In this work the model ASAM is enriched with new eddy viscosity based dynamic Smagorinsky subgrid-scale models. Therefore the model is more physically based to study atmospheric flow configurations at several atmospheric scales with main focus to urban scale flow with building-resolved resolution. The implemented dynamic procedures work well and showed good agreement to literature data. In a convective atmospheric boundary layer (ABL) the dynamic Smagorinsky coefficient reaches maximum values of 0.15 and decreases towards the surface or in stable stratified flow regimes. Vertical profiles of the Smagorinsky coefficient in a diurnal cycle of ABL depict typical behaviour of the dynamic Smagorinsky coefficient in near surface flow, free-stream, or stable stratified flow. Furthermore a modified inflow generation approach is proposed to produce fully turbulent flow fields. To modify a mean flow turbulent fluctuations are generated by superposition of sinusoidal and cosinesoidal modes. Due to the implementation of this inflow method the model ASAM has the ability to reproduce a given wind field with information from its mean wind speed and their fluctuation energy spectrum. The model configuration developed in this work is able to reproduce flow structure in a complex urban geometry. The Mock Urban Setting Test (MUST) experiment represent an urban roughness geometry by placing 120 shipping containers ordinary arranged in an array. The used building-resolved resolution is able to capture dynamic flow structures like specific wake flow, recirculation regions or eddy detachment. The dynamic fluctuating behaviour of the wind velocity components is reproduced by the model with regard to peak magnitudes and their temporal occurrence. Satisfying agreement is found between tracer gas dispersion field measurements and the model results by capturing the fluctuating concentration magnitude and in some extend the mean values.

In this work the model ASAM is enriched with new eddy viscosity based dynamic Smagorinsky subgrid-scale models. Therefore the model is more physically based to study atmospheric flow configurations at several atmospheric scales with main focus to urban scale flow with building-resolved resolution. The implemented dynamic procedures work well and showed good agreement to literature data. In a convective atmospheric boundary layer (ABL) the dynamic Smagorinsky coefficient reaches maximum values of 0.15 and decreases towards the surface or in stable stratified flow regimes. Vertical profiles of the Smagorinsky coefficient in a diurnal cycle of ABL depict typical behaviour of the dynamic Smagorinsky coefficient in near surface flow, free-stream, or stable stratified flow. Furthermore a modified inflow generation approach is proposed to produce fully turbulent flow fields. To modify a mean flow turbulent fluctuations are generated by superposition of sinusoidal and cosinesoidal modes. Due to the implementation of this inflow method the model ASAM has the ability to reproduce a given wind field with information from its mean wind speed and their fluctuation energy spectrum. The model configuration developed in this work is able to reproduce flow structure in a complex urban geometry. The Mock Urban Setting Test (MUST) experiment represent an urban roughness geometry by placing 120 shipping containers ordinary arranged in an array. The used building-resolved resolution is able to capture dynamic flow structures like specific wake flow, recirculation regions or eddy detachment. The dynamic fluctuating behaviour of the wind velocity components is reproduced by the model with regard to peak magnitudes and their temporal occurrence. Satisfying agreement is found between tracer gas dispersion field measurements and the model results by capturing the fluctuating concentration magnitude and in some extend the mean values.:1 Introduction 1 2 Fundamentals of Large-Eddy Simulation in atmospheric boundary layers 7 2.1 The atmospheric boundary layer . . . . . . . . . . . . . . . . . . . . . 7 2.2 Atmospheric turbulence . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Basic equations of LES . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Subgrid-scale modelling 15 3.1 Eddy viscosity subgrid-scale models . . . . . . . . . . . . . . . . . . . 15 3.1.1 Smagorinsky subgrid-scale model . . . . . . . . . . . . . . . . 16 3.1.2 Dynamic Smagorinsky subgrid-scale model . . . . . . . . . . . 18 3.1.3 Scale-dependent dynamic Smagorinsky subgrid-scale model . . 23 3.2 Implementation in the All Scale Atmospheric Model (ASAM) . . . . . 26 3.2.1 General description of ASAM . . . . . . . . . . . . . . . . . . 26 3.2.2 Subgrid-scale modelling in ASAM . . . . . . . . . . . . . . . . 27 3.3 Applications to meteorological situations . . . . . . . . . . . . . . . . 37 3.3.1 Stable and unstable stratified atmospheric boundary layers . . 37 3.3.2 Flow over periodic sinusoidal hill . . . . . . . . . . . . . . . . 49 4 Generation of turbulent inflow conditions 51 4.1 The necessity of turbulent inflow . . . . . . . . . . . . . . . . . . . . 51 4.2 Synthetic turbulent inflow generation method . . . . . . . . . . . . . 53 4.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4 2D simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5 Mock Urban Setting Test Experiment (MUST) 65 5.1 Micro-scale urban simulation . . . . . . . . . . . . . . . . . . . . . . . 65 5.2 Description of the experiment . . . . . . . . . . . . . . . . . . . . . . 68 5.3 Wind tunnel measurenments of MUST . . . . . . . . . . . . . . . . . 70 5.4 Numerical MUST simulation with ASAM . . . . . . . . . . . . . . . . 72 5.4.1 Choice of initial condition . . . . . . . . . . . . . . . . . . . . 75 5.4.2 Results of simulating case 2682353 . . . . . . . . . . . . . . . 81 5.4.3 Results of simulating case 2681829 . . . . . . . . . . . . . . . 98 5.4.4 Case resume . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6 Summary and outlook 111 6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 6.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 7 Bibliography 117 List of Figures 127 List of Tables 135 Acronyms 137 Nomenclature 139 Acknowledgement 143 List of Publications 145