BIBECHANA 18 (1) (2021) 159-169 

159 
 

BIBECHANA 
ISSN 2091-0762 (Print), 2382-5340 (Online) 

Journal homepage: http://nepjol.info/index.php/BIBECHANA 

Publisher: Department of Physics, Mahendra Morang A.M. Campus, TU, Biratnagar, Nepal 

 

Prediction of daily global solar radiation using different 

empirical models on the basis of meteorological parameters 

at Trans Himalaya Region, Nepal 
 

Usha Joshi1*, I. B. Karki2, N.P. Chapagain3
, K.N. Poudyal4 

 1Department of Physics, Patan Multiple Campus, TU, Patan, Nepal   

 2Nepal Open University, Nepal  

 3Department of Physics, Amrit Campus, TU, Kathmandu Nepal 
 4Department of Physics, Pulchowk Campus, IOE, TU, Patan, Nepal  
  *Email: ushajoshik1967@gmail.com 
 

 

Article Information: 

Received: May 30, 2020 

Accepted: August 9, 2020 

 

Keywords:  

Global solar radiation 

Pyranometer 

Meteorological parameters 

Regression technique 

Empirical constants 

 

 

 

 

 

 

 
 

 

ABSTRACT 

Global Solar Radiation (GSR) is the cleanest and freely available energy resource 

on the earth.  GSR  was measured for six years (2010 -2015) at the horizontal 

surface using calibrated first class CMP6 pyranometer at Kathmandu (Lat. 27.70o 

N, Long. 85.5oE and Alt. 1350m). This paper explains the daily, monthly and 

seasonal variations of GSR and also compares with sunshine hour, ambient 

temperature, relative humidity and precipitation to GSR. The annual average 

global solar radiation is about 4.16 kWh/m2/day which is significant amount to 

promote solar active and passive energy technologies at Trans-Himalaya region. 

In this study, the meteorological parameters are utilized in regression technique 

for four different empirical models and finally the empirical constants are found. 

Thus obtained coefficients are utilized to predict the GSR using meteorological 

parameters for the years to come. In addition, the predicted GSR is found to be 

closer with measured value of GSR. The values are justified by using statistical 

tools such as coefficient of determination (R2), root mean square error (RMSE), 

mean percentage error (MPE) and mean bias error (MBE). Finally, the values of 

R2, RMSE, MPE and MBE are found to be 0.792, 1.405, -1.014, and 0.011, 

respectively for model (D), which are based on sunshine hour, temperature and 

relative humidity. In this model, the empirical constants, a = 0.155, b = 0.134, c = 

0.014 and d = 0.0007 are determined which can be utilized at the similar 

geographical locations of Nepal.  

DOI: https://doi.org/10.3126/bibechana.v18i1.29203 
This work is licensed under the Creative Commons CC  BY-NC License. https://creativecommons.org/licenses/by-nc/4.0/ 
 

 
1. Introduction 
 

The sun is worshipped as god in Greek mythology. 

The sun is source of various kinds of energies 

which has been proven vital from the beginning of 

human civilization till now. It is mainly composed 

of hydrogen (90%), Helium and traces of other 

heavier elements. The fusion of hydrogen to helium 

http://nepjol.info/index.php/BIBECHANA
mailto:ushajoshik1967@gmail.com
https://doi.org/10.3126/bibechana.v18i1.29203
https://creativecommons.org/licenses/by-nc/4.0/


Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

160 
 

is believed to be source of energy of sun by 

reducing mass almost 5 million tons per second in 

the form of electromagnetic radiation [1]. 

Approximately 95% of solar radiation lies within 

the wavelength range of 0.3-2.4 µm, which 

comprises ultraviolet, visible and infrared portion 

of electromagnetic radiation, 1.2% lies in 

wavelength longer than 2.4µm and  3.6% lies in 

wavelength less than 2.4µm [1,2]. Solar radiation 

reaching the earth surface gets through the 

constituents of the atmosphere. The earth’s 

atmosphere consists of O2, N2, CO2, Ar as 

permanent constituents. Water vapor and 

particulate matters such as dust, soot, water drops 

and ice crystals which vary spatially as temporary 

constituents. These constituents modify solar 

radiation arriving at the earth surface by scattering, 

reflection and absorption [2-4].  
 

