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Satellite-derived solar resource maps for Myanmar
Renewable Energy 53 (2013) 132e140
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Technical note
Satellite-derived solar resource maps for Myanmar Serm Janjai*, Itsara Masiri, Jarungsaeng Laksanaboonsong Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 April 2012 Accepted 14 November 2012 Available online 12 December 2012
Solar resource maps for use in solar energy applications have been produced for Myanmar. A satellitebased solar radiation model, originally developed for the tropics, was improved and applied for the region. A 13-year period (1998e2010) of imagery data from GMS 5, GOES 9 and MTSAT-1R satellites was used as the main input in the model. The absorption and scattering of solar radiation by various atmospheric constituents was also taken into account. The absorption of solar radiation due to water vapour was estimated from precipitable water database obtained from the National Center for Environmental Protection (NCEP), USA. The total column ozone obtained from TOMS/EP and OMI/AURA satellites were used to calculate solar radiation absorption by ozone. The visibility data observed at meteorological stations in Myanmar and neighbouring countries were employed to estimate solar radiation depletion due to aerosols. In order to validate the model, five pyranometer stations were established in different regions of Myanmar and a two-year period of data from these stations were used for the model validation. Additionally, global solar radiation measured at 10 stations in a neighbouring country was also employed for the validation. It was found that monthly average global radiation obtained from the measurements and that estimated from the model was in good agreement, with a root mean square difference of 9.6% at monthly scale. After the validation, the model was used to estimate monthly average global radiation over Myanmar and the results were presented as solar resource maps. The maps revealed that geographical distribution of solar radiation was strongly influenced by the topography of the country and the tropical monsoons. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Solar radiation Mapping Satellite data Myanmar
1. Introduction The economics and industrial output of Myanmar rely essentially on oil. The country imports annually about 7,000,000 barrels of crude oil. The increase in oil price has prompted the government to seek other energy sources. Among various energy sources, solar energy is considered to be one of the most promising for the country. Myanmar is located near the equator where solar energy is abundant. In general, the amount of solar radiation incident at the earth surface varies with time, locations and atmospheric opacity. To select an optimum area for installing solar energy conversion systems such as solar PV or thermal power plants, it is necessary to have a solar resource map showing its geographical distribution over the area of interest. Ideally, such a map should be based on solar radiation data obtained from a dense network of solar radiation monitoring stations covering that area. However, prior to this study, there was no network of solar radiation monitoring stations in Myanmar. Consequently, there was
* Corresponding author. Tel.: þ66 34 270 761; fax: þ66 34 271 189. E-mail address: [email protected] (S. Janjai). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.11.014
no solar radiation data for constructing solar resource maps. An alternative solution to this problem is to use a model to estimate solar radiation from satellite data. This is because back-scattered solar radiation from the earth-atmospheric system captured by meteorological satellites can be statistically or physically related to solar radiation incident at the earth’s surface and relations can be used to estimate surface solar radiation, since, long-term satellite data over Myanmar are available for such estimation. In this work, a satellite-based solar radiation model was improved and used to estimate solar radiation in Myanmar. A 13year period of geostationary satellite data was employed as a main input in the model. Five pyranometer stations were established in Myanmar and a 2-year period of solar radiation from these stations was used to validate the model. Then, the model was employed to generate solar resource maps for Myanmar.
2. Methodology The methodology used to generate the solar resource maps of Myanmar may be partitioned into satellite data processing, solar radiation modelling, calculation of model parameters, model
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
validation and generation of solar resource maps. Details of each step are described as follows. 2.1. Processing of satellite data The satellite data used in this work were provided in 8-bit digital format. The data were obtained from the visible channel of three geostationary satellites, namely GMS 5, GOES 9 and MTSAT-1R. The data periods of GMS 5, GOES 9 and MTSAT-1R were between January 1998 and May 2003, June 2003 and July 2005 and August 2005 and December 2010, respectively. Nine hourly images per day were extracted between 8:30 to 16:30 h local time. The data could be displayed as images. Each image consists of pixels with different grey levels; ranging from 0 to 255. The original satellite data are in a satellite projection featuring the curvature of the earth’s surface. In order to facilitate the identification of the pixel coordinate, these images were transformed into a cylindrical projection. Then, they were navigated using the coastlines as references. After the navigation, each image comprising a matrix of pixels of 570 1,175 pixels was obtained. The size of each pixel corresponds to the area of 3 3 km2 on the ground. An example of the rectified image is shown in Fig. 1. 2.2. Solar radiation modelling In the last 30 years, a number of satellite-based solar radiation models have been developed [1e20]. These models have different complexities and accuracies. In addition, most of these models were developed and used in Europe and North and South America where the climate is different from that of the tropical Asian subcontinent. Therefore, in the case of Myanmar, a satellite-based solar radiation model developed for a tropical environment [21] was adopted. This model was improved by accounting for the
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solar radiation absorption by various atmospheric constituents in the upwelling path of reflected solar radiation, which was ignored in the original model. According to the estimation of the magnitude of this absorption, the inclusion of the absorption in the model causes the decrease of the estimated surface solar radiation about 1e4 % depending on locations and months, as compared to that calculated by the previous version of the model. This inclusion will make the model more realistic. The absorption and scattering of solar radiation in the atmosphere from the improved model is schematically shown in Fig. 2. To describe the model, the parameters with primed superscript denote their value in the satellite bands: 0.55e0.90 mm for GMS 5, 0.52e0.72 mm for GOES 9 and 0.55e0.90 mm for MTSAT-1R. The unprimed parameters refer to their value in the broad solar band (0.3e3.0 mm). According to the model, when solar radiation arrives at the top of the atmosphere, it is scattered by air molecules and clouds with the scattering coefficientr0A while passing through the atmosphere (see Fig. 2(a)). In addition, it is also scattered by aerosols with a scattering coefficient r0aer . The rest of the downwelling solar radiation is absorbed by ozone, gases, water vapour and aerosols with absorption coefficients of a0o ; a0g ; a0w and a0aer , respectively. The remaining solar radiation arrives at the ground and a fraction r0G , the surface reflectivity, is reflected back to the atmosphere and space. Similar to the downwelling path, the upwelling radiation is again absorbed by water vapour, aerosols, gases and ozone and scattered by air molecule, clouds and aerosols. Although, the visible band of GMS 5 and MTSAT-1R includes a small part of the near infrared solar spectrum which is absorbed by clouds, this absorption was assumed to be negligible. In addition, the effect of the multiple reflections between the ground and clouds was not taken into account in the model, as ground albedo in the tropics is relatively low. With this consideration and the processes mentioned above, the eartheatmospheric reflectivity as seen by the satellite can be written as:
r0EA ¼ r0A þ r0aer þ 1 r0A r0aer
2
1 a0w a0o a0aer a0g
2
r0G (1)
Rearranging Eq. (1) yields:
r0A ¼
2 h i 1 2 1 a0o a0g a0w a0aer r0G 1 r0aer
2 nh i2 1 r0aer 1 2r0G 1 a0o a0g a0w a0aer 2 h r0aer r0EA 4r0G 1 a0o a0g a0w a0aer 2 2 io1=2 1 r0aer þ r0G 1 a0o a0g a0w a0aer
þ
(2)
2 i1 h 2 1 a0o a0g a0w a0aer r0G
Fig. 1. Example of the rectified satellite imagery data covering the entire area of Myanmar.
