Preprints
https://doi.org/10.5194/gmd-2017-260
https://doi.org/10.5194/gmd-2017-260
Submitted as: methods for assessment of models
 | 
20 Nov 2017
Submitted as: methods for assessment of models |  | 20 Nov 2017
Status: this preprint was under review for the journal GMD. A revision for further review has not been submitted.

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Ross Noel Bannister, Stefano Migliorini, Alison Clare Rudd, and Laura Hart Baker

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Ross Noel Bannister, Stefano Migliorini, Alison Clare Rudd, and Laura Hart Baker
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Ross Noel Bannister, Stefano Migliorini, Alison Clare Rudd, and Laura Hart Baker

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, Stefano Migliorini, Alison Clare Rudd, and Laura Hart Baker

Viewed

Total article views: 1,570 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,036 424 110 1,570 95 111
  • HTML: 1,036
  • PDF: 424
  • XML: 110
  • Total: 1,570
  • BibTeX: 95
  • EndNote: 111
Views and downloads (calculated since 20 Nov 2017)
Cumulative views and downloads (calculated since 20 Nov 2017)

Viewed (geographical distribution)

Total article views: 1,486 (including HTML, PDF, and XML) Thereof 1,484 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Dec 2024
Download
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.