidity, and atmospheric water vapor pressure as input attributes are explored using meteorological data at 15 stations in China. These models significantly outperform the empirical models with an average 14 % higher accuracy. When sunshine duration data are available, model SVM2 using sunshine ratio and air temperature range is proposed. It significantly outperforms the empirical models with an average 26 % higher accuracy. When sunshine duration data are not available, model SVM19 using maximum temperature, minimum temperature and atmospheric water vapor pressure is proposed. It significantly outperforms the temperature-based empirical models with an average of 18 % higher accuracy. The remarkable improvement indicates that the SVM method would be a promising alternative over traditional approaches for estimation of solar radiation at any locations.     

Temporal and spatial variations of global solar radiation over the Qinghai–Tibetan Plateau during the past 40 years

Global solar radiation is of great significance to the balance of ground surface radiation, the energy exchange between the Earth’s surface and atmosphere, and the development of weather and climate systems in various regions. In this study, the monthly global radiation recorded at 23 stations over the Qinghai–Tibetan Plateau (QTP) was utilized to estimate global solar radiation (Q) from sunshine duration and to obtain improved fits to the variation coefficients of the monthly Angström–Prescott model (APM). The modeling results were evaluated by calculating the statistical errors, including mean bias error, mean absolute error, root mean square error, and mean relative error. We demonstrate that the monthly Q values can be predicted accurately by APM over the QTP. We also assess the variations of Q values at 116 meteorological stations by APM over the QTP during 1961–2000. The analysis shows that the annual mean sunshine duration amounted to more than 3,000 h over the whole plateau, implying promising prospects for economic applications of solar energy. During the past 40 years, the mean global solar radiation has been relatively high in the western QTP, extending northward to the Inner Mongolian Plateau. Although its decadal variations in the QTP and surrounding regions were inconsistent, the anomaly values of global solar radiation were generally positive during the 1960s and 1970s, indicating that the QTP’s global solar radiation has increased during those periods. The anomaly values were negative during the 1980s and 1990s, showing that the plateau’s global solar radiation has decreased during those periods. Global solar radiation over the QTP is negatively proportional to latitude but positively proportional to altitude and relative sunshine duration. Three factors, the sunshine duration, latitude, and altitude, exert great influence on global surface radiation, of which sunshine duration is most significant. A high-variation-coefficient zone of global solar radiation occurred in the western part of the QTP but, on average, the variation coefficient of the plateau’s global solar radiation was only 0.031, suggesting that the variation in global radiation was relatively stable over the whole QTP.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Daily global solar irradiation (R _s) is one of the main inputs in environmental modeling. Because of the lack of its measuring facilities, high-quality and long-term data are limited. In this research, R _s values were estimated based on measured sunshine duration and cloud cover of our synoptic meteorological stations in central and southern Iran during 2008, 2009, and 2011. Clear sky solar irradiation was estimated from linear regression using extraterrestrial solar irradiation as the independent variable with normalized root mean square error (NRMSE) of 4.69 %. Daily R _s was calibrated using measured sunshine duration and cloud cover data under different sky conditions during 2008 and 2009. The 2011 data were used for model validation. According to the results, in the presence of clouds, the R _s model using sunshine duration data was more accurate when compared with the model using cloud cover data (NRMSE = 11. 69 %). In both models, with increasing sky cloudiness, the accuracy decreased. In the study region, more than 92 % of sunshine durations were clear or partly cloudy, which received close to 95 % of total solar irradiation. Hence, it was possible to estimate solar irradiation with a good accuracy in most days with the measurements of sunshine duration.     

Retrieving air humidity,global solar radiation,and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part II: radiation

We propose a new model to estimate daily global radiation from daily temperature range measurements. This model combines that of Majumdar et al. (Sol Energy 13(4):383–394, 1972) to estimate clear sky radiation with a Gompertz function to estimate the relation between temperature range and cloud transmittance. Model parameters are estimated from historical weather data: maximum and minimum temperatures and, if available, relative humidity; no other calibration is required. The model was parametrized and validated using 788 weather stations in Mexico. When calibrated using historical humidity data, daily global radiation was estimated with a mean root mean square error of 3.06 MJ m^?2 day^?1. The model performed well in all situations, except for a few stations around the Gulf of Mexico and in mountain areas. When using estimated humidity, the root mean square error of prediction was only slightly degraded (3.07 MJ m^?2 day^?1). Possible theoretical basis and applicability of this model to other environments are discussed.     

山西省太阳能资源时空分布特征及利用潜力评估

按照中国气象局发布的太阳能资源评估方法,利用山西省近30 a 3个辐射观测站的太阳总辐射资料和105个站的日照资料,采用气候学方法计算了山西省的太阳总辐射,在分析山西省太阳总辐射的空间分布和时间演变特征的基础上,计算了山西省太阳能资源的各种参数,对区域太阳能资源潜力进行了客观评估。结果表明山西省太阳能资源储备丰富、稳定、可开发利用日数较多,特别是山西北部地区,尤其适合进行太阳能资源开发利用。     

Estimation of monthly average solar radiation in Libya

Summary In order to estimate the monthly average global radiation, regression equations for eleven stations in Libya are fitted, using monthly average hours of sunshine duration as predictors. Since only eight years of monthly average global radiation are available for each station, a reliable model for estimating solar radiation for each single month could not be obtained. The monthly data were merged together to produce a single regression equation for each station rather than twelve monthly regression equations. The results indicate that the regression equations for ten stations out of eleven hold very well.     