From Eq. (2) r0EA ; r0G ; a0o ; a0g ; a0w ; a0aer and r0aer can be calculated from ground and satellite based data which will be explained in the next section. Therefore, values of r0A can be consequently obtained from Eq. (2). Note that r0A is applicable only on the satellite band, i.e. 0.55e0.90 mm for GMS 5 and MTSAT-1R and 0.52e 0.72 mm for GOES 9. In the next step r0A was converted into broadband atmospheric albedo, rA (0.3e3.0 mm) by using an empirical formula explained in the next section. After the conversion, rA will be used in the model of broadband atmospheric transmittance.
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Fig. 2. Schematic diagram showing solar radiation absorption and scattering considered in the model: (a) solar radiation as detected by a satellite (b) solar radiation as detected by a surface pyranometer.
The broadband transmittance (s) of the atmosphere as seen by a pyranometer (Fig. 2(b)) can be modelled as:
s¼
ð1 rA raer Þ 1 aw ao aaer ag 1 ðrA þ raer ÞrG
- broadband
3:0 Z mm
(3)
ao ¼ 1 The denominator of Eq. (3) represents the multiple reflections between the ground and the atmosphere. The values of rG, raer, ao, ag, aw and aaer can be obtained using the method described in the next section. Finally, solar radiation on the ground (H) can be calculated from extraterrestrial radiation (H0) and the broadband atmospheric transmittance (s) as follows:
H ¼ sH0
(4)
All calculations in this model were on monthly average daily basis with values of H and H0 expressed in MJ m2 day1. 2.3. Calculation of model parameters 2.3.1. Absorption coefficient of ozone Ozone partly absorbs the ultraviolet and visible spectrum of solar radiation. The absorption coefficient due to ozone in satellite band (a0o ) and broadband (ao) was calculated from the following equations. - satellite band
Zl2
a0o ¼ 1
I0l sol dl
l1
Zl2 l1
(5) I0l dl
I0l sol dl
0:3 mm 3:0 Z mm
(6) I0l dl
0:3 mm
where I0l is extraterrestrial solar spectrum, s0l is spectral transmission coefficient of ozone, l1 and l2 are the wavelength band of the satellite sensors. The spectral transmission coefficient was calculated by the formula reported by Iqbal [22] as:
sol ¼ expð kol [mr Þ
(7)
where kol is extinction coefficient of ozone, [ is total column ozone and ma is air mass. The total column ozone data obtained from TOMS/EP and OMI/AURA satellite instruments were used in this work. These data were displayed as ozone maps with the resolution of 1.0 (latitude) 1.25 (longitude). Prior to utilisation, the pixels of ozone maps were spatially interpolated to match with the pixels of GMS5, GOES 9 and MTSAT-1R. As the EP and AURA satellites each passed over Myanmar only once per day, the ozone data at the overpass time were used to represent the ozone data of that day. 2.3.2. Absorption coefficient of water vapour Water vapour significantly absorbs the near infrared part of the solar spectrum. The amount of water vapour is normally quantified as the thickness of precipitable water (in cm). The estimation of solar radiation absorbed by water vapour is crucial for the calculation of solar radiation over Myanmar because the country is located in the tropics with high precipitable water year round. Similar to the case of ozone, the absorption coefficient of water vapour for satellite band (a0w ) and broadband (aw) was computed as:
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
where swl is spectral transmission coefficient due to water vapour which was calculated by the formula given by Leckner [23] as
- satellite band
Zl2
a0w ¼ 1
I0l swl dl
l1
Zl2
(8)
h
swl ¼ exp 0:2385kwl wmr =ð1 þ 20:07kwl wmr Þ0:45
i
(10)
I0l dl
l1
where kwl is extinction coefficient due to water vapour, w is precipitable water and mr is air mass. In this work, precipitable water data from the database of the National Centers for Environmental Protection (NCEP), USA (www.cdc.noaa.gov/data/gridded/data.ncep. reanalysis.html) were employed to estimate the absorption coefficient due to water vapour over Myanmar.
- broadband
3:0 Z mm
a0w ¼ 1
135
I0l swl dl
0:3 mm 3:0 Z mm 0:3 mm
(9) I0l dl
2.3.3. Absorption coefficient due to gases Like water vapour, the absorption coefficient of gases was estimated from the equations: - satellite band
Fig. 3. Pictorial view and positions ( ) of five new solar radiation monitoring stations in Myanmar established in this work. The symbol stations in Thailand whose radiation data were also used in the validation.
represents the location of 10 existing
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Table 1 Periods of solar radiation data and positions of solar radiation monitoring stations.
a
1. Yangon 2. Naypyidawa 3. Meiktilaa 4. Mandalaya 5. Shweboa 6. Chiang Raib 7. Chiang Maib 8. Mae Sariangb 9. Doi Muserb 10. Thong Phaphumb 11. Kanchanaburib 12. Nakhon Pathomb 13. Prachuabkirikhanb 14. Chumphonb 15. Ranongb a b
Latitude
Longitude
16 460 N 19 360 N 20 500 N 21 590 N 22 350 N 19 570 N 18 470 N 18 100 N 16 530 N 14 470 N 14 10 N 13 490 N 11 480 N 10 290 N 9 530 N
96 100 E 96 130 E 95 500 E 96 060 E 95 430 E 99 500 E 98 590 E 97 560 E 99 90 E 98 380 E 99 320 E 100 20 E 99 480 E 99 110 E 98 370 E
Period of data
30
September 2008eDecember 2010 September 2008eDecember 2010 September 2008eDecember 2010 September 2008eDecember 2010 September 2008eJune 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010
2
Position (degree)
Hmodel (MJ/m -day)
Station
35 MBD = 3.8% RMSD = 9.6%
25 20 15 10 5 0
Station in Myanmar. Stations in Thailand near MyanmareThai border.
