- Email: [email protected]
Solar radiation climate in Malawi
Solar Energy 80 (2006) 1055–1057 www.elsevier.com/locate/solener
Short Note
Solar radiation climate in Malawi A. Madhlopa
*
Department of Physics and Biochemical Sciences, Malawi Polytechnic, P/Bag 303, Blantyre 3, Malawi Received 5 March 2003; received in revised form 9 May 2005; accepted 30 August 2005 Available online 30 September 2005 Communicated by: Associate Editor David Renne
Abstract Recently, Diabate´ et al. [Diabate´, L., Blanc, Ph., Wald, L., 2004. Solar climate in Africa. Solar Energy 76, 733–744] created a map of solar radiation climate in Africa using clearness index for 62 sites. They established a solar climate class II located in Malawi and Madagascar. However, their analysis did not include any irradiation data from a site in Malawi. In this study, the solar radiation climate of Malawi has been studied using long-term global radiation data from a local site. The mean monthly (Ktm) and seasonal (Kts) daily clearness indices for the site were computed. It is observed that Ktm has two maxima in a year (Ktm = 0.58 in May and Ktm = 0.64 in September), in close conformity with findings of Diabate´ et al. (2004). Other results are presented and discussed. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Global radiation; Clearness index; Tropical country; Long-term data
1. Introduction The intensity of solar radiation is an important parameter in solar system design, testing and operation. However, this parameter varies with space and time. Consequently, many authors including Kudish et al. (1983), Al-Aruri (1990), Kudish and Ianetz (1996) and Ianetz et al. (2000) have studied solar climates of specific locations or regions for the development of solar technologies and other applications. Some work has also been done on solar radiation in Malawi, a tropical country located between *
Fax: +265 1 670578. E-mail address: [email protected]
latitudes 9°22 0 and 17°3 0 S and longitudes 33°40 0 and 35°55 0 E in Africa. Som (1979) showed that there is great potential for the utilization of solar systems in the country. Zingano (1986) studied the intensity of global radiation for twelve sites, mainly based on the sunshine duration. He found that there are: (a) a general gradient of solar radiation over the country in the north-to-south direction, and (b) local gradients of the mean monthly radiation due to altitude. Zingano (2001) observed that lowlands have the highest values of global solar radiation while uplands have the lowest in Malawi. Madhlopa (2001) evaluated piecewise polynomial models for estimating diffuse radiation in Malawi. Using data from one site, a new piecewise polynomial correlation was developed for estimating the diffuse fraction
0038-092X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.solener.2005.08.007
A. Madhlopa / Solar Energy 80 (2006) 1055–1057
from the daily clearness index. It was found that the new correlation performed better than the other similar correlations. Later, Madhlopa (2003) analyzed the distribution of hourly variability index of sky clearness. It was found that the beta probability model described the data well during most of the months. More recently, Diabate´ et al. (2004) created a map of solar radiation climate in Africa using clearness index for 62 sites, which did not include any site in Malawi. These authors used irradiation data from Sunsat 36 (Mozambique) to establish the solar climate class II located in Malawi and Madagascar. The data for this site was captured over a very short period (1985–1986), and it was extracted from a map. Consequently, their findings about Malawi need to be verified by using long-term data from a site within the country. The objective of this study is to examine solar radiation climate in Malawi in order to contribute to the international efforts in establishing solar radiation climates in many parts of the world. 