MEGAPOLI Scientific Report 11-03 Evaluation of Integrated Toolsmegapoli.dmi.dk/publ/MEGAPOLI_sr11-03.pdf · 2011-05-25 · MEGAPOLI Scientific Report 11-03 Evaluation of Integrated

MEGAPOLI Scientific Report 11-03

Evaluation of Integrated Tools MEGAPOLI Deliverable 7.2 Editors: K. Heinke Schlünzen, Michael Haller Contributing Authors: Matthias Beekmann, Gesa Bedbur, Kristina Conrady, Sandro Finardi, Sönke Gimmerthal, David Grawe, Michael Haller, Peter Hoffmann, Spyros Pandis, Marje Prank, Volker Reinhardt, Ole Ross, K. Heinke Schlünzen, Camillo Silibello, Guillaume Siour, Mikhail Sofiev, Ranjeet Sokhi, Jochen Theloke, Malte Uphoff

View above the European Green Capital 2011 Hamburg – visible wet deposition

Hamburg, 2011

FP7 EC MEGAPOLI Project

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Colophon Serial title: MEGAPOLI Project Scientific Report 11-03 Title: Evaluation of Integrated Tools Subtitle: MEGAPOLI Deliverable D7.2 Editors: K. Heinke Schlünzen, Michael Haller Contributing Authors: Matthias Beekmann, Gesa Bedbur, Kristina Conrady, Sandro Finardi, Sönke Gimmerthal, David Grawe, Michael Haller, Peter Hoffmann, Spyros Pandis, Marje Prank, Volker Reinhardt, Ole Ross, K. Heinke Schlünzen, Camillo Silibello, Guillaume Siour, Mikhail Sofiev, Ranjeet Sokhi, Jochen Theloke, Malte Uphoff Responsible institution:

Meteorological Institute, KlimaCampus, University of Hamburg Bundesstr. 55, 20146 Hamburg E-mail: [email protected] http://www.mi.uni-hamburg.de/memi

Language: English Keywords: MEGAPOLI model evaluation concept, integrated modelling framework, AQ model, operational evaluation, model evaluation, ozone, PM10, PM2.5, NO2, meteorology evaluation. Url: http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-03.pdf Digital ISBN: 978-87-92731-07-4 MEGAPOLI: MEGAPOLI-29-REP-2011-03 Website: www.megapoli.info Copyright: FP7 EC MEGAPOLI Project


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Content: Abstract ................................................................................................................................................4 1. Introduction and MEGAPOLI Evaluation Concept.....................................................................5 2. MEGAPOLI Integrated Tools to be Evaluated............................................................................7 3. Model Performances Based on Literature Study .......................................................................12

3.1. Measures used to calculate overall model performances...................................................12 3.2. Meteorological parameters.................................................................................................13 3.3. Concentrations ...................................................................................................................17

4. Evaluation Strategy for MEGAPOLI Integrated Tools .............................................................20 4.1. Objectives of evaluation.....................................................................................................20 4.2. Model evaluation to be performed by model developer (or deeply interested user) .........21

4.2.1 General evaluation .....................................................................................................21 4.2.2 Scientific evaluation...................................................................................................21 4.2.3 Benchmark test...........................................................................................................22

4.3. Operational evaluation .......................................................................................................22 4.3.1 Evaluated indicators and evaluation measures used ..................................................22 4.3.2 Pollutant concentration measurements.......................................................................23

4.4. Model evaluation neglecting timing ..................................................................................24 4.4.1 Evaluated indicators and evaluation measures used ..................................................24

4.5. Model evaluation for specific meteorological situations ...................................................25 4.5.1 Evaluation method and measures used ......................................................................25 4.5.2 Meteorology data .......................................................................................................25 4.5.3 Clustering of meteorological situations .....................................................................26

5. MEGAPOLI Model Evaluation Results ....................................................................................28 5.1. Operational evaluation .......................................................................................................28 5.2. Evaluation results neglecting timing..................................................................................32 5.3. Evaluation results for specific meteorological situations ..................................................34

5.3.1 Evaluation of meteorological parameters on an annual average basis.......................34 5.3.2 Evaluation of meteorological parameters for different clusters.................................35 5.3.3 Evaluation of concentrations for different clusters ....................................................38

6. Conclusions................................................................................................................................40 Acknowledgements............................................................................................................................41

Appendix 1. Indicators for Air Quality Assessment ......................................................................42 Appendix 2. Clusters Based on 925 hPa Geopotential Height Data for 2005 ...............................44

References..........................................................................................................................................46 Previous MEGAPOLI reports............................................................................................................48


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Abstract The evaluation of Air Quality (AQ) models is very important for checking their applicability. An evaluation concept and results of a literature survey on previous evaluations are presented here. Within MEGAPOLI model simulations were performed for the full year 2005, and the evaluation methodology is applied to results of the models CHIMERE, FARM and SILAM. These models are of somewhat different complexity and thereby differ in process requirements, operational aspects, levels of integration, interfaces between meteorological and the actual air quality models. The evaluation methodology follows the COST 728 / ACCENT concept and includes a general and scientific evaluation. The benchmark test case is the full year 2005, which is used for a diagnostic (ability to capture physical and chemical processes), dynamic (ability to capture changes in air quality levels by checking them dependent on different meteorological data) and probabilistic evaluation (ability to reflect statistical properties given in EU guidelines). Furthermore, the results for 2005 are used for the operational evaluation. All evaluations are done using routine observa-tional data. The operational evaluation includes not only annual means but also exceedances. Focus of the evaluation method is on concentrations of PM, O3, NO2. For diagnostic and dynamic evalua-tion meteorological parameters are evaluated as well and the evaluation is done in dependence of the meteorological situation, which is characterised by clustering all the days of 2005 in 9 classes.

The model evaluation developed for integrated tools within MEGAPOLI (Chapter 4) has success-fully been applied to three model results from the MEGAPOLI modelling team. All three models have not been evaluated in detail for the region selected here, which includes the megacity of the Rhine-Ruhr area plus its neighbouring rural areas. All in all the models agree quite well. The meteorology data used by the models as input are reliable in the range found for other meteorology model evaluations in literature (Section 3.2). Mean values of meteorological parameters tempera-ture and wind are well represented, the hit rates reach values over 50%. The differences found for the annual average of meteorological parameters are consistently found for different weather situa-tions. For this purpose the 9 classes of different weather types (Section 4.5.3) based on NCEP 925 hPa geopotential heights are used. Only for cluster 3 (high pressure system over the British Isles) a difference in wind direction from measured data was found in all investigated AQ model meteorol-ogy inputs. However, an impact of this on the AQ model performance with respect to (increased) concentration differences from measured data could not be found.

The AQ model reaches a different agreement with observations for the different chemical compo-nents. Systematic underestimations (overestimations) were found for PM10 for CHIMERE and SILAM (FARM). This results in too low (high) frequencies of exceedance. For ozone the low concentrations were simulated too high, the high concentrations slightly high (CHIMERE, FARM) or too low (SILAM). The differences are not only visible in the annual average data but also in different weather situations (all clusters) and might therefore not be a result of the meteorology, but of chemical reactions, of emission data composition or of a model internal origin. Since NO2 is systematically underestimated by all models the relation of NO/NO2 at the source might not be representative for the evaluation region. The too low NO2 values can also partly explain the too high ozone values found for many meteorological situations (but not for all and not for all models). For ozone annual averages agree well. Exceedances and AOT40 as well as SOMO35 consistently show that CHIMERE and FARM agree well with measured data, while SILAM underestimates the maxima. Reasons for this and for the other discrepancies of AQ model results and measured data are currently investigated.

From the evaluation study presented here it can be concluded that the introduction of weather clusters and frequency distributions of model results and observations into the model evaluation can indeed help to better understand reasons for differences. The focus on one evaluation domain only will in future be extended to several more evaluation regions of different character. The method will be further used in MEGAPOLI to analyse all model results available for 2005.


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1. Introduction and MEGAPOLI Evaluation Concept The evaluation of Air Quality (AQ) models is very important for checking the applicability of AQ models. Therefore, a methodology to evaluate AQ models is developed and applied here. An extensive review of model evaluation methodologies can be found in Schlünzen and Sokhi (2008) and lead to a model evaluation protocol that was developed through the COST 728 Action (Schlün-zen & Sokhi, 2008). This protocol has been widely accepted by the European Science community (e.g. adopted within ACCENT; Schlünzen et al., 2007) and will be adopted here for MEGAPOLI. The generic evaluation concept (Figure 1.1) consists of two parts. However, as a very first step the objectives of the evaluation are to be outlined. For MEGAPOLI the target parameters are particles with a diameter of up to 10 μm (PM10) and up to 2.5 μm (PM2.5) as well as ozone (O3) and nitro-gen dioxide (NO2). More details on the evaluation objectives are given in Section 4.1.

1. General evaluation

2. Scientific evaluation

3. Benchmark tests

• Evaluation ProtocolΣ

1. General evaluation

2. Scientific evaluation

3. Benchmark tests

• Evaluation ProtocolΣ

Part I: to be appliedby model developerPart I: to be appliedby model developer

Part II: to be appliedby model user & developer

Operational evaluation Part II: to be appliedby model user & developer

Operational evaluation

Objective

Figure 1.1: Structure of our evaluation concept (from Schlünzen & Sokhi, 2008).

The Part I of the evaluation concept includes three steps, and has to be applied by the model devel-oper. This part includes general evaluation, scientific evaluation and benchmark tests. The different parts will be outlined in this report (Section 4.1). However, the emphasis of the present evaluation concept details lies on Part II, the so named operational evaluation (Sections 4.3, 4.3.2). For this routine data are used, but the suggested MEGAPOLI operational evaluation step is based on the benchmark test case. Thus, it is also used to deeper understand differences in model performance and thus contributes to the diagnostic, dynamic and probabilistic evaluation. The different evalua-tion methods are distinguished following Dennis et al. (2010) and used in this report as follows: - operational evaluation: uses routine observational data. This evaluation is using routine data

and comparisons include the timing of observed and simulated values. MEGAPOLI employs this by calculating Bias, Root Mean Square Error (RMSE), Skill Variance SKVAR, and Hit rate (H). This is Part II of the evaluation concept (Figure 1.1). Details on the methodology are given in Section 4.3.

- diagnostic evaluation: checks the ability of the model to capture the physical and chemical processes. This normally will use data of higher resolution or measurements of processes and is typically performed in benchmark tests. Within MEGAPOLI we aim at a diagnostic evaluation by employing a cluster approach for the meteorological situations and making an operational evaluation for each of the single clusters. To better diagnose reasons for performance differ-ences the meteorological data are additionally evaluated. Details on the methodology are given in Section 4.3.2.

- dynamic evaluation: checks, if changes in air quality levels are simulated as a result of different meteorological situations. Due to the clustering approach applied in MEGAPOLI, model results


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are evaluated for different meteorological regimes and thus it can be checked, if the models have the same reliability for all the different meteorological situations. For this purpose the re-sults of the methodology introduced in Section 4.3.2 are used.

