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01-07 

1 

 

 

 

Article 

Validation of satellite-derived solar irradiance 

datasets: a case study in Saudi Arabia 
A. F. Almarshoud 
Department of EE, College of Engineering, Qassim University, Saudi Arabia 

               A R T I C L E   I N F O 
 

Article history: 
Received 01 December 2023  
Received in revised form 
31 December 2023 
Accepted 07 January 2024 
 
Keywords:  
GHI prediction, Solar irradiance prediction, 
Solar irradiance estimation, 
Satellite-derived irradiance 
 
*Corresponding author 
Email address: 
dr_almarshoud@qec.edu.sa 
dr_almarshoud@QU.edu.sa 
 
 
DOI: 10.55670/fpll.fusus.2.2.1 
 

A B S T R A C T 
 

A robust dataset of Surface Solar Irradiance is essential for secure competitive 

financing for solar energy projects. Rating agencies and lenders alike require 

verification of the solar-resource dataset for utilizing each solar energy project, 

as this can be translated directly into expected electrical energy and revenues. 

The accuracy of the dataset and the variability of solar radiation, as recorded by 

historical solar data, play a significant role in estimating the future performance 

of the project and its budget. The historical observed solar irradiance datasets 

by local stations are the best and most reliable for a specific site, but they are 

not always available for long and continuous periods in any location, especially 

in arid areas. So, the importance of historical solar radiation datasets derived 

from satellite-based models arises here. This paper validates the historical 

modeled datasets of the three most famous satellite-based commercial 

prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset 

by six ground stations in Saudi Arabia under different climatic zones. The 

validation method has been implemented using the standard error metrics: 

Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The 

validation process showed that, in the case of GHI, the discrepancy between 

observed and predicted values is narrow, while in the case of DNI, the 

discrepancy is wide. Also, the predicted GHI values are more accurate than 

predicted DNI values, and -in general- the values predicted by the SUNY model 

are less accurate than those predicted by SolarGIS and Solcast models for both 

GHI and DNI. The resultant of this validation process could be accepted not for 

the six locations under study only but, also for deserts and arid areas across 

Saudi Arabia and might be extended to similar arid areas around the world. 

 

1. Introduction 

The financing of large solar projects requires detailed 

diligence and allocation of technical and commercial risks; 

one of the principal risks is the knowledge of solar resources. 

The intermittent nature of the solar resource made its 

assessment essential for determining the performance of 

solar-power projects and securing financing for them in the 

long term. A robust dataset Surface Solar Irradiance (SSI) is 

essential for secure competitive financing for solar energy 

projects. Generally, financing communities consider solar 

resources to be stable on an annual basis when compared to 

other renewable energy resources. Therefore, rating agencies 

and lenders alike require verification of the solar-resource 

dataset for utilizing each solar energy project, as this can be 

translated directly into expected electrical energy and 

revenues. The accuracy of the dataset and the variability of 

the solar radiation, as recorded by historical solar data, play 

significant roles in estimating the future performance of the 

project and its budget. Information concluded from historical 

solar resource datasets may be used to make energy policy 

decisions, design solar energy systems for specific locations, 

choose optimum energy conversion technology, and operate 

installed solar energy projects. Historical solar resource 

datasets may be the result of local measurement stations, 

satellite-based estimation methods, or numerical weather 

prediction methods. There is no doubt that the observed solar 

irradiance datasets by local stations are the best and most 

reliable for a specific site, but they are not always available for 

long and continuous periods in any location, especially arid 

areas. So, the importance of historical solar radiation datasets 

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A. F. Almarshoud /Future Sustainability                                                                                        May 2024| Volume 02 | Issue 02 | Pages 01-07 

2 

 

derived from satellite-based models arises here. In the last 

decade, the King Abdullah City for Atomic and Renewable 

Energy [1], as the lead Saudi Arabia governmental agency for 

renewable energy, has developed the Renewable Resource 

Monitoring and Mapping (RRMM) Solar Measurement 

Network, which currently involves more than 50 metrological 

stations distributed over the area of Saudi Arabia. These 

metrological stations record the solar irradiance data in 

addition to other weather data; the details of the RRMM 

network are summarized in [2].  The RRMM network can be 

accessed via the Saudi Arabia Renewable Resource Atlas 

website [1]. The RRMM currently has a historical dataset that 

reaches up to 7 years for most locations with high resolution. 

