Insolation is the primary forcing variable driving surface energy balance estimates of evapotranspiration (ET). To generate climatological assessments of ET and changes in moisture patterns over continental scales, it is critical to have insolation inputs that have been well-calibrated over the full modeling domain. In this capacity, the pyranometer dataset collected by the U.S. Climate Reference Network has proven invaluable in providing high-quality radiation measurements over a long period of record and over a range in climate and elevation zones within the U.S.

In this paper, we will discuss an automated system used to validate and calibrate daily and hourly insolation estimates from GOES satellite imagery using USCRN datasets. These data have been used to identify and correct elevation-based biases in the GOES insolation algorithm related to column precipitable water inputs, and to intercalibrate insolation estimates from the GOES-E and W satellites, to produce a seamless hourly product over the continental U.S. The GOES insolation data are used as input to the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm to estimate daily ET and other fluxes for use in water resource studies and drought monitoring. Without rigorous calibration of insolation inputs, ET estimates from ALEXI would be unreliable, and trends in moisture conditions could not be assessed.