- probabilistic evaluation: checks for statistical properties. EU daughter directives focus not only on annual mean values, but also on e.g. the number of exceedances and thus the distribution functions. By checking for that, MEGAPOLI also contributes to the probabilistic evaluation. Details on the methodology are given in Section 4.3.2.

The evaluation concept will be applied to integrated tools as described in detail in the MEGAPOLI report by Sokhi et al. (2011). The focus is on meteorology-chemistry coupled models and less so on multi-scale models as outlined by Schlünzen et al. (2011). In general, off-line and online coupled models (Table A) and models of different integration level (Table B) are distinguished (Baklanov, 2010). As an example to demonstrate the evaluation methods developed in MEGAPOLI, results of the models CHIMERE, FARM and SILAM are used. All three models are one-way nested and calculate meteorology and emissions off-line (Integration type 1-A). They are regional scale models used to simulate the full year 2005 with focus on concentration simulations.

Table A: Overview on the models used in MEGAPOLI and their nesting level.

Level Nesting Scales MEGAPOLI Models 1 One-way Global → regional → urban All

2 Two-way Global ↔ regional l ↔ urban ECHAM5/MESSy, MATCH-MPIC, UM-WRF-CMAQ, SILAM, FARM

Table B: Level of integration for models in MEGAPOLI.

Order Integration type Information flow MEGAPOLI Models

A Off-line Meteorology/Emissions chemistry

All

B Partly online Meteorology → Chemistry & emissions

UKCA, DMAT, M-SYS, UM-WRF-Chem, SILAM

C Fully online Meteorology ↔ Chemistry & Emissions

UKCA, WRF-Chem, Enviro-HIRLAM, ECHAM5/MESSy

Before giving the details on the MEGAPOLI evaluation concept (Chapter 4), the models that are to be evaluated are shortly characterised in Chapter 2. Results on typical model evaluation perform-ances are summarised in Chapter 3. They are taken from a literature survey. The MEGAPOLI evaluation concept is applied to models CHIMERE, FARM and SILAM in Chapter 4, and the results are summarised in Chapter 5. Conclusions are drawn in Chapter 6.


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2. MEGAPOLI Integrated Tools to be Evaluated In MEGAPOLI the models CHIMERE, DMAT, ECHAM5/MESSy, Enviro-HIRLAM, FARM, FLEXPART, LOTOS-EUROS, MATCH-MPIC, M-SYS, PMCAMx, RegCM3, SILAM, UKCA, UM-WRF-CMAQ, WRF-Chem are employed. For the current evaluation results of a 2005 simula-tion of CHIMERE, FARM and SILAM are available. All three models are of integration type 1-A (Table A, Table B), thus they use pre-calculated meteorology with a one-way nesting. The MEGA-POLI models are documented in a web based meta-data information system that was introduced by COST728 and was extended for use in MEGAPOLI.

The web-based model meta-data information system (http://www.mi.uni-hamburg.de/costmodinv) allows users to create their own account, type in information on the numerical models they want to document and, after a checking period, the information is set online.

1 Creation of (new) user

2 Changes of info per model

Visible for users (online):

1 Creation of (new) user

2 Changes of info per model

Visible for users (online):

3 Changed model stored in temporary,

off-line data base

3 Changed model stored in temporary,

off-line data base

7 Summary tables

6 View info per model

7 Summary tables

6 View info per model

4 Checking changes to avoid privacy violation

5 Move online;automatic actualisation

of summary tables

4 Checking changes to avoid privacy violation

5 Move online;automatic actualisation

of summary tables

Figure 2.1: Sketch of the web based model meta-data inventory at http://www.mi.uni-amburg.de/costmodinv.

The bluish parts are available online.

The basics of the three models used here as examples for the MEGAPOLI evaluation are summa-rised in Table C. All models are 3-dimensional and use meteorological data as input data (Table D). All models are applicable for mesoscale applications, CHIMERE and SILAM also to the macro-scale. They all are applicable for long-term application and thus able to simulate the annual values investigated here. The models are also used for short-term simulations, which were defined in COST728 to be periods of a few days. Thus, the model results are not only usable for the probabil-istic evaluation, but can also be used for operational, diagnostic and dynamic evaluation.


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Table C: Characteristics of the models from which results are used for the MEGAPOLI evaluation.

CHIMERE FARM SILAM

Full name CHIMERE FARM (Flexible Air quality Regional Model)

System for Integrated modelling of Atmos-pheric Composition

Revision Chimere2008c 3.0 version 5.0 Date July 2009 October 2009 28.1.2011

Name Guillaume Siour Giuseppe Calori Mikhail Sofiev

Institute LISA/CNRS ARIANET Finnish Meteorological Institute

Detailed informa-tion on model

Schmidt et al. (2001), Bessagnet et al., (2008), web page1

Gariazzo et al. (2007) Sibello et al. (2008), web page2

Sofiev et al. (2004, 2006°,b) web page3

2D 3D

Meteorology Chemistry &

transport

Microscale Mesoscale Macroscale Short term Long term

Resolution used in present simulations

0.3° x 0.2° 0.3° x 0.2° 0.3°x0.2°

As mentioned, all models use meteorological data from other meteorological models (Table D, last line). Thus, when applying the diagnostic evaluation to meteorological parameters this will be more an evaluation of the meteorology input data used for the actual AQ simulations than of the AQ models CHIMERE, FARM, SILAM. The models differ in the meteorological input and the chemistry mechanism employed (All models are applied with a 0.3°x0.2° resolution (~24 km) and employ either the wind fields of the ECMWF data (analysis: FARM; forecast: SILAM) or those delivered by a mesoscale model (CHIMERE: MM5 forced with NCEP analyses). While CHIMERE uses the exchange coefficient from the meteorology model, FARM and SILAM calculate necessary values for the diffusion respectively in an interface pre-processor and inside the AQ model. Also the models differ in the use of the con-densed water in the CTMs: SILAM uses the ECMWF forecasted values, FARM uses the values for cloud cover and CHIMERE uses values for total water content qt derived in the AQ model from the MM5 relative humidity values.

Table E). The focus of the present investigations lies on PM, O3, NO2 (marked blue in All models are applied with a 0.3°x0.2° resolution (~24 km) and employ either the wind fields of the ECMWF data (analysis: FARM; forecast: SILAM) or those delivered by a mesoscale model (CHIMERE: 1 http://www.lmd.polytechnique.fr/chimere/ 2 http://www.aria-net.it/front/ENG/codes/modules.php 3 http://silam.fmi.fi


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MM5 forced with NCEP analyses). While CHIMERE uses the exchange coefficient from the meteorology model, FARM and SILAM calculate necessary values for the diffusion respectively in an interface pre-processor and inside the AQ model. Also the models differ in the use of the con-densed water in the CTMs: SILAM uses the ECMWF forecasted values, FARM uses the values for cloud cover and CHIMERE uses values for total water content qt derived in the AQ model from the MM5 relative humidity values.

Table E). While nitrogen dioxide and ozone are calculated by all three models, they differ some-what in the complexity of the particle calculation. The CHIMERE model calculates all aerosol species including secondary organic aerosol and dust. However, available observational data only include PM10 concentrations (Section 4.3.2), thus all models are included in that evaluation. To summarise the qualities of models whose results are used in the evaluation, all are three-dimensional chemistry transport models that include gas phase and aerosol chemistry. The aerosol chemistry is of quite different complexity and includes most species in FARM, but a better size distribution of the aerosol spectrum in SILAM. CHIMERE uses a condensed gas phase mechanism including 120 reactions. The aerosol scheme includes all major species (BC, dust, inorganic ions, several semi volatile VOC forming SOA), resolved over 8 size bins.


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Table D: Meteorological input data used in the models. Variables denote: .u, v, w: three components of the wind vector, T: real temperature, θ: potential temperature, p: pressure, Gph: geopotential height, ρ: density,

qv: specific humidity, qt: total liquid water content, qlc: cloud liquid water content, qsc: cloud solid water content, qlr: rain liquid water content, qss: snow, N: cloud cover, K: exchange coefficient, zi: inversion height.

CHIMERE FARM SILAM u v w T θ p

Gph ρ qv qt qlc qsc qlr qss N ε K zi

Meteorology

MM5 mesoscale Model output forced by AVN / NCEP global weather forecast has been used. Interfaces for various meso-scale (MM5, WRF, ...)

Hourly 2D/3D fields can be provided (through the GAP grid adaptor) by a wide series of diagnostic / prognostic meteorological models; among the others have been used: RAMS, MM5, Lokal-Modell, WRF, SWIFT, CALMET

ECMWF - operational and ERA-40 HIRLAM any other GRIB-formatted meteo input data

Meteorological input data used for the actual simulations

MM5 forced by NCEP 6-hourly global analysis

ECMWF operational analy-sis on IFS levels

ECMWF operational forecast 0.25° Boundary layer height and turbulence parame-ters were re-diagnosed by SILAM meteo-pre-processor

All models are applied with a 0.3°x0.2° resolution (~24 km) and employ either the wind fields of the ECMWF data (analysis: FARM; forecast: SILAM) or those delivered by a mesoscale model (CHIMERE: MM5 forced with NCEP analyses). While CHIMERE uses the exchange coefficient from the meteorology model, FARM and SILAM calculate necessary values for the diffusion respectively in an interface pre-processor and inside the AQ model. Also the models differ in the use of the condensed water in the CTMs: SILAM uses the ECMWF forecasted values, FARM uses the values for cloud cover and CHIMERE uses values for total water content qt derived in the AQ model from the MM5 relative humidity values.



Table E: Chemical compounds calculated in the models by prognostic equation. Variables denote: so2: sulphur dioxide, no nitrogen monoxide, no2: nitrogen dioxide, nox: nitrogen oxides, hno3:

nitric acid, o3: ozone, h2o2: hydrogen peroxide, voc: volatile organic compounds, c6h6: benzene, hcho: formaldehyde, co: carbon monoxide, pop: persistent organic pollutant, PM10: particles of up

to 10 μm in diameter, PM2.5: particles of up to 2.5 μm in diameter, PPM10: primary particulate matter particles of up to 10 μm in diameter , PM0.1: particles of up to 0.1 μm in diameter, PM1: particles of up to 1 μm in diameter, nh4: ammonium, so4: sulphate, bc: black carbon, soa: secon-

dary organic aerosol, no3: nitrate.

CHIMERE FARM SILAM so2 no

no2 nox nh3

hno3 o3

H2O2 voc

c6h6 hcho

co pop

PM10 PM2.5 PPM10 PM0.1 PM1 nh4 so4 dust

sea salt bc soa no3

Other gases probabilities 1st radioactivity

Other heavy metals (not pb, cd9

Pesticides



3. Model Performances Based on Literature Study It can be expected that model performances differ considerably for different meteorological situa-tions and/or emission situations. However, it is not easy to decide, if the results of a specific model evaluation are typical with respect to the received model performances or exceptional. To get an idea of the typical model performance, a literature survey was performed for concentrations in a similar way as already done by Conrady (2010) for meteorological parameters. For this purpose quantitative model evaluation results, published in refereed journals, were surveyed, summarised and overall model performances were quantified. The measures for the quantification of the overall performance are given in Section 3.1. While for the meteorological parameters (Section 3.1) the results presented here are based on the literature study by Conrady (2010), for concentrations a new survey was made (Section 3.3) based on refereed articles published after 2005. It should be noted that in both cases the number of evaluations considered is still small, and the results for the typical evaluations are not robust enough to determine dependencies e.g. of the publication year.