But these metrological stations are concentrated in the main 

cities and towns over the wide area of Saudi Arabia that 

reaches up to 2M km2; this means that the solar energy 

projects that are planned to be constructed in desert and arid 

areas are still suffering from a lack of reliable data of solar 

irradiance. So, the historical solar radiation datasets derived 

from satellite-based models are the only way to assess the 

performance of solar energy projects in arid areas. In this 

work, the researchers will validate three historically modeled 

datasets against ground measurements under different 

climatic zones in Saudi Arabia; if the validation result 

becomes acceptable, then we can depend on the modeled 

irradiance datasets for deserts and arid areas over Saudi 

Arabia; this is the aim of this study.   

2. Evolving of satellite-based solar irradiance 

estimation 

Estimating Surface Solar Irradiance (SSI) from satellites 

began in the 1960s with considerable errors in predicted 

data, but after 2000, satellite-based estimation of SSI had 

become increasingly mature, and many sensors have been 

employed. In addition to advances in sensors, many more 

sophisticated algorithms have been developed that take into 

account detailed radiative transfer processes. By these 

algorithms, multi-channel satellite observations are often 

combined to quantitatively determine the states of the 

surface and atmosphere. Satellite data from several sources 

are also combined to compensate for gaps [3-8]. The 

algorithms to estimate SSI from satellite data may be 

classified into two categories: Methods based on radiative 

transfer processes, which involve the acquisition of 

atmospheric spectral properties, and statistical methods that 

depend on the top of atmosphere reflectance observed by 

satellites, which is usually proportional to the cloud 

transmission such as Heliosat method [9-11] and Perez' 

method [12]. Statistical methods are more frequently used to 

estimate SSI on longer timescales such as daily, monthly, or 

yearly. In general, statistical methods have good accuracy due 

to their tuning property. To date, mature high-resolution SSI 

datasets with global coverage are still rare except for specific 

regions or are available on the basis of one specific satellite 

like the Geostationary Operational Environmental Satellite 

[13- 16]. The accuracy of SSI substantially improves as the 

timescale increases, the accuracy of hourly SSI is always 

slightly better than of instantaneous SSI, and the monthly SSI 

is more accurate than daily SSI when compared with ground 

observations. Also, the accuracy of SSI in clear-sky cases is 

better than cloudy-sky cases. Moreover, there are a few 

commercial high-resolution SSI datasets with near-global 

coverage. For example, the SolarGIS/SGIS 

(https://solargis.com), the SUNY/SolarAnywhere  

(https://data.solaranywhere.com), and Solcast 

(http://solcast.com). These commercial datasets are 

considered semi-empirical algorithms that typically include 

two operational models: the Clear-sky and Cloud-sky models. 

The early embodiment of semi-empirical models is referred 

to as the contribution of Cano et al. [9], which evolved over 

the years into the Heliosat model series [17-20]. These 

commercial datasets follow the same principle but differ in 

the source of data and in the fine details of operation models, 

so a difference in the accuracy of output data is expected. for 

more details about these commercial datasets [8, 21]. 

3. Validation methodology 

For the purpose of assessing the risk of solar-resource to 

support the project due diligence and financing, the most 

critical consideration is the total annual solar energy available 

in the location, which is typically characterized as total 

insolation (kWh/m2) or as daily average (kWh/m2/day) [21]. 

The type of solar irradiance to be estimated depends on the 

technology used for energy production. For concentrating 

solar power systems (CSP) or concentrating photovoltaic 

(CPV), the direct normal irradiance (DNI) must be estimated, 

while for the non-concentrating systems (PV), primarily 

global horizontal irradiance (GHI) must be estimated [21]. So, 

in this study, the historical dataset of (GHI & DNI) will only be 

validated on a monthly basis as daily-average irradiation 

(kWh/m2/day). For this purpose, the historical solar datasets 

of three commercial models (SolarGIS, SUNY, and Solcast) 

have been selected to be validated with historical datasets of 

six ground observation stations. These stations cover several 

distinct climatic environments ranging from arid (Buraydah) 

in the middle to humid (Jeddah) on the west coast and 

(Dhahran) on the east coast to cold (Tabuk) in the northern, 

to warm (Najran) in the southern to (Taif) in top of 

mountains. The validation period spans about five years, from 

June 1, 2013, to August 1, 2018. Table 1 illustrates the 

characteristics of the observation stations, and Figure 1 

shows their locations. 

