Description Usage Arguments Details Note References Examples

This toy model lets you create forecast-observation pairs with specified ensemble and forecast size, correlation skill, and overconfidence (underdispersion) for application with the verification functionality provided as part of the easyVerification package.

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`N` |
number of forecast instances |

`nens` |
number of ensemble members |

`alpha` |
nominal correlation skill of forecasts |

`beta` |
overconfidence parameter (see details) |

`dims` |
independent (e.g. spatial) dimensions for the toy model |

`...` |
additional arguments passed to |

The toy model is the TM2 model as introduced by Weigel and Bowler
(2009) with a slight modification to allow for forecasts with negative
correlation skill. In this toy model, the observations *x* and forecasts
*f_i* are defined as follows:

*x = μ_x + ε_x*

*f_i = α / |α| μ_x + ε_{β} + ε_i*

where

*μ_x ~ N(0, α^2)*

*ε_x ~ N(0, 1 - α^2)*

*ε_{β} ~ N(0, β^2)*

*ε_i ~ N(0, 1 - α^2 - β^2)*

*α^2 ≤ 1*

*0 ≤ β ≤ 1 - α^2*

This toy model is intended to provide example forecast observation pairs and not to serve as a conceptual model to study real forecasts. For models to do the latter, please refer to Siegert et al. (2015).

A. Weigel and N. Bowler (2009). Comment on 'Can multi-model
combination really enhance the prediction skill of probabilistic ensemble
forecasts?'. *Quarterly Journal of the Royal Meteorological Society*,
135, 535-539.

S. Siegert *et al.* (2015). A Bayesian framework for verification and
recalibration of ensemble forecasts: How uncertain is NAO predictability?
Preprint on ArXiv, http://arxiv.org/abs/1504.01933.

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MeteoSwiss/easyVerification documentation built on May 10, 2017, 1:05 a.m.

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