3.1. Measures used to calculate overall model performances The average value of a quality measure QM is given by:

( ) ∑=

=N

1iiQM

N1QMAve (1)

In addition to average model performances, the percentiles are calculated. The a percentile Pa is the value, below which a percent of the QMi fall. For example, the 10 percentile gives the value for which 10 percent of the model results have a QM below P10, the 50 percentile describes the median (50% of model results have a QM below and 50% above P50) and the 90th percentile, P90, describes the QM value for which 10% of the model results have higher values than QM90. The percentile Pa is calculated using Microsoft Excel. Following the definition given at http://en.wikipedia.org/wiki/Percentile#Alternative_methods 4 , the percentiles are calculated in Excel as follows: After sorting the QM values by size (QM1<QM2<….QMN), the a-th rank (eq. 2) is determined and split into its integer part n and its decimal part d:

( ) 11N100

aranka +−= dn += (2)

The percentile Pa(QN) as then given by:

( ) ( )⎪⎭

⎪⎬

⎫

=<<

=

⎪⎩

⎪⎨

⎧−+= +

NnNn0

0nfor

QMQMQMdQM

QMQNP

N

n1nn

1

a (3)

The averages and percentiles are calculated for the model performance measures, typically used in model evaluations (Schlünzen, Sokhi, 2008). The ones used in this report are repeated for the con-venience of the reader. The average difference between prediction Pi and observational value Oi is used to calculate the Bias (eq. 4). Here the average values of measurements O and model results P are calculated as given in Eq. (5) and (6), respectively.

4 Checked at 11. March 2011, 16:57



OPBias −= (4)

∑=

=N

1iiO

N1O (5)

∑=

=N

1iiP

N1P (6)

The correlation coefficient r is calculated using eq. (7), with using the standard deviations of ob-served and simulated data as given in eq. (8) and eq. (9).

( )( )

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

σσ

−−=

∑=

PO

N

1iii PPOO

N1

r (7)

( )2N

1iio OO

N1∑

=

−=σ (8)

( )2N

1iiP PP

N1∑

=

−=σ (9)

The root mean square error RMSE is calculated according to eq. (10), IOA using eq. (11) and skill variance SKVAR by using eq. (12).

( )∑=

−=N

1i

2ii OP

N1RMSE (10)

( )

∑

∑

=

=

⎟⎠⎞⎜

⎝⎛ −+−

−−=

N

1i

2

ii

N

1i

2ii

OOPP

OP1IOA (11)

O

PSKVARσσ

= (12)

3.2. Meteorological parameters In COST728 it was agreed upon the order of relevance of different meteorological quantities for air quality simulations (Table F). A diagnostic evaluation should aim at evaluating the most relevant meteorological parameters. However, the evaluation survey by Conrady (2010) has shown that this is rarely the case. She summarised the model performance of 60 published model evaluations for meteorological parameters. Some publications included several model versions that were evaluated, so that up to 221 published values are available for one of the performance measures (Bias). For all QM at least 100 evaluations were found (except SKVAR). As can be seen from Figure 3.1, the most frequent evaluations were not done for ABL height, arguably the most important parameter for the simulation of surface concentrations. Most evaluations were found for temperature. The reason for this is to be seen in the typical use of meteorology model results: mostly the results are used for weather forecast (NWP) and evaluations are done for that purpose. For NWP temperature is a very relevant parameter, while ABL height is of little interest to the normal NWP customer. Only 3 evaluations of ABL height were found (RMSE, Bias), too few to receive any robust information to compare with.



Table F: Meteorological parameters to be evaluated for EPISODES and LONG-TERM integration in order of relevance for air pollutant dispersion studies (from Schlünzen, Douros, 2011).

Meteorological Parameter

Where Relevance for concentration

forecasts

Influence on Averaging

ABL height - Very relevant All concentration values Per hour Wind direction At surface Very relevant Overall, per hour Upper air Very relevant

pollutant mixing, dispersion direction, chemical reactions Overall, per hour

Atmospheric stability

At surface Very relevant Per hour

Upper air Very relevant

All concentration values

Per hour Radiation Upper air Very relevant Photochemical reactions Overall, per hour Precipitation amount

At Surface Very relevant Deposition Overall

Cloud cover Upper air Relevant Photochemical reactions Overall Wind speed At surface Relevant Deposition Overall, per hour Upper air Relevant Pollutant mixing, dispersion Overall, per hour

At surface Relevant Deposition (dew) Overall, per hour Dew point tem-perature / relative humidity

Upper air Relevant Cloud formation, wet deposition

Overall, per hour

Mean sea level pressure

At surface Little relevance Chemical reactions Overall, per hour

Temperature At surface Little relevance Chemical reactions, phase partitioning

Overall, per hour

Upper air Little relevance Chemical reactions, phase partitioning

Overall, per hour

The second relevant parameter for AQ simulations is wind direction, which is also quite frequently evaluated (more than 50 evaluations for Bias, RMSE and r). Atmospheric stability has not been evaluated in the reviewed papers, the same is true for radiation and cloud cover. The reason is the too sparse observational data. Precipitation is also in the “very relevant” group (Table F), but again not often evaluated (Figure 3.1). A reason for this is not the too few data available, but the low spatial representativeness of the data and the need for long sampling times to receive reliable data (Bohnenstengel et al., 2011).

The relevant parameter cloud cover has not been found in the published evaluations surveyed, but wind speed (more than 100 evaluations for all performance measures except SKVAR) and some humidity measures are more frequently evaluated (more than 50 evaluations of specific humidity for Bias and r). The less relevant parameters such as the sea level pressure are also not frequently evaluated.

For the meteorological parameters more relevant for air quality studies, the evaluation measures Bias, RMSE and correlation coefficient r are more frequently used than index of agreement (IAO) or skill variance (SKVAR). The typical values received from published evaluations are summarized in Table G - Table I, based on the study performed by Conrady (2010). The summarised evaluations show that the model results have skewed error distributions even when averaging over at least 50 evaluations. While wind direction is on average very well hit (P50 = -1°, Average -4°), the certainty of the actual direction is not very large. This is clearly visible from the RMSE (average: 70°, Table H) and the correlation coefficient (average: 0.48, Table I). Wind speed is on average a little too high (average Bias 0.3 m/s, P50 = 0.1 m/s), correlation coefficient is 0.56 (average) and 0.63 (P50). This is somewhat better than for wind direction but lower as for temperature, where average correlation coefficients are 0.86 and P50=0.88. The larger correlation coefficients are not such a large surprise, as the temperature has very pronounced diurnal and annual signals, which are less visible for other



meteorological parameters. Since the correlation coefficient is mainly a measure for a reliable reproduction of a pattern, it can be expected that the values are larger for temperature than for, e.g. wind direction.

0

50

100

150

200

250

Bias RMSE r IOA SKVAR

Num

ber o

f eva

luat

ions

ABL height Wind direction PrecipitationWind speed Humidity Dew pointSurface pressure Temperature

Figure 3.1: Number of evaluations for different meteorological parameters using bias (Bias), root mean

square error (RMSE), correlation coefficient (r),index of agreement (IOA), skill variance(SKVAR) as included in the survey by Conrady (2010).

Table G: Overall model performances for Bias and different meteorological parameters (based on Conrady, 2010).

Ideal value No result Average P10 P50 P90 ABL height (m) 0.0 <50 - - - - Wind direction (°) 0.0 116 -4 -34 -1 22 Atmospheric stability (K/m) 0.0 <50 - - - - Radiation (W/m-2) 0.0 <50 - - - - Precipitation (mm) 0.0 <50 - - - - Cloud cover (1/8) 0.0 <50 - - - - Wind speed (m/s) 0.0 191 0.3 -0.6 0.1 1.3 Specific humidity (g/kg) 0.0 79 0.13 -0.40 0.03 0.92 Sea level pressure (hPa) 0.0 <50 - - - - Temperature (K) 0.0 221 -0.40 -1.8 -0.3 0.8

Table H: Overall model performances for RMSE and different meteorological parameters

(based on Conrady, 2010).

Ideal value No result Average P10 P50 P90 ABL height (m) 0.0 <50 - - - - Wind direction (°) 0.0 64 70 45 72 98 Atmospheric stability (K/m) 0.0 <50 - - - - Radiation (W/m-2) 0.0 <50 - - - - Precipitation (mm) 0.0 <50 - - - - Cloud cover (1/8) 0.0 <50 - - - - Wind speed (m/s) 0.0 149 1.9 1.3 1.8 2.5 Specific humidity (g/kg) 0.0 <50 - - - - Sea level pressure (hPa) 0.0 <50 - - - - Temperature (K) 0.0 120 2.7 1.5 2.5 4.4



Table I: Overall model performances for correlation coefficient r and different meteorological parameters (based on Conrady, 2010).

Ideal value No result Average P10 P50 P90 ABL height (m) 1.0 <50 - - - - Wind direction (°) 1.0 54 0.48 0.31 0.43 0.75 Atmospheric stability (K/m) 1.0 <50 - - - - Radiation (W/m-2) 1.0 <50 - - - - Precipitation (mm) 1.0 <50 - - - - Cloud cover (1/8) 1.0 <50 - - - - Wind speed (m/s) 1.0 114 0.56 0.27 0.63 0.77 Specific humidity (g/kg) 1.0 50 0.77 0.54 0.84 0.94 Sea level pressure (hPa) 1.0 <50 - - - - Temperature (K) 1.0 173 0.86 0.76 0.88 0.96

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Figure 3.2: Dependence of Bias on averaging time (a, c, d) or grid resolution (b) for wind direction (a, b, wind speed (c), temperature (d). Correlation coefficient for wind direction in dependence on grid size (b).

The bias of wind direction shows no clear dependence on averaging time (Figure 3.2a). However, this decision can not easily be made, since the number of results found by Conrady (2010) for long averaging times is small. For wind speed some more results were found in the literature surveyed by Conrady (2010), so that a tendency for smaller deviations at larger averaging times might be de-rived (Figure 3.2c). For temperature it seems that larger averaging times decrease the bias (Figure 3.2d).

Since wind direction is one of the more relevant meteorological parameters for air quality studies, the dependence of bias and correlation coefficient on grid resolution is also presented (Figure 3.2b).



No dependence can be derived for the correlation coefficient, since too few results were found in the publications. For the bias it seems that very high resolutions (order of 1 km) and about 12 km seem better – but this might be an artefact due to the limited number of publications and the large number of published evaluations found for about 3 km grid size. To determine any relation of model performance on e.g. averaging time or resolution in a statistically significant manner more publications are needed.

