A new method for determining the optimal lagged ensemble

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

doi:10.1002/2016MS000838

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A new method for determining the optimal lagged ensemble

2017
By Trenary, L. ; DelSole, T. ; Tippett, M. K. ; ...
Source: Journal of Advances in Modeling Earth Systems 9(1), 291-306, 2017

[PDF-1.06 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:

We propose a general methodology for determining the lagged ensemble that minimizes the mean square forecast error. The MSE of a lagged ensemble is shown to depend only on a quantity called the cross-lead error covariance matrix, which can be estimated from a short hindcast data set and parameterized in terms of analytic functions of time. The resulting parameterization allows the skill of forecasts to be evaluated for an arbitrary ensemble size and initialization frequency. Remarkably, the parameterization also can estimate the MSE of a burst ensemble simply by taking the limit of an infinitely small interval between initialization times. This methodology is applied to forecasts of the Madden Julian Oscillation (MJO) from version 2 of the Climate Forecast System version 2 (CFSv2). For leads greater than a week, little improvement is found in the MJO forecast skill when ensembles larger than 5 days are used or initializations greater than 4 times per day. We find that if the initialization frequency is too infrequent, important structures of the lagged error covariance matrix are lost. Lastly, we demonstrate that the forecast error at leads 10 days can be reduced by optimally weighting the lagged ensemble members. The weights are shown to depend only on the cross-lead error covariance matrix. While the methodology developed here is applied to CFSv2, the technique can be easily adapted to other forecast systems.
Keywords:

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Atmospheric Science Mathematical Models
Source:

Journal of Advances in Modeling Earth Systems 9(1), 291-306, 2017
DOI:

https://doi.org/10.1002/2016MS000838
Document Type:

Journal Article
Funding:

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

Grant no. NA10OAR4310264 ; Grant no. NA14OAR4310160 ; Grant no. NA14OAR4310184 Less -
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CC BY-NC-ND
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PMC
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urn:sha256:401d7c1f47a07da0f7e3e2ec4445b74eaa11c80242dfe7f701d6bb1d24cfb534
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https://repository.library.noaa.gov/view/noaa/25310/noaa_25310_DS1.pdf
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A new method for determining the optimal lagged ensemble

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