Retrieving daily global solar radiation from routine climate variables

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m² day^?1 and 2.25 MJ m² day^?1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.     

Ultraviolet radiation in the background region over Northeast China

Measurements of broadband global solar and ultraviolet radiation (UV-KZ) by a Kipp & Zonen radiometer from 2005 to 2011 at three sites in the background region over Northeast China were used to investigate the characteristics of the temporal variability of UV-KZ radiation in Northeast China. The highest annual mean daily values of UV-KZ (0.53 MJ m^?2 d^?1) were observed at Sanjiang and Changbai Mountain. The lowest value (0.49 MJ m^?2 d^?1) was measured at Hailun due to the high aerosol burden and the long path of solar radiation. The diurnal variation in UV-KZ radiation on four clear days near the equinoxes and solstices exhibited bell-shaped curves, with a maximum at approximately noon. There was little difference in UV-KZ radiation between representative spring and autumn days except that the amount of UV-KZ radiation during the spring was higher than that during the autumn. The relationship between the maximum values of UV-KZ radiation and clearness index can be accurately demonstrated with three polynomial equations. An empirical estimation model suited for all weather conditions was developed using measurements collected at Hailun. The slope of the linear regression between the measured and modeled UV-KZ radiation was approximately 1, the intercept of the linear regression equation was near zero, and the relative error of the equation was less than 8.5 %. These validation results suggest that this model can accurately estimate the UV-KZ radiation based on more conventional measured radiation data. The empirical estimation model can also serve as a valuable method for the study of ecological processes in other regions.     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region

Downward longwave radiation (LW _↓ ) is a relevant variable for meteorological and climatic studies. Good estimates of this term are vitally important in correct determining of the net radiation, which, in turn, modulates the magnitude of the terms in the surface energy budget (e.g., evaporation). In remote sensing applications, the determination of daytime LW_↓ is required for estimation of the net radiation using satellite data. LW_↓ is not directly measured in weather stations and then is estimated using models with surface air temperature and humidity as input. In this paper, we identify the best models to estimate daytime downward longwave radiation from meteorological data in the sub-humid Pampean region. Several well-known models to estimate LW_↓ under clear and cloudy skies were tested. We use downward radiation components and meteorological data registered at Tandil (Argentina) from 2006 to 2010 (840 days). In addition, we propose two multiple linear regression models (MLRM-1 and MLRM-2) to estimate LW_↓ at the surface for all sky conditions. The new equations show better performance than the others models tested with root mean square errors between 12 and 16 W m^?2, bias close to zero and best agreements with measured data (r ²?≥?0.85).     

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.     

Characteristics of UV radiation at Tazhong of the Tarim Basin,west China

Tazhong station, located at the hinterland of the Taklimakan Desert in northwest China, experiences frequent dusty weather events during spring and summer seasons (its dusty season) caused by unstable stratified atmosphere, abundant sand source and strong low-level wind. On average, it has 246.2 dusty days each year, of which 16.2 days are classified as sand and dust storm days. To better understand the characteristic of solar ultraviolet (UV) radiation and factors influencing its variations under such an extreme environment, UV radiation data were collected continuously from 2007 to 2011 at Tazhong station using UVS-AB-T radiometer by Kipp and Zonen. This study documents observational characteristics of the UV radiation variations observed during the five-year period. Monthly UV radiation in this region varied in the range of 14.1–37.8 MJ m^?2 and the average annual amount was 320.7 MJ m^?2. The highest value of UV radiation occurred in June (62.5 W m^?2) while the lowest one in December (29.3 W m^?2). It showed a notable diurnal cycle, with peak value at 12:00–13:00 LST. Furthermore, its seasonal variation exhibited some unique features, with averaged UV magnitude showing an order of summer > spring > autumn > winter. The seasonal values were 37.0, 29.1, 24.9 and 15.9 MJ m^?2, respectively. In autumn and winter, its daily variations were relatively weak. However, significant daily variations were observed during spring and summer associated with frequent dust weather events occurring in the region. Further analysis showed that there was a significant correlation between the UV radiation and solar zenith angle under different weather conditions. Under the same solar zenith angle, UV radiation was higher during clear days while it was lower in sand and dust storm days. Our observations showed that there was a negative correlation between UV radiation and ozone, but such a relationship became absent in dusty days. The UV radiation was reduced by 6 % when cloud amount was 1–4 oktas, by 12 % when the cloud amount was 5–7 oktas, and by 24 % when the cloud amount was greater than 8 oktas. The relative reduction of UV radiation reached 26, 38, and 45 % in dust day, blowing sand day and sand and dust storm day, respectively. The results revealed that decrease in UV radiation can be attributed to cloud coverage and dust aerosols. Moreover, the reduction of UV radiation caused by dust aerosols was about 2–4 times greater than that caused by cloud coverage. These observational results are of value for improving our understanding of processes controlling UV radiation over sand desert and developing methods for its estimation and prediction.     

