A novel, particle based, probabilistic approach for the simulation of cloud microphysics is proposed, which is named the Super-Droplet Method (SDM). This method enables accurate simulation of cloud microphysics with less demanding cost in computation. SDM is applied to a warm-cloud system, which incorporates sedimentation, condensation/evaporation, and stochastic coalescence. The methodology to couple super-droplets and a non-hydrostatic model is also developed. It is confirmed that the result of our Monte Carlo scheme for the stochastic coalescence of super-droplets agrees fairly well with the solutions of the stochastic coalescence equation. The behavior of the model is evaluated using a simple test problem, that of a shallow maritime cumulus formation initiated by a warm bubble. Possible extensions of SDM are briefly discussed. A theoretical analysis suggests that the computational cost of SDM becomes lower than the spectral (bin) method when the number of attributes - the variables that identify the state of each super-droplet - becomes larger than some critical value, which we estimate to be in the range $2\sim4$.
Boundary-layer scheme selection outweighs microphysics in WRF simulations of the August 2018 Kerala extreme rainfall event
Indian Institute of Technology Bhubaneswar（印度布巴内斯瓦尔理工学院）；Indian Institute of Technology Bombay（印度孟买理工学院）；Cochin University of Science and Technology（科钦科技大学）
On the possible role of condensation-related hydrostatic pressure adjustments in intensification and weakening of tropical cyclones
Theoretical Physics Division, Petersburg Nuclear Physics Institute（彼得堡核物理研究所理论物理部）；Institute for Advanced Study, Technical University of Munich（慕尼黑工业大学高等研究院）
From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Deutsches Zentrum für Luft- und Raumfahrt e.V.（德国航空航天中心）；University of Oxford（牛津大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；University of Bremen（不来梅大学）
PRecover 1.0: Process Rate Recovery with Machine Learning
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology（卡尔顿理工学院气象与气候研究所）；Faculty of Geosciences and Environment, University of Lausanne（洛桑大学地球科学与环境学院）；Expertise Center for Climate Extremes, University of Lausanne（洛桑大学气候极端研究中心）
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations
