Using Distributional Regression Networks to Retrieve Cloud Properties from Solar Satellite Channels for Data Assimilation

Abstract

Satellite observations in the solar spectrum (including visible and near-infrared channels) offer high-resolution information on clouds and atmospheric properties valuable for data assimilation. While forward operators for a direct assimilation of solar images have become available recently and a first visible channel is already used operationally, their assimilation remains challenging due to strong non-linearities, ambiguities and high inter-channel correlations. This study addresses two central questions: what is the potential impact of assimilating multiple solar channels jointly, and can observed reflectances be transformed into physically meaningful, uncertainty-quantified variables better suited to assimilation than the raw reflectances themselves? As a proof of concept, we assess the joint information content of six solar channels from the Flexible Combined Imager (FCI) onboard Meteosat Third Generation and introduce a novel "Backward Operator" (BO) for probabilistic retrievals of cloud-related variables. The BO is implemented in a machine learning approach as a distributional regression network that is trained on synthetic images from a NWP regional model run and produces multivariate Gaussian estimates of total optical thickness, column cloud fraction, ice fraction, and effective radii of water and ice. The BO predictions are unbiased and well-calibrated, with realistic, situation-dependent and non-trivial covariance structures. The retrieved variables can be overall usefully constrained. Despite strong inter-channel correlations, combining multiple channels yields substantial performance improvements. As the BO does not require prior information, is consistent with an existing forward operator, and yields cloud variables more linearly related to the NWP model state, assimilating these variables could be a viable alternative to direct reflectance assimilation.