 
Figure 1. Locations of selected observation stations 

 



A. F. Almarshoud /Future Sustainability                                                                                        May 2024| Volume 02 | Issue 02 | Pages 01-07 

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Table 1. Characteristics of selected observation locations 

Location Latitude Longitude Elevation Avg. 
Temp. 
O C 

Annual 
GHI 
kWh/m2 

Annual 
DNI 
kWh/m2 

Tabuk 28.38 o 
N 

36.48 o E 781 m 23 2285 2617 

Jeddah 21.49 o 
N 

39.24 o E 76 m 30.7 2117 1774 

Taif 21.43 o 
N 

40.49 o E 1518 m 23.7 2310 2288 

Najran 17.63 o 
N 

44.54 o E 1187 m 26.7 2449 2263 

Buraydah 26.34 o 
N 

43.76 o E 688 m 26.5 2219 2055 

Dhahran 26.30 o 
N 

50.14 o E 75 m 27.9 2037 1847 

 

4. Validation metrics 

Various metrics have been proposed in the literature to 

quantify the accuracy of solar irradiance forecasts. Overall 

bias and dispersion are the criteria that have been used to 

gauge the accuracy of solar irradiance models. The metrics 

recommended to quantify these criteria are the mean bias 

error (MBE) and its relative (rMBE) for quantifying the 

overall bias, and the root mean square error (RMSE) and the 

mean absolute error (MAE) for quantifying the dispersion 

[22, 23]. Many researchers prefer the mean absolute error 

(MAE) over the RMSE as a measure of dispersion because it is 

less sensitive to distant outliers and less subject to 

interpretation when expressed as a percentage [21, 24]. So, in 

this study, the two error metrics (MAE & rMBE) have been 

selected for the validation process and defined as [25]: 

𝑀𝐴𝐸 =  
1

𝑁
∑ |�̂�𝑡 − 𝑦𝑡|𝑁

𝑡=1                                                (1) 

 

𝑀𝐵𝐸 =  
1

𝑁
∑ �̂�𝑡 − 𝑦𝑡

𝑁
𝑡=1                                                   (2) 

 
Where  �̂�𝑡 and  𝑦𝑡 are predictions and observations at time 
step t, respectively, and N is the total number of samples.                               
then,  

𝑟𝑀𝐵𝐸 = 100
𝑀𝐵𝐸

�̅�
                                                          (3) 

Where MBE is normalized by the mean of observations: 
 

�̅� =
1

𝑁
∑ 𝑦𝑡

𝑁
𝑡=1                                                                  (4) 

  
5. Results and Discussion 

The Maximum Absolute Error (MAE) for the GHI and DNI 

has been calculated using formula (1), and the results are 

represented in Figures 2 & 3 for all regions under study; it is 

clear from these two figures that the SUNY model has the 

highest MAE value in most regions under study, while the 

SGIS and Solcast are the less. Figures from (4) – (15) show the 

variation of observed values of GHI & DNI and predicted ones 

of the three estimation models (SIGS, SUNY, and Solcast) in 

addition to the percentage of their relative Main Bias Error 

rMBE for the six regions under study using formula (3). In 

Tabuk City, Figure 4 shows no significant difference between 

observed and predicted values of GHI for the three estimation 

models; this conclusion is clear from the small values of rMBE 

in most months. Also, it is clear from the small values of MAE 

shown in Figure 2. While in the case of DNI, as shown in Figure 

5, there are some differences between observed and 

predicted values in a few months, especially in the case of the 

SUNY model, where its rMBE may reach up to 16%. The values 

of the Solcast model satisfy the best matching with observed 

values where its rMBE does not exceed 8%; this result is 

confirmed by the value of MAE, as shown in Figure 3. 

 
Figure 2. Maximum absolute error of GHI for locations under study 

 

 
Figure 3. Maximum absolute error of DNI for locations under study 

 

 
Figure 4. The relative maximum bias error of GHI for Tabuk City 

 

In Jeddah city, Figure 6 shows no significant difference 

between observed and predicted values of GHI for the Solcast 

model, but for SUNY and SGIS models, the rMBE reached up to 

14% and 9%, respectively.  This big discrepancy between 

observed and predicted values in the case of SUNY and SGIS 

models is confirmed by their MAE shown in Figure 2. In the 

case of DNI, as shown in Figure 7, SGIS and Solcast models are 

in the best matching with observed values for most of the 

year, but SUNY model values are very far, with rMBE reaching 



A. F. Almarshoud /Future Sustainability                                                                                        May 2024| Volume 02 | Issue 02 | Pages 01-07 

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up to 45%; this result is confirmed by the value of MAE as 

shown in Figure 3 (Taif City: Figure 8 and Figure 9). 

