Estimation of turbulence dissipation rate within shallow cumulus using airborne Particle Image Velocimetry

Accurately estimating cloud microphysics is crucial for reducing uncertainties in weather and climate models. A particular challenge is capturing the interactions between the cloud microphysics and turbulence. The pervasive shallow cumulus clouds over tropical oceans play a critical role in the Earth’s energy budget, making their study crucial for understanding atmospheric dynamics. In this context, the EUREC4A field campaign, conducted over the Atlantic Ocean close to Barbados from January to February 2020, gathered approximately 200 hours of unique observational data from these clouds [1]. The data was collected by two Max Planck CloudKites deployed from ships. A subset of this data includes Particle Image Velocimetry (PIV) and holographic measurements taken within clouds, providing unique insight into cloud dynamics. To our knowledge, our data represent the first application of airborne PIV in atmospheric clouds and provide an unprecedented opportunity to link cloud turbulence and microphysics.

In this study, we explore the feasibility and accuracy of estimating high-resolution turbulence energy dissipation rates within clouds based on the PIV data. We used the approximate 100k PIV image pairs from both precipitating and non-precipitating clouds collected during the EUREC4A campaign. We employed several established dissipation rate estimating methods, including the second-order structure function method [2] and the 2D gradient method used in the field of planar PIV [3, 4]. The turbulence energy dissipation rate across different cloud types (or flight segments) observed during the campaign is computed.

In addition, we have performed a detailed comparative analysis of the dissipation rate estimated with different techniques, including a 1- and 3-dimensional pitot tube. We also investigate the two-dimensional spatial distribution of cloud droplets and its correlation with turbulence features. We believe that these findings will improve our understanding of turbulence in shallow cumulus clouds and its impact on their formation and evolution.

 

References

[1] Bony, S., Stevens, B., Ament, F., Bigorre, S., Chazette, P., Crewell, S., ... & Wirth, M. EUREC 4 A: A field campaign to elucidate the couplings between clouds, convection and circulation., Surveys in Geophysics, 38, 1529–1568 (2017).

[2] Schröder, M., Bätge, T., Bodenschatz, E., Wilczek, M., & Bagheri, G. Estimating the turbulent kinetic energy dissipation rate from one-dimensional velocity measurements in time., Atmospheric Measurement Techniques, 17, 2, 627-657 (2024).

[3] Tanaka, T. & Eaton, J. K. A correction method for measuring turbulence kinetic energy dissipation rate by PIV: Validated by random Oseen vortices synthetic image test., Experiments in Fluids, 42, 6, 893-902 (2007).

[4] Verwey, C., & Birouk, M. Dissipation rate estimation in a highly turbulent isotropic flow using 2D-PIV. Flow., Turbulence and Combustion, 109, 3, 647-665 (2022).