2. Data collection and processing The Department of Meteorological Services in Malawi is responsible for measurement and management of a data base of solar radiation. However, long-term continuous data captured by pyranometers is scarce. So, mean monthly daily global solar radiation (Hm) is commonly computed from the mean monthly daily duration of sunshine (S) using ˚ ngstro¨m, 1924) the equation (A H m ¼ ða þ b S=S o ÞH o ;
ð1Þ
where Ho is the mean monthly daily extraterrestrial radiation over the site, So is the mean monthly daily possible sunshine duration, a and b are constants. The ratio Hm/Ho yields the mean monthly daily clearness index (Ktm). In this study, the mean monthly daily global solar radiation (Hm) data captured at Makoka weather station (15°32 0 S, 35°11 0 E, 1029 m above mean sea level) was used. This data was calculated from measurements of sunshine duration over the period 1961–1990, with a = 0.25 and b = 0.50. These values of a and b have been established by the Department of Meteorological Services as suitable for this site. There are two main seasons in Malawi: dry and rainy seasons. So, based on these major climatic conditions and the observed values of Ktm, four seasonal classes were identified:
(a) Dry season (i) May through July (D1) (ii) August through October (D2) (b) Rainy season (i) January through April (R1) (ii) November through December (R2) In order of increasing time in a year, these seasonal classes are as follows: R1 < D1 < D2 < R2. The mean seasonal daily clearness index (Kts) was calculated for each class. 3. Results and discussion 3.1. Monthly clearness index Fig. 1 shows the variation of the mean monthly daily clearness index (Ktm) for Makoka weather station. It is observed that Ktm is relatively low from January through February, and then it increases to a maximum (Ktm = 0.58) in May. There is a slight drop in the level of Ktm in June/July, with a distinct maximum in September. The level of Ktm decreases from 0.64 in September to 0.46 in December. The variation in Ktm is attributed to the level of humidity and position of the sun relative to the site. It is rainy season from November through April, with generally high levels of moisture in the air from December through February (which reduces atmospheric transparency). The air is dry in May, which results in the observed minor peak. In addition, the air mass is highest with frequent cloudy days in June, when the sun is farthest from the site. This leads to a significant attenuation of the solar radiation before it reaches the ground. The atmospheric air is very dry in September, with a relatively low air 0.65
0.60
Ktm
1056
0.55
0.50
0.45 1
2
3
4
5
6
7
8
9
10
11
12
Month
Fig. 1. Variation of the mean monthly daily clearness index (Ktm) at Makoka weather station in Malawi.
A. Madhlopa / Solar Energy 80 (2006) 1055–1057
(Kts) daily clearness indices. It was found that Ktm has maxima in May (0.58) and September (0.64), in very close conformity with findings of Diabate´ et al. (2004). Generally, the agreement between the solar climate reported by these authors and the present one is most satisfactory during the dry season. It appears that their findings about Malawi are relatively accurate.
0.65
0.60
Kts
1057
0.55
0.50
Acknowledgement
0.45 R1
D1
D2
R2
Seasonal class
Fig. 2. Variation of the mean seasonal daily clearness index (Kts) during the rainy R1 (January–April), dry-cold D1 (May–July), dry-hot D2 (August–October) and rainy-hot R2 (November– December) for Makoka weather station in Malawi.
