Mobile Cloud Observatory (MCO)

E. Bodenschatz, G. (Mohsen) Bagheri


Weather forecasts have substantially improved over the years due to the rise of faster computing resources and improved numerical models that are continuously fed by inputs from weather satellites and ground stations all around the globe. However, it is our everyday experience that forecasts can fail to predict the weather a few days (and sometimes even a few hours) in advance. Why is weather forecasting so challenging? It turns out that much of the challenge in weather models stems from difficulties simulating moist convection and cloud formation in the atmosphere [1]. Similarly, the same issues make it harder to predict climate. The cloud challenge, therefore, must be solved if we want to better understand the weather and climate.

Why are clouds challenging to understand? The answer to this question can be summarized in two main cloud features: the large separation of scales and the highly turbulent nature of clouds [2]. Clouds are suspensions of water drops and ice crystals in a thermodynamically- and physically-complex medium over a wide range of scales. The relevant scales start at sub-micrometer, where microphysical processes such as condensation on cloud condensation nuclei take place, to hundreds of kilometers where clouds become part of the weather and climate. To make matters worse, turbulence strongly affects cloud processes at all scales.

As a fluid-physics group interested in turbulent flows, we want to better understand the interplay between clouds and turbulence, in particular at meter and sub-meter scales. These are the scales at which long-standing riddles of cloud physics such as the size gap problem [3] and mechanisms of entrainment and mixing in clouds exist. The main tool available at the MCO for tackling this is the MPIDS CloudKites, which are tethered kite-balloon hybrids equipped with a host of state-of-the-art instruments. The MPIDS CloudKites will be also accompanied with the Mobile Cloud Observatory (MCO) to facilitate field operations and also to provide complementary surface measurements.

 

Active research projects in our group are:
  · The CloudKite
  · The Mobile Cloud Laboratory (MCL)
  · Multi-pulse Particle Image Velocimetry (PIV) combined with Interferometric Particle Imaging (IPI)
  · Investigation of bottleneck phenomenon occurring in turbulence flows using Laser Doppler Velocimetry (LDV)

 

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

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