Articles | Volume 27, issue 2
Nonlin. Processes Geophys., 27, 187–207, 2020
https://doi.org/10.5194/npg-27-187-2020
Nonlin. Processes Geophys., 27, 187–207, 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 et al.

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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
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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.