Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-260
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
20 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble
Ross Noel Bannister1, Stefano Migliorini1,a, Alison Clare Rudd1,b, and Laura Hart Baker1 1Dept. of Meteorology, Univ. of Reading, Earley Gate, RG6 6BB, UK
anow at: Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
bnow at: Centre for Ecology and Hydrology, Wallingford, OX10 8BB, UK
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.


Citation: Bannister, R. N., Migliorini, S., Rudd, A. C., and Baker, L. H.: Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-260, in review, 2017.
Ross Noel Bannister et al.
Ross Noel Bannister et al.

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
Ross Noel Bannister et al.

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Short summary
An ensemble of weather forecasts (i.e. multiple forecasts) contains useful information that a traditional single forecast does not have. Most existing forecast ensembles though have few members (ensemble too small), meaning that the information that they contain is noisy. This paper shows how more ensemble members can be generated from an existing (small) ensemble, and how the value added by the extra members can be assessed in a quantitative way.
An ensemble of weather forecasts (i.e. multiple forecasts) contains useful information that a...
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