Cloud discrimination and spectral radiance estimation from a digital sky images

Abstract:

Clouds cover more than 60% of the globe with high impacts on incoming solar irradiance on the ground as well as the radiative energy transfer in the Earth–atmosphere system. Several method for detecting clouds from sky images have been developed, and digital signals available from the JPEG image have nonlinear relationship with the corresponding spectral radiances, which may lead to cloud misclassifications. In this work, a method for cloud discrimination from sky images in RAW format taken from a commercial digital camera is developed. The method uses the clear sky index (CSI). In order to take into account the spectral response in red–green–blue (RGB) channels of the camera as well as lens characteristics, these characteristics are first inferred very accurately with a laboratory experiment. Spectral radiance is represented in a simple form with spectra of incoming solar radiation at the top of atmosphere and ozone transmittance and a polynominal with three coefficients that include the intensity index, the molecular index (MI) and the small particle index (SPI). These coefficients can be obtained from the digital RGB RAW counts by linear transformation. The MI and the SPI can be converted to the CSI, which takes different value from that at clear sky and cloudy pixels. Simultaneous observations with the lidar and the digital camera at Tohoku University show that the CSI can discriminate cloud and clear sky at every pixel with correct discrimination rate more than 90%. Furthermore, spectral distribution of sky radiance can also be estimated at every pixel, and estimated ones are consistent with those from spectrometer and those from radiative transfer simulations under various sky conditions in a wavelength range of 430–680 nm with mean biases lower than 3% and bias standard deviations smaller than 1%.