Elevated Moist Layers (EMLs) are free tropospheric moisture anomalies that typically occur in the vicinity of deep convection in the tropics. They significantly effect the spatial structure of radiative heating, which is considered a key driver for meso-scale dynamics, in particular convective aggregation. Since aggregated convective states are on average drier, enhancing radiative cooling, understanding whether aggregated states will be more common in the future is important for quantifying climate feedbacks. Hence, we want to understand the driving mechanisms for the occurrence of EMLs that range from microscale cloud processes to meso-scale dynamics. The entry point of my research is marked by the sparsity of observational EML studies that is currently limited to in-situ measurement campaign data. Significant insight into spatial and temporal EML occurrence rates could be gained based on satellite observations. In particular, hyperspectral infrared observations offer rich vertically resolved water vapor information. Recent retrieval results, however, suggest a somewhat fundamental EML blindspot in these type of satellite observations. We show that the suggested EML blindspot is linked to the simultaneous occurrence of temperature and water vapor inversions that are difficult to distinguish in the satellite spectra. We show that there is no EML blindspot since it can be circumvented by including sufficient independent temperature information. We follow this finding up with an assessment of EML representation in operational satellite retrieval and reanalysis products with reference to long-term radiosonde data. We find varying results in terms of EML representation in the investigated data products, with ERA5 showing the most promising performance. We also introduce a method for quantifying the moist layers’ meso-scale dynamical impact, showing that EML associated radiative cooling drives subsidence rates that are about double the observed mean meso-scale subsidence rate in tradewind regions.