Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems (HI‐SCALE) field campaign are simulated by using an observation‐constrained cloud‐system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four‐dimensional ensemble‐variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow‐to‐deep transitions of convective clouds.

中文翻译：

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基于观测约束云系统解析模型的大陆浅层积云种群模拟

使用观测受限的云系统解析模型模拟了2016年8月30日在浅云，气溶胶和陆地生态系统的整体相互作用（HI-SCALE）野战期间观测到的大陆浅积云（ShCu）。在这一天，ShCu在俄克拉荷马州和堪萨斯州南部形成，其中一些云层过渡到更深的地方，并在下午导致对流。我们在社区网格点统计插值（GSI）系统中应用了四维整体变分（4DEnVar）混合技术，以吸收操作数据集和独特的边界层测量值，包括拉曼激光雷达，雷达风廓线仪，无线电探空仪和地面站收集的数据美国能源部 s（DOE）大气辐射测量（ARM）大南部平原（SGP）大气观测站纳入天气研究和预报（WRF）模型，以确定改善的环境条件如何影响ShCu人口的预报以及向更深对流的过渡。来自飞机，卫星以及ARM的远程传感器的独立观测值用于评估模型在不同方面的性能。进行了几个模型实验，以识别数据同化（DA）对云演化预测的影响。分析表明，从云的开始时间和云的基本高度来看，DA后ShCu种群得到了更精确的复制，这可以归因于环境气象条件和对流边界层的更好表示。