3.3. Concentrations Similar to the evaluation survey performed by Conrady (2010) for meteorological parameters, a survey on quantitative evaluations of concentrations were made that have been published in refe-reed journals. Fewer papers were found here. Figure 3.3 shows the number of evaluations. At least 10 individual evaluations were found for some gases NO2 (Bias, MAE, RMSE, IOA), SO2 (Bias, RMSE), O3 (Bias, MSE, RMSE) with ozone being by far the most frequently evaluated gas (more than 100 evaluations using Bias or RMSE). For particles more than 10 evaluations were found for PM1_sulphate (Bias), Nitrate (Bias), PM2.5 (Bias, MAE, RMSE).

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O3 PM1_sulfate

PM10_Suflate Ammonium

Nitrate PM10

PM2.5

Figure 3.3: Number of evaluation studies included in the literature survey using the quality measures bias

(Bias), Mean Absolute Error (MAE), root mean square error (RMSE), Index of Agreement (IOA) and correlation coefficient (r). The red (green) line denotes 10 (25) different evaluation values per evaluation

measure.

Figure 3.3 gives the evaluation measures in the order of use. For most gases or particles Bias is applied as evaluation measure, then MAE, RMSE, IOA, correlation coefficient. The last does not include more than 10 evaluation values for any of the gases or particles and is thus left out. None-theless, it should be kept in mind that average or percentile values that are based on only, e.g. 20 evaluations are quite restricted in their meaning, since e.g. the 10 percentile only includes two values.

The overall performances are summarized in Table J - Table L. The summarised evaluations show that the model results have a less skewed distribution of performance measures as found for the meteorological parameters. The Bias (Table J) suggests some systematic underestimation for NO2 and SO2. Some slight underestimation of ozone and PM2.5 seems also to be a common feature of several models. Furthermore, an (in model average) unbiased simulation of nitrate and PM1_sulfate



is found. However, as mentioned before, the number of evaluations included in the survey is very small, and therefore our conclusions are rather tentative. An additional limitation of the PM evalua-tion studies is that PM is the sum of a range of components. Therefore, overpredictions of some components and underpredictions of others often cancel each other out to some degree. As a result the PM evaluation metrics may not be representative of the usefulness of the model as a tool for the design of PM mitigation strategies.

Table J: Overall model performances for Bias and different concentrations.

Unit Ideal values No results Average P10 P50 P90 NO2 ppb 0.0 22 -7.0 -14.4 -5.6 -1.4 SO2 ppb 0.0 11 -0.5 -0.9 -0.5 -0.2 O3 ppb 0.0 121 -3.5 -10.4 -5.4 7.8 PM1_sulfate µgm-³ 0.0 16 74.3 -0.3 0.2 0.9 Nitrate µgm-³ 0.0 10 2.5 -0.8 0.0 8.3 PM2.5 µgm-³ 0.0 21 -2.1 -8.9 -3.2 4.8

Table K: Overall model performances for Mean Absolute Error (MAE) and different concentrations.

Unit Ideal values No results Average P10 P50 P90NO2 ppb 0.0 19 14.2 8.6 9.6 21.6 SO2 ppb 0.0 <10 - - - - O3 ppb 0.0 27 18.7 12.2 17.1 25.5 PM1_sulfate µgm-³ 0.0 <10 - - - - Nitrate µgm-³ 0.0 <10 - - - - PM2.5 µgm-³ 0.0 13 8.1 4.0 9.0 11.1

Table L: Overall model performances for RMSE and different concentrations.

Unit Ideal values No results Average P10 P50 P90NO2 ppb 0.0 27 22.5 12.5 14.5 36.7 SO2 ppb 0.0 16 11.0 8.6 10.2 16.9 O3 ppb 0.0 118 18.8 15.2 18.6 20.8 PM1_sulfate µgm-³ 0.0 <10 - - - - Nitrate µgm-³ 0.0 <10 - - - - PM2.5 µgm-³ 0.0 19 11.4 8.8 11.7 14.8

Table M: Overall model performances for IOA and different concentrations.

Unit Ideal values No results Average P10 P50 P90NO2 - 1.0 15 0.77 0.62 0.74 0.91 SO2 - 1.0 <10 - - - - O3 - 1.0 <10 - - - - PM1_sulfate - 1.0 <10 - - - - Nitrate - 1.0 <10 - - - - PM2.5 - 1.0 <10 - - - -

For the systematically underestimated NO2, the RMSE is large meaning that timing and/or spacing of measured data is not well reflected by several model results. Nonetheless, the IOA is quite high, showing that the general variability of the measured field is relatively well reflected by the models. Due to the very few results this survey is based on, the dependence of model performance on averaging time and grid size can only be illustrated for ozone (Figure 3.4). A clear dependence of Bias or RMSE on averaging time (Figure 3.4a) or grid size (Figure 3.4b) can not be derived; the spread of Bias/RMSE values seems more dependent on the number of evaluations than on averag-ing time or grid size. More evaluations need to be surveyed before conclusions can be drawn on this.



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Figure 3.4: Dependence of Bias and RMSE on averaging time (a) or grid resolution (b) for ozone.



4. Evaluation Strategy for MEGAPOLI Integrated Tools 4.1. Objectives of evaluation All the AQ indicators required by EU directives or needed for a deeper understanding of model results or model results differences are given in Table N. Additional indicators are summarised in Appendix A1. Several of these indicators are defined by MEGAPOLI WP8 with respect to the AQ and climate change policies (CC)5. These indicators are required for the application of the model-ling results on an aggregated level in the impact assessment model like ECOSENSE. In MEGA-POLI WP8 the efficiency of different AQ and CC policies will be assessed by the ECOSENSE model. Table N summarises the indicators most relevant for urban and vegetation effects of pollutants or that have been defined in the AQ directives for model evaluation. This table also summarizes all the AQ indicators that are used in the evaluation presented in Chapter 4. Annual average values as well as frequencies of exceedances are evaluated by comparing measured and modelled data. Target concentrations are those of particles (PM2.5, PM10), ozone, and –to a lesser account– NO2 and SO2. Reasons for differences in meteorological parameters like wind direction, wind speed and tempera-ture are also evaluated to provide additional context. For other meteorological parameters compari-son data are missing.

Table N: Relevant indicators as suggested by EU directives with some additions from MEGAPOLI WGs. Column “Error” denotes the allowed error when model and measurement results are compared following

EU directives, u shows the error is not clear up to now. Blue lines highlight indicators used in Chapter 5 for model evaluation.

Pol-lutant

Indicator Unit Definition Description EU Limit Value

Error

PM2.5 A_PM2.5 µg/m³ Annual average concentra-tion of PM2.5

Protection of human health 25 50%

PM10 A_PM10 µg/m³ annual average concentra-tion of PM10

Protection of human health 40 50%

NOD_PM10 # number of days (NOD) above 50 µg/m³ daily average

Protection of human health 35 u

Ozone SOMO35 ppb * days

Accumulated values of means above 35 ppb (daily maximum 8-hour average; accumulation period )

Indicator for ozone relevant for human health exposure and related health effects

AOT40 ppb h Accumulated Ozone concentration over a threshold of 40 ppb; accumulation period is May to July

Indicator for ozone to assess ozone-related impacts; default setting will be according to the growth season

18000 µg/m³ h

Ozone NOD_O3 # Number of days (NOD) above 120 µg/m³ for maximum 8-hour average value

Protection of human health 25

Max8 Maximum 8-hourly daily value

For model evaluation 50%

H_O3 Hourly average values For model evaluation 50% 5 Climate change policies have impacts to AQ policies and vice versa.



Pol-lutant

Indicator Unit Definition Description EU Limit Value

Error

during daytime NO2 A_NO2 µg/m³ Annual average concentra-

tion of NO2 Protection of human health 40 30%

NOH_NO2 # Number of hourly averaged concentrations above 200 µg/m³


H_NO2 µg/m³ Hourly data For model evaluation 50% NOx A_NOx µg/m³ Yearly average concentra-

tion of NOx Protection of vegetation 30

SO2 NOD_SO2 # Number of days with a daily average concentration above 125 µg/m³


NOH_SO2 # Number of hours with an hourly average concentra-tion above 350 µg/m³


H_SO2 µg/m³ Hourly average data For model evaluation 50% D_SO2 µg/m³ Daily average data For model evaluation 50% Winter_SO2 µg/m³ Average concentration

from 1 October to 31 March

Protection of vegetation 20

A_SO2 µg/m³ Annual average concentra-tion

Protection of vegetation 20 50%

The evaluations method outlined here shall be applicable to assess AQ model results of all scales, but not for obstacle-resolving models. Also it shall help to determine, which meteorological situa-tions are simulated well and which ones are not.

4.2. Model evaluation to be performed by model developer (or deeply interested user)

4.2.1 General evaluation The general evaluation mainly concerns the overall picture of a model to the scientific and user community. Based on the ideas of Fay et al. (2011), the pedigree of a model needs to be docu-mented, refereed publications should be available and the model needs to be documented. To get insight if this is the case for a model the model inventory at http://www.mi.uni-hamburg.de/costmodinv is a good help, since it includes information of the pedigree, on publica-tions and the available documentation.

4.2.2 Scientific evaluation In the scientific evaluation the model-predicted variables and processes essential for the simulations are defined. While this is relatively clear for the meteorological models (e.g. Table 3 in Schlünzen, Douros, 2011), the corresponding set of variables and processes essential for AQ models remains a topic of debate. The minimum set of parameters that should be predicted by an AQ model to allow its scientific evaluation using the set of indicators given in Table N is presented in Table O. These variables are the objectives of the evaluation and if a model does not deliver the corresponding data it is not usable in this framework.



Table O: Minimal set of variables predicted by an AQ model.

Pollutant Description of variable PM annual average concentration of PM2.5 PM daily average concentration of PM10 O3 maximum 8-hourly daily value O3 hourly average value during daytime NO2 hourly averaged concentrations NOx yearly average concentration SO2 hourly average concentration

As can be seen for NO2 and SO2 hourly data are needed. Most models will then also provide the other concentrations at the same time resolution, however, from the point of the AQ indicators and with respect to model evaluation these are less useful.

4.2.3 Benchmark test The Benchmark test defined in MEGAPOLI and used in this report is a simulation of AQ for the full year 2005, covering west and central Europe (Figure 4.1). This test is operationally evaluated (Section 4.3), but also gives insights about the differences and similarities in model performance by the three models considered here. The model results are also evaluated without considering timing (probabilistic evaluation, Section 4.3.2) and analysed for specific meteorological situations (Section 4.5). This aids the diagnostic and the dynamic evaluation. Since only routine meteorological observations are available, a compromise had to be made: only wind direction, wind speed and temperature have been used in the evaluation. Other parameters (e.g. boundary layer height) are not evaluated due to a lack of available comparison data.

4.3. Operational evaluation The operational evaluation compares the predictions with exact timing and spacing. It provides a first idea on model performance without providing explanations about the discrepancies between predictions and observations.

