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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Cited articles

Baker, L. H., Rudd, A. C., Migliorini, S., and Bannister, R. N.: Representation of model error in a convective-scale ensemble prediction system, Nonlin. Processes Geophys., 21, 19–39, https://doi.org/10.5194/npg-21-19-2014, 2014. a, b, c, d
Banacos, P. C. and Schultz, D. M.: The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives, Weather Forecast., 20, 351–366, https://doi.org/10.1175/WAF858.1, 2005. a
Berner, J., Shutts, G. J., Leutbecher, M., and Palmer, T. N.: A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System, J. Atmos. Sci., 66, 603–626, https://doi.org/10.1175/2008JAS2677.1, 2009. a
Berner, J., Achatz, U., Batté, L., Bengtsson, L., Cámara, A. D. L., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., Palmer, T. N., Penland, C., Sakradzija, M., von Storch, J.-S., Weisheimer, A., Weniger, M., Williams, P. D., and Yano, J.-I.: Stochastic Parameterization: Toward a New View of Weather and Climate Models, B. Am. Meteorol. Soc., 98, 565–588, https://doi.org/10.1175/BAMS-D-15-00268.1, 2017. a
Boisserie, M., Arbogast, P., Descamps, L., Pannekoucke, O., and Raynaud, L.: Estimating and diagnosing model error variances in the Météo-France global NWP model, Q. J. Roy. Meteor. Soc., 140, 846–854, https://doi.org/10.1002/qj.2173, 2014. a
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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.