0
5
10
15
20
25
30
35
2
Hmeas (MJ/m -day) Zl2
a0g ¼ 1
Fig. 4. A comparison between the monthly average daily global radiation from the model (Hmodel) and the measurements (Hmeas).
I0l sgl dl
l1
Zl2
(11) I0l dl
l1
Daer ¼
- broadband
3:0 Z mm
ag ¼ 1
In this work, a solar radiation depletion coefficient of aerosols introduced in our previous work [21] was used to determine the effect of aerosols on solar radiation. This coefficient (Daer) was defined as
I0l sgl dl
0:3 mm 3:0 Z mm
(12) I0l dl
Hclean Hact Hclean
(14)
where Hclean is daily radiation under clean atmosphere without aerosols and Hact is daily radiation under actual atmosphere with the presence of aerosols. According to our previous work [21], Daer related well with the visibility and the following empirical equation was proposed as:
Daer ¼ 0:3631 0:0222 VIS þ 0:002ðVISÞ2
(15)
0:3 mm
where sgl is the spectral transmission coefficient of gases. This coefficient was computed from a formula proposed by Leckner [23] as:
h
sgl ¼ exp 1:41 kgl ma = 1 þ 118:93kgl ma
0:45 i
(13)
where kgl is the spectral extinction coefficient of gases and ma is air mass. 2.3.4. Absorption and scattering coefficients of aerosols Most aerosols are fine particles which are suspended in the atmosphere. They can be produced from human activities or natural processes such as volcanic eruptions, bush fire, oceanic haze and sands storm. In general, aerosols produced over land areas are called “continental aerosols. In cities and industrial areas, most aerosols are produced from combustion processes and they are called “urban aerosols”. Small sea salt particles produced from oceans are called “maritime aerosols”. Aerosols play an important role in depleting solar radiation in the atmosphere. Aerosol amount and properties in the atmosphere can be measured by using ground-based instruments such as sunphotometers. However, these instruments are costly to deploy over large regions.
where VIS is the visibility in km. This equation was used to calculate Daer in this study. The visibility observed in Myanmar and neighbouring countries was used and an interpolation technique was applied to determine visibility over the country.
Table 2 Root mean square difference (RMSD) and mean bias difference (MBD) between calculated and measured solar radiation. Station
RMSD (%)
MBD (%)
1. Yangon 2. Naypyidaw 3. Meiltila 4. Mandalay 5. Shwebo 6. Chiang Rai 7. Chiang Mai 8. Mae Sariang 9. Doi Muser 10. Thong Phaphum 11. Kanchanaburi 12. Nakhon Pathom 13. Prachuabkirikhan 14. Chumphon 15. Ranong Combined data from all stations
11.9 8.4 8.5 15.6 12.6 11.5 10.2 14.9 10.9 6.9 6.8 10.5 6.4 8.8 7.3 9.6
4.7 3.6 2.4 8.1 3.9 6.3 2.4 9.2 2.2 1.1 3.7 5.6 1.4 4.8 2.1 3.8
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
The depletion of solar radiation by aerosols is caused by absorption and scattering processes. To partition Daer into the absorption coefficient (aaer) and scattering coefficient (raer), the single scattering albedo (SSA) of aerosols was required. In this work, SSA data from aerosol monitoring stations of AERONET (Aerosol Robotic Network) of NASA [24] located in Myanmar and neighbouring countries were used for the partition.
137
2.3.5. Surface albedo The method for deriving surface albedo over Myanmar is similar to that used for deriving surface albedo for Thailand [25]. This can be briefly explained as follows: The surface albedo over Myanmar was estimated on a monthly basis from the satellite images collected at 12:30 h local time. For each month, digital data of the rectified images in that month were
Fig. 5. Geographical distribution of monthly average solar irradiation.
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examined pixel by pixel, and the pixels were chosen with the lowest value to construct the cloud free composite image for that month. Then, the grey level of every pixel in the composite image was converted into the earth-atmospheric albedo by using conversion tables provided by the satellite data agency. In the last step, the effect of the atmospheric reflectivity was removed from the cloud-free earth-atmospheric albedo by using 5S radiative transfer model [26] to obtain the surface albedo. 2.3.6. Broadband atmospheric reflectivity Atmosphere reflectivity in satellite bands (r0A ) was converted into the broadband atmospheric reflectivity (rA) by using empirical formula developed by Janjai [27] as:
8 < 1:4846r0A 0:0768 rA ¼ 1:3756r0A 0:0793 : 1:3847r0A 0:0624
for GMS5 for GOES9 for MTSAT 1R
(16)
2.5. Generation of solar resource maps The validated model was used to estimate monthly average global irradiance over Myanmar. A 13-year period of satellite and other ancillary data required by the model were employed to calculate values of monthly average global radiation. For each month, these values were again averaged over the period of 13 years to obtain the long-term monthly average radiation for that month. The results were displayed as solar resource maps which will be shown in the next section. 3. Results and discussion The solar resource maps showing the geographical distribution of long-term monthly and yearly average irradiation are shown in Figs. 5 and 6, respectively. From January to March, solar radiation of 14e16 MJ m2 day1 is generally observed in the North of Myanmar due to a change in sun’s declination which moves northwards from the equator causing an
2.4. Model validation In order to obtain solar radiation data for model validation, we established five solar radiation monitoring stations at different locations in Myanmar, namely Yangon (16 460 N, 96 100 E), Naypyitaw (19 360 N 96 130 E), Meiktila (20 500 N, 95 500 E), Mandalay (21 590 N 96 060 E) and Shwebo (22 350 N 95 430 E) (Fig. 3). For each station, a Kipp&Zonen pyranometer (model CMP11) was used to measure solar radiation. The voltage signal from the pyranometer was recorded in a data logger manufactured by Campbell Scientific, Inc (Model CR1000). The signal from the pyranometer was captured every second and averaged over 10 min. The 10-min average values were recorded in the memory of the data logger. These data were sent to our laboratory at Silpakorn University by email every month. The data were converted into solar irradiance and was integrated over a day to obtain daily irradiation. As the period of solar radiation data obtained from these stations was relatively short, a longer period of solar radiation data collected at ten existing Thai stations located near Myanmare Thailand border were also employed (Fig. 3). Details are shown in Table 1. The datasets both from Myanmar and Thailand were subjected to a quality control. The data which violated physical laws were discarded from the datasets. In order to obtain satellite-derived solar radiation for the model validation, monthly average of the earth atmospheric albedo (r0EA ) was determined from the satellite data. The data were selected over sub-array of 3 3 pixels, centreed at the 15 target solar radiation monitoring stations (5 stations in Myanmar and 10 stations in Thailand). For each station, the values of r0EA in the sub-array were averaged to obtain the average earth-atmospheric albedo. The values of a0w , a0o , a0aer , r0aer , and r0G at that station were also calculated. Values of these coefficients were substituted into Eq. (2) to obtain the values of r0A . Moreover they were converted into broadband atmospheric albedo (rA) by using Eq. (16). The values of rA were used to compute the broadband atmospheric transmittance (s) from Eq. (3). Finally, broadband surface solar radiation (H) was estimated for all stations by using Eq. (4). All coefficients were calculated on monthly average basis. Values of monthly average solar radiation calculated from the model were compared to those obtained from the measurements. The results are shown in Fig. 4. From Fig. 4, it is noticed that the data points of all stations scatter around the 1:1 line in a narrow band. Results in Table 2 also show values of RMSD and MBD which vary in the range from 6.4% to 15.6% and 3.6% to 9.2%, respectively. This indicates that the estimated and measured irradiance are in reasonable agreement.
