Clouds remain one of the largest sources of uncertainty in Earth’s radiation budget, largely due to persistent inconsistencies across observing systems and retrieval methods. This session targets that bottleneck by focusing on the integration of ground-based, airborne, and satellite observations with physically consistent radiative transfer frameworks to constrain cloud optical and microphysical properties across scales. We invite contributions on advanced sensing approaches (radar, lidar, hyperspectral and sky imaging), retrieval algorithm development, and cross-platform validation, with particular emphasis on multi-sensor fusion and spectroscopic constraints. Submissions on cloud studies that deal with improving physical interpretability or generalization across environments using machine learning approaches are encouraged. We also welcome studies addressing long-term, multi-site observations and coordinated international measurement strategies that enable reproducible, interoperable cloud datasets. The long-term goal is to move toward unified, observation-driven constraints on cloud–radiation interactions relevant for next-generation climate models.

• GC - Global Environmental Change
• H - Hydrology
• IN - Informatics
• OS - Ocean Sciences