UCLA-LES shallow cumulus dataset with 3D cloud output data

The dataset features 6 hours of single layer shallow cumulus clouds with an ever increasing cloud deck. I.e. domain average cloud fraction ranges from 0% in the beginning to 100% towards the end of the simulation. A key feature of the dataset is its very high temporal resolution of 3D output fields (every 10 seconds). The vision is that the high temporal frequency and spatial resolution of cloud and wind variables will allow for a wide range of offline benchmarks. Applications that come to our mind are offline benchmark for 1D and 3D radiative heating rate computations, 3D radiative transfer effects on retrievals as well as cloud motion tracking algorithms. Access to dataData files are available for download at: https://opendata.physik.lmu.de/5d0k9-q2n86该数据集涵盖6小时的单层浅积云演化过程，云量覆盖范围持续拓展。具体而言，模拟初始时刻的区域平均云量为0%，至模拟结束时升至100%。本数据集的核心特征为其三维输出场具备极高的时间分辨率——每10秒输出一组数据。我们期望，该数据集所拥有的高时间频率与云、风变量的高空间分辨率，可支撑多类离线基准测试任务。经初步设想的应用场景包括：一维与三维辐射加热率计算的离线基准、检索任务中的三维辐射传输效应分析，以及云运动追踪算法的验证。数据集文件可通过以下链接下载：https://opendata.physik.lmu.de/5d0k9-q2n86

搜集汇总

背景与挑战

背景概述

该数据集是一个2022年发布的浅积云模拟数据集，包含6小时的单层浅积云演变过程，云量从初始0%逐步增加到100%。其核心特点是具有每10秒输出一次的高时间分辨率3D云和风变量数据，适用于辐射加热计算、3D辐射传输效应分析和云运动跟踪算法等离线基准测试。