Comparison of ensemble‐MOS methods in the Lorenz '96 setting
Open accessAbstract
A suite of methods that have been proposed for statistical post‐processing of ensemble forecasts based on historical verification data (i.e. ensemble‐MOS methods) are compared with each other, and with direct probability estimates using ensemble relative frequencies, in the idealised Lorenz '96 setting. The three most promising methods are logistic regressions predicting probabilities associated with selected quantiles, ensemble dressing (a kernel density estimation approach), and linear regressions with non‐constant prediction errors that depend on the ensemble variance. Copyright © 2006 John Wiley & Sons, Ltd.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
More recently, ensemble MOS approaches have been introduced to adjust the whole forecast distribution.
“Recently “ensemble MOS” methods have been developed to correct the entire forecast distribution (Wilks 2006;Wilks and Hamill 2007).”
From Skill of Multimodel ENSO Probability Forecasts (2008) · cites this paper as Wilks 2006
Cites
Atmospheric Modeling, Data Assimilation and Predictability
Kalnay, E. (2002) · doi:10.1017/cbo9780511802270 · Cited by 3,391 · see citing works
Richardson, D. S. (2001) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712757715 · Cited by 212 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bayesian multi-model projection of climate: bias assumptions and interannual variability
Buser, C. M., Künsch, H. R., Lüthi, D., Wild, M., & Schär, C. (2009) · Climate Dynamics · doi:10.1007/s00382-009-0588-6 · Cited by 181 · see citing works
Skill of Multimodel ENSO Probability Forecasts
Tippett, M. K., & Barnston, A. G. (2008) · Monthly Weather Review · doi:10.1175/2008mwr2431.1 · Cited by 64 · see citing works
- Authors
- Wilks, D. S.
- Affiliations
- Cornell University (US)
- Corresponding author
- Wilks, D. S.
- ORCID
- Wilks, D. S.
- Venue
- Meteorological Applications
- Publisher
- Wiley
- Published
- 2006-09
- Volume
- 13
- Issue
- 3
- Pages
- 243-256
- ISSN
- 1350-4827 (ISSN-L)
- 1469-8080
- OpenAlex
- W2170593763
- Cited by
- 142
- References
- 2
- Citations by year
- 2025: 3 · 2024: 8 · 2023: 2 · 2022: 7 · 2021: 11 · 2020: 10 · 2019: 13 · 2018: 9 · 2017: 13 · 2016: 6 · 2015: 3 · 2014: 6 · 2013: 7 · 2012: 10
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Meteorological Applications (publishedVersion)
- Funders
- National Oceanic and Atmospheric Administration
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Keywords
- ensemble model output statistics, statistical post-processing, ensemble forecasting, Lorenz 96 model, logistic regression, kernel density estimation, ensemble variance, forecast verification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
