Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (N d ) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent N d observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and airquality, weather, and climate predictions.air quality | indirect effect | weather prediction | stratiform cloud | microphysics A mbient aerosols are important air pollutants with direct impacts on human health (1). They also play important roles in Earth's weather and climate systems through their direct (2), semi-direct (3), and indirect effects (4) on radiative transfer and clouds. Their role is dependent on their size, number, phase, and composition distributions, which vary significantly in space and time. There remain large uncertainties in predictions of aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal (5-9). These uncertainties in aerosol distributions lead to large uncertainties in weather and air-quality predictions and in estimates of health and climate-change impacts (10).Constraining ambient aerosol distributions with current Earthobserving systems is a difficult task. The most common approach is to assimilate satellite retrievals of aerosol optical depth (AOD) (11, 12), a quantity that represents total aerosol mass and composition in the atmospheric column. The requirement of cloudfree conditions for a successful retrieval and the remaining challenges of relating AOD to aerosol mass, size, and composition limit the utility of AOD retrievals (by themselves) in constraining aerosol mass and composition (13,14). These shortcomings are particularly true in regions of persistent marine stratocumulus, such as the southeast Pacific off the coast of Chile and Peru, where aerosol-cloud interactions are important to the energy balance (15), and limitations in current observing and modeling capabilities adversely impact regional and global weather and climate predictions (16). A typical MODIS AOD scene in this region ( Fig.