The author is grateful to the Department of Meteorological Services in Malawi for providing the irradiation data. References
mass, which accounts for the observed major peak during this month. Thereafter, the level of moisture in the air starts increasing from October through December, thereby reducing Ktm. Diabate´ et al. (2004) also found a minor peak in the values of Ktm in May and major peak in September for the class II solar climate located in Malawi and Madagascar, which is in close conformity with the present results. It should also be mentioned that the agreement between the corresponding values of Ktm is most satisfactory during the dry season. 3.2. Seasonal daily clearness index The variation of mean seasonal daily clearness index (Kts) is shown in Fig. 2. It is seen that Kts is maximum (0.62) during the dry hot season D2, intermediate (0.57) during the dry cold season D1 and lowest (Kts 0.50) during the rainy season (R1 and R2). It is observed that the major seasons (dry and rainy) are reflected in the solar climate of Malawi. The clearness of the sky is generally higher during the dry season than the rainy season. 4. Conclusion The solar radiation climate of Malawi has been studied using long-term global radiation data from Makoka weather station. This data was used to compute the mean monthly (Ktm) and seasonal
Al-Aruri, S.D., 1990. The empirical relationship between global radiation and global ultraviolet (0.290–0.385) lm solar radiation components. Solar Energy 45, 61–64. ˚ ngstro¨m, A., 1924. Solar and extraterrestrial radiation. QuarA terly Journal of the Royal Meteorological Society 50, 121– 125. Diabate´, L., Blanc, Ph., Wald, L., 2004. Solar climate in Africa. Solar Energy 76, 733–744. Ianetz, A., Lyubansky, V., Setter, I., Evseev, E.G., Kudish, A.I., 2000. A method for characterization and inter-comparison of sites with regard to solar energy utilization by statistical analysis of their solar radiation data as performed for three sites in the Israel Negev region. Solar Energy 69, 283–293. Kudish, A.I., Ianetz, A., 1996. Analysis of daily clearness index, global and beam radiation for Beer Sheva, Israel: partition according to day type and statistical analysis. Energy Conversion and Management 37, 405–416. Kudish, A.I., Wolf, D., Machlav, Y., 1983. Solar radiation data for Beer Sheve, Israel. Solar Energy 30, 33–37. Madhlopa, A., 2001. Evaluation of piecewise polynomial models for computation of daily diffuse solar radiation in Malawi. In: Proceedings of the 2001 ISES Solar World Congress, pp. 2183–2189. Madhlopa, A., 2003. Distribution of hourly variability index of sky clearness. In: Proceedings of the International Conference on Partnership in Scientific Research Capacity Development, 4–7 August, 2003, Dar es Salaam, Tanzania. Som, A.K., 1979. Solar utilization potential in Malawi. Malawi Journal of Science 3, 103–104. Zingano, B.W., 1986. An appraisal of solar water heating in Malawi, M.Sc. Thesis. University of Malawi, Zomba. Zingano, B.W., 2001. A discussion on thermal comfort with reference to bath water temperature to deduce a mid-point of the thermal comfort temperature zone. Renewable Energy 23, 41–47.
Short Note
Solar radiation climate in Malawi A. Madhlopa
*
Department of Physics and Biochemical Sciences, Malawi Polytechnic, P/Bag 303, Blantyre 3, Malawi Received 5 March 2003; received in revised form 9 May 2005; accepted 30 August 2005 Available online 30 September 2005 Communicated by: Associate Editor David Renne
Abstract Recently, Diabate´ et al. [Diabate´, L., Blanc, Ph., Wald, L., 2004. Solar climate in Africa. Solar Energy 76, 733–744] created a map of solar radiation climate in Africa using clearness index for 62 sites. They established a solar climate class II located in Malawi and Madagascar. However, their analysis did not include any irradiation data from a site in Malawi. In this study, the solar radiation climate of Malawi has been studied using long-term global radiation data from a local site. The mean monthly (Ktm) and seasonal (Kts) daily clearness indices for the site were computed. It is observed that Ktm has two maxima in a year (Ktm = 0.58 in May and Ktm = 0.64 in September), in close conformity with findings of Diabate´ et al. (2004). Other results are presented and discussed. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Global radiation; Clearness index; Tropical country; Long-term data
1. Introduction The intensity of solar radiation is an important parameter in solar system design, testing and operation. However, this parameter varies with space and time. Consequently, many authors including Kudish et al. (1983), Al-Aruri (1990), Kudish and Ianetz (1996) and Ianetz et al. (2000) have studied solar climates of specific locations or regions for the development of solar technologies and other applications. Some work has also been done on solar radiation in Malawi, a tropical country located between *
