High-resolution numerical studies on the effect of turbulence on the structure of nocturnal radiation fogs
Final Report Abstract
Fog, perhaps one of the most underestimated meteorological phenomena, is characterized by the presence of liquid water close to the surface, that leads to a reduction in visibility of less than 1 km. Despite the absence of high wind speeds or heavy precipitation, this reduction in visibility poses a significant threat to humans, especially in transportation. However, common numerical weather prediction models still have enormous difficulties in properly predicting fog. This can be attributed in particular to the multitude of processes, such as turbulent mixing, cloud microphysics, radiation, and land-surface feedback, which interact with each other on different scales. The research in this project ties in precisely with the point of representing, understanding, and quantifying the significant processes during the life cycle of fog. For this purpose we used high-resolution turbulence-resolving numerical modeling. In this project this was achieved by extending and applying the well-established model system PALM. Through the efforts of several tens of thousands of cores on high performance computing (HPC) systems and the most advanced modeling approaches, our research has succeeded in representing fog with unprecedented level of detail. We were able to show that stronger turbulent mixing, i.e. the strength of eddies in the lower atmosphere (the so-called boundary layer), leads to thicker fog. We were also able to quantify the role of land surface properties: our research shows that soil temperature and soil moisture play key roles in the formation of fog. Furthermore, the number of cloud droplets was found to be a crucial parameter determining the strength and accordingly the time of fog dissipation. However, as in common numerical weather forecast models, this parameter was fixed in PALM. Consequently, we improved the representation of cloud microphysics for the application of fog in numerical models. After major model development we could quantify the error made by commonly used parameterizations and give suggestions what schemes performed best. Notwithstanding, those cloud microphysics schemes are not able to overcome some inherent limitations, as assuming a certain size distribution of cloud droplets. We thus applied the most advanced method in cloud modeling (so called particle-based approach) for the first time for simulating fog (which could not be done before due to excessive HPC demand). Therewith, we could show that cloud droplet size distributions develop in time, and that there is a large amount of swollen, but not activated (i.e. not able to grow to sizes of a cloud droplet), aerosols. This could be a reason for the often reported overestimation of the cloud droplet number in common schemes simulating fog. The last part of our study shows the influence of nocturnal fog on the evolution of the daytime boundary layer, which revealed that the nocturnal radiation fog has the potential to modify the daytime boundary layer for several hours. We could estimate how large the errors in the prediction are, if the fog at night is not properly represented. In summary, the scientific results from this project represent an important contribution to an improved understanding of fog processes.
Publications
- 2017: Key parameters for the life cycle of nocturnal radiation fog: a comprehensive large-eddy simulation study. Q.J.R. Meteorol. Soc., 143, 2463-2480
Maronga, B. and F.C. Bosveld
(See online at https://doi.org/10.1002/qj.3100) - 2019: Large-eddy simulation of radiation fog with comprehensive two-moment bulk microphysics: Impact of different aerosol activation and condensation parameterizations. Atmos. Chem. Phys., 19, 7165-7181
Schwenkel, J. and B. Maronga
(See online at https://doi.org/10.5194/acp-19-7165-2019) - 2020: Towards a better representation of fog microphysics in large-eddy simulations based on an embedded Lagrangian cloud model. Atmosphere, 11 (5), 466
Schwenkel, J. and B. Maronga
(See online at https://doi.org/10.3390/atmos11050466) - 2021: Demistify: an LES and SCM intercomparison of radiation fog. Atmos. Chem. Phys. Discuss.
Boutle, I., W. Angevine, J.-W. Bao, T. Bergot, R. Bhattacharya, A. Bott, L. Duconge, R. Forbes, T. Goecke, E. Grell, A. Hill, A. Igel, I. Kudzotsa, C. Lac, B. Maronga, S. Romakkaniemi, J. Schmidli, J. Schwenkel, G.-J. Steeneveld, and B. Vie
(See online at https://doi.org/10.5194/acp-2021-832) - 2021: Modeling of land–surface interactions in the PALM model system 6.0: land surface model description, first evaluation, and sensitivity to model parameters. Geosci. Model Dev., 14 (8), 5307–5329
Gehrke, K. F., Sühring, M., and B. Maronga
(See online at https://doi.org/10.5194/gmd-14-5307-2021)
