1. Bruce Rolstad Denby FAIRMODE 4th Plenary, Norrkjoping Sweden June2011
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 N. Name e-mail 1. BRUCE ROLSTAD DENBY bde@nilu.no2.
WOLFGANG SPANGL 3.
BERTRAND BESSAGNET 4.
HENDRIK ELBERN 5.
THILO ERBERTSEDER 6.
ALBERTO GONZÁLEZ 7.
MARKE HONGISTO 8.
FERNANDO MARTIN 9.
CAMILLO SILIBELLO
10.
MARTIJN SCHAAP
11.
KEITH VINCENT
12.
GABRIELE ZANINI
13.
HILDE FAGERLI
14.
AMELA JERICEVIC
15.
CLARA RIBEIRO
16.
HANS VAN JAARSVELD
17.
MIREIA UDINA SISTACH
18.
ONDREJ VLCEK
19.
SUZANNE CALABRETTA-JONGEN
20.
MARIA LUISA VOLTA
21.
RAHELA ZABKAR
22.
DAVID CARRUTHERS
23.
JOSE M. BALDASANO
24.
MARIA GONCALVES-AGEITOS N.
Name
25.
DICK VAN DEN HOUT
26.
Ari Karppinen
27.
CLAUDIO CARNEVALE
28.
ARNO GRAFF
29.
LINTON CORBET
30.
ENRICO PISONI
31.
GUUS VELDERS
32.
MIKHAIL SOFIEV
33.
SUGANYA ILANGO
34.
AMY TALBOT
35.
THANASIS SFETSOS
36.
LUIS BELALCAZAR
37.
EVANGELOS MARAZIOTIS
38.
PETER BUILTJES
39.
SIMONE MANTOVANI
40.
STIJN JANSSEN
41.
MARK SCERRI
42.
DAN GALERIU
43.
MIHAELA MIRCEA

5. Expertise required for methods
Data assimilation
Bayesian heirarchical approaches
Monte Carlo Markov Chain
4D var
Ensemble Kalman filter
Optimal interpoaltion
Data fusion
Kriging methods
Increasing statistical expertise
GIS based methods
Regression
IDW
Modelling
Increasing model expertise

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. For information and contributions contact
Bruce Rolstad Denby
and register interest on the website

9. Example inter-comparison
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
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,

10. Example inter-comparison
Maps of annual mean concentrations
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,

11. Example inter-comparison
Scatter plots all daily means from validation stations
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,

12. Example inter-comparison
Annual statistics per validation station
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,

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
0.2
σc/c
NO2
PM10
SOMO35
47%
At 5km resolution variability is 34%
24%
diameter
200 km
A random sampling within a 5km grid in an average European city will give this variability