4.3.1 Evaluated indicators and evaluation measures used The evaluation measures Bias, RMSE, skill variance (SKVAR) are calculated as defined in Section 3.1. The concentrations to be evaluated are those of particulate matter components (PM10 and PM2.5), ozone (O3), Nitrogen Dioxide (NO2) and Sulphur Dioxide (SO2). In addition to the performance measures typically found in published model evaluations the models should be evaluated as suggested in the EU guidelines. The allowed discrepancy is 50%, except for A_NO2 where 30% is prescribed as the maximum difference (Table N). To evaluate the models correspondingly we use the hit rate (equation 12), which should be 1 if all model results meet the measured data within the allowed discrepancy. This is needed following the EU directives for a model to be applicable.

∑= ⎪⎩

⎪⎨

⎧≤<

−=

m

1i

iii

ii

else0

DPandOorErrO

OPfor1

m1H (12)

As can be seen from eq. (12) the differences can be very small for small observed values Oi, but the relative error might still be large. However, small concentration values Oi are in fact not very relevant for AQ purposes. Therefore, below a minimum measured concentration D the results are assumed to be valid, if the simulated value is also below D. This minimum value is assumed to be



10% of the EU limit value. Table P gives the values for the minimum concentration D and the values for Err, the allowed discrepancies following EU directives (Table N). Note that the value for Winter_SO2 is not in the guidelines but assumed in consistency with the annual value A_SO2.

Table P: Minimum concentration D and allowed relative difference Err for calculating hit rates

Pollutant Indicator Unit Definition D Err PM2.5 A_PM2.5 µg/m³ Annual average concentration of PM2.5 2.5 0.5 PM10 A_PM10 µg/m³ Annual average concentration of PM10 4 0.5 O3 Max8 µg/m Maximum 8-hourly daily value 12 0.5 O3 H_O3 µg/m³ Hourly average values during daytime 12 0.5 NO2 A_NO2 µg/m³ Annual average concentration of NO2 4 0.3 NO2 H_NO2 µg/m³ Hourly data of NO2 20 0.5 SO2 H_SO2 µg/m³ Hourly average data SO2 35 0.5 SO2 D_SO2 µg/m³ Daily average data SO2 12.5 0.5 SO2 A_SO2 µg/m³ Annual average concentration 2 0.5 SO2 Winter_SO2 µg/m³ Average concentration from 1 October to 31 March 2 0.5

4.3.2 Pollutant concentration measurements Observations of the concentrations of various pollutants have been provided by the Landesamt für Natur, Umwelt und Verbraucherschutz (LANUV) of Northrhine-Westfalia (NRW), Germany, for NRW (Figure 4.1b). This area was selected since is includes both areas with very high concentra-tions and rural background ones. With the Rhine-Ruhr area being one of Europe’s megacity areas, it is well-suited for a model evaluation within MEGAPOLI. Furthermore, the models that are evalu-ated here do normally not use these data in their in-house evaluations, since the data are not open to the scientific community. The data were specifically provided by LANUV to the group at the University of Hamburg to perform model evaluations. It is important to note that the data are not covering the whole of Europe but only a tiny part of it. Thus, the evaluation results presented here do not necessarily reflect the performance of these models in other areas of Europe. However, the corresponding insights from this exercise are still valuable.

Table Q: Number of stations at which the different chemical compounds were measured.

Chemical component Number of stations Time resolution PM10 20 hourly PM2.5 6 yearly

O3 11 hourly NO2 18 hourly SO2 10 yearly

Data from 6 (PM2.5) to 20 (PM10) stations were used in the evaluations (Table Q). The simulated concentrations were bi-linearly interpolated with an inversely weighted distance approach from the surrounding four model grid points to the actual position of the measurement site.



4.4. Model evaluation neglecting timing This evaluation concerns the number of exceedances and is a probabilistic evaluation. For this it is not essential to have the timing of the measured concentrations field correctly simulated. This is also true for the average values which will be evaluated as described in Section 4.3.1. For evalua-tion of exceedances the same data as for the operational evaluation are used (Section 4.3.2).

4.4.1 Evaluated indicators and evaluation measures used For the AQ indicators that are to be calculated without timing no quality measures for model evalu-ation are suggested by the EU guidelines. The indicators fall in two groups: a) the number of ex-ceedances above a threshold is to be calculated and to be compared with the number derived from measured data and b) yearly accumulated values above a threshold are to be calculated and, for AOT40, to be compared with a threshold value.

Number of exceedances The number of exceedances N is an indicator very sensitive to small differences in simulated concentrations. A small concentration difference may determine, if a day (hour) needs to be counted or left out in the number of exceedances. Therefore, the counting needs to account for allowed model uncertainties. As a simple solution to this problem we count three times, with and without inclusion of the error Err. This is assumed to be 10% of the threshold value T. The values for T and Err are given in The approaches suggested here for evaluating exceedances and accumulated values will help to overcome the problems caused in the evaluation by small errors in the model results. They are a first attempt to tackle this problem and might eventually help to identify the model quality measure that should be included in the EU AQ guidelines.

Table R for the different indicators. The average value for the number of exceedances, N, should then be as derived from measured data as given in Eq. (13).

∑= ⎩⎨⎧ +<−>>

=m

1i

iii

else0ErrTPorErrTPorTPfor1

m31N (13)

Accumulated values In this case also a threshold T is involved when the accumulated values <P> are determined. This makes the measure again somewhat arbitrary, since little differences in the simulated concentrations from the threshold determine if an hour/day is counted or not. Therefore, it is suggested to follow a similar approach for evaluating accumulated values as for the number of exceedances. Again some error of the simulated values should be allowed (Err), the accumulated values are then calculated in consideration of the allowed error Err (The approaches suggested here for evaluating exceedances and accumulated values will help to overcome the problems caused in the evaluation by small errors in the model results. They are a first attempt to tackle this problem and might eventually help to identify the model quality measure that should be included in the EU AQ guidelines.

Table R) and eventually lead to the accumulated values as defined in eq. (14).

∑= ⎩⎨⎧ +>−>>

=m

1i

iiii

else0ErrTPorErrTPorTPforP

m31P (14)

The approaches suggested here for evaluating exceedances and accumulated values will help to overcome the problems caused in the evaluation by small errors in the model results. They are a



first attempt to tackle this problem and might eventually help to identify the model quality measure that should be included in the EU AQ guidelines.

Table R: Minimum concentration D and allowed relative difference Err for calculating hit rates

Indicator type

Pollutant Indicator Unit Description T Err

a PM10 NOD_PM10 µg/m³ Daily average 50 5 a O3 NOD_O3 µg/m³ Maximum 8-hour average value of a day 120 12 a NO2 NOH_NO2 µg/m³ Hourly average 200 20 a SO2 NOD_SO2 µg/m³ Daily average 125 12.5a SO2 NOH_SO2 µg/m³ Hourly average 350 35 b O3 SOMO35 ppb Daily maximum 8-hour 35 3.5 b O3 AOT40 ppb Hourly value 40 4

4.5. Model evaluation for specific meteorological situations

4.5.1 Evaluation method and measures used This evaluation provides deeper insight into the reasons for model performance and aids the diag-nostic evaluation of the models. Here, two different approaches are used. First the meteorological data are analysed which are the drivers of pollutant advection and dispersion. For this purpose routine meteorological data are used (Section 4.5.2) and the model’s meteorological – in the present case input – data are evaluated very much in an operational way. Evaluation measures used are Bias, RMSE, correlation coefficient r and skill variance. At second the meteorological situations are clustered (Section 4.5.3) and the operational evaluation is repeated for each of the clusters separately. This shall help to identify, which meteorological situations are better or worse simulated than others and see if changes of concentration that are connected with different clusters are properly reproduced. The same evaluation measures as for the operational evaluation are used.

4.5.2 Meteorology data For the evaluation of integrated tools meteorological data are used from the SYNOP station network of Europe, provided by the German Weather Service (DWD). The station data of the area of 15°W - 20°E and 40°N - 60°N are used.



(a) (b)

Figure 4.1: Map of SYNOP stations (a) and concentration measurement sites (b) used in the evaluation.

This includes data from 1422 stations (Figure 4.1a). The meteorological data contain the surface values of temperature T, dew point temperature Td, wind speed ff, wind direction dd, sea level pressure p and specific humidity q, but wind speed and temperature are considered as well. The model data are interpolated from the four neighbouring grid points to the station coordinates by using a bi-linear inversely weighted distance approach. For evaluation daily average data are used.

4.5.3 Clustering of meteorological situations A clustering algorithm is employed to divide the whole year of 2005 into several typical weather types. The purpose is to distinguish differences in the model performance depending on the influ-ence of certain meteorological situations in Europe. For the classification the clustering software of COST7336 (Philipp et al., 2010) is used. Input data for the cluster analyses are the NCEP reanalyses data (Kalnay et al., 1996) with a 2.5° resolution. The 925 hPa geopotential height data have been clustered using the k-means method. This method samples the data points into k groups so that the sum of squares from data points to the assigned cluster centres is minimized. Further details on the clustering method used in this work can be found in Hartigan and Wong (1979). Nine classes were assigned for the year 2005, with every day of 2005 in a single class. An example for the distribution of the 925 hPa geopotential for the most common weather pattern of 2005 is given in Figure 4.2. The fields for the other eight weather types are shown in Appendix A1.

6 http://geo23.geo.uni-augsburg.de/cost733class-1.0



Figure 4.2: Clustered weather type 2 of the 925 hPa geopotential. This is the most common weather type in

2005. Additional clusters of the 925 hPa geopotential are presented in Appendix A1.

The total number of occurrences for every cluster determined for 2005 is shown in Figure 4.3. The first three clusters appeared for more than 60 days in 2005, whereas the classes 7 to 9 represent fewer than 20 days each. Classes 7 and 9 both show a strong low over the North Atlantic and the North Sea, respectively (Appendix A1). These weather situations appear rarely, but can imply severe weather. The most common weather type 2 represents a typical south-westerly atmospheric flow with low pressure over the North Atlantic and high pressure over Eastern Europe. Weather type 1 is connected with a high pressure system situated over southern Denmark, Weather type 3 with a low pressure system over the northern Baltic Sea.

Figure 4.3: Rate of appearance of weather types in 2005.



5. MEGAPOLI Model Evaluation Results The model evaluation results presented here focus on operational evaluation in Section 5.1. They do not consider timing (Section 5.2), and the model results are evaluated for different meteorological situations (Section 5.3).

5.1. Operational evaluation As outlined in Section 4.3.1, Bias, RMSE, correlation coefficient, skill variance and hit rates are calculated. For the evaluation concentration measurements of the main pollutants are used. The observed values contain measurement data of up to 20 stations situated in the Rhine-Ruhr area, which is close to the centre of the European model domain of most CTMs. Figure 5.1 shows the annual mean values of the observed and predicted concentrations of PM10, PM2.5, NO2, O3 and SO2 for the year 2005. Following Table P, the allowed errors Err for the annual means are ±13 μg m-3 (PM10), ±9 μg m-3 (PM2.5), ±11 μg m-3 (NO2), ±4 μg m-3 (SO2). An allowed error is not given by the EU directives for annual average O3 values. For PM2.5 and PM10 the mean values of CHIMERE and SILAM agree quite well with measured data. FARM is way too high for PM10 and PM2.5. Accordingly, the bias and the RMSE for FARM are high: 17 µg/m3 and 21 µg/m3, respectively. In the case of CHIMERE and SILAM the bias is negative. The skill variance (Figure 5.2d) for FARM is higher than 1, indicating that the observed variance is smaller than the simulated one. The other models, CHIMERE and SILAM, show values of 0.6 and 0.8, respectively, which indicate an underestimation of the observed variability. The correlation with observations amounts to 0.5-0.6 with best results for FARM.