Fig. 6. Geographical distribution of yearly average global irradiation.
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
increase in extraterrestrial solar radiation over the region. By contrast, values of high solar radiation (18e22 MJ m2 day1) are seen in most part of the country, especially in the South and Southwest. Solar radiation increases progressively from February to reach a peak of 20e24 MJ m2 day1 in April. This feature occurs mostly in the western part of the country. From May to September, the south-west monsoon brings rains and cloudy skies. This causes low solar radiation (14e 17 MJ m2 day1) in the southern region extending from 8 N to 22 N. From September onwards, solar radiation increases progressively reaching a magnitude of 18e20 MJ m2 day1 in October and November, marking the beginning of the dry season. Examine the yearly average map, Fig. 6, it can be seen that daily global solar radiation in Myanmar varies from 15 to 16 MJ m2 day1 in the North and the East to 19e20 MJ m2 day1 in the West of the country. High solar radiation (>20 MJ m2 day1) is observed mostly in the Central dry zone of the country. High cloud cover related to mountainous areas is likely to explain the relatively low values obtained for the North (14 MJ m2 day1) and in the East and the South of the country (16e18 MJ m2 day1). In contrast, high solar radiation (20e22 M J m2 day1) in the Central part of the country is probably related to rain shadow effects caused by the mountain range in the West. The yearly average of solar radiation in Myanmar was found to be 18.3 MJ m2 day1, when averaged over the entire area of the country. 4. Conclusion A satellite-based model was improved and employed to generate solar resource maps for the region of Myanmar. Long-term satellite data obtained from GMS5, GOES 9 and MTSAT-1R from January 1998 to December 2010 were used as the main input to the model. The model considers the processes of scattering and absorption due to clouds, aerosols, water vapour and ozone. Model validation was made against the measurements at 15 sites and the root mean square difference was 9.6%. The model was employed to calculate the surface solar radiation for the entire country, and monthly averages of daily solar radiation of Myanmar were computed. Then the results were displayed as solar resource maps. Variability of solar radiation in Myanmar is mostly influenced by the topography of the country and the tropical monsoons. Long-term annual average of solar radiation, when averaged over the entire areas of the country, in Myanmar was found to be 18.3 MJ m2 day1. Acknowledgements The authors would like to thank Department of Alternative Energy Development and Efficiency, Ministry of Energy of Thailand for inviting Silpakorn University to carry out this project. The authors are grateful to Dr. Tun Lwin, former Director General of the Department of Meteorology and Hydrology, Ministry of Transport of Myanmar, and Mr. Soe Aung, Director general of the Department of Energy Planning, Ministry of Energy of Myanmar for their support to this project. Nomenclature Daer H Hclean Hact
coefficient of solar radiation depletion by aerosols [-] monthly average of daily global solar radiation [MJ m2 day1] daily global radiation under clean atmosphere without aerosols [MJ m2 day1] daily global radiation under actual atmosphere with the presence of aerosols [MJ m2 day1]
H0 I0l kol kgl kwl [ ma mr MBD RMSD VIS w
aaer
a0aer ag a0g ao a0o aw a0w l rA r0A raer r0aer r0EA rG r0G s s gl sol s wl
139
monthly average of daily extraterrestrial solar radiation [MJ m2 day1] extraterrestrial spectral solar irradiance [W m2 ] extinction coefficient of ozone [-] extinction coefficient of gases [-] extinction coefficient of water vapour [-] total column ozone [cm] optical air mass under actual condition [-] optical air mass at standard condition [-] mean bias difference relative to the mean value [%] root mean square difference relative to the mean value [%] visibility [km] precipitable water [cm] absorption coefficient due to aerosols in broadband (0.3e3.0 mm) [-] absorption coefficient due to aerosols in satellite band [-] absorption coefficient due to gases in broadband [-] absorption coefficient due to gases in satellite band [-] absorption coefficient due to ozone in broadband [-] absorption coefficient due to ozone in satellite band [-] absorption coefficient due to water vapour in broadband [-] absorption coefficient due to water vapour in satellite band [-] wavelength [mm] cloud-atmospheric albedo in broadband [-] cloud-atmospheric albedo in satellite band [-] scattering coefficient of aerosols in broadband [-] scattering coefficient of aerosols in satellite band [-] earth-atmospheric albedo [-] surface albedo in broadband [-] surface albedo in satellite band [-] broadband atmospheric transmittance [-] spectral transmittance of gases [-] spectral transmittance of ozone [-] spectral transmittance of water vapour [-]
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[21] Janjai S, Laksanaboonsong J, Nunez M, Thongsathitya A. Development of a method for generating operational solar radiation maps from satellite data for a tropical environment. Solar Energy 2005;78:739e51. [22] Iqbal M. An introduction to solar radiation. New York: Academic Press; 1983. [23] Leckner B. The spectral distribution of solar radiation at the earth’s surfaceelements of a model. Solar Energy 1978;20(2):143e50. [24] Holben BN, Eck TF, Slutsker I, Tanre D, Buis JP, Setzer A, et al. AERONETa federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment 1998;66:1e16. [25] Janjai S, Wanvong W, Laksanaboonsong J. The determination of surface albedo of Thailand using satellite data. In: Proceeding of the 2nd Joint International Conference on Sustainable Energy and Environment (SEE2006) Bangkok, Thailand; 2006. p. 156e61. [26] Tanre0 D, Deroo C, Duhaut P, Herman N, Morcrette JJ, Perbos J, et al. 1986. Simulation of the satellite signal in the solar spectrum, technical report. Laboratoire d0 Optique Atmospherique, Universite0 des Science et Technique de Lille; 59655 Villeneuve d0 Ascq Cedex, France. [27] Janjai S. Generation of solar radiation maps from long-term satellite data. In: Badescu V, editor. Modeling solar radiation on earth surface: recent advances. Berlin: Springer; 2008.
