Two-Layer Quasigeostrophic Ocean Turbulence Streamfunctions

Manucharyan et al. (2020) provide a dataset of 10-day-separated snapshots of surface and deep ocean streamfunctions, generated from a two-layer quasigeostrophic model of ocean turbulence. The data supports the development and testing of deep learning methods for temporal interpolation and state estimation from sparse sea surface height observations. It is a companion repository for a publication in the Journal of Advances in Modeling Earth Systems.

Use Cases

• Temporal interpolation of sparse ocean observations using deep learning models.
• State estimation of deep ocean flows from surface observations.
• Benchmarking machine learning algorithms against simulated quasigeostrophic turbulence.
• Studying the dynamics of geostrophic turbulence in a two-layer ocean model.

Strengths

• Dataset is directly linked to a peer-reviewed publication in a scientific journal.
• Includes Python scripts demonstrating specific use cases for deep learning applications.
• Data is generated from a controlled, physics-based model (two-layer quasigeostrophic).

Limitations

• Specific column names, row counts, and file sizes are not provided in the available metadata.
• The temporal and spatial resolution of the model snapshots is not explicitly detailed.
• Metadata on the last update date is missing across all sources.

Provenance

Source

Georgy E. Manucharyan, University of Washington

Collection Method

Simulated data from a two-layer quasigeostrophic model of ocean turbulence.

Geography

Model domain (likely a simulated ocean basin), specific geography not stated.