Articles | Volume 31, issue 2
https://doi.org/10.5194/npg-31-287-2024
https://doi.org/10.5194/npg-31-287-2024
Research article
 | 
01 Jul 2024
Research article |  | 01 Jul 2024

Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)

Man-Yau Chan

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Short summary
Forecasts have uncertainties. It is thus essential to reduce these uncertainties. Such reduction requires uncertainty quantification, which often means running costly models multiple times. The cost limits the number of model runs and thus the quantification’s accuracy. This study proposes a technique that utilizes users’ knowledge of forecast uncertainties to improve uncertainty quantification. Tests show that this technique improves uncertainty reduction.
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