A088
Processes of (Sub) Cloud Scales: Modeling, Observations and Parameterizations
Session ID#: 249291
Session Description:
Clouds continue to challenge our understanding and are major sources of uncertainty in numerical weather prediction and climate projection. Key to addressing such outstanding challenges associated with clouds are to understand the processes that occur at space-time scales of individual clouds or smaller, including entrainment/detrainment, turbulent mixing, turbulence, cloud microphysics, radiation, aerosol activation/deactivation, and boundary layer processes. The temporal and spatial scales of these processes are essentially smaller compared to the time and grid scales in numerical models. These sub-grid cloud processes pose particular challenges to numerical simulations, observations, as well as parameterization development. Representations of these sub-grid cloud processes will likely become more important as models increase their resolutions. This session focuses on process-level modeling, observations (both laboratory and field), machine learning and parameterizations of cloud processes. Also invited are studies on unified treatment of different processes, process coupling, and integration from observations, theories to models.
Index Terms:
3311 Clouds and aerosols [ATMOSPHERIC PROCESSES]
3314 Convective processes [ATMOSPHERIC PROCESSES]
3323 Large eddy simulation [ATMOSPHERIC PROCESSES]
3379 Turbulence [ATMOSPHERIC PROCESSES]
Primary Convener: Chunsong Lu, NUIST Nanjing University of Information Science and Technology, Nanjing, China
Conveners: Yangang Liu, Brookhaven National Laboratory, Upton, United States, Lulin Xue, NSF National Center for Atmospheric Research, Boulder, United States and Alexei Korolev, Environment and Climate Change Canada, Toronto, Canada
Frequent Bimodal Droplet Size Distributions in Fogs and Clouds: Mechanisms, Dispersion Effects and Parameterization (1894103)
Dantong Liu1, Shitong Zhao1, Ping Tian2, Paul Connolly3, Yangzhou Wu4, Siyuan Li5, Xiaotong Jiang6, Kang Hu7 and Chunsong Lu8, (1)Zhejiang University, Department of Atmospheric Sciences, School of Earth Sciences, Hangzhou, China, (2)Precipitation and Atmospheric Water Resources, Beijing, China, (3)University of Manchester, Manchester, United Kingdom, (4)University of Shanghai for Science and Technology (USST), School of Environment and Architecture, Shanghai, China, (5)Henan University, State Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering, Zhengzhou, China, (6)Shandong University of Aeronautics, Shandong Key Laboratory of Eco-Environmental Science for the Yellow River Delta, Binzhou, China, (7)NUIST Nanjing University of Information Science and Technology, Nanjing, China, (8)Nanjing Univerisity of Information Science and Technology (NUIST), Nanjing, China
A framework for modeling aerosol-cloud-lightning interactions: Validation of charge structure and aerosol effects (1908691)
Weishan Wang, Fudan University, Department of atmospheric and oceanic sciences, Shanghai, China, Guoxing Chen, Fudan University, Institute of Atmospheric Sciences & Department of Atmospheric and Oceanic Sciences, Albany, United States, Yijun Zhang, Fudan University, Institute of Atmospheric Sciences, Shanghai, China, Dong Zheng, State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China and guoxing chen
Interaction of Microphysics and Turbulencein High-Resolution Simulations of a Dense Fog Event (1909962)
Meng Tian, University of Turkish Aeronautical Association, Ankara, Turkey, Li Shun, Ocean University of China, Qingdao, China, Ismail Gultepe, Ontario Tech University, Engineering and Applied Sci., and ACE, Oshawa, ON, Canada and Bingui WU, Tianjin Institute of Meteorological Sciences, Tianjin, China
Inverse Mapping of the Collision Kernel and Wall Flux Scaling in a Large-Scale Convection-Cloud Chamber Using Local Sensors and Knowledge-Informed Deep Learning (1965249)
Dr. Aaron Wang, PhD1, Peishi Jiang1, Susannah M Burrows2, Susanne Glienke1, Mikhail Ovchinnikov1 and Naser Mahfouz1, (1)Pacific Northwest National Laboratory, Richland, United States, (2)Pacific Northwest National Laboratory, Richland, WA, United States
Relationship between Turbulent Entrainment-Mixing and Droplet Clustering (1981725)
Yangang Liu, Brookhaven National Laboratory, Upton, United States, Sinan Gao, Guangzhou Institute of Tropical and Marine Meteorology, Guangzhou, China, Chunsong Lu, Nanjing Univerisity of Information Science and Technology (NUIST), Nanjing, China and Yaohui Shu, Stony Brook University, Stony Brook, NY, United States