 

Figure 5. The relative maximum bias error of DNI for Tabuk City 

 

 

Figure 6. The relative maximum bias error of GHI for Jeddah City 

 

 
Figure 7. The relative maximum bias error of DNI for Jeddah City 

 

In Najran city, Figure 10 shows no significant difference 

between observed and predicted values of GHI for all models 

most of the year in general; the best matching is done by the 

SUNY model, while the maximum rMBE is done by the Solcast 

model. The MAE values in Figure 2 show the superiority of the 

SUNY model in this case.  In the case of DNI, as shown in 

Figure 11, SGIS and Solcast are close to observed values most 

of the year, with rMBE reaching up to 16%, but the SUNY 

model did the best matching for half of the year and in another 

half did the worst with rMBE reaches to 38%, this result is 

confirmed by its MAE value which reaches up to 1.6 kWh as 

shown in Figure 3. 

 

Figure 8. The relative maximum bias error of GHI for Taif City 

 

 

Figure 9. The relative maximum bias error of DNI for Taif City 

 

 

Figure 10. The relative maximum bias error of GHI for Najran City 

 

In Buraydah City, Figure 12 shows no significant 

difference between observed and predicted values of GHI all 

the year for SGIS and Solcast models and most of the year for 

SUNY models where the rMBE is less than 10% for all models. 

The MAE values in Figure 2 confirm this conclusion. In the 

case of DNI, as shown in Figure 13, the rMBE is less than 15% 

for all models in the second half of the year, while in the first 

half, it reaches high values: 45% for SUNY, 28% for SGIS and 

25% for Solcast. Figure 13 shows the superiority of SGIS and 

Solcast models over the SUNY model in this case. 



A. F. Almarshoud /Future Sustainability                                                                                        May 2024| Volume 02 | Issue 02 | Pages 01-07 

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Figure 11. The relative Maximum Bias Error of DNI for Najran City 

 

 

Figure 12. The relative maximum bias error of GHI for Buraydah City 

 

 

 

Figure 13. The relative maximum bias error of DNI for Buraydah City 

 

In Dhahran City, Figure 14 shows the best matching was 

done by SGIS with rMBE less than 8%, then Solcast with rMBE 

less than 11%, while SUNY did the worst matching with rMBE 

which reaches up to 18%, the MAE in Figure 2 confirms the 

superiority of SGIS and Solcast in this case. Regarding DNI, 

Figure 15 shows a high discrepancy for all models; the rMBE 

reaches up to 45% for SUNY, 28% for Solcast, and 18% for 

SGIS; this result is confirmed by their MAE values in Figure 3, 

which reaches up to 2.2 kWh for SUNY model. 

 

 

 

Figure 14. The relative maximum bias error of GHI for Dhahran City 

 

 

Figure 15. The relative maximum bias error of DNI for Dhahran City 

 

6. Conclusions 

The historical predicted solar irradiance datasets for the 

last five years of six locations across Saudi Arabia have been 

validated against the observed dataset by local stations for 

the same period. The validation process has been 

implemented using the standard error metrics: Maximum 

Absolute Error (MAE) and relative Maximum Bias Error 

(rMBE). The predicted data sets have been collected from the 

most famous commercial solar irradiance datasets: SolarGis, 

SUNY, and Solcast. The validation process showed that, In the 

case of GHI, the discrepancy between observed and predicted 

values is narrow, its rMBE is in the range of 10%, and its MAE 

is less than 0.6 kWh, especially for SolarGIS and Solcast 

models, while in the case of DNI, the discrepancy between 

observed and predicted values is wide, especially in case of 

SUNY model which its rMBE reaches up to 45%, and its MAE 

reaches up to 2.2 kWh. So, the GHI-predicted values are more 

accurate than the DNI-predicted values, and the values 

predicted by the SUNY model are less accurate than those 

predicted by SolarGIS and Solcast models for both GHI and 

DNI. It is clear from these results that GHI values, especially 

those predicted by SolarGIS and Solcast, can be accepted not 

only for locations under study but also for deserts and arid 

areas across Saudi Arabia. Is this conclusion valid for the 

whole globe? We think it is valid for deserts that are less 

cloudy between the Cancer and Capricorn lines. 

 



A. F. Almarshoud /Future Sustainability                                                                                        May 2024| Volume 02 | Issue 02 | Pages 01-07 

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Acknowledgment  

Researchers would like to thank all the entities who provided 

us with data used in this research: the King Abdullah City for 

Atomic and Renewable Energy K.A. CARE (http:// 

rratlas.energy.gov.sa), SolarGIS (https://solargis.com), SUNY 

(https://data.solaranywhere.com), and Solcast 

(http://solcast.com).  

Ethical issue 

The author is aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the author. 

Conflict of interest 

The author declares no potential conflict of interest. 

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A. F. Almarshoud /Future Sustainability                                                                                        May 2024| Volume 02 | Issue 02 | Pages 01-07 

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