Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-187-2020
https://doi.org/10.5194/npg-27-187-2020
Research article
 | 
16 Apr 2020
Research article |  | 16 Apr 2020

Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia

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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 Michiel Van Ginderachter on behalf of the Authors (24 Jan 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (04 Feb 2020) by Juan Restrepo
RR by Anonymous Referee #1 (12 Feb 2020)
ED: Publish as is (24 Feb 2020) by Juan Restrepo
AR by Michiel Van Ginderachter on behalf of the Authors (02 Mar 2020)
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Short summary
A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.