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 [email protected]. WOLFGANG SPANGL [email protected]. BERTRAND BESSAGNET [email protected]. HENDRIK ELBERN [email protected]. THILO ERBERTSEDER [email protected]. ALBERTO GONZÁLEZ [email protected]. MARKE HONGISTO [email protected]. FERNANDO MARTIN [email protected]. CAMILLO SILIBELLO [email protected]. MARTIJN SCHAAP [email protected]. KEITH VINCENT [email protected]. GABRIELE ZANINI [email protected]. HILDE FAGERLI [email protected]. AMELA JERICEVIC [email protected]. CLARA RIBEIRO [email protected]. HANS VAN JAARSVELD [email protected]. MIREIA UDINA SISTACH [email protected]. ONDREJ VLCEK [email protected]. SUZANNE CALABRETTA-
20. MARIA LUISA VOLTA [email protected]. RAHELA ZABKAR [email protected]. DAVID CARRUTHERS [email protected]. JOSE M. BALDASANO [email protected]. MARIA GONCALVES-
N. Name e-mail25. DICK VAN DEN HOUT [email protected]. ARI KARPPINEN [email protected]. CLAUDIO CARNEVALE [email protected]. ARNO GRAFF [email protected]. LINTON CORBET [email protected]. ENRICO PISONI [email protected]. GUUS VELDERS [email protected]. MIKHAIL SOFIEV [email protected]. SUGANYA ILANGO [email protected]. AMY TALBOT [email protected]. THANASIS SFETSOS [email protected]. LUIS BELALCAZAR [email protected]. EVANGELOS MARAZIOTIS [email protected]. PETER BUILTJES [email protected]. SIMONE MANTOVANI [email protected]. STIJN JANSSEN [email protected]. MARK SCERRI [email protected]. DAN GALERIU [email protected]. MIHAELA MIRCEA [email protected]
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
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
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