DOI: 10.5281/zenodo.3634813
title: JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.

description: The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes We deliver (1) data collection and (2) numerical codes in the following files attached: (1) Data collection: A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature. A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <strong>JUMP-JetsInTheLab.zip</strong> A netcdf file of velocity fields of our Saturn reference simulation <strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong> Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong> <strong>StatisticalData-JupObs.nc</strong> A zip file of potential vorticity profiles for Saturn and Jupiter observations <strong>IPV-QGPV-Jupiter-Saturn.zip</strong> (2) Numerical codes: Codes for statistical analysis in spherical geometry on Github. --&gt; https://github.com/scabanes/POST Codes for statistical analysis in cylindrical geometry on Github. --&gt; https://github.com/scabanes/JUMP Codes for statistical analysis in cartesian geometry on Github. --&gt; https://github.com/scabanes/JUMP The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows. <strong>Acknowledgments</strong> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.