Determination and Evaluation of Surface Solar Irradiance With the MAGIC-Heliosat Method Adapted to MTSAT-2/Imager and Himawari-8/AHI Sensors

Surface solar irradiance (SSI) is a crucial component of the radiation budget at the surface, which governs water and energy exchanges with the atmosphere. Good estimates of SSI at regional-to-global scales are needed for modeling land surface processes, climate and weather predictions, or management of solar power plants. This article presents the adaptation of the Mesoscale Atmospheric Global Irradiance Code (MAGIC)-Heliosat method used by the Climate Monitoring Satellite Application Facility (CM-SAF) for Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) to the Multifunction Transport Satellite 2 (MTSAT-2)/Imager and Himawari-8/Advanced Himawari Imager (AHI) sensors managed by the Japanese Meteorological Agency. The method allows providing estimates of global horizontal irradiance (GHI) and direct normal irradiance (DNI) over the Asian Pacific coast and Oceania. These estimates were evaluated by comparison to ground data measured at six baseline surface radiation network (BSRN) stations during years 2014 and 2016. The results showed that GHI can be determined with an accuracy of -5 Wmiddotm(-2), a precision of 160 Wmiddotm(-2), and a relative absolute error of 30% in an hourly basis. They improved to an accuracy of -5 Wmiddotm(-2) (-5 Wmiddotm(-2)), a precision of 70 Wmiddotm(-2 )(40 Wmiddotm(-2)), and a relative error of 10% (7%) in daily (monthly) estimates. The results for DNI showed an accuracy of -45 Wmiddotm(-2) and a precision of 330 Wmiddotm(-2), which represent a relative absolute error of 38%. These results improved for longer time steps, with an accuracy of +15 Wmiddotm(-2) (+30 Wmiddotm(-2)), a precision of 150 Wmiddotm(-2 )(130 Wmiddotm(-2)), and a relative error of 35% (20%) in daily (monthly) estimations.

Automated summary

This study presents the adaptation of the MAGIC-Heliosat method to MTSAT-2/Imager and Himawari-8/AHI sensors, and evaluates the accuracy of GHI and DNI estimates through comparison with ground data. The results show that the accuracy of GHI and DNI estimates improved with longer time steps.