Analysis of CM SAF data over Senegal

By Steffen Kothe and Mariane Diop-Kane

The EUMETSAT African User Forum (UFA) took place from 12 to 16 September 2016 in Kigali, Rwanda. At the UFA 2016 the Satellite Application Facility on Climate Monitoring (CM SAF) presented their Climate Data Records and Services to the African community.

The CM SAF provides several data records of essential climate variables of the global energy and water cycle, which are suitable for multiple climatic applications.

To support and simplify the usage of CM SAF climate data, an R-based toolbox was developed. This R-toolbox can be used to prepare, analyse and visualise CM SAF NetCDF formatted data.

Mariane Diop-Kane, from the Senegal National Meteorological Agency, took the advantage of this meeting to do some hands-on exercises with the R-toolbox and the CM SAF Climate Data Records. The diversity of functions, the easy usage and the quality of the CM SAF data were the inspiration for this case study.

CM SAF Data for Senegal

Mariane Diop-Kane was interested in the possibilities of CM SAF Climate Data Records in the field of solar energy production.

Solar energy, as an important source of renewable energy, can be a large opportunity, especially for countries in the tropics and subtropics. But, as there are several influencing factors on the productivity of a solar energy plant, extensive planning is essential to reach the most effective and economic solution. Here CM SAF surface radiation data are very helpful.

The SARAH-2 climate data record includes, among others, ‘Surface Incoming Shortwave radiation’ (SIS) and ‘Direct Normalized Irradiance’ (DNI) (Figure 1).

SIS is the most important input parameter for the calculation of the solar energy potential. DNI provides information on the direct irradiance on a surface, which is perpendicular to the sun. This is important for planning and production forecast of solar thermal power plants (STP), where solar radiation is concentrated using mirrors or lenses, and the resulting heat drives generators.

In comparison, photovoltaics (PV) convert solar energy directly into electricity. PV can also use solar diffuse radiation, while STP can only convert solar direct irradiance into energy. As clouds and aerosols have a much higher impact on DNI than on SIS, areas with low cloud coverage are more important for STP.

The Senegal is a transition zone to the arid Sahel region (north of the country) with low cloud coverage (high insulation). However, the West African Monsoon leads to a strong seasonal impact of clouds in coastal areas. This explains the difference in the insulation pattern between JJA and DJF.

SARAH-2 SIS and DNI (Figure 2) are well suited to display characteristic gradients in surface insulation, and its regional and seasonal differences in space and time.

Regarding the influencing factors on the solar energy potential, Senegal is a very interesting country. On the one hand, the Senegal is situated at the transition zone to the Sahel, an arid region to the north of the country with low cloud coverage throughout the year, as illustrated in Figure 2 (a) and (b)

On the other hand, there is the strong influence of the West African Monsoon, this leads to a seasonally strong impact of clouds at the Atlantic coastal region and in the south of the country (see Figure 2 (c) and (d)).

While the annual range in SIS in the northern parts is dominated by the solar zenith angle (reflected in the latitudinal pattern structure), in the southern regions the higher insulation in summer is partly compensated by more clouds, due to the influence of the West African Monsoon. This results in a lower SIS standard deviation in the south (Figure 3 (a)).

The overall values of DNI standard deviation (Figure 3 (b)) are higher than for SIS, but the gradients are vice versa, which shows the stronger impact of clouds on DNI: i.e. fewer clouds in the arid region in the north, more clouds in the south.

This information helps identifying the best locations for solar energy power plants in Senegal, and to decide which kind of plants (PV or STP) will be most efficient.

All analyses and figures in this case study were done with the CM SAF R Toolbox, which is freely available via http://www.cmsaf.eu/tools