Figure 5.1: Annual mean concentration values of the pollutants PM10, PM2.5, NO2, O3 and SO2 at LANUV

stations.

The reasons for the high particle concentrations in FARM are currently investigated.



(a) (b)

(c) (d)

Figure 5.2: Different evaluation measures calculated from the model results on the basis of LANUV stations for the year 2005. (a) Bias, (b) Root mean square error , (c) correlation coefficient, (d) skill variance.

All three models underestimate the mean value of NO2 (negative bias); here FARM is slightly better than the other models, SILAM slightly worse. The observed mean value is 35 µg/m3, the simulated NO2 values lay between 20 and 28 µg/m3. Thus, all the differences are within or close to the accept-able range. The RMSE (around 15), correlation coefficient (around 0.5) and skill variance (around 0.7) are similar for each for the three models. O3 concentrations are generally overestimated, with CHIMERE showing the highest mean value of nearly 50 µg/m3. FARM and SILAM show mean values slightly above the observed 35 µg/m3. The skill variance is best for CHIMERE and SILAM amounting to around 0.9. In case of FARM, the skill variance of O3 concentrations is as high as for PM10. Correlation coefficients are high for all three models indicating that the annual cycle is well captured by the models. For SO2 (as for PM2.5) only annual average values are available for comparison, thus only average concentrations can be compared. They show quite good agreement for FARM and SILAM but an underestimation just out of the error range for CHIMERE.



To gain some insight into the distribution of the discrepancies, frequency distributions of measured and observed values are presented in Figure 5.3 (PM10) and Figure 5.4 (O3, NO2). While the frequency distribution is well represented for the small PM10 concentrations by CHIMERE, high PM10 concentration values are underestimated. FARM, in contrast, frequently predicts too high concentrations and misses the frequently occurring lower concentration values. This explains the PM10 overestimation by FARM. SILAM predicts too frequently low concentrations, also does not reproduce the frequency of high concentrations properly.

(a) (b)

(c)

Figure 5.3: Frequency distribution for 5 μm m-3 intervals for observed data of PM10 (black) and

CHIMERE (a), FARM (b), SILAM (c). Ideal value of the median black line, median (per interval) of the

different model results coloured.

Figure 5.4 shows observed and simulated frequency distributions for ozone (a, c, e) and NO2 (b, d, f). The medians reflect a good agreement between the observed and simulated O3 concentrations. All three models show an underestimation of the frequency of values below 30 µg/m3 and an overestimation of values above about 50 µg/m3. SILAM shows the best results for the frequency distribution, but clearly underestimates high ozone concentrations. The reasons for this underesti-mation are currently investigated. The overestimation of the annual average O3 values by CHI-MERE can be explained by the frequency distribution, which is shifted to larger concentration values.



(a) (b)

(b) (c)

(e) (f)

Figure 5.4: Frequency distribution for O3 (a, c, e) and NO2 (b, d, f) with 5 μg m-3 intervals, for observed data (black) and CHIMERE (a, b), FARM (c, d), SILAM (e, f). Ideal value of the median black line, median

(per interval) of the different model results coloured.

The frequency distribution of NO2 (Figure 5.4b, d, f) shows a shift to smaller concentration values



for all three models – this explains the underestimation of the annual average values. For these small concentrations the measured data agree relative to the larger concentrations quite well with results from CHIMERE, FARM and SILAM. Low values (below 20 µg/m3) are too frequently simulated and high values too rarely predicted.

5.2. Evaluation results neglecting timing In this section the results of the evaluation without timing are shown. Also for this evaluation the results of the three models CHIMERE, FARM and SILAM, described in Chapter 2, have been used. The main focus is on annual averages and frequency of exceedances. Furthermore, the correlation of measured and simulated data, skill variance, hit rates and root mean square error (RMSE) are determined. Input for this are daily average values. Figure 5.5 shows the frequency of exceedances for PM10. As can be seen, none of the three models CHIMERE, FARM and SILAM is able to accurately simulate the frequency of exceedances for PM10 concentrations over 50 µg/m3. As it is indicated by the observations, there are large differ-ences between different stations. Some of them record more than 80 days with average concentra-tions above 50 µg/m3, while other stations count no more than 10 exceedances each. Not surpris-ingly, when having the high annual average values in mind, FARM overestimates the frequencies by far. At most stations the predicted frequency exceeds 80-120. The other two models, on the other hand, predict frequencies that are too low. SILAM performs quite well at stations 1, 4, 8, 10 to 13.

Figure 5.5: Frequency of exceedances of the threshold value of 50 µg/m3 for daily PM10 concentrations at

20 LANUV stations.

The frequency of exceedances for ozone (number of days with maximum 8-hourly mean concentra-tion larger 120 µg/m3) is well reproduced by the models FARM and CHIMERE (Figure 5.6), with CHIMERE being closer to the observed data with some overestimations (e.g. Station 8, 11). This is consistent with its overestimation of the annual average values. SILAM clearly underestimates ozone exceedances, which is visible from the frequency distribution (Figure 5.6). Viewing the frequency distribution in Figure 5.4 already shows that the high ozone concentrations are lower than measured.



Figure 5.6: Frequencies of exceedances of the daily maximum 8 hour mean of over 120 µg/m3 for ozone

concentrations at 11 LANUV stations.

AOT40, an integral value which accumulates the hourly ozone concentration values above 40 ppb is in its performance similar as the frequency of exceedances: FARM and CHIMERE (except at station 3, reason for this is currently investigated) are well reflecting the AOT40 values derived from measured data (Figure 5.7). They are slightly overestimating them, but SILAM considerably underestimates the values derived from measured data (by a factor of about 5).

Figure 5.7: O3 AOT40 values at the LANUV stations in 2005 (EMEP, 2008).

SOMO35, an integral value of the maximum 8-hour concentrations above 35 ppb (Figure 5.8), confirms the performance results for the models at high ozone concentrations: FARM and CHI-MERE (except site 3) are well within the range derived from measured data, while SILAM underes-timates the SOMO35 values. The SILAM values are a factor of 2-3 below the measured data, indicating that these concentration levels are already in the well simulated range of SILAM (Figure 5.4).



Figure 5.8: O3 SOMO35 values at the LANUV stations in 2005 (EMEP, 2008).

5.3. Evaluation results for specific meteorological situations In this section the dependence of the model results on the prevailing weather situation as clustered in Section 4.5.3 is analysed.

5.3.1 Evaluation of meteorological parameters on an annual average basis Comparisons of the mean annual values of meteorological variables show differences between the predictions and observations (Table S). The differences among the three model simulations are rather small. The simulated temperatures are up to 1.6 K higher, the simulated mean wind speed is too low. The two groups of meteorological input data are reflected in the similarity of the differ-ences: CHIMERE using MM5 results is separate from forcings of FARM (ECMWF analyses) and SILAM (ECMWF forecasts), which are similar with respect to temperature and wind speed. Differ-ences in wind direction are small.

Table S: Mean annual values of wind direction, wind speed and temperature for SYNOP stations.

Wind direction in ° Wind speed in m/s Temperature in °C CHIMERE 235 3.3 9.2 FARM 245 3.8 9.7 SILAM 235 3.9 9.7 Observations 225 4.6 8.1

Wind direction is derived from a histogram as the most frequent direction found in the histogram. The sample width of 10° is used in the histogram. Model results and measured data agree to mean south-westerly wind direction (Table S); the bias is in the range typically found also in other model results (Table G). It is for all model input data somewhat more turned to the right. The histogram of the wind direction is shown in Figure 5.13: It can be seen that the three model input data show a very similar wind direction distribution, which does not for all wind directions correspond well to the observed wind direction distribution. While the broad maximum of south-westerly winds is well represented, northerly wind directions are too frequently simulated and easterly to southerly too rare. The cluster analyses (Section 5.3.2) will show that the deficiencies can mainly be attributed to single weather clusters.



Figure 5.9: Distribution of the mean daily wind direction of the year 2005, sampled in 10° steps. Note that the meteorological input data of CHIMERE, FARM, SILAM are evaluated, not the AQ models themselves.

We further investigated the hit rates as defined in eq. (12) for the meteorological variables. There-fore, it was necessary to determine threshold values Err and D for each variable. For meteorological variables the values introduced by Cox et al. (1998) and also employed by Schlünzen and Katzfey (2003), Dierer et al. (2005), Schlünzen and Meyer (2007), Ries and Schlünzen (2009) using models METRAS and MM5 were used. As can be seen in Figure 5.10, the hit rate for the temperature reaches 75% (MM5 results as input for CHIMERE) to 80% (ECMWF analyses as input for FARM, ECMWF forecast as input for SILAM). This is in the same range as reported by Schlünzen and Meyer (2007) for an episode of 5 days. The hit rates of the wind speed and wind direction account to 40-50%, which is in the range of the median of the above mentioned evaluations. The wind direction is slightly better represented by the three meteorology data sets used as input for the models than the wind speed, also in agreement with the above mentioned evaluations.

Figure 5.10: Hit rates of temperature T, wind speed ff, wind direction dd for 2005. Note that the meteoro-

logical input data of CHIMERE, FARM, SILAM are evaluated, not the AQ models themselves.

5.3.2 Evaluation of meteorological parameters for different clusters The aim of this section is to analyse the influence of specific weather types on the simulation skills of the integrated tools. As it was introduced in Section 5.1.3., the geopotential height data of 2005



were sampled into nine classes. The daily data of the three models CHIMERE, FARM and SILAM and the comparison data of meteorology have been correspondingly sampled for the nine weather type classes. In Figure 5.11 the dependence of the 2 m temperature on the weather types is presented. The clus-ter-mean temperatures show a distinct dependence on the weather type. The classes 1, 2, 3, 5 and 7 represent a weather situation with temperatures above the annual average temperature (8.1°C as derived from the measured data), whereas class 6 indicates a rather cold weather situation with a mean 2 m temperature at freezing point. With a look at the typical geopotential fields of the differ-ent classes (figures in Appendix A1 and Figure 4.2), one can easily see that the classes 2, 5 and 7 represent typical west wind situations that transport maritime air masses from the Atlantic to Europe. Contrarily, high pressure over the British Isles causes cold air outbreaks from the subpolar regions straight to Europe (Cluster 6). The mean temperatures of the classes are well presented in all three models. The models FARM and SILAM use both ECMWF data for their simulations and are hardly to be distinguished in Figure 5.11. They show a larger difference from the observations than the CHIMERE meteorology input, which was already expected from the larger bias (Table S).

