Illuminated Supercells

I found a few new buttons on Paraview the other day. I subsequently went on a 'render bender' and this is the final result. Last year the software engineers at Kitware, authors of Paraview, included some GPU-native new lighting code that does things I've mostly only seen done with hard to use expensive proprietary software. Coupled with an NVIDIA Titan RTX GPU that had been spending far too much time idling, I was able to make this entire video in only a couple days. I was able to render up to 4 movies in parallel and the GPU only used up about half its available memory. Some technical details: This is a CM1 numerical model simulation in a highly idealized environment. Paraview, the visualization software creating each PNG frame that makes up each movie, is reading ZFP-compressed CF-compliant NetCDF files created by my own software (that I call LOFS). I found the best performance by saving individual files for each variable at each time rather than putting all the variables in one file at each time. I am using Volume rendering with shading turned on, with the Global Illumination Reach parameter set at about 0.2, Volumetric Scattering Blending around 1.1, and Volume Anisotropy around 0.0 (settings varied between movies). All software used in running, processing, and visualizing this simulation is open source. My sincere thanks to all programmers who were involved in these projects! Regarding the supercell simulation: Just recently I began using a different microphysics option (NSSL microphysics, authored by Ted Mansell) in CM1. What you see here is a large eddy simulation of a supercell (75 meter grid spacing, which is actually a bit coarse for most of my work, which has gone up to 10 meters) using NSSL microphysics, with data saved every 2 seconds, starting at time=0. The storm is initiated with a "warm bubble" perturbation. The model domain is 120km by 120km by 30km with visualized data extending up to 22 km. Lots of interesting physics is revealed in this simulation (showing supercells split, following by the dominant right mover). A few highlights: 1. The AACP behavior by both the right and left mover. Just, wow. There is graupel in those plumes in addition to cloud ice. [NOTE: After talking with Ted Mansell, this excess of graupel/hail is likely to go away with newer versions of his code, which I am going to be using soon.] 2. The relationship between hydrometeors (rain, hail, graupel) and the cold pool (temperature perturbations, outflow patterns). Clearly, some downdrafts are dominated by thermodynamics, while others are dominated by dynamics. 3. The 'waterfall effect' of hail and graupel at the edge of the cloud 4. Tornadogenesis is associated with some explosive updraft growth at the tropopause 5. Isn't it cool how environmental vorticity gets reorganized (and quickly depleted) in the environment surrounding the rapidly growing updraft near the beginning of the simulation These types of movies reinforce my long-held belief that visualization, when done right, can foster true scientific discovery that is often missed by theory and observations. This work was supported by NSF grant AGS-2114757 and a Texas Advanced Computing Center (TACC) subaward from NSF grant OAC-2139536.

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