Atmospheric particles (aerosols) modify the climate through absorption and scattering of sunlight, and interaction with clouds. They are also harmful to human health if present close to the surface, and affect the remote sensing of greenhouse gases by changing the photon paths between the sun and the observer. Satellite observations have enabled frequent, global monitoring of basic aerosol properties such as the column-integrated aerosol loading. These provide critical constraints for climate and air quality forecast models, especially in terms of quantifying aerosol sources and sinks. However, aerosol remote sensing is a highly multidimensional challenge with opportunities for new algorithm approaches (incl. Machine Learning, multi-sensor retrievals). The high spatiotemporal variability of aerosol properties and interactions with solar radiation make them a major source of uncertainty in climate modeling and prediction. The uncertainty related to absorption by aerosols is particularly large, owing to the difficulty of inferring absorption from observations and of assessing its impact on changes in atmospheric heating and stability, influencing cloud formation and lifetime. The uncertainty in cloud adjustments due to aerosols is similarly high. Overall, there is a lack of (1) sustained global data for aerosols in the planetary boundary layer with the most direct impacts on human health; (2) vertically resolved aerosol data with the spatial coverage necessary to ascertain trends relevant for radiative forcing estimates; and (3) consistency between data products for extreme events such as wildfires. We solicit contributions that push the frontiers of aerosol remote sensing. These include, but are not limited to aerosol vertical profiling, absorbing aerosols, and extreme events.