Figure 5.11: Mean values of 2 m temperature for the various weather types. Note that the meteorological

input data of CHIMERE, FARM, SILAM are evaluated, not the AQ models themselves.

The differences in wind speed between the 9 weather types are less than 2 m/s (Figure 5.12). As already expected when looking at the annual averages (Table S), the models underestimate the wind speeds. They do that in all weather types. As for the annual averaged values CHIMERE meteorol-ogy input data show the largest differences between model input and observations. The relations between the 9 different weather types are again well represented in all three input data sets. The highest mean wind speed occurs in class 7 and 9, in which low pressure dominates the North Atlantic. It is accompanied by a strong westerly air flow over Europe.



Figure 5.12: Mean values of wind speed in dependence of weather types. Note that the meteorological input

data of CHIMERE, FARM, SILAM are evaluated, not the AQ models themselves.

Figure 5.13: Most common wind direction in dependence of weather types. The bin size of each histogram member accounts to 10°. Note that the meteorological input data of CHIMERE, FARM, SILAM are evalu-

ated, not the AQ models themselves.

In most classes of the weather type classification the most common wind direction is in the range of 230-270°, except of the classes 1, 2 and 6 (Figure 5.13). The agreement between the model meteor-ology input data is, however, not as persistent as the wind direction. Remember when looking at the visible differences in Figure 5.13 that wind direction is periodic. The agreement is consistently within 30° for clusters 1, 2, 4, 5, 6, 7. Lowest agreement between model results and observations is found for class 3. In this case high pressure systems over the British Isles cause north-westerly winds which are indicated by the observations but all three models show north-easterly winds. The weather type 8 has a large difference for CHIMERE’s input data. For cluster 9 a slightly larger difference than 30° is found for FARM’s input data.



5.3.3 Evaluation of concentrations for different clusters Figure 5.14 shows that the relation between the different weather types is correctly reproduced by all three models. The pattern agrees best in FARM, when neglecting the bias already found, e.g. in the frequency of exceedances (Figure 5.5). As found for the exceedances, the models CHIMERE and SILAM are underestimating the PM10 concentration also in each class. The highest observed mean PM10 concentrations are found in class 6, which mainly occurs in winter as the average temperatures have shown (Figure 5.11). This is also found in FARM simulations, but not as pro-nounced as in CHIMERE and SILAM. Since Class 6 represents a winter weather situation we can expect that the anthropogenic emissions of PM10 are higher, the boundary layer and thus the vertical mixing is lower and, thus, PM concentrations increase. Similar to class 6, high pressure systems in Europe also occur in class 1 and 5; they tend to cause higher PM10 concentrations as well. Since they occur not only in winter, the average concentrations are somewhat lower than for cluster 6.

Figure 5.14: Mean values of PM10 concentration in dependence of weather types at LANUV stations.

Figure 5.15: Mean values of O3 concentration in dependence of weather types at LANUV stations.



Figure 5.15 shows the mean values of O3 concentrations in the different clusters. The observed differences account to 15 µg/m3. The highest observed mean concentrations are found for weather type 2 with over 40 µg/m3, the lowest for type 9. The dependence of the mean values on the weather type is also somewhat reproduced by the models, however, some (e.g., FARM) reproduce them with a larger amplitude. CHIMERE shows a good relation, but produces overall too high O3 con-centrations as already pointed out in Section 5.2. SILAM shows lowest differences between model and observations, but overestimates especially the concentrations in class 7. FARM simulations show some changes between over- and underestimation, which partly compensate each other so that the evaluation of results with neglect of timing shows quite good results (Section 5.2). The dependence of the mean values of NO2 concentrations (Figure 5.16) on the weather type class is similarly small as for O3 (Figure 5.15). The mean values range between 30 and 39 µg/m3. The highest mean NO2 concentration is found for weather type cluster 1, but then clusters 2, 5, 6, 8 give values that are very close. As could already be expected form the average values (Figure 5.1), all models underestimate the observed concentrations in every cluster. FARM simulations show the lowest differences between simulations and observations, but the best agreement of the relations between the different weather types is achieved by CHIMERE. This model shows a nearly constant bias of 10 µg/m3. SILAM achieves agreement only for some weather types.

Figure 5.16: Mean values of NO2 concentration for the various weather types at LANUV stations.

Summarizing, our evaluation results are consistent with previous studies evaluating the performance of a model for a new region. For the model results analysed here, the concentration differences found for the annual average concentrations are also found for most single weather clusters. This systematic error might be connected to representation of emissions and the chemical transforma-tions. The systematic underestimations can also be the result of gaps in the simulated PM10 com-ponents. Systematic overestimations like those for PM10 in FARM may be the result of problems with the simulated chemical transformations or the emission data. These problems may occur either inside or outside the focus region. One of the major problems in such evaluation exercises is the lack of chemical composition measurements for PM. This is a major obstacle in identifying the areas in which model improvement is needed. Problems in the emission data is most probably the cause for the errors in the NO2 predictions. Here, all models underestimate the measured data. These errors are probably local in the focus region but may also exist in other parts of Europe.



6. Conclusions The model evaluation approach developed for integrated tools within MEGAPOLI (Chapter 4) has been successfully applied to three models in the MEGAPOLI model suite. All three models have not been evaluated in detail for the region selected here, which includes the megacity of the Rhine-Ruhr area plus its neighbouring rural areas. The meteorology input data are consistent with each other and their discrepancies from the meas-urements are in the range found in literature for other model evaluations (Section 3.2). Mean values of temperature and wind are well represented with the hit rates reaching values over 50%. The differences between meteorology input data and observations for the averages of meteorological parameters were consistent for all weather situations. The whole year 2005 has been clustered in 9 classes of different weather types (Section 4.5.3) using NCEP 925 hPa geopotential heights. Only for cluster 3 (high pressure system over the British Isles) a difference in wind direction from meas-ured data was found in all investigated model inputs. However, an impact of this on the AQ model performance with respect to (increased) concentration differences could not be found. The model performance for the concentrations of the major pollutants depends on the pollutant examined. Systematic underestimations (overestimations) were found for PM10 for CHIMERE and SILAM (FARM). This results for CHIMERE and SILAM in too low values for the frequency of exceedance of the threshold of 50 μg m-3 for daily PM10 concentrations. For FARM the frequency of exceedance is overestimated. The differences are not only visible in the annual averages but also in different weather situations (all clusters) and might therefore not be a result of the meteorology, but of errors in atmospheric chemistry, emission data or model internal origin. Since NO2 is sys-tematically underestimated by all models the relation of NO/NO2 at the source might not to be representative for the study region. The low NO2 values can also partially explain the high ozone values found for many meteorological situations (but not for all and not for all models). The fre-quency distributions (Section 5.1) show that low values are overestimated, while high values are underestimated (except FARM for PM10). Reasons for this are currently investigated. Ozone annual averages agree well. Simulated ozone maximum values also agree well with measured data. Exceedances and AOT40 as well as SOMO35 consistently show that CHIMERE and FARM agree well, while SILAM underestimates the maxima derived from measurements. Reasons for this are currently investigated. From the study presented here it can be concluded that the introduction of clusters and frequency distributions for the simulated and measured data in model evaluation can indeed help to better understand reasons for differences. This method and concentration data from other regions of Europe will be further used in MEGAPOLI to analyse all model results of the MEGAPOLI model suite available for 2005.



Acknowledgements The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP/2007-2011 within the project MEGAPOLI, grant agreement n°212520. The ESF funded action COST 728 (Enhancing mesoscale meteorological modelling capabilities for air pollution and dispersion applications), the excellence cluster CliSAP (Integrated Climate System Analysis and Prediction; EXC177) at University of Hamburg, funded through the German Science Foundation (DFG), and the German BMBF funded project KLIMZUG-NORD (grant number 01LR0805D), gave valuable input to this report. The authors would like to thank Sabine Wilhelm of the Landesamt für Natur, Umwelt und Verbrauch-erschutz (LANUV) for providing the concentration measurement data for the model evaluation, and the German Meteorological Service (DWD) for providing the SYNOP meteorological measurement data for use in model evaluation.



Appendix 1. Indicators for Air Quality Assessment Table T: Indicators in addition to Table N needed for concentration of pollutants as suggested by MEGA-

POLI WG 8.

Concentrations of pollutants

Indicator

Unit

Definition

Description

Ozone

SOMO0

ppb * days

the yearly sum of means (over 0 ppb) (daily maximum 8-hour)

Indicator for ozone relevant for human health exposure and related health effects

AOT40crops ppb*h for crops over 3 months (e.g. May–July)

AOT40forest ppb*h for forest trees over 6 months (April–September)

PPM2.5 (without sea salt)

µg/m³ yearly average concentra-tion of Particulate Matter < 2.5 µm

PPMcoarse (without sea salt)

µg/m³ yearly average concentra-tion of Particulate Matter > 2.5 µm and < 10 µm

PM2.5 (sulfates, total mass of salt, not only S or SO4)


PM Coarse (sulfates, total mass of salt, not only S or SO4)

µg/m³ PM(coarse) = PM10-PM2.5, yearly average concentration

PM2.5 (nitrates, total mass of salt, not only S or SO4)


PM Coarse (nitrates, total mass of salt, not only S or SO4)


SIA - total Secondary Inorganic Aerosols (PM2.5)


SOA - total Secondary Organic Aerosols (PM2.5)


SIA - total Secondary Inorganic Aerosols (PM Coarse)


SOA - total Secondary Organic Aerosols (PM Coarse)


Benzene µg/m³ yearly average concentra-

tion of benzene



Table U: Indicators needed for deposition as suggested by MEGAPOLI WG 8.

Depositions (dry & wet)

Unit

Definition

Description

OXN_dry µgN/m² Yearly accumulated amount of depos-

ited oxidized N deposition of oxidized N compounds

Nred_dry µgN/m² Yearly accumulated amount of depos-ited reduced N

deposition of reduced N com-pounds

OXS_dry µgS/m² Yearly accumulated amount of depos-ited oxidized S

deposition of oxidized S com-pounds

Sred_dry µgS/m² Yearly accumulated amount of depos-ited reduced S

deposition of reduced S com-pounds

OXN_wet µgN/m² Yearly accumulated amount of depos-ited oxidized N

deposition of oxidized N compounds

Nred_wet µgN/m² Yearly accumulated amount of depos-ited reduced N

deposition of reduced N com-pounds

OXS_wet µgS/m² Yearly accumulated amount of depos-ited oxidized S

deposition of oxidized S com-pounds

Sred_wet µgS/m² Yearly accumulated amount of depos-ited reduced S

deposition of reduced S com-pounds

deposition of Ions eq [H+]/m² Yearly accumulated amount of depos-ited Ion in H+ equivalents

Table V: Indicators needed for diagnostic evaluation as suggested by MEGAPOLIM WG8. Blue lines

indicate evaluations performed here.