Parameterisation of incoming longwave radiation over glacier surfaces in the semiarid Andes of Chile

A good understanding of radiation fluxes is important for calculating energy, and hence, mass exchange at glacier surfaces. This study evaluates incoming longwave radiation measured at two nearby glacier stations in the high Andes of the Norte Chico region of Chile. These data are the first published records of atmospheric longwave radiation measurements in this region. Nine previously published optimised parameterisations for clear sky emissivity all produced results with a root mean square error (RMSE) ~20 W?m^?2 and bias within ±5 W m^?2, which is inline with findings from other regions. Six optimised parameterisations for incoming longwave in all sky conditions were trialled for application to this site, five of which performed comparably well with RMSE on daytime data <18 W?m^?2 and bias within ±6 W?m^?2 when applied to the optimisation site and RMSE <20 W?m^?2 and bias within ±10 W m^?2 when applied to the validation site. The parameterisation proposed by Mölg et al. (J Glaciol 55:292-302, 2009) was selected for use in this region. Incorporating the proposed elevation modification into the equation reduced the bias in the modelled incoming longwave radiation for the validation site. It was found that applying the parameterisation optimised in the original work at Kilimanjaro produced good results at both the primary and validation site in this study, suggesting that this formulation may be robust for different high mountain regions.     

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.     

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.     

Biogenic volatile compound emissions from a temperate forest,China: model simulation

Emissions of biogenic volatile organic compounds (BVOC) were measured using a relaxed eddy accumulation (REA) technique on an above-canopy tower in a temperate forest (Changbai Mountain, Jilin province, China) during the 2010 and 2011 summer seasons. Solar global radiation and photosynthetically active radiation (PAR) were also measured. Based on PAR energy dynamic balance, an empirical BVOC emission and PAR transfer model was developed that includes the processes of BVOC emissions and PAR transfer above the canopy level, including PAR absorption and consumption, and scattering by gases, liquids, and particles (GLPs). Simulated emissions of isoprene and monoterpenes were in agreement with observations. The averages of the relative estimator biases for the flux were 39.3 % for isoprene, and 27.1 % for monoterpenes in the 2010 and 2011 growing seasons, with NMSE (normalized mean square error) values of 0.133 and 0.101, respectively. The observed and simulated mean diurnal variations of isoprene and monoterpenes in the 2010 and 2011 growing seasons were evaluated for the validation of the empirical model. Under observed atmospheric conditions, the sensitivity analysis showed that emissions of isoprene and monoterpenes were more sensitive to changes in PAR than to water vapor content or to the magnitude of the scattering factor. The emissions of isoprene and monoterpenes in the 2010 and 2011 growing seasons (from June to September) were estimated using this empirical model along with hourly observational data, with mean hourly emissions of 1.71 and 1.55 mg m^?2 h^?1 for isoprene, and 0.48 and 0.47 mg m^?2 h^?1 for monoterpenes in 2010 and 2011, respectively. As formaldehyde (HCHO) is considered as the main oxidation product of isoprene and monoterpenes, it is necessary to investigate the link between HCHO and BVOC emissions. GOME-2 HCHO vertical column densities (VCDs) can be used to estimate BVOC emission fluxes in the Changbai Mountain temperate forest.     