Fax: +265 1 670578. E-mail address: [email protected]
latitudes 9°22 0 and 17°3 0 S and longitudes 33°40 0 and 35°55 0 E in Africa. Som (1979) showed that there is great potential for the utilization of solar systems in the country. Zingano (1986) studied the intensity of global radiation for twelve sites, mainly based on the sunshine duration. He found that there are: (a) a general gradient of solar radiation over the country in the north-to-south direction, and (b) local gradients of the mean monthly radiation due to altitude. Zingano (2001) observed that lowlands have the highest values of global solar radiation while uplands have the lowest in Malawi. Madhlopa (2001) evaluated piecewise polynomial models for estimating diffuse radiation in Malawi. Using data from one site, a new piecewise polynomial correlation was developed for estimating the diffuse fraction
0038-092X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.solener.2005.08.007
A. Madhlopa / Solar Energy 80 (2006) 1055–1057
from the daily clearness index. It was found that the new correlation performed better than the other similar correlations. Later, Madhlopa (2003) analyzed the distribution of hourly variability index of sky clearness. It was found that the beta probability model described the data well during most of the months. More recently, Diabate´ et al. (2004) created a map of solar radiation climate in Africa using clearness index for 62 sites, which did not include any site in Malawi. These authors used irradiation data from Sunsat 36 (Mozambique) to establish the solar climate class II located in Malawi and Madagascar. The data for this site was captured over a very short period (1985–1986), and it was extracted from a map. Consequently, their findings about Malawi need to be verified by using long-term data from a site within the country. The objective of this study is to examine solar radiation climate in Malawi in order to contribute to the international efforts in establishing solar radiation climates in many parts of the world. 2. Data collection and processing The Department of Meteorological Services in Malawi is responsible for measurement and management of a data base of solar radiation. However, long-term continuous data captured by pyranometers is scarce. So, mean monthly daily global solar radiation (Hm) is commonly computed from the mean monthly daily duration of sunshine (S) using ˚ ngstro¨m, 1924) the equation (A H m ¼ ða þ b S=S o ÞH o ;
ð1Þ
where Ho is the mean monthly daily extraterrestrial radiation over the site, So is the mean monthly daily possible sunshine duration, a and b are constants. The ratio Hm/Ho yields the mean monthly daily clearness index (Ktm). In this study, the mean monthly daily global solar radiation (Hm) data captured at Makoka weather station (15°32 0 S, 35°11 0 E, 1029 m above mean sea level) was used. This data was calculated from measurements of sunshine duration over the period 1961–1990, with a = 0.25 and b = 0.50. These values of a and b have been established by the Department of Meteorological Services as suitable for this site. There are two main seasons in Malawi: dry and rainy seasons. So, based on these major climatic conditions and the observed values of Ktm, four seasonal classes were identified:
(a) Dry season (i) May through July (D1) (ii) August through October (D2) (b) Rainy season (i) January through April (R1) (ii) November through December (R2) In order of increasing time in a year, these seasonal classes are as follows: R1 < D1 < D2 < R2. The mean seasonal daily clearness index (Kts) was calculated for each class. 3. Results and discussion 3.1. Monthly clearness index Fig. 1 shows the variation of the mean monthly daily clearness index (Ktm) for Makoka weather station. It is observed that Ktm is relatively low from January through February, and then it increases to a maximum (Ktm = 0.58) in May. There is a slight drop in the level of Ktm in June/July, with a distinct maximum in September. The level of Ktm decreases from 0.64 in September to 0.46 in December. The variation in Ktm is attributed to the level of humidity and position of the sun relative to the site. It is rainy season from November through April, with generally high levels of moisture in the air from December through February (which reduces atmospheric transparency). The air is dry in May, which results in the observed minor peak. In addition, the air mass is highest with frequent cloudy days in June, when the sun is farthest from the site. This leads to a significant attenuation of the solar radiation before it reaches the ground. The atmospheric air is very dry in September, with a relatively low air 0.65
0.60
Ktm
1056
0.55
0.50
0.45 1
2
3
4
5
6
7
8
9
10
11
12
Month
Fig. 1. Variation of the mean monthly daily clearness index (Ktm) at Makoka weather station in Malawi.
