Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, USA.
Proc Natl Acad Sci U S A. 2012 Jul 24;109(30):11939-43. doi: 10.1073/pnas.1205877109. Epub 2012 Jul 9.
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.
目前限制气溶胶的能力对大气模拟产生了不利影响。通常，模型中的气溶胶负荷是通过卫星气溶胶光学特性来限制的，但在云层条件下无法获得这些特性。在这里，我们迈出了克服长期以来的限制的第一步，即在云层条件下，气溶胶无法通过卫星遥感进行限制。我们引入了一种独特的数据同化方法，该方法利用云滴数（N(d)）反演来改进预测的云下气溶胶质量和数浓度。该同化方法使用伴随气溶胶激活参数化，提高了与独立 N(d)观测和浅层积云下风场的气溶胶测量数据的一致性。单次同化对气溶胶和云的预报影响可超过 24 小时。与以前的方法不同，该技术可以直接改进对近地表细颗粒气溶胶的预测，这些气溶胶对人类健康影响和低云辐射强迫负责。更好地限制气溶胶分布将有助于改善健康影响研究、大气排放估计以及空气质量、天气和气候预测。
