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 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 air-quality, weather, and climate predictions.
Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, USA. pablo-saide@uiowa.edu
Pablo E
PE
Saide
Gregory R
GR
Carmichael
Scott N
SN
Spak
Patrick
P
Minnis
J Kirk
JK
Ayers
D000336:Aerosols / Q000737:chemistry*; D000393:Air Pollutants / Q000737:chemistry*; D001272:Atmosphere / Q000032:analysis*; D003198:Computer Simulation; D003625:Data Collection; D055904:Meteorology / Q000379:methods*; D008962:Models, Theoretical; D010140:Pacific Ocean; D018531:Spacecraft
D000336:Aerosols; D000393:Air Pollutants