期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 109, 期 30, 页码 11939-11943出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1205877109
关键词
air quality; indirect effect; weather prediction; stratiform cloud; microphysics
资金
- National Science Foundation (NSF) [0748012]
- Fulbright-CONICYT (Comision Nacional de Investigacion Cientifica y Tecnologica de Chile) [15093810]
- NASA [NNX08AL05G, NNX11AI52G]
- Department of Energy (DoE)
- NASA [NNX08AL05G, 99573, NNX11AI52G, 144582] Funding Source: Federal RePORTER
- Div Atmospheric & Geospace Sciences
- Directorate For Geosciences [0748012] Funding Source: National Science Foundation
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.
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