Toward a stochastic parameterization of ocean mesoscale eddies
By PierGianLuca Porta-Mana, L. Zanna. 2014
173 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Oceanography
View Paper: https://www.semanticscholar.org/paper/a2bb68bda961dfc9f8bc04ef91bb0b7a6861fad6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- T. Bolton
- L. Zanna
- A. Bellucci
- B. Fox‐Kemper
- Eric P. Chassignet
- Till Kuhlbrodt
- Arne Biastoch
- M. Roberts
In Oceanography
- T. Bolton
- L. Zanna
- A. Treguier
- B. Fox‐Kemper
- Eric P. Chassignet
- Till Kuhlbrodt
- Arne Biastoch
- D. Marshall
Citations in the corpus
- Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization (2018, methodology): “The time mean stream function, which illustrates the double-gyre flow structure, can be seen in Figure 1a of Mana and Zanna (2014).”
- Resolving and Parameterising the Ocean Mesoscale in Earth System Models (2020, background): “For example, stochastic eddy parametrisations have been developed for both eddy-parameterising and eddy-present simulations [160, 164].”