Cloud properties such as cloud optical thickness (COT) and cloud effective radius (CER) are essential for weather forecasting, climate monitoring, and Earth’s energy budget estimation. Traditional physics-based retrievals using independent pixel approximation (IPA) often incur biases due to three-dimensional radiative effects. While existing deep learning approaches reduce these biases, they demand large annotated datasets and high computational cost. This study frames cloud property retrieval as an information-limited learning problem (limited spectral information and limited training samples) and incorporates CloudUNet with Attention Module (CAM), a compact deep learning model with channel attention for joint estimation of COT, CER, and cloud mask from bi-spectral radiance observations. Using synthetic datasets from large-eddy simulation (LES) cloud fields, CAM outperforms state-of-the-art models in both direct radiance-based retrieval and IPA correction, achieving 38% better performance in terms of mean absolute errors (MAE) and higher correlation with true properties. Ablation studies demonstrate that CAM-based IPA correction achieves 73% and 80% MAE reduction relative to the IPA baseline when using no radiance input and single-band radiance, respectively. Including cloud mask information as input improves COT retrieval across deep learning models (except CAM) but degrades CER retrieval for all models except CAM, which shows a slight 3% MAE improvement. These findings highlight the advantage of joint retrievals of multiple cloud properties and IPA correction models under limited labeled data constraints.
Tushar et al. (Sun,) studied this question.