The radiation which evades absorption and 

scattering and arriving on ground directly in the 

line from the solar disk is called direct or beam 

radiation [3]. The radiation received from sun after 

its direction has been changed by scattering from 

atmosphere is diffused solar radiation. 

Consequently, the solar radiation arriving at the 

earth surface also known as global solar 

radiation(GSR) which is the sum of direct solar 

radiation and diffused solar radiation[3,4]. The 

GSR is of enormous importance as it can direct out 

development on low carbon trajectory [5]. Study on 

GSR is most essential in order to design and 

operate the solar energy devices. Hydrologists, 

engineers, agriculturists use solar radiation data for 

various purposes such as solar heating, cooking, 

drying, and interior illumination of buildings [5, 6]. 

Also, manufacturers of solar equipment need to 

know the average GSR available in different and 

specific region. It is used to develop database in 

order to use it in active and passive energy form 

which could be a giant step for development of 

many sectors like tourism, education, industry, 

communication, health and scientific agriculture. 

One of the most used practical possibilities of 

renewable energy is the photovoltaic system to 

lighten the rural areas [7, 8]. In Nepal, 83% of total 

population use clean energy and rest of them (22%) 

live in live dark region [9]. Many people are still 

deprived of hydroelectricity, petroleum and so on. 

So people are forced to use kerosene lamp as an 

alternative to electricity. In Nepal, 87% of total 

energy is consumed in residential areas, 6% in 

transportation, and 5 % in industry. In FY 2017/18, 

the consumption of traditional, commercial and 

renewable energies are 70%, 27.8% and 2.2% of 

the total energy respectively. The above data shows 

that the large percentage of consumption of 

traditional energy is not environment friendly and 

unsustainable. It creates indoor and outdoor air 

pollution. The consumption of imported petroleum 

product has increased by 5.6% as compared to 

previous year and huge amount of hard earned 

foreign currency is used to fulfil this energy 

demand [9].  
 

Nepal lies at most favorable solar belt in global 

map. Hence, Nepal receives sufficient and adequate 

amount of solar radiation throughout the country. 

The range of solar insolation and sunny days are 

3.6 - 6.2 kWhr/m2/day and about 300 days in a year 

[10]. The annual average sunshine hour and solar 

insolation are 6.8 hour/day and 4.23 kWhr/m2/day 

respectively [11].  The solar irradiance at eastern 

highland is lesser than central mid-hill region and 

western hill of Nepal. This is because of higher 

range of humidity, precipitation and wind at the 

eastern part than at western part of Nepal [12].  
 

The reliable and genuine information regarding the 

solar radiation for particular site can be applied in 

various field like hydrology, modern agriculture, 

solar active and passive energy, irrigation system 

and climate change. Further, specific understanding 

on GSR is required to design solar energy system. 

Even though we have necessity of solar radiation 

data, only few number of measuring equipment are 

available due to financial constraints and 

maintenance. So, we have to rely on several models 

to estimate the solar radiation based on the easily 

available meteorological parameters [8]. 



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

161 
 

The commonly used empirical models  to estimate 

solar radiation using  sunshine hour with varying 

degree of  complexity is followed by classical work 

of Angstrom and Prescott [13,14].So far various 

models have been developed by a number of 

researchers with different regression  coefficients 

depending on meteorological parameters and 

locations. [7,8,15,16]. Estimation of GSR using 

temperature and precipitation for year 2005 and 

2007, using RadEst 3.0 software  was observed 

[17]. Similarly, estimation of GSR using empirical 

model based on sunshine hour, temperature and 

relative humidity (Abdalla Model) for four 

different places of Nepal has been done [18]. 
 

The available literature shows that there is limited 

study of solar radiation in Nepal. That is why it is 

necessary to develop authentic empirical constants 

on the basis of meteorological parameters such as 

temperature, sunshine hour and relative humidity. 

This is due to deficiency of solar energy data and 

lack of sense of importance of solar energy 

technique. In addition that, the empirical constants 

should be developed for every 200m altitude 

because of abruptly varying geographical structure 

as well as complex terrain with variation of altitude 

(60m-8848m). 
 

Different types of empirical models are used to 

estimate daily average solar radiation using 

meteorological parameters for different locations of 

Nepal. Thus found empirical constants are used to 

predict global solar radiation. The better 

performing model are those models, using sunshine 

duration [6, 13, 14]. The main focus of this 

research is to validate the performance of four 

empirical models based on temperature, relative 

humidity and sunshine hour. 
 
