Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions and microphysics

This study aims to identify whether model parameters describing atmospheric conditions such as wind shear or model parameters related to cloud microphysics such as the fall velocity of hail lead to larger uncertainties in the prediction of deep convective clouds.
In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of three sets of input parameters (environmental conditions, microphysics, combined) on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, the size distribution of hail and diabatic heating rates.
This dataset contains the processed model output and the generated emulators when the convection is triggered by a warm bubble.

本研究旨在识别：描述风切变等大气状态的模型参数，抑或冰雹下落速度等云微物理相关模型参数，是否会在深对流云（deep convective clouds）的数值预测中带来更大不确定性。
在搭载双矩微物理方案（two-moment microphysics scheme）的理想云分辨模式（cloud-resolving model）设置中，我们采用统计仿真（statistical emulation）方法实现参数空间的蒙特卡洛（Monte Carlo）采样，借此可开展全面的敏感性分析。我们分析了三类输入参数（环境条件、微物理参数及二者组合）对云属性（六种水成物类别的垂直积分含量）、降水、冰雹粒径分布以及非绝热加热率的影响。
本数据集包含由暖气泡（warm bubble）触发对流时的经处理模式输出结果，以及所生成的统计仿真器（emulator）。