Bruce Rolstad Denby FAIRMODE 4 th Plenary, Norrkjoping Sweden June2011

1. The combined use of models and monitoring for applications related to the European air quality Directive: SG1-WG2 FAIRMODE FAIRMODE Forum for air quality modelling in Europe Bruce Rolstad Denby FAIRMODE 4th Plenary, Norrkjoping Sweden June2011

2. Content Aim of SG1-WG2 Registered FAIRMODE participants Response to ‘request for information’ Institute and activities list Agenda for today Example of an inter-comparison Representativeness and the Directive

3. Aims of SG1 To promote ‘good practice’ when combining models and monitoring (Directive related) To provide a forum for modellers and users interested in applying these methodologies To develop and apply quality assurance practices when combining models and monitoring To provide guidance on station representativeness and station selection

4. Registered for SG1

5. Expertise required for methods Data assimilation Monte Carlo MarkovChain Bayesianheirarchicalapproaches 4D var Ensemble Kalman filter Optimal interpoaltion Data fusion Kriging methods Increasingstatisticalexpertise GIS based methods Regression IDW Modelling Increasingmodelexpertise

6. Request for information SG1 21 responses to the survey Summary tabulation of the information Summary information in figures Will help to plan bench marking activities Will help to understand the state-of-the-science

7. Agenda today in SG1-WG2 • Progress through the WG2-SG1 discussion document • Results of the ‘request for information’ concerning data assimilation activities in Europe. Results, discussion and additions • Methods for quality assurance when combining monitoring and modelling. Proposed methods and implication for Bench marking activities of SG4 • Contribution of SG1 to the revision of the Directive. Providing recommendations on methods and quality assurance. • Defining representativeness of monitoring stations for modeling and data assimilation. Results of the survey and implications for the Directive • Contributions and discussion points from other members • SG1 + SG4 discussion • Station representativeness for quality assurance and implementation in the bench marking activities • How to bench mark data assimilation methods? How to implement these? • Conclusions and recommendations • Bench marking activity for SG1 • Contribution to the Directive review • Representativeness

8. http://fairmode.ew.eea.europa.eu/ For information and contributions contact Bruce Rolstad Denby bde@nilu.no and register interest on the website

9. Example inter-comparison Available stations Denby B., M. Schaap, A. Segers, P. Builtjes and J. Horálek (2008). Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale. Atmos. Environ. 42, 7122-7134. Comparison of Residual kriging and Ensemble Kalman Filter for assessment of regional PM10 in Europe (2003) Indicator for daily mean PM10 concentration: r2, RMSE, total bias Validation data subset: Random selection of 20% of available Airbase stations (127) No two validation stations within 100 km No station above 700 m Repeated random cross-validation: Applied only using the residual kriging to assess the representativeness of the validation subset

10. Example inter-comparison Residual kriging EnKF Model (LOTOS-EUROS) Denby B., M. Schaap, A. Segers, P. Builtjes and J. Horálek (2008). Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale. Atmos. Environ. 42, 7122-7134. Maps of annual mean concentrations

11. Example inter-comparison Residual kriging EnKF Model (LOTOS-EUROS) Denby B., M. Schaap, A. Segers, P. Builtjes and J. Horálek (2008). Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale. Atmos. Environ. 42, 7122-7134. Scatter plots all daily means from validation stations

12. Example inter-comparison Denby B., M. Schaap, A. Segers, P. Builtjes and J. Horálek (2008). Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale. Atmos. Environ. 42, 7122-7134. Annual statistics per validation station

13. Some concepts • ’Combination’ used as a general term • Data integration • Refers to any ‘bringing together’ of relevant and useful information for AQ modelling in one system (e.g. emissions/ meteorology/ satellite/ landuse/ population/ etc.) • Data fusion • The combination of separate data sources to form a new and optimal dataset (e.g. models/monitoring/satellite/land use/etc.). Statistically optimal but does not necessarily preserve the physical characteristics • Data assimilation • The active, during model integration, assimilation of observational data (e.g. monitoring/satellite). Physical laws are obeyed

14. Representativeness and the AQD • For monitoring the AQ Directive states: • For industrial areas concentrations should be representative of a 250 x 250 m area • for traffic emissions the assessment should be representative for a 100 m street segment • Urban background concentrations should be representative of several square kilometres • For rural stations (ecosystem assessment) the area for which the calculated concentrations are valid is 1000 km2 (30 x 30 km) • These monitoring requirements also set limits on model resolution

15. Defining spatial representativeness • The degree of spatial variability within a specified area • e.g. within a 10 x 10 km region surrounding a station the variability is  30% • Useful for validation and for data assimilation • The size of the area with a specified spatial variability • e.g. < 20% of spatial mean (EUROAIRNET) or < 10% of observed concentration range in Europe (Spangl, 2007) • Useful for determining the spatial representativeness of a site

16. Observed spatial variability Coefficient of variation σc/c for annual indicators as a function of area (diameter) for all stations (Airbase) 0.5 47% NO2 PM10 SOMO35 At 5km resolution variability is 34% 24% 0.2 σc/c 0 200 km diameter A random sampling within a 5km grid in an average European city will give this variability