If you use this data set in your publications, please acknowledge with the following:
Red Sea data courtesy of Red Sea Modeling and Prediction Group (PI Prof. Ibrahim Hoteit), KAUST available at https://kaust-vislab.github.io/SciVis2020/data.html.and cite the paper
H. Toye, P. Zhan, G. Gopalakrishnan, A. Kartadikaria, H. Huang, O. Knio, and I. Hoteit: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics, 67, 915-933, 2017.-
Download the data from here:
Data Repository
Password: SciVisContest2020
You can either download all ensembles plus the bathymetry in a zipped archive (~64GB) by pressing the "Download" button on the upper right, or as individual files. Each ensemble member extracts to one netcdf file (~32GB), the whole data set extracts to approximately 1.5 TB.
+Download the data from here:
Data Repository
Password: SciVisContest2020
You can either download all ensembles plus the bathymetry in a zipped archive (~64GB) by pressing the "Download" button on the upper right, or as individual files. Each ensemble member extracts to one netcdf file (~32GB), the whole data set extracts to approximately 1.5 TB.
After you have downloaded some or all ensemble members as .tgz files you can check against the provided md5 checksum to see if the download succeeded.
As an example, we demonstrate how to load the data in ParaView. ParaView can load the netcdf file, but will not assemble all the data correctly. This requires a few steps which are shown in this Python script (resampling the staggered grid, requires ParaView 5.7 or later). Run ParaView from the directory where the data was extracted (i.e., containing the Folder SciVisContest2020), or update the file names, and load the script as a state.