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- ECMWF operational ensemble forecasts treat uncertainty in both the initial data and the model.
- Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA) methods. Singular Vectors are a way of representing the fastest growing modes in the initial state.
- Model uncertainty: sampled by use of stochastic parametrizations In IFS this means Stochastically Perturbed Physical Tendencies (SPPT) and the spectral backscatter scheme (SKEB)
- Ensemble mean : the average of all the ensemble members. Where the spread is high, small scale features can be smoothed out in the ensemble mean.
- Ensemble spread : the standard deviation of the ensemble members and represents how different the members are from the ensemble mean.
Ensemble exercise tasks
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The ensemble forecasts
In this case study, there are two operational ensemble datasets and additional datasets created with the OpenIFS model where the initial and model uncertainty are switched off. The OpenIFS ensembles are discussed in more detail in latter exercises and are not covered here.
2012 Operational ensemble
The dataset labelled 'ens_oper' in the macros uses the operational ensemble from 2012 and was used in the Pantillon et al. publication. A key feature of this ensemble is use of a climatological SST field (you should have seen this in the earlier tasks!).
2016 Operational ensemble
The dataset labelled 'ens_2016' in the macros is a reforecast of the 2012 event using the ECMWF operational ensemble from March 2016. Two key differences between the 2016 and 2012 operational ensembles are: higher horizontal resolution, coupling of NEMO ocean model to provide SST.
Note that the analysis was not rerun for 20-Sept-2012. This means the reforecast using the 2016 ensemble will be using the original 2012 analyses. Also important is that only 10 EDA members were in use at that time, whereas 25 would be used in the 2016 operational ensemble. This will impact on the spread and clustering seen in the tasks in this exercise.
Ensemble exercise tasks
Visualising ensemble forecasts can be done in various ways. During this exercise, in order to understand the errors and uncertainties in the forecast, we will use a number of visualisation techniques.
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