Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical depth (AOD), 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 new data assimilation method that uses cloud droplet number (Nd) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent Nd 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 hours. 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. It can also be coupled with simultaneous AOD assimilation enabling the observing system to “see aerosols” even under cloudy conditions. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates and air quality, weather and climate predictions. The image below shows an example of the Nd satellite product assimilated, over the persistent Southeast Pacific stratocumulus deck off the coasts of Chile and Peru.