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Technical note
Satellite-derived solar resource maps for Myanmar Serm Janjai*, Itsara Masiri, Jarungsaeng Laksanaboonsong Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 April 2012 Accepted 14 November 2012 Available online 12 December 2012
Solar resource maps for use in solar energy applications have been produced for Myanmar. A satellitebased solar radiation model, originally developed for the tropics, was improved and applied for the region. A 13-year period (1998e2010) of imagery data from GMS 5, GOES 9 and MTSAT-1R satellites was used as the main input in the model. The absorption and scattering of solar radiation by various atmospheric constituents was also taken into account. The absorption of solar radiation due to water vapour was estimated from precipitable water database obtained from the National Center for Environmental Protection (NCEP), USA. The total column ozone obtained from TOMS/EP and OMI/AURA satellites were used to calculate solar radiation absorption by ozone. The visibility data observed at meteorological stations in Myanmar and neighbouring countries were employed to estimate solar radiation depletion due to aerosols. In order to validate the model, five pyranometer stations were established in different regions of Myanmar and a two-year period of data from these stations were used for the model validation. Additionally, global solar radiation measured at 10 stations in a neighbouring country was also employed for the validation. It was found that monthly average global radiation obtained from the measurements and that estimated from the model was in good agreement, with a root mean square difference of 9.6% at monthly scale. After the validation, the model was used to estimate monthly average global radiation over Myanmar and the results were presented as solar resource maps. The maps revealed that geographical distribution of solar radiation was strongly influenced by the topography of the country and the tropical monsoons. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Solar radiation Mapping Satellite data Myanmar
1. Introduction The economics and industrial output of Myanmar rely essentially on oil. The country imports annually about 7,000,000 barrels of crude oil. The increase in oil price has prompted the government to seek other energy sources. Among various energy sources, solar energy is considered to be one of the most promising for the country. Myanmar is located near the equator where solar energy is abundant. In general, the amount of solar radiation incident at the earth surface varies with time, locations and atmospheric opacity. To select an optimum area for installing solar energy conversion systems such as solar PV or thermal power plants, it is necessary to have a solar resource map showing its geographical distribution over the area of interest. Ideally, such a map should be based on solar radiation data obtained from a dense network of solar radiation monitoring stations covering that area. However, prior to this study, there was no network of solar radiation monitoring stations in Myanmar. Consequently, there was
* Corresponding author. Tel.: þ66 34 270 761; fax: þ66 34 271 189. E-mail address: [email protected] (S. Janjai). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.11.014
no solar radiation data for constructing solar resource maps. An alternative solution to this problem is to use a model to estimate solar radiation from satellite data. This is because back-scattered solar radiation from the earth-atmospheric system captured by meteorological satellites can be statistically or physically related to solar radiation incident at the earth’s surface and relations can be used to estimate surface solar radiation, since, long-term satellite data over Myanmar are available for such estimation. In this work, a satellite-based solar radiation model was improved and used to estimate solar radiation in Myanmar. A 13year period of geostationary satellite data was employed as a main input in the model. Five pyranometer stations were established in Myanmar and a 2-year period of solar radiation from these stations was used to validate the model. Then, the model was employed to generate solar resource maps for Myanmar.
2. Methodology The methodology used to generate the solar resource maps of Myanmar may be partitioned into satellite data processing, solar radiation modelling, calculation of model parameters, model
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
validation and generation of solar resource maps. Details of each step are described as follows. 2.1. Processing of satellite data The satellite data used in this work were provided in 8-bit digital format. The data were obtained from the visible channel of three geostationary satellites, namely GMS 5, GOES 9 and MTSAT-1R. The data periods of GMS 5, GOES 9 and MTSAT-1R were between January 1998 and May 2003, June 2003 and July 2005 and August 2005 and December 2010, respectively. Nine hourly images per day were extracted between 8:30 to 16:30 h local time. The data could be displayed as images. Each image consists of pixels with different grey levels; ranging from 0 to 255. The original satellite data are in a satellite projection featuring the curvature of the earth’s surface. In order to facilitate the identification of the pixel coordinate, these images were transformed into a cylindrical projection. Then, they were navigated using the coastlines as references. After the navigation, each image comprising a matrix of pixels of 570 1,175 pixels was obtained. The size of each pixel corresponds to the area of 3 3 km2 on the ground. An example of the rectified image is shown in Fig. 1. 2.2. Solar radiation modelling In the last 30 years, a number of satellite-based solar radiation models have been developed [1e20]. These models have different complexities and accuracies. In addition, most of these models were developed and used in Europe and North and South America where the climate is different from that of the tropical Asian subcontinent. Therefore, in the case of Myanmar, a satellite-based solar radiation model developed for a tropical environment [21] was adopted. This model was improved by accounting for the
133
solar radiation absorption by various atmospheric constituents in the upwelling path of reflected solar radiation, which was ignored in the original model. According to the estimation of the magnitude of this absorption, the inclusion of the absorption in the model causes the decrease of the estimated surface solar radiation about 1e4 % depending on locations and months, as compared to that calculated by the previous version of the model. This inclusion will make the model more realistic. The absorption and scattering of solar radiation in the atmosphere from the improved model is schematically shown in Fig. 2. To describe the model, the parameters with primed superscript denote their value in the satellite bands: 0.55e0.90 mm for GMS 5, 0.52e0.72 mm for GOES 9 and 0.55e0.90 mm for MTSAT-1R. The unprimed parameters refer to their value in the broad solar band (0.3e3.0 mm). According to the model, when solar radiation arrives at the top of the atmosphere, it is scattered by air molecules and clouds with the scattering coefficientr0A while passing through the atmosphere (see Fig. 2(a)). In addition, it is also scattered by aerosols with a scattering coefficient r0aer . The rest of the downwelling solar radiation is absorbed by ozone, gases, water vapour and aerosols with absorption coefficients of a0o ; a0g ; a0w and a0aer , respectively. The remaining solar radiation arrives at the ground and a fraction r0G , the surface reflectivity, is reflected back to the atmosphere and space. Similar to the downwelling path, the upwelling radiation is again absorbed by water vapour, aerosols, gases and ozone and scattered by air molecule, clouds and aerosols. Although, the visible band of GMS 5 and MTSAT-1R includes a small part of the near infrared solar spectrum which is absorbed by clouds, this absorption was assumed to be negligible. In addition, the effect of the multiple reflections between the ground and clouds was not taken into account in the model, as ground albedo in the tropics is relatively low. With this consideration and the processes mentioned above, the eartheatmospheric reflectivity as seen by the satellite can be written as:
r0EA ¼ r0A þ r0aer þ 1 r0A r0aer
2
1 a0w a0o a0aer a0g
2
r0G (1)
Rearranging Eq. (1) yields:
r0A ¼
2 h i 1 2 1 a0o a0g a0w a0aer r0G 1 r0aer
2 nh i2 1 r0aer 1 2r0G 1 a0o a0g a0w a0aer 2 h r0aer r0EA 4r0G 1 a0o a0g a0w a0aer 2 2 io1=2 1 r0aer þ r0G 1 a0o a0g a0w a0aer
þ
(2)
2 i1 h 2 1 a0o a0g a0w a0aer r0G
Fig. 1. Example of the rectified satellite imagery data covering the entire area of Myanmar.