 

2. Methodology 
 

Description of site 
 

Kathmandu Valley is capital city, and a hub of 

cultural as well as historical heritage, inhabited by 

2.5 million people. It lies at 27.70oN and 85.5oE at 

mean elevation of about 1350m from sea level [19, 

20]. It is characterized by subtropical cool 

temperate climate where temperature normally 

varies from a minimum of 2.4oC for January to 

maximum of 35oC for June with annual average of 

mean temperature normal of 18.4oC. The annual 

average of precipitation normal is 1454.8mm, most 

of which falls during monsoon [21, 22]. 
 

The data of hourly solar radiation on horizontal 

surface for Kathmandu were collected from 

archives of the Department of Hydrology and 

Meteorology, Government of Nepal (DHM/GoN) 

for the year 2010 to 2015. Daily data of Sunshine 

hour, maximum temperature, minimum 

temperature and relative humidity for the site were 

obtained from DHM/GoN. The GSR were 

measured using first class CMP6 Pyranometer [23]. 
 

Theory 
 

Hg (MJ/m2/day) is the measured global solar 

radiation at the ground surface. Ho is 

Extraterrestrial GSR which is found at the top of 

atmosphere of site measured in (MJ/m2/day).It is 

calculated theoretically as [3] 
 

𝐻𝑜 =
24

𝜋
 𝐼𝑠𝑐 [1 + 0.033 cos

360 𝑛𝑑

365
] × 

               [cos ∅ cos 𝛿  sin 𝜔 + 𝜔 sin ∅ sin 𝛿]        (1) 
 

where,  𝐼𝑠𝑐is the solar constant which is given as, 
 

𝐼𝑠𝑐 =
1367×3600

106  MJ/m2/day                          (2) 
 

where, nd is the day of the year, Julian days starting 

from January 1st (nd =1) to December 31th (nd 

=365). ω is hour  angle, ∅(rad) being latitude and 

δ(rad) is the declination angle whose expressions 

are given as, 
 

𝛿 =  23.45 sin [
360

365
 (284 +  𝑛𝑑)]                  (3) 

and hour angle is, 
 

𝜔 =  cos−1[− tan ∅ tan 𝛿]                         (4)   
 

The relation of day length, the maximum feasible 

sunshine length for given day is given by equation, 
 

Daylength(𝑁) =
2

15
𝜔 =  

2

15
 cos−1[− tan ∅ tan 𝛿]   (5) 

    



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

162 
 

There are many empirical models to estimate solar 

radiation reaching the earth’s surface. Among 

them, in this research work the empirical models 

are selected on the basis of availability of 

meteorological parameters for given sites are listed 

in Table 1.  
 

The solar radiation data which are analyzed to 

check the performance of above models consist of 

daily average global solar radiation for Kathmandu 

for year (2010 - 2015). The regression technique 

for the models used were performed by using 

Matlab2015Rb and advanced Microsoft Excel 

software analysis. The graph is obtained with help 

of Origin software as well as Matlab. We obtained 

statistical errors such as root mean square error 

(RMSE), mean percentage error (MPE), mean bias 

error (MBE) and coefficient of determination (R2) 

to validate the efficiency of used model. 
 

3. Results and Discussion 
 

 

The correlation of daily GSR and clearness index 

(KT) with relative sunshine hour, difference of 

ambient temperature and relative humidity is 

shown in Table 2.  It shows that the clearness index 

on average is more correlated than GSR with given 

parameters. Thus, the above four empirical models 

are utilized to predict the GSR at this region. 
 

Empirical constants are found by using regression 

technique, and those values are used   for the 

estimation of daily GSR from different models for 

different years at trans-Himalaya region separately. 

The estimated GSR were compared with 

corresponding value of measured radiation. To 

analyze the validity of used models we perform 

different statistical test. The statistical indices 

employed in all applied models are MPE, MBE, 

RMSE and coefficient of determination (R2). The 

outcomes of these statistical analysis and values of 

empirical constants are mentioned in Table 3. 
 

In the study for the determination of GSR, we have 

taken various single and multiple meteorological 

parameter models. Among those models, model D 

is multiple parameter model of sunshine hour, 

difference of temperature and average relative 

humidity which perform better than other models 

and indicates least value of RMSE, MPE, MBE and 

highest value of coefficient of determination.  
 

