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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.
The weather of the atmospheric boundary layer significantly affects our life on Earth. Thus, a realistic modelling of the atmospheric boundary layer is crucial. Hereby, the processes of the atmospheric boundary layer depend on an accurate representation of the land-atmosphere coupling in the model. In this context the land surface temperature (LST) plays an important role. In this thesis, it is examined if the assimilation of LST can lead to improved estimates of the boundary layer and its processes.
To properly assimilate the LST retrievals, a suitable model equivalent in the weather prediction model is necessary. In the weather forecast model of the German Weather Service used here, the LST is modelled without a vegetation temperature. To compensate for this deficit, two different vegetation parameterizations were investigated and the better one, a conductivity scheme, was implemented. In order to make optimal use of the influence of the assimilation of the LST observation on the model system, it is useful to pass on the information of the observation to land and atmosphere already in the assimilation step. For that reason, a fully coupled land-atmosphere prediction model was used. Therefore, the existing control vector of the assimilation system, a local ensemble transform Kalman filter, was extended by the soil temperature and moisture. In two-day case studies in March and August 2017, different configurations of the augmented assimilation system were evaluated based on observing system simulation experiments (OSSE).
LST was assimilated hourly over two days in the weakly and strongly coupled assimilation system. In addition, every six hours a free 24-hour forecast was simulated. The experiments were validated with the simulated truth (a high-resolution model run) and compared against an experiment without assimilation. It was shown that the prediction of the boundary layer temperature, especially during the day, and the prediction of the soil temperature, during the whole day and night, could be improved.
The best impact of LST assimilation was achieved with the fully coupled system. The humidity variables of the model benefited only partially from the LST assimilation. For this reason, covariances in the model ensemble were investigated in more detail. To check their compatibility with the high-resolution model run the ensemble consistency score was introduced. It was found that the covariances between the LST and the temperatures of the high-resolution model run were better represented in the ensemble than those between the LST and the humidity variables.