Geospatial modeling of solar radiation to explore solar energy potential in Papua New Guinea

Abstract

Climatology and meteorology are essentially driven by the incoming solar radiation and the latter’s latitudinal distribution. The total amount of incoming solar radiation, duration or day-length and the seasonal distribution of radiation have also the leading implication in researches in agricultural sciences. With the reality of climate change looming large, with its plausible paraphernalia on the humankind, it is obvious that the rising demand for clean energy sources will swing the needle of research towards ‘the optimum harnessing of solar energy’ regime. In a specified latitudinal expanse, topography is a major factor that determines the spatial distribution of insolation. Spatial variability of topographic elevation, slope, aspect and shadows influence the amount of insolation received at different point locations. At a given atmospheric impediment, the amount of solar insolation received at a particular geographical location is a function of ‘time of the day’ and the ‘season’. It determines the variability of microclimate as emanated from different parameters such as soil temperature, soil moisture, near surface air temperature, evapotranspiration, and direct-sun or sky-light available for photosynthesis (absorbed photosynthetically active radiation). The variation that occurs in incoming solar radiation over space and time for power generation using photovoltaic panels also warrants proper feasibility assessment prior to investment. In this scenario the solar radiation modeling is attempted in this paper to map and analyse the effects of the sun over a geographic area for specific time periods. Different parameters that are mandatory, like atmospheric condition, latitudinal position, elevation, slope and aspect, sun angle, and topographic shadows are taken in account for the modeling purpose. Two types of output are generated at the end of the modeling, (1) point layer and (2) raster representation while inputting monthly average of the ‘daily solar insolation’. For the first type of output, interpolation process is applied to complete the insolation map and compared with surface meteorology and solar energy (SSE 6.0) database. In this case spatial variations of shadows are not prominently indicated, while the latter is taken care of in the second type of output.