The linear variation of monthly average measured 

and estimated GSR is shown in Fig. 1 for model D 

for different year. It shows that there is remarkable 

agreement between monthly average daily GSR for 

different years for model D with highly acceptable 

value of coefficient of determination. 
 

Fig: 2 shows monthly variation of GSR from 2010 

to 2015. The GSR gradually increases from 

December to May and sharply decrease due to high 

frequency of rainfall as well as cloudy days in June, 

July, and August till September. Above graph 

shows the value of global solar radiation received is 

maximum in the month of May,  it is mainly due to 

less cloud, slow wind, less rainfall and less solar 

zenith angle. However, the normal trend deviates 

for the months of June, July and August for all the 

studied sites due to the fact that maximum rainfall 

of the year occurs in these months. In addition, 

lower values of GSR is found in winter season for 

the year 2010 to 2014. Moreover, there is lower 

values of GSR found during August for the year 

2015.  

Fig: 3(a), (b), (c), and (d) gives the variation of 

GSR and maximum temperature with month. There 

is close agreement between maximum temperature 

and GSR. In June, July and August, summer season 

although temperature is high, there is decrease of 

GSR due to heavy rainfall and cloudy sky. 
 

Fig: 4 (a), (b), (c) and (d) show variation of GSR 

and relative humidity with month. There is opposite 

relation between GSR and relative humidity. In 

these figures, there is minimum relative humidity 

and higher GSR in May due to dry season. 

However, in rainy season, there is higher relative 

humidity and lower GSR which is usual practice in 

weather.  



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

163 
 

Table 1: List of models used for the determination of global solar radiation. 
 

Model Symbol Parameters Relation 

Angstrom-Prescott Model [14, 15] A n, N 
𝐻𝑔

𝐻𝑜
= 𝑎 + 𝑏 (

𝑛

𝑁
 ) 

Swarthman-Oguniada Model [21] B n, N, RH 
𝐻𝑔

𝐻𝑜
= 𝑎 + 𝑏 (

𝑛

𝑁
) + 𝑐(𝑅𝐻) 

Modified Angstrom Model C n, N, ΔT 
𝐻𝑔

𝐻𝑜
= 𝑎 + 𝑏 (

𝑛

𝑁
) + 𝑐(∆T) 

Modified Angstrom Model D n, N, ΔT, RH 
𝐻𝑔

𝐻𝑜
= 𝑎 + 𝑏 (

𝑛

𝑁
) + 𝑐 (𝛥𝑇)   + 𝑑(𝑅𝐻) 

 

Table 2: Correlation of GSR and clearness index with given parameters. 
 

Variables 
Hg relate with variables for year KT relate with variables for year 

2010 2012 2014 2015 2010 2012 2014 2015 

n/N 0.2417 0.2921 -0.0041 0.7066 0.607 0.7638 0.4896 0.811 

ΔT 0.1435 0.2473 0.0731 0.6174 0.5845 0.6193 0.4532 0.7784 

RH -0.6278 -0.491 -0.3686 -0.4095 -0.5593 -0.3401 -0.1508 -0.1443 

 

Table 3: Statistical indicators and empirical constants for empirical models used in research for different 

year using daily measured data. (Optimal values are bold-faced). 
 

Year Model 

Statistical Tools Empirical Constants 

MPE (%) 

MBE 

(MJ/m2/da

y) 

RMSE 

(MJ/m2/

day) 

