Solar radiation climate in Africa

doi:10.1016/j.solener.2004.01.002

Solar Energy

Volume 76, Issue 6, 2004, Pages 733-744

https://doi.org/10.1016/j.solener.2004.01.002 Get rights and content

Abstract

This work is a first attempt to propose a map of the solar radiation climate in Africa. Such a map is very useful for preliminary assessment and modeling of solar energy systems. Following the approach adopted for Europe, a data set of monthly means of the daily clearness index has been assembled for 62 sites. A cluster analysis was applied to create 20 classes that were reported on a map of Africa. These classes were geographically extended, thus creating a solar radiation climate map, comprising 20 climates. Known features and other atlases, global or local, were used in this mapping. This map and its companion tables permit to perform elementary energy calculations. Further improvements are discussed.

Introduction

Knowing the solar climate in Africa is a challenge. Only few measuring sites are available with long-term time-series of accurate measurements. For such a site, the time structure may be derived that characterizes the solar climate. The monthly mean of the daily clearness index (KT_d)_m is an appropriate variable in that respect. It is defined by the ratio of the monthly mean daily global irradiation (G_d)_m and the monthly mean daily extraterrestrial irradiation (G_0d)_m following the notations of the European solar radiation atlas (ESRA, 2000): $(KT_{d})_{m} =(G_{d})_{m} /(G_{0d})_{m}$ A typical value of (KT_d)_m for Paris, France, is 0.4 (ESRA, 2000). That means that 40% of the extraterrestrial irradiation reaches the ground.

(KT_d)_m is a dimensionless quantity. Its use helps suppress variations in monthly mean due to variations in extraterrestrial irradiation, which are also strongly dependent on latitude. This index brings out with greater clarity global radiation variations due to climate impacts, i.e., site altitude, site cloudiness and atmospheric turbidity.

The difficulty in solar climate lies in the comprehension of the spatial dimension. Due to the scarcity of the network, the boundaries of a climatic area are difficult to draw. However, zoning helps in a better understanding of the distribution of the clearness index in space. It also guides the selection of appropriate measuring stations for a given geographical location. Incidentally, it also helps with the Ångström coefficients by defining the geographical limits of the validity of a given set of coefficients.

Such a zoning effort was recently performed for Europe and has been published under the form of maps (ESRA, 2000). The present paper reports on a similar effort made for Africa, though the resources spent and the number of stations used are limitations to the analogy.

Solar radiation climatic zoning is of large importance for preliminary assessment and modeling of systems using solar radiation, whether they are natural systems, e.g., vegetation and fauna (Thompson and Perry, 1997), or energy conversion artificial system, e.g., photovoltaics. Some atlases of solar irradiation are available for Africa (Ba et al., 2001; Bahraoui-Buret et al., 1983; Raschke et al., 1991). They are either limited to a country or are made of isolines or colored maps at large scale (1° or so). Values are difficult to handle and these atlases are not offering such a zoning for the whole Africa. The maps provided by the World Meteorological Organization (WMO, 1981) are covering the whole world but exhibit very limited spatial details. That is why an effort was made to produce a map of solar radiation climate in Africa, benefiting from these atlases and other available information. As for the ESRA and Dogniaux and Lemoine (1983), the present study uses the monthly mean of the daily clearness index (KT_d)_m as a basic quantity to define the zoning.

Section snippets

Data

The WMO published a CD-ROM containing global climate normals (CLINO) for the period 1961–1990 (WMO, 1998). This CLINO database would have been a good source of data; unfortunately, it does not contain solar irradiation values for Africa. The possibility to use sunshine duration observations was examined. There are approximately 400 stations measuring the sunshine duration in the CLINO. However, there were two obstacles. Firstly, the spatial distribution is very heterogeneous. There is no

Clustering and processing procedures

As for the ESRA, the method selected for the creation of the climatic zones is a cluster analysis. It arranges the 62 stations in classes or clusters. The procedure aims at forming classes such that items of a given class are as similar as possible, while they are as different as possible from items of other classes. Because it makes no sense to perform a cluster analysis with correlated variables, a correlation matrix was calculated on the time-series of (KT_d)_m. The results showed that the

Results and discussion

The final map is displayed in Fig. 3. The 20 classes (clusters) are reported by means of Roman numbers: I, II etc. Fig. 4, Fig. 5, Fig. 6, Fig. 7 display the monthly values of (KT_d)_m for each class.

There is a marked latitudinal effect that is consistent with what is known of the climate of Africa and also of the distribution of (KT_d)_m. One may see links between the known climates and the solar radiation climates shown in this figure. According to Trewartha (1954), the following rough climatic

Conclusion

This work provides a first estimate of the solar radiation climate in Africa. Based on an approach adopted for Europe, it permits to increase the knowledge available in Africa in agreement with preliminary studies and other atlases.

The climate zone classification only gives information about similar values of the monthly mean of the daily clearness index. It is therefore unsuitable for direct energy calculations. If the user wants to make energy calculations he is recommended to use a

Acknowledgements

This work was partly supported by the Service de Coopération et d'Action Culturelle de l'Ambassade de France au Mali. We are thankful to Djénéba Tounkara and Rokia Berthé for the manual digitizing of the data used in this study. We thank one of the reviewers whose comments improve the quality of the paper.

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View full text

Solar radiation climate in Africa

Abstract

Introduction

Section snippets

Data

Clustering and processing procedures

Results and discussion

Conclusion

Acknowledgements

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