An efficient method to simulate the Earth spectral reflectance over large spatiotemporal scales and application to fingerprinting of climate change

Solar fingerprinting is a new method to attribute the radiative spectral changes to individual climate parameters based on the spectral characteristics (fingerprints) of each component. The mean spectral reflectance averaged over large spatiotemporal scales is required for solar fingerprinting of climate changes. However, it is difficult to derive the mean solar spectra in large climate domain by explicit radiative transfer computations from the large volume of instantaneous satellite data. We propose a procedurally simple but effective method to compute the solar spectral reflectance in large climate domains, in which the probability distribution function (PDF) of cloud optical depth is used to account for the wide variation of cloud properties in different sensor footprints, and to avoid the repeated computations for footprints with similar conditions. This PDF-based approach provides a simple, fast, and effective way to simulate the mean spectral reflectance over large time and space scales with a large volume of high-resolution satellite data. The application of this method to the solar fingerprinting studies are presented.