With the aim of contributing to the improvement of subgrid-scale gravity wave (GW) parameterizations in numerical weather prediction and climate models, the comparative relevance in GW drag of direct GW–mean flow interactions and turbulent wave breakdown are investigated. Of equal interest is how well Wentzel–Kramer–Brillouin (WKB) theory can capture direct wave–mean flow interactions that are excluded by applying the steady-state approximation. WKB is implemented in a very efficient Lagrangian ray-tracing approach that considers wave-action density in phase space, thereby avoiding numerical instabilities due to caustics. It is supplemented by a simple wave-breaking scheme based on a static-instability saturation criterion. Idealized test cases of horizontally homogeneous GW packets are considered where wave-resolving large-eddy simulations (LESs) provide the reference. In all of these cases, the WKB simulations including direct GW–mean flow interactions already reproduce the LES data to a good accuracy without a wave-breaking scheme. The latter scheme provides a next-order correction that is useful for fully capturing the total energy balance between wave and mean flow. Moreover, a steady-state WKB implementation as used in present GW parameterizations where turbulence provides by the noninteraction paradigm, the only possibility to affect the mean flow, is much less able to yield reliable results. The GW energy is damped too strongly and induces an oversimplified mean flow. This argues for WKB approaches to GW parameterization that take wave transience into account.

Near-surface temperature and humidity observations over Germany, coming on the one hand from the citizen weather station's network Netatmo and on the other hand from synoptic weather stations, were assimilated into the limited are mode of the Icosahedral Nonhydrostatic Model with 2-km resolution (ICON-D2). For that we use the Kilometre-Scale Ensemble Data Assimilation (KENDA) system and a bias-correction approach that improves the assimilation of the observations by taking into account the diurnal cycle of temperature and humidity variables. Our results show that the assimilation of bias-corrected observations from Netatmo stations reduces the forecast error considerably; meanwhile, the assimilation of Netatmo observations without bias correction leads to a strong warm bias with a negative impact on forecast performance.In contrast, for the assimilation of synoptic observations the usage of our bias-correction approach does not lead to any significant decrease in the forecast error, yet reduces the bias for the diurnal cycle of synoptic stations. Overall, it can be concluded that the forecast quality can gain from assimilating Netatmo data, provided an effective bias-correction approach is applied.

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. K E Y W O R D S data assimilation, land-atmosphere coupling, land surface temperature, LETKF 1 INTRODUCTION The coupling between soil and atmosphere plays an important role for atmospheric boundary layer (ABL) development (