A daily global mesoscale ocean eddy dataset from satellite altimetry

By James H. Faghmous, Ivy Frenger, Yuanshun Yao, R. Warmka, Aron Lindell, Vipin Kumar. 2015

322 citations recorded by the source corpus.

Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System. Design Type(s) observation design • data integration objective • time series design Measurement Type(s) hydrographic profiling Technology Type(s) software method Factor Type(s) Sample Characteristic(s) mesoscale marine eddy Design Type(s) observation design • data integration objective • time series design Measurement Type(s) hydrographic profiling Technology Type(s) software method Factor Type(s) Sample Characteristic(s) mesoscale marine eddy Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Atmospheric Science, Oceanography

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Anuj Karpatne
2. Vipin Kumar
3. Gowtham Atluri
4. Arindam Banerjee
5. Michael S. Steinbach
6. James H. Faghmous

In Oceanography

1. Anuj Karpatne
2. Vipin Kumar
3. James H. Faghmous

Citations in the corpus

• Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “ples include the discovery of novel climate patterns and relationships [18], [19], closure of knowledge gaps in turbulence modeling efforts [20], [21], discovery of novel com-”
• Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “One such approach has been successfully used for finding spatio-temporal patterns in sea surface height data [32], [33], resulting in the creation of a global catalogue of mesoscale ocean eddies [34].”