R2 a b c d 

2015 

A -7.559 0.278 2.705 0.587 0.118 0.566   

B -7.593 0.262 2.706 0.586 0.065 0.573 0.001  

C -5.056 0.204 2.363 0.684 -0.017 0.364 0.018  

D -4.871 0.148 2.310 0.698 -0.191 0.363 0.020 0.002 

2014 

A -3.577 0.057 2.282 0.563 0.337 0.221     

B -3.582 0.051 2.281 0.564 0.323 0.225 0.0002  

C -3.367 0.049 2.235 0.581 0.299 0.154 0.006  

D -3.277 -0.013 2.179 0.602 0.134 0.148 0.009 0.002 

2012 

A -4.911 0.007 2.885 0.606 0.295 0.414     

B -4.907 0.011 2.884 0.606 0.303 0.412 0.0001  

C -4.837 0.006 2.873 0.609 0.282 0.386 0.002  

D -4.824 -0.003 2.870 0.610 0.254 0.383 0.003 0.0002 

2010 

A -3.584 -0.180 2.969 0.684 0.348 0.299     

B -3.058 -0.040 2.740 0.731 0.681 0.213 -0.004  

C -3.410 -0.219 2.972 0.683 0.306 0.197 0.007  

D -3.005 -0.070 2.759 0.727 0.636 0.167 0.004 -0.003 

all 

Daily 

average 

A -1.525 0.089 1.628 0.720 0.325 0.272     

B -1.439 0.125 1.578 0.737 0.429 0.254 -0.001  

C -1.025 0.039 1.408 0.791 0.229 0.140 0.013  

D -1.014 0.011 1.405 0.792 0.155 0.134 0.014 0.0007 



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

164 
 

 

 

 

 

 

 

 

 

 (a) (b) 

 

 

 

 

 

 

 

 (c) (d) 

Fig. 1: Linear variation of monthly average measured and estimated GSR at Kathmandu Valley 

 

Fig. 2: Monthly variation measured GSR at Kathmandu Valley for the years (2010-2015). 



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

165 
 

 

  

  

Fig 3: Monthly variation of GSR and maximum  

temperature at  Kathmandu Valley  for the years 

 (2010, 2012, 2014, 2015). 

Fig. 4: Monthly variation of GSR and average relative 

humidity at Kathmandu Valley for the years (2010, 

2012, 2014, 2015).  

(a) 

(c) 

(b) 

(d) 

(b) 

(c) 

(d) 

(a) 



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

166 
 

  

  
Fig 5: Monthly variation of GSR and rainfall at Kathmandu Valley (2010, 2012, 2014, 2015). 

 

Fig: 5(a), (b), (c), and (d) shows variation of GSR 

and precipitation with month. Among the four 

figures rain fall is maximum in July, August and 

September in Fig 5(a), (c) and (d). However in Fig 

(b) rainfall is lower in August. At this time, there is 

lesser GSR due to local weather condition. In the 

summer or monsoon season the average rainfall is 

about 70% to 90% of whole year and remaining 

months remain almost dry. Hence normal trend 

deviate for these months [24].  
 

The maximum and minimum value of GSR are 

16.79 ± 3.9 MJ/m2/day and 9.35 ± 3.08 MJ/m2/day 

in May and August respectively for year 2015. 

Similarly, the monthly average GSR is maximum 

in May is 22.97 ± 4.59 MJ/m2/day and minimum in 

December is 9.22 ± 1.74 MJ/m2/day for year2010.  

The clearness index in July and August is low 

(0.25) and for those months GSR is ((10.01 ± 3.75) 

and (9.35± 3.08) MJ/m2/day) respectively. The 

lower value of GSR in this month is due to frequent 

rain fall in monsoon season.  It is mainly due to less 

cloud, slow wind, less rainfall and less solar zenith 

angle. However, the normal trend deviates for the 

months of June, July and August for all the studied 

sites due to the fact that they are rainy seasons for 

the site and maximum rainfall of the year occurs in 

these months. In table 4, total and average GSR is 

lower in year 2015 and more rainfall in same year. 
 

In rainy season, sky is covered by cloud and this 

reduces the solar radiation reaching the ground 

surface. Not only the meteorological factors such as 

rainfall, sunshine duration and temperature affect 

the global solar radiation but also astronomical, 

geometrical and physical.  
 

Fig: 6, shows the comparative study of seasonal 

variation of GSR at four different years at 

Kathmandu Valley. In the figure the maximum and 

minimum GSR is found at spring and winter season 

(a) 
(b) 

(c) 
(d) 



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

167 
 

respectively. However, there is maximum 

fluctuation of GSR at summer season, it happens 

due to the presence of clouds and more rain fall 

during this time. In figure, standard deviation is 

used to compare the fluctuations in GSR. Among 

the years 2015 has less GSR than others. 
 

Fig. 7,8 shows the variation of GSR and Clearness 

index with years in box plot. In year 2015 GSR is 

lower (12.662 ± 4.21 MJ/m2/day) than in other 

year. It also seen from Table 3. In year 2015, 

Clearness index or transmittance of atmosphere is 

also low due to more rainfall. In 2015, GSR is 

slightly overestimated likewise for the year 2014, 

2012, 2010 there is gradual increase in 

underestimation, it is due to variation of rainfall 

pattern and local weather condition. Due to the 

earthquake in Nepal (April 25, 2015, Saturday), the 

air pollutants escalated, which reduced the 

clearness index (KT) and as a result lower global 

solar radiation.  
 

