Assimilating novel ground-based remote sensing observations into a numerical weather prediction model for improving model predictions and advancing knowledge of atmospheric boundary layer processes

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Although the atmospheric boundary layer plays a crucial role for the diurnal cycle of clouds and precipitation, screen level predictions and severe weather events, its initial state is under-constrained in numerical weather prediction (NWP) models. Especially for nowadays short-range convective-scale NWP models a realistic initialization with respect to the vertical stratification and stability of the boundary layer is expected to prove highly valuable. Ground-based remote sensing devices deliver high-frequent thermodynamic and momentum profile observations of the boundary layer. However no strategies exist yet, how to exploit those data optimally for numerical weather prediction, as the development of affordable high-quality devices by commercial manufacturers has taken place only in the recent years.Moreover many processes in the boundary layer are still not completely understood and, this holds in particular for the stable boundary layer, for subgrid-scale orographic effects, gravity waves, thermal circulations and subgrid-scale surface heterogeneity effects. Often, the simulation of subgrid-scale boundary layer processes in atmospheric models shows deficiencies.The objective of the proposed project is twofold:In a first phase the usefulness of state-of-the-art ground-based remote sensing boundary layer observations (microwave radiometer and Doppler lidar) to improve the initial state and subsequent forecast of the boundary layer in atmospheric models will be exploited, making use of recent developments in data assimilation (DA) and ground-based remote sensing, including the development of an optimal assimilation strategy. The chance to capture small-scale structures typical for the boundary layer in the DA system in such a way is now possible due to developments in the DA community to employ Ensemble Kalman Filters for convective-scale data assimilation, providing high-resolution flow-dependent covariances and allowing high-frequent update cycles.In a second phase we propose to exploit the enormous potential of this expanded NWP framework including data assimilation system and continuously observed quality-checked observations as a novel approach to advance basic boundary layer research. Both, the information obtained from the DA system to correct the model first guess towards the observations but also the analysis itself as a self-consistent three-dimensional state at the kilometer-scale close to the true state, will be exploited. The O-B (observation minus background) statistics point to systematic differences between model first guess and observations, the increments added to the first guess are an indicator of errors in the system leading to deviations from the true state. To obtain information on single processes, the “initial tendency approach” as in the study by Klocke and Rodwell (2013) will be applied.