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

Long-term,ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region.The fixed station was established in 1989,and conventional radiation observations started much later in 2008.In this study,a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data,and a highprecision,long-term DGSR dataset is constructed.Then,the trend of DGSR from 1990 to 2019 at Zhongshan Station,Antarctica is analyzed.The RF model,which performs better than other models,shows a desirable performance of DGSR hindcast estimation with an R~2 of 0.984,root-mean-square error of 1.377 MJ m~(-2),and mean absolute error of 0.828 MJ m~(-2).The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004.Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station.In addition to clouds and water vapor,bad weather conditions (such as snowfall,which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station.The high-precision,longterm estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow,ice,and atmosphere.     

几种水平面太阳总辐射量计算模型的对比分析

利用中国区域1961-1999年39 a间98个常规气象观测数据,建立6个模型分别以天文辐射、干洁大气总辐射和湿洁大气总辐射为起始数据,进行太阳辐射日总量的模拟,对比分析了6个水平面太阳总辐射量计算模型的性能.结果表明:在三种起始数据中,干洁大气总辐射和湿洁大气总辐射均能较好地体现宏观地势对太阳辐射空间分布的影响,以湿洁大气总辐射为起始数据的计算模型拟合精度相对较高.对6个水平面太阳总辐射量计算模型的对比分析发现:2个以日照百分率为主导因子,气温日较差为修正项的综合模型拟合误差最小,精度最高;经典的日照百分率模型次之,但其模型系数最稳定可靠;3个气温日较差模型拟合效果最差.最终选用经验系数稳定、拟合精度较高的日照百分率模型,制作了2001年中国水平面太阳辐射日总量空间分布图.     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

洛阳地区太阳能资源分析与评估

根据1981—2010年郑州辐射站太阳辐射和日照资料、洛阳地区9个气象观测站日照资料,采用气候学计算、线性趋势分析等方法和资源丰富程度、利用价值、稳定程度等指标,对洛阳地区太阳能资源进行了计算、分析和评估。结果表明:洛阳地区多年平均总辐射为4842.4MJ·m-2·a-1,总体呈显著减少趋势,减少速率为每年1.4MJ·m-2。夏季辐射丰富,冬季偏少,夏季总辐射量是冬季的2倍;月平均总辐射5月最多(569.7MJ·m-2),12月最少(241.9MJ·m-2);年总辐射孟津最高,为4922.8MJ·m-2·a-1,宜阳最少,为4681.1MJ·m-2·a-1。多年平均日照时数为2064.7h,总体呈显著减少趋势。春季日照时数多,冬季少,春季日照时数比冬季多33.37%;月平均日照时数5月最多(217.3h),2月最少(138.2h);年日照时数孟津最多(2144.9h),宜阳最少(1909.2h)。洛阳各地属太阳能资源丰富区,利用价值较高,各月日照时数6h的天数为9.8~18.1天,全年为162.7~185.3天;太阳能资源也比较稳定,月最大日照时数12月6h的天数为16.9天,是月最小日照时数7月的1.48倍,12月至次年1月不利于太阳能利用。     

我国太阳日总辐射计算方法的研究

对全国23个站点的日照时数、日最高气温、日最低气温、太阳日总辐射量等气象要素实测资料进行统计分析,利用回归分析法建立了以日照百分率和气温日较差为主要相关因子的各地日总辐射估算模型。结果表明：除了高原站拉萨以外,推算模型的复相关系数均介于0．80～0．93之间,拟合效果较好。在春、夏季使用独立的季节模型有一定的必要性,该方法适用于我国各地太阳日总辐射的推算,     

Simple new methods to estimate global solar radiation based on meteorological data in Egypt

Three simple methods to estimate global solar radiation are proposed in addition to (Solar Energy 63 (1998) 147). All were tested seasonally and at different sky conditions at seven locations in Egypt. The methods use ground-based measurements of maximum and minimum temperature, daily mean of cloud cover and extraterrestrial global radiation. Average of root mean square differences (RMSD) for a comparison between observed and estimated global radiation for all locations tested was around 10% for the new methods and 13% for Supit–Van Kappel method. The coefficient of determination R² is higher for the new methods for all tested locations. Better results were obtained when applying the new methods to different seasons. The differences in root mean square error (RMSE) between the new methods and Ångstrom–Prescott method that is based on sunshine duration data were less than 1.0 MJ m⁻² day⁻¹ at all sites. On the whole, the performance statistics demonstrate that the new methods are better when compared by Ångstrom–Prescott method.     

1960~2000年黄河流域太阳总辐射气候变化规律研究

运用数据集群技术，在黄河流域建立了用日照百分率拟合太阳总辐射的不同时空尺度估算式，对比分析了不同数据集群下总辐射的拟合精度。利用逆距离加权插值法，将获得的黄河流域及其周边35个日射站1~12月总辐射拟合的经验系数进行空间内插，获得了黄河流域1~12月总辐射拟合经验系数的空间分布。结合黄河流域及其周边164个常规气象站日照百分率观测资料，对黄河流域1960~2000年太阳总辐射进行了计算，分析了其气候变化的时空分布规律。结果表明:黄河流域近年来太阳总辐射呈下降趋势，在季节上主要表现在夏季和冬季。