From Eq. (2) r0EA ; r0G ; a0o ; a0g ; a0w ; a0aer and r0aer can be calculated from ground and satellite based data which will be explained in the next section. Therefore, values of r0A can be consequently obtained from Eq. (2). Note that r0A is applicable only on the satellite band, i.e. 0.55e0.90 mm for GMS 5 and MTSAT-1R and 0.52e 0.72 mm for GOES 9. In the next step r0A was converted into broadband atmospheric albedo, rA (0.3e3.0 mm) by using an empirical formula explained in the next section. After the conversion, rA will be used in the model of broadband atmospheric transmittance.
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S. Janjai et al. / Renewable Energy 53 (2013) 132e140
Fig. 2. Schematic diagram showing solar radiation absorption and scattering considered in the model: (a) solar radiation as detected by a satellite (b) solar radiation as detected by a surface pyranometer.
The broadband transmittance (s) of the atmosphere as seen by a pyranometer (Fig. 2(b)) can be modelled as:
s¼
ð1 rA raer Þ 1 aw ao aaer ag 1 ðrA þ raer ÞrG
- broadband
3:0 Z mm
(3)
ao ¼ 1 The denominator of Eq. (3) represents the multiple reflections between the ground and the atmosphere. The values of rG, raer, ao, ag, aw and aaer can be obtained using the method described in the next section. Finally, solar radiation on the ground (H) can be calculated from extraterrestrial radiation (H0) and the broadband atmospheric transmittance (s) as follows:
H ¼ sH0
(4)
All calculations in this model were on monthly average daily basis with values of H and H0 expressed in MJ m2 day1. 2.3. Calculation of model parameters 2.3.1. Absorption coefficient of ozone Ozone partly absorbs the ultraviolet and visible spectrum of solar radiation. The absorption coefficient due to ozone in satellite band (a0o ) and broadband (ao) was calculated from the following equations. - satellite band
Zl2
a0o ¼ 1
I0l sol dl
l1
Zl2 l1
(5) I0l dl
I0l sol dl
0:3 mm 3:0 Z mm
(6) I0l dl
0:3 mm
where I0l is extraterrestrial solar spectrum, s0l is spectral transmission coefficient of ozone, l1 and l2 are the wavelength band of the satellite sensors. The spectral transmission coefficient was calculated by the formula reported by Iqbal [22] as:
sol ¼ expð kol [mr Þ
(7)
where kol is extinction coefficient of ozone, [ is total column ozone and ma is air mass. The total column ozone data obtained from TOMS/EP and OMI/AURA satellite instruments were used in this work. These data were displayed as ozone maps with the resolution of 1.0 (latitude) 1.25 (longitude). Prior to utilisation, the pixels of ozone maps were spatially interpolated to match with the pixels of GMS5, GOES 9 and MTSAT-1R. As the EP and AURA satellites each passed over Myanmar only once per day, the ozone data at the overpass time were used to represent the ozone data of that day. 2.3.2. Absorption coefficient of water vapour Water vapour significantly absorbs the near infrared part of the solar spectrum. The amount of water vapour is normally quantified as the thickness of precipitable water (in cm). The estimation of solar radiation absorbed by water vapour is crucial for the calculation of solar radiation over Myanmar because the country is located in the tropics with high precipitable water year round. Similar to the case of ozone, the absorption coefficient of water vapour for satellite band (a0w ) and broadband (aw) was computed as:
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
where swl is spectral transmission coefficient due to water vapour which was calculated by the formula given by Leckner [23] as
- satellite band
Zl2
a0w ¼ 1
I0l swl dl
l1
Zl2
(8)
h
swl ¼ exp 0:2385kwl wmr =ð1 þ 20:07kwl wmr Þ0:45
i
(10)
I0l dl
l1
where kwl is extinction coefficient due to water vapour, w is precipitable water and mr is air mass. In this work, precipitable water data from the database of the National Centers for Environmental Protection (NCEP), USA (www.cdc.noaa.gov/data/gridded/data.ncep. reanalysis.html) were employed to estimate the absorption coefficient due to water vapour over Myanmar.
- broadband
3:0 Z mm
a0w ¼ 1
135
I0l swl dl
0:3 mm 3:0 Z mm 0:3 mm
(9) I0l dl
2.3.3. Absorption coefficient due to gases Like water vapour, the absorption coefficient of gases was estimated from the equations: - satellite band
Fig. 3. Pictorial view and positions ( ) of five new solar radiation monitoring stations in Myanmar established in this work. The symbol stations in Thailand whose radiation data were also used in the validation.
represents the location of 10 existing
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Table 1 Periods of solar radiation data and positions of solar radiation monitoring stations.
a
1. Yangon 2. Naypyidawa 3. Meiktilaa 4. Mandalaya 5. Shweboa 6. Chiang Raib 7. Chiang Maib 8. Mae Sariangb 9. Doi Muserb 10. Thong Phaphumb 11. Kanchanaburib 12. Nakhon Pathomb 13. Prachuabkirikhanb 14. Chumphonb 15. Ranongb a b
Latitude
Longitude
16 460 N 19 360 N 20 500 N 21 590 N 22 350 N 19 570 N 18 470 N 18 100 N 16 530 N 14 470 N 14 10 N 13 490 N 11 480 N 10 290 N 9 530 N
96 100 E 96 130 E 95 500 E 96 060 E 95 430 E 99 500 E 98 590 E 97 560 E 99 90 E 98 380 E 99 320 E 100 20 E 99 480 E 99 110 E 98 370 E
Period of data
30
September 2008eDecember 2010 September 2008eDecember 2010 September 2008eDecember 2010 September 2008eDecember 2010 September 2008eJune 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010 January 2002eDecember 2010
2
Position (degree)
Hmodel (MJ/m -day)
Station
35 MBD = 3.8% RMSD = 9.6%
25 20 15 10 5 0
Station in Myanmar. Stations in Thailand near MyanmareThai border.
