1. EFnet: an added value of multi-model simulation
Mikhail Sofiev
Finnish Meteorological Institute

2. Content Introduction: why the ensemble modelling
Examples of single- and multi-model ensembles
EFnet: ensemble forecasting and model development
Conclusions

3. Ensemble modelling: why?
Atmospheric processes are stochastic
The smaller scale and the shorter averaging the higher uncertainty
small-scale processes, as well as some chemical chains of reactions can be chaotic by nature
Deterministic models work poor at small scales, with short averages and complicated chemical chains.
Reason is NOT (well, not only) model weaknesses but rather stochastic nature of the atmosphere
Right form of question: probability terms
Ways to answer the probabilistic questions
make probabilistic models (what about physics?)
run ensembles of existing deterministic model(s)

4. Types of ensembles Single-model multi-setup ensemble
One deterministic model
Input forcing, initial and/or boundary conditions are perturbed in a “reasonable” way or taken from several sources
Each perturbed set of data is computed in a normal way
Output datasets are considered as realizations of a stochastic process
Example: ECMWF ensemble weather forecast (operational !)
Multi-model ensemble
Several deterministic (and/or other) models are used
Each model uses own input datasets and/or common set(s)
Example: EU FP5 ENSEMBLE project, NKS MetNet network, EMEP Pb-1996 model inter-comparison

5. Single-model ensemble: ECMWF
Source:

6. Multi-model ensemble: NKS MetNet

7. Multi-model ensemble: EU-ENSEMBLE project
Source: Galmarini, 2004

8. Multi-model ensemble: EMEP Pb model inter-comparison
Source: Sofiev et al., 1996

9. Multi-model debugging
Source term are the same for both models
Meteorological data are the same but:
Models used own meteo pre-processors
Source: Potempski, 2005

10. EFnet: forecasting and model development
Validation of the model results
Operational Quality Assurance QA:
simple, basic statistics, provides the basic quality scores for individual models
Scientific assessment of the model quality:
detailed speciation, precursor analysis, detailed statistics
annual model inter-comparison exercises during high O3, PM seasons
Statistical correction of the model forecasts
Based on past model quality scores – both operational and scientific QA
Model-specific
some models may already have it
depends on the model individual quality score

11. EFnet: forecasting and model development (2)
Ensemble forecast (feasibility study)
Straightforward generation of statistically-sounding ensemble is far beyond the reach of current computers
Multi-model results allow approaching an uncertainty-disclosing problem in air quality prediction problem
Predictability of ozone and PM levels
Points of chaos, multi-track developments, etc
Output: ensemble average (if possible) + uncertainty range

12. Conclusions
Multi-model ensemble is a brand-new tool in air quality assessment
First ensembles were applied in emergency modelling
deterministic models work very poorly
established co-operation, need for mutual backup
comparatively straightforward (cheap) application
Outcome from the ensemble usage
MUCH better stability than individual models
show uncertainty ranges, areas/times of instability, errors or model failures
often agrees with measurements better than individual models
EFnet ensemble approach
Build and understand the multi-model ensemble for O3 and PM
If successful, generate the ensemble air quality forecast