Scale Interactions and Atmospheric Predictability: An Updated Perspective
By Joseph J. Tribbia, D. Baumhefner. 2004
155 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/1ae1d69f8ea8a685c2d15a4da8f51673114c7710
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
- Martin Leutbecher
- Marc Bocquet
- Tim N. Palmer
- Carlos A. L. Pires
- Lin Wu
- Mark Gingrich
- Dale R. Durran
- P. V. Leeuwen
In Global and Planetary Change
- Martin Leutbecher
- Marc Bocquet
- Tim N. Palmer
- Carlos A. L. Pires
- Lin Wu
- Mark Gingrich
- Dale R. Durran
- P. V. Leeuwen
Citations in the corpus
- Stochastic climate theory and modeling (2015, unknown): “Meteor.718 Soc., 74, 2317–2330.719 [162] Tribbia, J. J. and D. P. Baumhefner, 2004: Scale interactions and atmospheric predictability: an updated per-720 spective.”
- Atmospheric Predictability: Why Butterflies Are Not of Practical Importance (2014, background): “In particular, Tribbia and Baumhefner (2004) found that the role of small-scale errors is primarily to perturb the baroclinically unstable scales, which then grow rapidly and dominate the loss of large-scale predictability.”
- Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “In the context of meteorology, the finer the horizontal space scale, the bigger the error growth rate (Lorenz 1969; Tribbia and Baumhefner 2004; Lorenc and Payne 2007).”
- Ensemble forecasting (2008, background): “Small spatial scales tend to be predictable for shorter time scales than the larger spatial scales [30].”