0
5
10
15
20
25
30
35
2
Hmeas (MJ/m -day) Zl2
a0g ¼ 1
Fig. 4. A comparison between the monthly average daily global radiation from the model (Hmodel) and the measurements (Hmeas).
I0l sgl dl
l1
Zl2
(11) I0l dl
l1
Daer ¼
- broadband
3:0 Z mm
ag ¼ 1
In this work, a solar radiation depletion coefficient of aerosols introduced in our previous work [21] was used to determine the effect of aerosols on solar radiation. This coefficient (Daer) was defined as
I0l sgl dl
0:3 mm 3:0 Z mm
(12) I0l dl
Hclean Hact Hclean
(14)
where Hclean is daily radiation under clean atmosphere without aerosols and Hact is daily radiation under actual atmosphere with the presence of aerosols. According to our previous work [21], Daer related well with the visibility and the following empirical equation was proposed as:
Daer ¼ 0:3631 0:0222 VIS þ 0:002ðVISÞ2
(15)
0:3 mm
where sgl is the spectral transmission coefficient of gases. This coefficient was computed from a formula proposed by Leckner [23] as:
h
sgl ¼ exp 1:41 kgl ma = 1 þ 118:93kgl ma
0:45 i
(13)
where kgl is the spectral extinction coefficient of gases and ma is air mass. 2.3.4. Absorption and scattering coefficients of aerosols Most aerosols are fine particles which are suspended in the atmosphere. They can be produced from human activities or natural processes such as volcanic eruptions, bush fire, oceanic haze and sands storm. In general, aerosols produced over land areas are called “continental aerosols. In cities and industrial areas, most aerosols are produced from combustion processes and they are called “urban aerosols”. Small sea salt particles produced from oceans are called “maritime aerosols”. Aerosols play an important role in depleting solar radiation in the atmosphere. Aerosol amount and properties in the atmosphere can be measured by using ground-based instruments such as sunphotometers. However, these instruments are costly to deploy over large regions.
where VIS is the visibility in km. This equation was used to calculate Daer in this study. The visibility observed in Myanmar and neighbouring countries was used and an interpolation technique was applied to determine visibility over the country.
Table 2 Root mean square difference (RMSD) and mean bias difference (MBD) between calculated and measured solar radiation. Station
RMSD (%)
MBD (%)
1. Yangon 2. Naypyidaw 3. Meiltila 4. Mandalay 5. Shwebo 6. Chiang Rai 7. Chiang Mai 8. Mae Sariang 9. Doi Muser 10. Thong Phaphum 11. Kanchanaburi 12. Nakhon Pathom 13. Prachuabkirikhan 14. Chumphon 15. Ranong Combined data from all stations
11.9 8.4 8.5 15.6 12.6 11.5 10.2 14.9 10.9 6.9 6.8 10.5 6.4 8.8 7.3 9.6
4.7 3.6 2.4 8.1 3.9 6.3 2.4 9.2 2.2 1.1 3.7 5.6 1.4 4.8 2.1 3.8
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
The depletion of solar radiation by aerosols is caused by absorption and scattering processes. To partition Daer into the absorption coefficient (aaer) and scattering coefficient (raer), the single scattering albedo (SSA) of aerosols was required. In this work, SSA data from aerosol monitoring stations of AERONET (Aerosol Robotic Network) of NASA [24] located in Myanmar and neighbouring countries were used for the partition.
137
2.3.5. Surface albedo The method for deriving surface albedo over Myanmar is similar to that used for deriving surface albedo for Thailand [25]. This can be briefly explained as follows: The surface albedo over Myanmar was estimated on a monthly basis from the satellite images collected at 12:30 h local time. For each month, digital data of the rectified images in that month were
Fig. 5. Geographical distribution of monthly average solar irradiation.