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.     

Error assessment of climate variables for FAO-56 reference evapotranspiration

Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ET_o) using the Food and Agriculture Organization Penman?CMonteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ET_o estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ET_o showed small differences [i.e., mean bias error (MBE) varying ?0.22 to 0.25?mm?day^?1 and root-mean-square-error (RMSE) varying 0.06?C0.50?mm?day^?1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ET_o (i.e., MBE of ?0.50?mm?day^?1 and RMSE of 0.60?C0.73?mm?day^?1). Estimated ET_o showed different sensitivity to variations of the meteorological parameters??in order of vapor pressure?>?wind speed?>?radiation. It is clear that the FAO56-PM method is applicable for reasonable ET_o estimation at a daily time scale especially in data-limited regions in Korea.     

Assessment of GEWEX/SRB version 3.0 monthly global radiation dataset over China

NASA/GEWEX (National Aeronautics and Space Administration/Global Energy and Water Cycle Experiment) Surface Radiation Budget (SRB) has released its latest radiation dataset, version 3.0. We examine the accuracy of the monthly mean global radiation in China using surface-observed radiation (SOR) data at 42 stations during the period 1984?C2004. Overall comparison shows a general overestimation of satellite retrieval radiation data with a bias of 14.6?W?m^?2 and a root mean square error of 25.9?W?m^?2. Differences at individual stations suggested satellite data are consistently higher than surface measurements over eastern China (110°E), but occasional underestimation occurs in Western China, especially Southwest China. Intra-annual variation analysis indicates that SRB satellite radiation can capture the annual cycle well. For trend of global radiations, there are evident discrepancies between satellite retrievals and surface measurements for both the entire period and segmental terms. For the entire period from 1984 to 2004, most stations show a positive trend based on surface measurements, while the majority of collocated pixels show a negative trend. Segmental trends demonstrated that the principal difference occurred during the first period of 1981?C1994. After 1994, the two datasets change similarly. Therefore, trend analysis in terms of detecting global dimming/brightening remains very difficult as surface measurements and satellite products do not agree yet. In addition, some proposals are made towards better understanding of the bias of satellite products and to improve further the satellite retrieval algorithm with better representation of both cloud and aerosol properties.     

Long-term comparisons of net radiation calculation schemes

Six commonly used models for calculating daily net radiation were tested against measured net radiation. Meteorological data from 32 and 7 consecutive years obtained at two temperate sites were used. The extensive duration of the datasets ensured that all weather conditions and extreme events were captured. A set of statistical procedures was used to evaluate the performance of the models. The mean bias errors ranged from 0.0 W m⁻² to 24.8 W m⁻² and 0.1–24.7 W m⁻² and root mean square error from 11.0 W m⁻² to 28.1 W m⁻² and 10.0–27.9 W m⁻² at the two sites respectively, for days without snow cover on the ground. The best agreement was found when locally calibrated model coefficients were used. Only negligible differences in model performances were found between the two sites and the differences were lower than the inaccuracies of the net radiation instruments used. Including days with snow cover in the analysis lead to a slight increase in the bias and scatter of the predictions. Model performances were in general better during summertime than wintertime. Altered albedo values during winter caused by generally low sun angles were likely the cause of this. Analysis showed that at least 5 years of data were needed to obtain stable calibration coefficients for local calibration of the models. Based on the results from this study, and due to their physical background, two physical based models were recommended for calculating daily values of net radiation under temperate climate regimes. A simple adjustment of the calibration coefficients based on climate regime was suggested for these models.     

