Monthly ENSO Forecast Skill and Lagged Ensemble Size

Trenary, L.; DelSole, T.; Tippett, M.K.; Pegion, K.

doi:10.1002/2017MS001204

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Monthly ENSO Forecast Skill and Lagged Ensemble Size

2018
By Trenary, L. ; DelSole, T. ; Tippett, M.K. ; ...
Source: Journal of Advances in Modeling Systems 10(4), 1074-1086, 2018

[PDF-1.45 MB]

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Journal Title:

Journal of Advances in Modeling Earth Systems
Personal Author:

Trenary, L. ; DelSole, T. ; Tippett, M.K. ; Pegion, K.

Trenary, L. ; DelSole, T. ; Tippett, M.K. ; Pegion, K. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research)
Description:

The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real‐time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real‐time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8–10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.
Keywords:

[+]

Atmospheric Science Climatology Mathematical Models
Source:

Journal of Advances in Modeling Systems 10(4), 1074-1086, 2018
DOI:

https://doi.org/10.1002/2017MS001204
Pubmed Central ID:

PMC5993225
Document Type:

Journal Article
Funding:

Grant no. NA10OAR4310264 ; Grant no. NA14OAR4310160 ; Grant no. NA14OAR4310184

Grant no. NA10OAR4310264 ; Grant no. NA14OAR4310160 ; Grant no. NA14OAR4310184 Less -
Rights Information:

CC BY-NC-ND
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PMC
Main Document Checksum:

[+]

urn:sha256:9491a2004635fe2a955afbfd7b066bdfdf55ebe70696f12e84a7237e2a61cbdf
Download URL:

https://repository.library.noaa.gov/view/noaa/25288/noaa_25288_DS1.pdf
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Monthly ENSO Forecast Skill and Lagged Ensemble Size

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