C​loud ​D​roplet ​N​umber ​C​oncentration – satellite retrievals Advanced by Atmospheric models for Assessing ​A​erosol-​C​loud ​I​nteractions

气溶胶-云相互作用意味着一种有效的辐射强迫，这在理解和解释观测到的气候变化时是一个关键的不确定性。需要全球数据来更好地量化相关过程，但从可操作的产品中无法获得一个关键数量--云滴数量浓度(CDNC，ND)。在前期工作的基础上，CDNC4aci将致力于在近距离观测-模式相互作用中可靠地从卫星上反演ND：新提供的云分辨模拟将为反演的发展和改进提供信息，而这些数据将反过来用于增进对模型和统计分析中气溶胶-云相互作用的理解和量化。具体地说，该项目将包括多角度和偏振观测以获得更好的ND数据，它将使用以云制度为条件的模型信息云垂直分层来修订反演方法，彻底量化和纠正反演误差和偏差，并根据数据评估气溶胶-云相互作用过程。该项目将使用模式敏感性分析来评估反演数据中的云过程信息，它将通过正向模拟测量的偏振辐射和反演产品来使模式和数据具有可比性，并利用模式数据评估中的数据和过程理解来评估全球模式中的气溶胶-云相互作用。最终目标是在模式和数据分析之间对气溶胶-云强迫进行一致的量化。

Aerosol-cloud interactions imply an effective radiative forcing that is a key uncertainty when un­derstanding and interpreting observed climate change. Global data are needed to better quan­tify the relevant processes, but a key quantity – the cloud droplet number concentration (CDNC, Nd) - is not available from operational products. Building on preliminary work, CDNC4aci will work towards reliable retrievals of Nd from satellites in close observations – model interaction: newly-available cloud-resolving simulations will inform the retrieval development and refinement, and the data, in turn, will be used to improve understanding and quantification of aerosol-cloud inter­actions in the model and from statistical analysis. Specifically, the project will include multi-angle and polarimetric observations for better Nd data, it will revise retrieval approaches using model-informed cloud vertical stratification conditioned on cloud regime and thoroughly quantify and correct retrieval errors and biases and assess aerosol-cloud interaction processes from data. The project will assess the cloud-process information in the retrieved data using model sensitiv­ity analyses, it will make model and data comparable by forward-simulating measured polarized radiances and retrieval products, and assess aerosol-cloud interactions in a global model evalu­ated using the data and the process understanding in model-data assessment. The final goal is a consistent quantification of the aerosol-cloud forcing between model and data analysis.