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examined pixel by pixel, and the pixels were chosen with the lowest value to construct the cloud free composite image for that month. Then, the grey level of every pixel in the composite image was converted into the earth-atmospheric albedo by using conversion tables provided by the satellite data agency. In the last step, the effect of the atmospheric reflectivity was removed from the cloud-free earth-atmospheric albedo by using 5S radiative transfer model [26] to obtain the surface albedo. 2.3.6. Broadband atmospheric reflectivity Atmosphere reflectivity in satellite bands (r0A ) was converted into the broadband atmospheric reflectivity (rA) by using empirical formula developed by Janjai [27] as:
8 < 1:4846r0A 0:0768 rA ¼ 1:3756r0A 0:0793 : 1:3847r0A 0:0624
for GMS5 for GOES9 for MTSAT 1R
(16)
2.5. Generation of solar resource maps The validated model was used to estimate monthly average global irradiance over Myanmar. A 13-year period of satellite and other ancillary data required by the model were employed to calculate values of monthly average global radiation. For each month, these values were again averaged over the period of 13 years to obtain the long-term monthly average radiation for that month. The results were displayed as solar resource maps which will be shown in the next section. 3. Results and discussion The solar resource maps showing the geographical distribution of long-term monthly and yearly average irradiation are shown in Figs. 5 and 6, respectively. From January to March, solar radiation of 14e16 MJ m2 day1 is generally observed in the North of Myanmar due to a change in sun’s declination which moves northwards from the equator causing an
2.4. Model validation In order to obtain solar radiation data for model validation, we established five solar radiation monitoring stations at different locations in Myanmar, namely Yangon (16 460 N, 96 100 E), Naypyitaw (19 360 N 96 130 E), Meiktila (20 500 N, 95 500 E), Mandalay (21 590 N 96 060 E) and Shwebo (22 350 N 95 430 E) (Fig. 3). For each station, a Kipp&Zonen pyranometer (model CMP11) was used to measure solar radiation. The voltage signal from the pyranometer was recorded in a data logger manufactured by Campbell Scientific, Inc (Model CR1000). The signal from the pyranometer was captured every second and averaged over 10 min. The 10-min average values were recorded in the memory of the data logger. These data were sent to our laboratory at Silpakorn University by email every month. The data were converted into solar irradiance and was integrated over a day to obtain daily irradiation. As the period of solar radiation data obtained from these stations was relatively short, a longer period of solar radiation data collected at ten existing Thai stations located near Myanmare Thailand border were also employed (Fig. 3). Details are shown in Table 1. The datasets both from Myanmar and Thailand were subjected to a quality control. The data which violated physical laws were discarded from the datasets. In order to obtain satellite-derived solar radiation for the model validation, monthly average of the earth atmospheric albedo (r0EA ) was determined from the satellite data. The data were selected over sub-array of 3 3 pixels, centreed at the 15 target solar radiation monitoring stations (5 stations in Myanmar and 10 stations in Thailand). For each station, the values of r0EA in the sub-array were averaged to obtain the average earth-atmospheric albedo. The values of a0w , a0o , a0aer , r0aer , and r0G at that station were also calculated. Values of these coefficients were substituted into Eq. (2) to obtain the values of r0A . Moreover they were converted into broadband atmospheric albedo (rA) by using Eq. (16). The values of rA were used to compute the broadband atmospheric transmittance (s) from Eq. (3). Finally, broadband surface solar radiation (H) was estimated for all stations by using Eq. (4). All coefficients were calculated on monthly average basis. Values of monthly average solar radiation calculated from the model were compared to those obtained from the measurements. The results are shown in Fig. 4. From Fig. 4, it is noticed that the data points of all stations scatter around the 1:1 line in a narrow band. Results in Table 2 also show values of RMSD and MBD which vary in the range from 6.4% to 15.6% and 3.6% to 9.2%, respectively. This indicates that the estimated and measured irradiance are in reasonable agreement.
Fig. 6. Geographical distribution of yearly average global irradiation.
S. Janjai et al. / Renewable Energy 53 (2013) 132e140
increase in extraterrestrial solar radiation over the region. By contrast, values of high solar radiation (18e22 MJ m2 day1) are seen in most part of the country, especially in the South and Southwest. Solar radiation increases progressively from February to reach a peak of 20e24 MJ m2 day1 in April. This feature occurs mostly in the western part of the country. From May to September, the south-west monsoon brings rains and cloudy skies. This causes low solar radiation (14e 17 MJ m2 day1) in the southern region extending from 8 N to 22 N. From September onwards, solar radiation increases progressively reaching a magnitude of 18e20 MJ m2 day1 in October and November, marking the beginning of the dry season. Examine the yearly average map, Fig. 6, it can be seen that daily global solar radiation in Myanmar varies from 15 to 16 MJ m2 day1 in the North and the East to 19e20 MJ m2 day1 in the West of the country. High solar radiation (>20 MJ m2 day1) is observed mostly in the Central dry zone of the country. High cloud cover related to mountainous areas is likely to explain the relatively low values obtained for the North (14 MJ m2 day1) and in the East and the South of the country (16e18 MJ m2 day1). In contrast, high solar radiation (20e22 M J m2 day1) in the Central part of the country is probably related to rain shadow effects caused by the mountain range in the West. The yearly average of solar radiation in Myanmar was found to be 18.3 MJ m2 day1, when averaged over the entire area of the country. 4. Conclusion A satellite-based model was improved and employed to generate solar resource maps for the region of Myanmar. Long-term satellite data obtained from GMS5, GOES 9 and MTSAT-1R from January 1998 to December 2010 were used as the main input to the model. The model considers the processes of scattering and absorption due to clouds, aerosols, water vapour and ozone. Model validation was made against the measurements at 15 sites and the root mean square difference was 9.6%. The model was employed to calculate the surface solar radiation for the entire country, and monthly averages of daily solar radiation of Myanmar were computed. Then the results were displayed as solar resource maps. Variability of solar radiation in Myanmar is mostly influenced by the topography of the country and the tropical monsoons. Long-term annual average of solar radiation, when averaged over the entire areas of the country, in Myanmar was found to be 18.3 MJ m2 day1. Acknowledgements The authors would like to thank Department of Alternative Energy Development and Efficiency, Ministry of Energy of Thailand for inviting Silpakorn University to carry out this project. The authors are grateful to Dr. Tun Lwin, former Director General of the Department of Meteorology and Hydrology, Ministry of Transport of Myanmar, and Mr. Soe Aung, Director general of the Department of Energy Planning, Ministry of Energy of Myanmar for their support to this project. Nomenclature Daer H Hclean Hact
coefficient of solar radiation depletion by aerosols [-] monthly average of daily global solar radiation [MJ m2 day1] daily global radiation under clean atmosphere without aerosols [MJ m2 day1] daily global radiation under actual atmosphere with the presence of aerosols [MJ m2 day1]
H0 I0l kol kgl kwl [ ma mr MBD RMSD VIS w
aaer
a0aer ag a0g ao a0o aw a0w l rA r0A raer r0aer r0EA rG r0G s s gl sol s wl
139
monthly average of daily extraterrestrial solar radiation [MJ m2 day1] extraterrestrial spectral solar irradiance [W m2 ] extinction coefficient of ozone [-] extinction coefficient of gases [-] extinction coefficient of water vapour [-] total column ozone [cm] optical air mass under actual condition [-] optical air mass at standard condition [-] mean bias difference relative to the mean value [%] root mean square difference relative to the mean value [%] visibility [km] precipitable water [cm] absorption coefficient due to aerosols in broadband (0.3e3.0 mm) [-] absorption coefficient due to aerosols in satellite band [-] absorption coefficient due to gases in broadband [-] absorption coefficient due to gases in satellite band [-] absorption coefficient due to ozone in broadband [-] absorption coefficient due to ozone in satellite band [-] absorption coefficient due to water vapour in broadband [-] absorption coefficient due to water vapour in satellite band [-] wavelength [mm] cloud-atmospheric albedo in broadband [-] cloud-atmospheric albedo in satellite band [-] scattering coefficient of aerosols in broadband [-] scattering coefficient of aerosols in satellite band [-] earth-atmospheric albedo [-] surface albedo in broadband [-] surface albedo in satellite band [-] broadband atmospheric transmittance [-] spectral transmittance of gases [-] spectral transmittance of ozone [-] spectral transmittance of water vapour [-]
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