Articles | Volume 25, issue 3
Nonlin. Processes Geophys., 25, 481–495, 2018
https://doi.org/10.5194/npg-25-481-2018

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 481–495, 2018
https://doi.org/10.5194/npg-25-481-2018

Research article 09 Jul 2018

Research article | 09 Jul 2018

Parametric covariance dynamics for the nonlinear diffusive Burgers equation

Olivier Pannekoucke et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Olivier Pannekoucke on behalf of the Authors (29 May 2018)  Author's response    Manuscript
ED: Publish subject to technical corrections (08 Jun 2018) by Michael Ghil

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Olivier Pannekoucke on behalf of the Authors (25 Jun 2018)   Author's adjustment  
EA: Adjustments approved (01 Jul 2018) by Michael Ghil
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Short summary
The forecast of weather prediction uncertainty is a real challenge and is crucial for risk management. However, uncertainty prediction is beyond the capacity of supercomputers, and improvements of the technology may not solve this issue. A new uncertainty prediction method is introduced which takes advantage of fluid equations to predict simple quantities which approximate real uncertainty but at a low numerical cost. A proof of concept is shown by an academic model derived from fluid dynamics.