Model Error and Predictability over Different Timescales in the Lorenz '96 Systems
By David Orrell. 2003
71 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change, Oceanography
View Paper: https://www.semanticscholar.org/paper/a01abd0acb5ab7d06410aaebdaa1507b096e5951
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
- Stephen G. Penny
- Milija Zupanski
- D. Wilks
- Dusanka Zupanski
- Tilmann Gneiting
- Achim Zeileis
- Jakob W. Messner
- Georg J. Mayr
In Global and Planetary Change
- Stephen G. Penny
- Milija Zupanski
- D. Wilks
- Dusanka Zupanski
- Tilmann Gneiting
- Achim Zeileis
- Jakob W. Messner
- Georg J. Mayr
In Oceanography
Citations in the corpus
- Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, methodology): “…system exhibits sensitive dependence on initial conditions (‘chaotic dynamics’), and has been used as an idealisation of atmospheric behaviour in a number of previous studies (e.g. Lorenz 1996; Orrell 2003; Palmer 2001: Smith 2001; Vannitsem & Toth 2002; Roulston & Smith 2003; Wilks 2005).”
- The Hybrid Local Ensemble Transform Kalman Filter (2014, methodology): “Because this model’s internal doubling time varies strongly with forcing (Orrell 2003), we note that our results were qualitatively similar using F5 8 with a forecast time stepDt5 0.05 (as originally used by Lorenz).”
- Model Error Estimation Employing an Ensemble Data Assimilation Approach (2006, methodology): “1999), and in predictability (Orrell 2003).”