Articles | Volume 14, issue 12
Geosci. Model Dev., 14, 7377–7389, 2021
https://doi.org/10.5194/gmd-14-7377-2021
Geosci. Model Dev., 14, 7377–7389, 2021
https://doi.org/10.5194/gmd-14-7377-2021
Model experiment description paper
01 Dec 2021
Model experiment description paper | 01 Dec 2021

Recalculation of error growth models' parameters for the ECMWF forecast system

Hynek Bednář et al.

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Cited articles

Alligood, K. T., Sauer, T. D., and Yorke, J. A.: Chaos an Introduction to Dynamical System, Springer, New York, USA, 1996. 
Bednář, H.: Recalculation of error growth models’ parameters for the ECMWF forecast system, OSF [code and data set] https://doi.org/10.17605/OSF.IO/CEK32, 2020. 
Bengtsson, L. K., Magnusson, L., and Kallen, E.: Independent Estimations of the Asymptotic Variability in an Ensemble Forecast System, Mon. Weather Rev., 136, 4105–4112, https://doi.org/10.1175/2008MWR2526.1, 2008. 
Brisch, J. and Kantz, H.: Power law error growth in multi-hierarchical chaotic system-a dynamical mechanism for finite prediction horizon, New J. Phys., 21, 1–7, https://doi.org/10.1088/1367-3630/ab3b4c, 2019. 
Buizza, R.: Horizontal Resolution Impact on Short- and Long-range Forecast Error, Q. J. Roy. Meteorol. Soc., 136, 1020–1035, https://doi.org/10.1002/qj.613, 2010. 
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Short summary
Forecast errors in numerical weather prediction systems grow in time. To quantify the impacts of this growth, parametric error growth models may be employed. This study recalculates and newly defines parameters for several statistic models approximating error growth in the ECMWF forecasting system. Accurate values of parameters are important because they are used to evaluate improvements of the forecasting systems or to estimate predictability.