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- Scientifically feasible due to improvement in numerical modeling
- Higher uncertainty compared to short-range forecasts

- Main approach to improve forecast skill
- Single model ensembles reduce the errors due to initial conditions
- Systematic errors are more important for longer-term forecasts

¡æ Multi-model ensemble prediction is one of the solution to reduce these errors!

- An empirical statistical post-processing to produce an optimal forecast from a set of ensemble predictions
- Computing the weights during training phase for each grid point of each model
- Linear regression methods: simple ensemble mean, multiple linear regression , EOF-based multiple regression, Singular Value Decomposition
- Non-linear regression methods: Artificial Neural Network, Genetic Algorithm, Fuzzy Theory
- Notion of general MME scheme (See Fig.1)

- Training phase: observed fields provide statistical relationships

Figure 1. The vertical dotted line denotes time t = 0; the area to the left denotes the training phase where a large number of forecast experiments are carried out. During this period, the available observed fields provide statistical relationships, which are then passed on to the area t > 0 (on the right).

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