the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble
Abstract. Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is large enough
to be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)-variance statistics in the spatial and spectral domains.
The work here extends the Met Office's 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts.
It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation purposes.
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EC1: 'Brief Editor comments', James Annan, 20 Nov 2017
- AC3: 'Response to editor', Ross Bannister, 05 Feb 2018
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RC1: 'Referee comment', Anonymous Referee #1, 13 Dec 2017
- AC1: 'Response to reviewer 1', Ross Bannister, 05 Feb 2018
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RC2: 'review comments', Anonymous Referee #2, 12 Jan 2018
- AC2: 'Response to reviewer 2', Ross Bannister, 05 Feb 2018
- EC2: 'Editor comments', James Annan, 05 Mar 2018
-
EC1: 'Brief Editor comments', James Annan, 20 Nov 2017
- AC3: 'Response to editor', Ross Bannister, 05 Feb 2018
-
RC1: 'Referee comment', Anonymous Referee #1, 13 Dec 2017
- AC1: 'Response to reviewer 1', Ross Bannister, 05 Feb 2018
-
RC2: 'review comments', Anonymous Referee #2, 12 Jan 2018
- AC2: 'Response to reviewer 2', Ross Bannister, 05 Feb 2018
- EC2: 'Editor comments', James Annan, 05 Mar 2018
Model code and software
Ensemble-diagnostics: Code selection of Met Office UM ensemble diagnostics R. Bannister, S. Migliorini, A. Rudd, and L. Baker https://doi.org/10.5281/zenodo.1013435
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Cited
5 citations as recorded by crossref.
- Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble T. Necker et al. 10.1175/MWR-D-19-0154.1
- Distributions and convergence of forecast variables in a 1,000‐member convection‐permitting ensemble G. Craig et al. 10.1002/qj.4305
- The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics J. Zhu & R. Bannister 10.5194/gmd-16-6067-2023
- Guidance on how to improve vertical covariance localization based on a 1000-member ensemble T. Necker et al. 10.5194/npg-30-13-2023
- A convective‐scale 1,000‐member ensemble simulation and potential applications T. Necker et al. 10.1002/qj.3744