The combined use of models and monitoring for applications related to the European air

quality Directive: SG1-WG2 FAIRMODE

Bruce Rolstad Denby

FAIRMODE 4th Plenary, Norrkjoping SwedenJune2011

FAIRMODEForum for air quality modelling in Europe

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

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

Registered for SG1N. Name e-mail

1. BRUCE ROLSTAD DENBY bde@nilu.no2. WOLFGANG SPANGL wolfgang.spangl@umweltbundesamt.at3. BERTRAND BESSAGNET bertrand.bessagnet@ineris.fr4. HENDRIK ELBERN he@eurad.uni-koeln.de5. THILO ERBERTSEDER thilo.erbertseder@dlr.de6. ALBERTO GONZÁLEZ agortiz@mma.es7. MARKE HONGISTO marke.hongisto@fmi.fi8. FERNANDO MARTIN fernando.martin@ciemat.es9. CAMILLO SILIBELLO c.silibello@aria-net.it10. MARTIJN SCHAAP martijn.schaap@tno.nl11. KEITH VINCENT keith.vincent@aeat.co.uk12. GABRIELE ZANINI gabriele.zanini@enea.it13. HILDE FAGERLI h.fagerli@met.no14. AMELA JERICEVIC jericevic@cirus.dhz.hr15. CLARA RIBEIRO clararibeiro@ua.pt16. HANS VAN JAARSVELD hans.vanjaarsveld@pbl.nl17. MIREIA UDINA SISTACH mudina@am.ub.es18. ONDREJ VLCEK vlcek@chmi.cz19. SUZANNE CALABRETTA-

JONGENsuzanne.jongen@vito.be

20. MARIA LUISA VOLTA lvolta@ing.unibs.it21. RAHELA ZABKAR rahela.zabkar@fmf.uni-lj.si22. DAVID CARRUTHERS david.carruthers@cerc.co.uk23. JOSE M. BALDASANO jose.baldasano@bsc.es24. MARIA GONCALVES-

AGEITOSmaria.goncalves@bsc.es

N. Name e-mail25. DICK VAN DEN HOUT dick.vandenhout@tno.nl26. ARI KARPPINEN ari.karppinen@fmi.fi27. CLAUDIO CARNEVALE carneval@ing.unibs.it28. ARNO GRAFF arno.graff@uba.de29. LINTON CORBET linton.corbet@chmi.cz30. ENRICO PISONI enrico.pisoni@ing.unibs.it31. GUUS VELDERS guus.velders@pbl.nl32. MIKHAIL SOFIEV mikhail.sofiev@fmi.fi33. SUGANYA ILANGO isu.ilango19@gmail.com34. AMY TALBOT amy.talbot@mepa.org.mt35. THANASIS SFETSOS ts@ipta.demokritos.gr36. LUIS BELALCAZAR lcbelalcazarc@unal.edu.co37. EVANGELOS MARAZIOTIS maraziot@mech.upatras.gr38. PETER BUILTJES peter.builtjes@tno.nl39. SIMONE MANTOVANI mantovani@sistema.at40. STIJN JANSSEN stijn.janssen@vito.be41. MARK SCERRI mark.scerri@mepa.org.mt42. DAN GALERIU dangaler@yahoo.com43. MIHAELA MIRCEA mihaela.mircea@enea.it

Expertise required for methods

Increasing model expertise

Incre

asin

g s

tati

sti

cal exp

ert

ise

Data fusionData fusion

Data assimilationData assimilation

Optimal interpoaltion

Optimal interpoaltion

4D var4D var

Ensemble Kalman filter

Ensemble Kalman filter

Kriging methods Kriging

methods

GIS based methodsGIS based methods

Bayesian heirarchical approaches

Bayesian heirarchical approaches

Monte Carlo Markov ChainMonte Carlo

Markov Chain

Modelling Modelling

RegressionRegressionIDWIDW

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

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

http://fairmode.ew.eea.europa.eu/

For information and contributions contactBruce Rolstad Denby

bde@nilu.no

and register interest on the website

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 biasValidation data subset:

Random selection of 20% of available Airbase stations (127)No two validation stations within 100 kmNo station above 700 m

Repeated random cross-validation:Applied only using the residual kriging to assess the

representativeness of the validation subset

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.

Example inter-comparison

Available stations

Example inter-comparisonMaps of annual mean concentrations

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.

Model (LOTOS-EUROS)

Residual kriging EnKF

Scatter plots all daily means from validation 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.

Model (LOTOS-EUROS)

Residual kriging EnKF

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, 7122-7134.

Example inter-comparison

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

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

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

Observed spatial variability

0 20 40 60 80 100 120 140 160 180 2000.2

0.25

0.3

0.35

0.4

0.45

0.5

Co

effi

cien

t of v

aria

tion

Lag distance (km)

Variability of mean NO2 (2006)

NO2

0 20 40 60 80 100 120 140 160 180 2000.2

0.25

0.3

0.35

0.4

0.45

0.5

Co

effic

ien

t of v

aria

tion

Lag distance (km)

Variability of mean PM10

(2006)

PM10

0 20 40 60 80 100 120 140 160 180 2000.2

0.25

0.3

0.35

0.4

0.45

0.5

Co

effic

ien

t of v

aria

tion

Lag distance (km)

Variability of SOMO35 (2006)

SOMO35

Coefficient of variation σc/c for annual indicators as a function of area (diameter) for all stations (Airbase)0.5

0.2

σc/c

diameter0 200 km

At 5km resolution variability is 34%

24%

47%

A random sampling within a 5km grid in an average European city will give this

variability