Skip to content
Research Article Open access CC BY 4.0

Assessing the Potential of Using Satellite Data on Radiation in the Analysis of Earth’s Climate System to Complement the Scarce Radiation Data Measured from the Ground Stations

Collins Ochieng Onyaga, Samson W. Wanyonyi, Roger Stern

Asian Journal of Probability and Statistics · pp. 1–21 · Published 17 Nov 2018

10.9734/ajpas/2018/v2i228768

Abstract

High quality solar radiation data is required for the appropriate monitoring and analysis of the Earth’s climate system as well as efficient planning and operation of solar energy systems. However, well maintained radiation measurements are rare in many regions of the world. Therefore, satellite-derived radiation estimates are an alternative to these scarce solar radiation measurements from the weather stations. Satellite estimates of solar radiation have an advantage over solar radiation measurements from weather stations because of their high spatial and temporal resolutions. These satellite radiation estimates at approximately 5-6 Km resolution derived from geostationary Meteosat satellites are available through the EUMETSAT Satellite Application Facilities (SAFs). CM-SAF (SAF on Climate Monitoring) provides consistent dataset of hourly, daily and monthly solar radiation from 1983 to 2013. In this study, we examined the potential of using satellite estimates of solar radiation to fill in the data gaps in records from the weather stations as well as the areas where radiation data is not available. The analysis carried out showed that the satellite data had fewer missing values than the ground data, and that they are both similar in distribution. The average correlation between the two data sets was found to be 0.71 for both monthly and daily analysis. However, the month of September showed a very low correlation of 0.21. Mean percentage error, mean bias error and mean absolute deviation were found to be 2.46, 18.84, 50.32 and 3.08, 559.87, 1135.93 for daily and monthly analysis, respectively. The solar radiation distribution in Dodoma was found to follow Weibull distribution throughout the year.

Weibull Distribution CM-SAF EUMETSAT geostationary Meteosat satellites

Cited by 0

No indexed citations yet.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

0

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.