The prediction of GSR for 2017 for Kathmandu 

and Khumaltar is observed from empirical 

constants obtained from above analysis shown in 

Fig.9. The empirical constants for model E-19, a = 

0.115, b = 0.134, c = 0.014 and d = 0.0007, is used 

for prediction. The predicted values are slightly less 

than observed values for Kathmandu for the month 

March, June and October as it depends upon 

meteorological parameters. The yearly predicted 

average value of GSR for Kathmandu and 

Khumaltar is14.56 MJ/m2/day and 14.16 

MJ/m2/day for year 2017. The total predicted yearly 

GSR for Kathmandu and Kumaltar are 5315.16 

MJ/m2/day and 5169.78 MJ/m2/day respectively. 

Fig 6: Seasonal variation of GSR at Kathmandu for 

year (2010-2015). 
 

 

 

 

 

Table 4: Variation of GSR and rain fall in different year at Kathmandu Valley. 

 

Year  2015 2014 2012 2010 

 yearly mean GSR 

(MJ/m2/day) 

12.662 ± 4.21 14.499 ± 3.45 16.683 ± 4.6 16.033 ± 5.7 

 Yearly total GSR  

(MJ/m2/day)  

4621.594 ± 0.22 5292.228 ± 0.18 6105.823 ± 0.24 5852.224 ± 0.3 

max GSR  

(MJ/m2/day) 

22.23 - May 4 19.97 – 8 May 26.99 - 13 May 30.533- Sep 26 

min GSR  

(MJ/m2/day) 

1.43 - March 1 4.65 - Dec 16 2.95 -July 13 0.026 - Sep 29 

 Total rain  

(mm) 

1649.4 1579.7 1465.94 1480.9 

 max Rain 

 (mm) 

115.5 - May 11 93.1 - Jul 1 75mm - Sep 14 80.7 - Jul 22 

 

Winter Spring Summer Autumn

0

5

10

15

20

25

G
S

R
 (

M
J
/m

2
/d

a
y
)

Seasons

 2010

 2012

 2014

 2015

TIA



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

168 
 

GSR-2015 GSR-2014 GSR-2012 GSR-2010
6

8

10

12

14

16

18

20

22

24

26

G
S

R
 (

M
J
/m

2
/d

a
y
)

 Range within 3IQR

 Median Line

 Mean

 Outliers

Year

TIA

Fig. 7: Yearly variation of GSR for different year 

for given location 

2015 2014 2012 2010

0.2

0.3

0.4

0.5

0.6

0.7

C
le

a
rn

e
s
s
 I
n

d
e
x
(K

T
)

 Range within 1.5IQR

 Median Line

 Mean

 Outliers

Year

TIA

 Fig. 8: Variation of clearness index for different 

year at Kathmandu Valley. 

 

 

 
Fig. 9: Monthly variation of measured and predicted 

GSR for Kathmandu and Khumaltar for year 2017.  

 

 

4. Conclusion 
 

The maximum and minimum values of monthly 

average global solar radiation are found (19.29 ± 

3.91) MJ/m2/day and (11.15 ± 1.55) MJ/m2/day in 

May and December, respectively. The annual 

average global Solar Radiation is about 14.97 ± 

4.49 MJ/m2/day which is significant amount to 

operate many more energy based devices .The 

overall performance of parameters RMSE, MBE, 

MPE and R2 are found to be 1.405, 0.011, -1.014 

and 0.792, respectively in the model D. These 

statistical errors showed that this research work is 

meaningful for the estimation of global solar 

radiation on the basis of sunshine hour, difference 

of ambient temperature and relative humidity. It 

indicates that the estimated values of global solar 

radiation can be very efficiently used to 

compensate for the energy deficiency in the 

country. It concluded that the availability of global 

solar radiation is very encouraging from application 

point of view. At the end, the predicting regression 

coefficients could be employed for the estimation 

of global solar radiation at the similar climatic 

locations of Nepal. 
 