A. Madhlopa / Solar Energy 80 (2006) 1055–1057
(Kts) daily clearness indices. It was found that Ktm has maxima in May (0.58) and September (0.64), in very close conformity with findings of Diabate´ et al. (2004). Generally, the agreement between the solar climate reported by these authors and the present one is most satisfactory during the dry season. It appears that their findings about Malawi are relatively accurate.
0.65
0.60
Kts
1057
0.55
0.50
Acknowledgement
0.45 R1
D1
D2
R2
Seasonal class
Fig. 2. Variation of the mean seasonal daily clearness index (Kts) during the rainy R1 (January–April), dry-cold D1 (May–July), dry-hot D2 (August–October) and rainy-hot R2 (November– December) for Makoka weather station in Malawi.
The author is grateful to the Department of Meteorological Services in Malawi for providing the irradiation data. References
mass, which accounts for the observed major peak during this month. Thereafter, the level of moisture in the air starts increasing from October through December, thereby reducing Ktm. Diabate´ et al. (2004) also found a minor peak in the values of Ktm in May and major peak in September for the class II solar climate located in Malawi and Madagascar, which is in close conformity with the present results. It should also be mentioned that the agreement between the corresponding values of Ktm is most satisfactory during the dry season. 3.2. Seasonal daily clearness index The variation of mean seasonal daily clearness index (Kts) is shown in Fig. 2. It is seen that Kts is maximum (0.62) during the dry hot season D2, intermediate (0.57) during the dry cold season D1 and lowest (Kts 0.50) during the rainy season (R1 and R2). It is observed that the major seasons (dry and rainy) are reflected in the solar climate of Malawi. The clearness of the sky is generally higher during the dry season than the rainy season. 4. Conclusion The solar radiation climate of Malawi has been studied using long-term global radiation data from Makoka weather station. This data was used to compute the mean monthly (Ktm) and seasonal
Al-Aruri, S.D., 1990. The empirical relationship between global radiation and global ultraviolet (0.290–0.385) lm solar radiation components. Solar Energy 45, 61–64. ˚ ngstro¨m, A., 1924. Solar and extraterrestrial radiation. QuarA terly Journal of the Royal Meteorological Society 50, 121– 125. Diabate´, L., Blanc, Ph., Wald, L., 2004. Solar climate in Africa. Solar Energy 76, 733–744. Ianetz, A., Lyubansky, V., Setter, I., Evseev, E.G., Kudish, A.I., 2000. A method for characterization and inter-comparison of sites with regard to solar energy utilization by statistical analysis of their solar radiation data as performed for three sites in the Israel Negev region. Solar Energy 69, 283–293. Kudish, A.I., Ianetz, A., 1996. Analysis of daily clearness index, global and beam radiation for Beer Sheva, Israel: partition according to day type and statistical analysis. Energy Conversion and Management 37, 405–416. Kudish, A.I., Wolf, D., Machlav, Y., 1983. Solar radiation data for Beer Sheve, Israel. Solar Energy 30, 33–37. Madhlopa, A., 2001. Evaluation of piecewise polynomial models for computation of daily diffuse solar radiation in Malawi. In: Proceedings of the 2001 ISES Solar World Congress, pp. 2183–2189. Madhlopa, A., 2003. Distribution of hourly variability index of sky clearness. In: Proceedings of the International Conference on Partnership in Scientific Research Capacity Development, 4–7 August, 2003, Dar es Salaam, Tanzania. Som, A.K., 1979. Solar utilization potential in Malawi. Malawi Journal of Science 3, 103–104. Zingano, B.W., 1986. An appraisal of solar water heating in Malawi, M.Sc. Thesis. University of Malawi, Zomba. Zingano, B.W., 2001. A discussion on thermal comfort with reference to bath water temperature to deduce a mid-point of the thermal comfort temperature zone. Renewable Energy 23, 41–47.