Turbulence and Particles in Fluids

E. Bodenschatz, G. (Mohsen) Bagheri

Weather forecasting has improved significantly over the years as faster computing capabilities and improved numerical models become available, continuously fed with data from weather satellites and ground stations around the globe. Yet, our daily experience is that forecasts fail to predict the weather several days (and sometimes even several hours) in advance. Why is weather forecasting so difficult? It has been found that much of the problems with weather models are due to difficulties in simulating moist convection and cloud formation in the atmosphere. These same problems also complicate climate prediction. Thus, the problem of clouds must be solved if we are to better understand weather and climate.

Why is it difficult to sufficiently understand clouds? The answer to this question can be summarized in two main characteristics of clouds: the large scale separation and the highly turbulent nature of clouds. Clouds are suspensions of water droplets and ice crystals in a thermodynamically and physically complex medium on a variety of scales. The relevant scales begin at the submicrometer scale, where microphysical processes such as condensation occur at cloud condensation nuclei, and extend to hundreds of kilometers, where clouds become part of the weather and climate. To complicate matters, turbulence strongly influences cloud processes at all scales.

As a group of fluid physicists interested in turbulent flows, we want to better understand the interaction between clouds and turbulence, particularly at meter and submeter scales. These are the scales at which long-standing puzzles in cloud physics exist, such as the size gap problem and the mechanisms of entrainment and mixing in clouds. To study clouds, we use the Max Planck CloudKites, tethered kite-balloon hybrids equipped with a variety of imaging and non-imaging instruments.

 

Active research projects in our group are:
  · The CloudKite
  · The Mobile Cloud Laboratory (MCL)

 

References

[1] B. Stevens, S. Bony, What Are Climate Models Missing?, Science. 340 (2013) 1053–1054. 10.1126/science.1237554
[2] E. Bodenschatz, S.P. Malinowski, R. a Shaw, F. Stratmann, Can we understand clouds without turbulence?, Science. 327 (2010) 970–971. 10.1126/science.1185138
[3] A. Pumir, M. Wilkinson, Collisional Aggregation Due to Turbulence, Annu. Rev. Condens. Matter Phys. 7 (2016) 141–170. 10.1146/annurev-conmatphys-031115-011538