Estimating net radiation at surface using artificial neural networks: a new approach

This study describes the results of artificial neural network (ANN) models to estimate net radiation (R _n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R _n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R _n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m^?2 and correlation coefficient (R ²) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m^?2 and R ² between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R _n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R _n prediction using commonly measured meteorological parameters.     

Influence of weather conditions and spatial variability on glacier surface melt in Chilean Patagonia

In order to clarify how differences in weather conditions affect the surface heat balance of a large maritime glacier, meteorological observations were carried out in the ablation area of Glaciar Exploradores in the Chilean Patagonia during the austral summer of 2006/2007. Under cloudy/rainy weather, when the air temperature and wind speed were high due to advection, the average melting heat was 18.8 MJ m^?2 day^?1 and the turbulent heat fluxes contributed 35% of the total melt. During clear weather, the average melting heat was 16.9 MJ m^?2 day^?1 and 13% of the total was the turbulent heat fluxes. A decrease in air temperature due to the development of the glacier boundary layer on clear days will lead to an overestimation of the melt using the air temperature at a weather station outside of the glacier.     

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

We evaluated two methods to estimate evapotranspiration (ETo) from minimal weather records (daily maximum and minimum temperatures) in Mexico: a modified reduced set FAO-Penman-Monteith method (Allen et al. 1998, Rome, Italy) and the Hargreaves and Samani (Appl Eng Agric 1(2): 96–99, 1985) method. In the reduced set method, the FAO-Penman-Monteith equation was applied with vapor pressure and radiation estimated from temperature data using two new models (see first and second articles in this series): mean temperature as the average of maximum and minimum temperature corrected for a constant bias and constant wind speed. The Hargreaves-Samani method combines two empirical relationships: one between diurnal temperature range ΔT and shortwave radiation Rs, and another one between average temperature and the ratio ETo/Rs: both relationships were evaluated and calibrated for Mexico. After performing a sensitivity analysis to evaluate the impact of different approximations on the estimation of Rs and ETo, several model combinations were tested to predict ETo from daily maximum and minimum temperature alone. The quality of fit of these models was evaluated on 786 weather stations covering most of the territory of Mexico. The best method was found to be a combination of the FAO-Penman-Monteith reduced set equation with the new radiation estimation and vapor pressure model. As an alternative, a recalibration of the Hargreaves-Samani equation is proposed.     

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.     

Analysis of solar radiation on the surface estimated from GWNU solar radiation model with temporal resolution of satellite cloud fraction

Preliminary analysis with a solar radiation model is generally performed for photovoltaic power generation projects. Therefore, model accuracy is extremely important. The temporal and spatial resolutions used in previous studies of the Korean Peninsula were 1 km × 1 km and 1-h, respectively. However, calculating surface solar radiation at 1-h intervals does not ensure the accuracy of the geographical effects, and this parameter changes owing to atmospheric elements (clouds, aerosol, ozone, etc.). Thus, a change in temporal resolution is required. In this study, one-year (2013) analysis was conducted using Chollian geostationary meteorological satellite data from observations recorded at 15-min intervals. Observation data from the intensive solar site at Gangneung-Wonju National University (GWNU) showed that the coefficient of determination (R²), which was estimated for each month and season, increased, whereas the standard error (SE) decreased when estimated in 15-min intervals over those obtained in 1-h intervals in 2013. When compared with observational data from 22 solar sites of the Korean Meteorological Administration (KMA), R² was 0.9 or higher on average, and over- or under-simulated sites did not exceed 3 sites. The model and 22 solar sites showed similar values of annual accumulated solar irradiation, and their annual mean was similar at 4,998 MJ m^?2 (3.87 kWh m^?2). These results show a difference of approximately ± 70 MJ m^?2 (± 0.05 kWh m^?2) from the distribution of the Korean Peninsula estimated in 1-h intervals and a higher correlation at higher temporal resolution.