Acknowledgment 
 

Authors sincerely express deep thanks to 

Department of Hydrology and Meteorology 

(DHM), Government of Nepal for providing 

relevant meteorological data. Authors would like to 

give special thanks to R. Bachchan, P.M. Shrestha, 

faculty members and staffs of Dept. of Physics, 

Patan Multiple Campus, TU, Nepal and faculty 

members of Applied Sciences, IOE, Pulchowk 

Campus, TU. I would like to thank NAST for 

providing financial support to forward my research 

work. 
 

References 
 

[1] K. N. Liou, An introduction to atmospheric 

radiation, Academic press (2002). 

[2] M. Iqbal, An introduction to solar radiation, 

Academic press, New York (1983). 



Usha Joshi et al. / BIBECHANA 18 (1) (2021) 159-169  

169 
 

[3] J. A. Duffie and W.A. Beckman, Solar Engineering 

of Thermal Processes, John Wiley & sons, Ins 

(2013). 

[4] M.L. Slaby, Fundamentals of Atmosphiric Physics, 

Acedemic press,New York  (1995). 

[5] K. .N. Poudyal., Estimation of Global Solar 

Radiation Potential in Nepal, Ph.D.Dissertation 

IOE, TU (2015). 

[6] D. Banjade et al., Estimation of Global Solar 

radiation using Empirical Models at Benevento, 

Italy, National Seminar on Power and 

Communication Sectors Development (PCSD), 

Kathmandu, Nepal  (2010) 41-44. 

[7] F. Besharat et al., Empirical models for estimating 

global solar radiation: A review and case study. 

Renewable and Sustainable Energy Reviews 

21(2013)798–821. 

[8] M.J. Ahmad and G.N. Tiwari, Solar radiation 

models–review. International journal of energy and 

environment 1(3) (2010) 513-532. 

[9] Economic Survey 2018/19, Gov, Ministry of 

Finance. 

[10] WECS. Water and Energy Commission Secretariat, 

Energy Sector Synopsis Report, Gov of Nepal 

(2010). 

[11] J. N. Shrestha et al., Renewable energy in Nepal-

Progress at a glance from 1998 to 2003, 

RETRUD03, 12-14 October, Kathmandu, Nepal, 

(2003). 

[12] International Energy Association (2010). Key 

world energy statistics, International Energy 

Agency, Paris. (2017).  

[13] A. Angstrom, Solar and terrestrial radiation, 

Quarterly Journal of the Royal Meteorological 

Society 50 (1924) 121-125. 

[14] J. A. Prescott, Evaporation from a water surface in  

 

 

 

 

 

 

 

 

 

 

 

 

 

relation to solar radiation, Transactions of the  

Royal Society of South Australia 64 (1940) 114-

118.  

[15] M.S. Okundamiya et al., Evaluation of various 

global solar radiation models for Nigeria, 

International Journal of Green Energy 13(5) (2016) 

505- 512. 

[16] C. Augustine and M. N. Nnabuchi, Relationship 

between Global Solar Radiation and  Sunshine 

Hours for Calabar, Port Harcourt and Enugu, 

Nigeria, International Journal   of Physical 

Sciences 4(4) (2009) 182-188.  

[17] K. N. Poudyal et al., Estimation of daily global 

solar radiation; Nepal experience,             

Measurement, 46 (2013) 1807-1817.  

[18] Krishna R. Adhikari et al., Estimation of  global 

solar radiation for four selected sites in Nepal 

using sunshine hour, temperature and relative 

humidity, Journal of Power and Energy 

Engineering 1 (2013) 1-9. 

[19] Central Bureau of Statistics, National Population 

and Housing Census 2011, Government of Nepal, 

National Planning Commission Secretariat (2012). 

[20] Introduction (2018). www.kathmandu.gov.np.  

[21] P. R. Pant and D. Dongal, Kathmandu Valley 

Profile, Kathmandu Metropolitan City (2009). 

[22] Climate (2018). [Online]. www.dhm.gov.np. 

[23]  Kipp and Zonen, Manual CMA and CMP series, 

Instruction Manual, Version:  the Netherlands 

(2006).     

[24]  J. L. Nayava et al., Impact of Climate, Climate 

Change and Modern Technology on Wheat 

Production in Nepal: A Case Study at Bhairahawa, 

Journal of Hydrology and Meteorology 6(1) (2009) 

1-14.  

http://www.kathmandu.gov.np/
http://www.dhm.gov.np/