State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning

This is a data repository in support of the publication "State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning" by Manucharyan et al. (2020), Journal of Advances in Modeling Earth Systems. The zipped file contains 10-day-separated snapshots of surface and deep ocean streamfunctions from the two-layer quasigeostrophic model of ocean turbulence. The included Python scripts demonstrate the efficacy of Deep Learning in temporal interpolation and state estimation given partial observations of the surface ocean turbulence.