A simple radiative model designed to estimate insolation from geostationary satellite data has been applied to GOES-East calibrated visible data. Insolation results for 90 days are presented and compared with pyranometer measurements for three stations in southern Canada. The root-mean-square of the difference between satellite-estimated insolation and pyranometer measurements is within 8% of the mean measurements. The advantage of the satellite approach to obtaining insolation for regions where no measurements are available is also demonstrated and quantified. Mean monthly and seasonal insolation maps have been obtained for southeastern Canada and northeastern United States using the method described, and maps for May, October and spring (April–June) 1978 are presented. They distinctively show the influence of Lake Ontario on the mean insolation and the effects of orography in northern New York State. The “natural variability” of insolation, defined as the ratio of the standard deviation to the ... Abstract A simple radiative model designed to estimate insolation from geostationary satellite data has been applied to GOES-East calibrated visible data. Insolation results for 90 days are presented and compared with pyranometer measurements for three stations in southern Canada. The root-mean-square of the difference between satellite-estimated insolation and pyranometer measurements is within 8% of the mean measurements. The advantage of the satellite approach to obtaining insolation for regions where no measurements are available is also demonstrated and quantified. Mean monthly and seasonal insolation maps have been obtained for southeastern Canada and northeastern United States using the method described, and maps for May, October and spring (April–June) 1978 are presented. They distinctively show the influence of Lake Ontario on the mean insolation and the effects of orography in northern New York State. The “natural variability” of insolation, defined as the ratio of the standard deviation to the ...