A realistic simulation of the atmospheric boundary layer (ABL) depends on an accurate representation of the land–atmosphere coupling. Land surface temperature (LST) plays an important role in this context and the assimilation of LST can lead to improved estimates of the boundary layer and its processes. We assimilated synthetic satellite LST retrievals derived from a nature run as truth into a fully coupled, state‐of‐the‐art land–atmosphere numeric weather prediction model. As assimilation system a local ensemble transform Kalman filter was used and the control vector was augmented by the soil temperature and humidity. To evaluate the concept of the augmented control vector, two‐day case‐studies with different control vector settings were conducted for clear‐sky periods in March and August 2017. These experiments with hourly LST assimilation were validated against the nature run and overall, the RMSE of atmospheric and soil temperature of the first‐guess (and analysis) were reduced. The temperature estimate of the ABL was particularly improved during daytime as was the estimate of the soil temperature during the whole diurnal cycle. The best impact of LST assimilation on the soil and the ABL was achieved with the augmented control vector. Through the coupling between the soil and the atmosphere, the assimilation of LST can have a positive impact on the temperature forecast of the ABL even after 15 hr because of the memory of the soil. These encouraging results motivate further work towards the assimilation of real satellite LST retrievals.

中文翻译：

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在陆-气耦合模型中吸收合成地表温度

大气边界层（ABL）的真实模拟取决于陆地与大气耦合的精确表示。在这种情况下，地表温度（LST）起着重要作用，LST的同化可以改善边界层及其过程的估计。我们同化运行从一个自然的合成的卫星LST检索真相完全耦合，最新的陆地大气数值天气预报模型。作为同化系统，使用了局部集成变换卡尔曼滤波器，并且通过土壤温度和湿度增加了控制向量。为了评估增强控制向量的概念，于2017年3月和2017年8月在晴空期间进行了为期两天的案例研究，其中使用了不同控制向量设置。这些每小时LST同化的实验已针对自然运行和总体情况进行了验证。降低了一次猜测（和分析）的大气和土壤温度的均方根误差。ABL的温度估计值在白天特别好，整个昼夜周期的土壤温度估计值也得到了改善。LST同化对土壤和ABL的最佳影响是通过增强控制载体实现的。通过土壤与大气之间的耦合，由于对土壤的记忆，LST的同化甚至可以在15小时后对ABL的温度预测产生积极影响。这些令人鼓舞的结果激励着人们为同化真实的卫星LST检索做进一步的工作。