Guidance Document on Model Quality Objectives and Benchmarking
Applying the DELTA tool v4.0 to NINFA Air Quality System
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- 7.3.JOAQUIN Model comparison PM10 NW Europe
7.2.Applying the DELTA tool v4.0 to NINFA Air Quality SystemMichele Stortini, Giovanni Bonafè, Enrico Minguzzi, Marco Deserti ARPA Emilia Romagna (Italy), Regional Agency for Environmental Protection and Prevention Background Information
The Emilia-Romagna Environmental Agency has implemented since 2003 an operational air quality modelling system, called NINFA, for both operational forecast and regional assessment. NINFA recently has been used for the assessment of regional air quality action plan.
O3, PM10, PM25 and NO2
Chimère version 2008c
Not provided
Meteorological inputs are from the COSMO-I7, the meteorological Italian Limited Area Model. Chemical boundary conditions are provided by Prev’air data and emission input data are based on regional Emilia-Romagna Inventory (INEMAR), national (ISPRA) and European inventories (MACC).
MDS link for Chimère: http://pandora.meng.auth.gr/mds/showlong.php?id=144
The simulation domain (640*410 km) covers the northern Italy, with a horizontal resolution of 5 km.
Hourly resolution: the model runs daily at ARPA and provides concentration for the previous day (hind cast) and the following 72 hours (forecast).
Concentration maps of PM10, Ozone and NO2 are produced
Not used Evaluation
All the observations from the active Emilia-Romagna regional background stations have been used in this study. 13 monitoring station are rural, 13 are urban and 10 suburban.
Not applicable
Often the station names in bar plot diagrams are not readable because they overlap. The results for PM10 are presented in the figures below (Figure , Figure and Figure ) 
Figure Target diagram for daily average PM10 concentrations. Model NINFA, year 2012. Red stations are located in the hills, blue in Bologna area, orange in the east, cyan in the west 
Figure Scatter plot of the modelled versus measured PM10 concentrations. NINFA, year 2012 
Figure Summary statistics for daily PM10. NINFA, year2012 Feedback
The tool is useful for assessing air quality models, especially because in this way is it possible to use standard methodologies to intercompare air quality model performances. Other comments relate to the implementation of the method: it would be useful to be able to use the tool in batch mode as well as on other operating systems (e.g. Linux).
The evaluation is usually performed on statistical index (Bias, correlation, rmse).
It could be useful to have time series for a group of stations as well as time series of mean daily values both for individual stations and station groups. 7.3.JOAQUIN Model comparison PM10 NW EuropeElke Trimpeneers (IRCEL, Belgium) Background Information
Joaquin (Joint Air Quality Initiative) is an EU cooperation project supported by the INTERREG IVB North West Europe programme (www.nweurope.eu). The aim of the project is to support health-oriented air quality policies in Europe.
The scope of the exercise is to compare model performances for the pollutant PM10 for the NW-Europe domain.
Four models are used in the exercise: Chimère, Aurora, LotosEuros and Beleuros.
Both hourly and yearly data were produced for the 2009.
PM10.
No data assimilation used only raw model results. Evaluation
We selected all background stations within the NW Europe (Joaquin) domain from Airbase data 2009. This resulted in 300 stations to be used for the model comparison.
Raw model results were used, no data-assimilation was applied.
IRCEL provides feedback on the evaluation of the model results using the DELTA tool. The evaluation is based on the ‘raw (=not calibrated, data assimilated) model results’ of the four models. None of the models not meet the model quality objective (=target value ≤ 1) in 90% of the stations for the PM10 daily mean model evaluation (Chimère 81 %, Aurora 54 %, BelEuros 80 %, LotosEuros 62 %). The target plots are presented in Figure . 
Figure Target plots for the daily average results CHIMERE, BELEUROS, AURORA and LOTOS EUROS. 
Figure Scatterplots for yearly average results for CHIMERE, BELEUROS, AURORA and LOTOS EUROS. Noticeable is that the model quality objective for yearly average model results is apparently even harder to comply to in this particular case. For all models the evaluation result based on yearly average model values is worse than the evaluation based on the daily average values. This can be seen from the annual mean scatterplots (Figure ) where the MQO is only met in respectively 10 % (Chimère), 9 % (Aurora), 46 % (BelEuros)and 6 % (LotosEuros) of the stations. This might seem strange but can be explained by the measurement uncertainty which is lower for the annual mean observed PM10 than for the daily mean values. Feedback
Most of the feedback is on the actual implementation of the method. Special about this exercise is that so many points (300) are considered. The DELTA Tool implementation considered was able to handle such a large amount of stations but it is difficult to interprete individual station results in this case as legends become cluttered and in practice useless.
IRCEL was already using another implementation of DELTA, the ATMOSYS tool. Concerning the daily mean PM10 results, two models perform relatively well considering the model quality objectives as set in the Delta tool. The results for these same models based on the annual PM10 values are however a lot worse (Figure ). In the latest template an indicator (MQOperc) was added to assess whether a model can correctly calculate exceedances. It was noticed in this specific example that even though the model would apparently comply to the MQOperc objective it still significantly underestimates the number of exceedances. For example in the Belgian station BETR012 that measures the suburban background concentration the 50 µg/m3 PM10 daily limit value was exceeded 24 times in 2009 while Chimère or Beleuros predict respectively only 4 and 0 exceedances. Both models however comply with the MQOperc model quality objective for the station BETR012.
IRCEL would like to see additional output with statistics for individual stations. This is also useful to be able to do some complementary calculations. Yüklə 189,05 Kb. Dostları ilə paylaş:
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