Abstract
The design and operation of any solar energy system requires a good knowledge of the solar radiation data in a location. This data finds application in agriculture, climatology, meteorology, etc. Since the solar radiation reaching the earth’s surface varies with climatic conditions of a place, a study of solar radiation under local climatic condition is essential. Global solar radiation is of economic importance as renewable energy alternatives. In this research 14 Iraqi climatic stations radiation data were used for the years 2013 to 2015. Data have been designed and calculated by using Excel. ArcGIS 10.2 is used for spatial interpolation and mapping activities. Surface radiation map have been generated by using ordinary kriging interpolation technique. Different models are tested, namely Spherical, Gaussian and Circular model. Creation of digital grid maps makes it possible to obtain climatic information at any point, whether there is a weather station or not. Results show that the spherical model outperforms Gaussian and circular models.
Highlights
Solar radiation data on the earth’s surface is required for solar engineers, agriculturists and hydrologists in many applications
Measured values of solar radiation can be in the form of global solar radiation, diffused solar radiation or beam solar radiation [5]
Spatial interpolation is an essential tool in processing the data of natural and social science
Summary
Solar radiation data on the earth’s surface is required for solar engineers, agriculturists and hydrologists in many applications. Since the solar radiation reaching the earth’s surface varies with climatic conditions of a place, a study of solar radiation under local climatic condition is essential [3, 12]. The average daily values of these three parameters are sought after for various applications. Spatial interpolation is an essential tool in processing the data of natural and social science. It has been widely used especially in the discipline of hydrological, meteorological climate, ecology, environment, geology etc. The essence of spatial interpolation is to estimate the values of unobserved points based on known sample data. The following errors, Prediction Errors include: Mean, Root Mean Square, Average Standard Error, Mean Standardized, Root Mean Square Standardized were calculated
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