Concentrations of pollutants Unit Definition PPM1 µg/m³ yearly average concentration of Particulate Matter < 1µm PM1 (sulfates) µg/m³ yearly average concentration of Particulate Matter < 1µm PM1 (Nitrates) µg/m³ yearly average concentration of Particulate Matter < 1µm Secondary Inorganic Aerosols (PM1)

µg/m³ yearly average concentration of Particulate Matter < 1µm

Secondary Organic Aerosols (PM1) µg/m³ yearly average concentration of Particulate Matter < 1µm PN (distinguished by different size modes)

#/m³ yearly average particle numbers

EC µg/m³ yearly average concentration of EC OC µg/m³ yearly average concentration of OC O3 µg/m³ yearly average concentration of O3 SO2 µg/m³ yearly average concentration of SO2



Appendix 2. Clusters Based on 925 hPa Geopotential Height Data for 2005

Figure A.1: Weather type 1 FigureA.2: Weather type 3

FigureA.3: Weather type 4 FigureA.4 Weather type 5



FigureA.5 Weather type 6 FigureA.6 Weather type 7

FigureA.7 Weather type 8 FigureA.8: Weather type 9



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Thornes, J.E. and D.B. Stephenson (2001): How to judge the quality and value of weather forecast products. Meteorol. Appl., 8, 307-314



Previous MEGAPOLI reports Previous reports from the FP7 EC MEGAPOLI Project can be found at: http://www.megapoli.info/

Collins W.J. (2009): Global radiative forcing from megacity emissions of long-lived greenhouse gases. Deliverable 6.1, MEGAPOLI Scientific Report 09-01, 17p, MEGAPOLI-01-REP-2009-10, ISBN: 978-87-992924-1-7

http://megapoli.dmi.dk/publ/MEGAPOLI_sr09-01.pdf

Denier van der Gon, HAC, AJH Visschedijk, H. van der Brugh, R. Dröge, J. Kuenen (2009): A base year (2005) MEGAPOLI European gridded emission inventory (1st version). Deliverable 1.2, MEGAPOLI Scientific Report 09-02, 17p, MEGAPOLI-02-REP-2009-10, ISBN: 978-87-992924-2-4


Baklanov A., Mahura A. (Eds) (2009): First Year MEGAPOLI Dissemination Report. Deliverable 9.4.1, MEGAPOLI Scientific Report 09-03, 57p, MEGAPOLI-03-REP-2009-12, ISBN: 978-87-992924-3-1


Allen L., S Beevers, F Lindberg, Mario Iamarino, N Kitiwiroon, CSB Grimmond (2010): Global to City Scale Urban Anthropogenic Heat Flux: Model and Variability. Deliverable 1.4, MEGA-POLI Scientific Report 10-01, MEGAPOLI-04-REP-2010-03, 87p, ISBN: 978-87-992924-4-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-01.pdf

Pauli Sievinen, Antti Hellsten, Jaan Praks, Jarkko Koskinen, Jaakko Kukkonen (2010): Urban Morphological Database for Paris, France. Deliverable D2.1, MEGAPOLI Scientific Report 10-02, MEGAPOLI-05-REP-2010-03, 13p, ISBN: 978-87-992924-5-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-02.pdf

Moussiopoulos N., Douros J., Tsegas G. (Eds.) (2010): Evaluation of Zooming Approaches De-scribing Multiscale Physical Processes. Deliverable D4.1, MEGAPOLI Scientific Report 10-03, MEGAPOLI-06-REP-2010-01, 41p, ISBN: 978-87-992924-6-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-03.pdf

Mahura A., Baklanov A. (Eds.) (2010): Hierarchy of Urban Canopy Parameterisations for Different Scale Models. Deliverable D2.2, MEGAPOLI Scientific Report 10-04, MEGAPOLI-07-REP-2010-03, 50p, ISBN: 978-87-992924-7-9 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-04.pdf

Dhurata Koraj, Spyros N. Pandis (2010): Evaluation of Zooming Approaches Describing Multi-scale Chemical Transformations. Deliverable D4.2, MEGAPOLI Scientific Report 10-05, MEGAPOLI-08-REP-2010-01, 29p, ISBN: 978-87-992924-8-6 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-05.pdf

Igor Esau (2010): Urbanized Turbulence-Resolving Model and Evaluation for Paris. Deliverable D2.4.1, MEGAPOLI Scientific Report 10-06, MEGAPOLI-09-REP-2010-03, 20p, ISBN: 978-87-992924-9-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-06.pdf

Grimmond CSB., M. Blackett, M.J. Best, et al. (2010): Urban Energy Balance Models Comparison. Deliverable D2.3, MEGAPOLI Scientific Report 10-07, MEGAPOLI-10-REP-2010-03, 72p, ISBN: 978-87-993898-0-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-07.pdf

Gerd A. Folberth, Steve Rumbold, William J. Collins, Tim Butler (2010): Determination of Radia-tive Forcing from Megacity Emissions on the Global Scale. Deliverable D6.2, MEGAPOLI



Scientific Report 10-08, MEGAPOLI-11-REP-2010-03, 19p, ISBN: 978-87-993898-1-0 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-08.pdf

Thomas Wagner, Steffen Beirle, Reza Shaiganfar (2010): Characterization of Megacity Impact on Regional and Global Scales Using Satellite Data. Deliverable D5.1, MEGAPOLI Scientific Report 10-09, MEGAPOLI-12-REP-2010-03, 25p, ISBN: 978-87-993898-2-7 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-09.pdf

Baklanov A., Mahura A. (Eds.) (2010): Interactions between Air Quality and Meteorology, Deliv-erable D4.3, MEGAPOLI Scientific Report 10-10, MEGAPOLI-13-REP-2010-03, 48p, ISBN: 978-87-993898-3-4 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-10.pdf

Baklanov A. (Ed.) (2010): Framework for Integrating Tools. Deliverable D7.1, MEGAPOLI Scien-tific Report 10-11, MEGAPOLI-14-REP-2010-03, 68p, ISBN: 978-87-993898-4-1 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-11.pdf

Sofiev M., Prank M., Vira J., and MEGAPOLI Modelling Teams (2010): Provision of global and regional concentrations fields from initial baseline runs. Deliverable D5.2, MEGAPOLI Tech-nical Note 10-12, MEGAPOLI-15-REP-2010-03, 10p. http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-12.pdf

H.A.C. Denier van der Gon, J. Kuenen, T. Butler (2010): A Base Year (2005) MEGAPOLI Global Gridded Emission Inventory (1st Version). Deliverable D1.1, MEGAPOLI Scientific Report 10-13, MEGAPOLI-16-REP-2010-06, 20p, ISBN: 978-87-993898-5-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-13.pdf

Lawrence M. G., Butler T. M., Collins W., Folberth G., Zakey A., Giorgi F. (2010): Meteorological Fields for Present and Future Climate Conditions. Deliverable D6.5, MEGAPOLI Technical Note 10-14, MEGAPOLI-17-REP-2010-09, 9p. http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-14.pdf

Beekmann M., Baltensperger U., and the MEGAPOLI campaign team (2010): Database of Chemi-cal Composition, Size Distribution and Optical Parameters of Urban and Suburban PM and its Temporal Variability (Hourly to Seasonal). Deliverable D3.1, MEGAPOLI Scientific Report 10-15, MEGAPOLI-18-REP-2010-10, 21p, ISBN: 978-87-993898-6-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-15.pdf

Beekmann M., Baltensperger U., and the MEGAPOLI campaign team (2010): Database of the Impact of Megacity Emissions on Regional Scale PM Levels. Deliverable D3.4, MEGAPOLI Scientific Report 10-16, MEGAPOLI-19-REP-2010-10, 29p, ISBN: 978-87-993898-7-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-16.pdf

Kuenen J., H. Denier van der Gon, A. Visschedijk, H. van der Brugh, S. Finardi, P. Radice, A. d’Allura, S. Beevers, J. Theloke, M. Uz-basich, C. Honoré, O. Perrussel (2010): A Base Year (2005) MEGAPOLI European Gridded Emission Inventory (Final Version). Deliverable D1.6, MEGAPOLI Scientific Report 10-17, MEGAPOLI-20-REP-2010-10, 37p, ISBN: 978-87-993898-8-9 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-17.pdf

Karppinen A., Kangas L., Riikonen K., Kukkonen J., Soares J., Denby B., Cassiani M., Finardi S., Radice P., (2010): Evaluation of Methodologies for Exposure Analysis in Urban Areas and Application to Selected Megacities. Deliverable D4.4, MEGAPOLI Scientific Report 10-18, MEGAPOLI-21-REP-2010-11, 29p, ISBN: 978-87-993898-9-6 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-18.pdf

Soares J., A. Karppinen, B. Denby, S. Finardi, J. Kukkonen, M. Cassiani, P. Radice, M.Williams



(2010): Exposure Maps for Selected Megacities. Deliverable D4.5, MEGAPOLI Scientific Re-port 10-19, MEGAPOLI-22-REP-2010-11, 26p, ISBN: 978-87-92731-00-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-19.pdf

Rumbold S.T., W.J. Collins, G.A. Folberth (2010): Comparison of Coupled and Uncoupled Models. Deliverable D6.4, MEGAPOLI Scientific Report 10-20, MEGAPOLI-23-REP-2010-11, 15p, ISBN: 978-87-92731-01-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-20.pdf

Baklanov A., Mahura A. (Eds) (2010): Second Year MEGAPOLI Dissemination Report. Deliver-able D9.4.2, MEGAPOLI Scientific Report 10-21, MEGAPOLI-24-REP-2010-12, 89p, ISBN: 978-87-92731-02-9


Moussiopoulos N., Douros J., Tsegas G. (Eds) (2010): Evaluation of Source Apportionment Meth-ods. Deliverable D4.6, MEGAPOLI Scientific Report 10-22, MEGAPOLI-25-REP-2010-12, 54p, ISBN: 978-87-92731-03-6


Theloke J., M.Blesl, D. Bruchhof, T.Kampffmeyer, U. Kugler, M. Uzbasich, K. Schenk, H. Denier van der Gon, S. Finardi, P. Radice, R. S. Sokhi, K. Ravindra, S. Beevers, S. Grimmond, I. Coll, R. Frie-drich, D. van den Hout (2010): European and megacity baseline scenarios for 2020, 2030 and 2050. Deliverable D1.3, MEGAPOLI Scientific Report 10-23, MEGAPOLI-26-REP-2010-12, 57p, ISBN: 978-87-92731-04-3


Galmarini S., Vinuesa J.F., Cassiani M., Denby B., Martilli A., (2011): Evaluation of Sub-Grid Models with Interactions between Turbulence and Urban Chemistry. Recommendations for Emission Inventories Improvement. Deliverable D2.6, MEGAPOLI Scientific Report 11-01, MEGAPOLI-27-REP-2011-01, 41p, ISBN: 978-87-92731-05-0


Butler T., H.A.C. Denier van der Gon, J. Kuenen (2011): The Base Year (2005) Global Gridded Emission Inventory used in the EU FP7 Project MEGAPOLI (Final Version). MEGAPOLI Scientific Report 11-02, MEGAPOLI-28-REP-2011-01, 27p, 978-87-92731-06-7


Schlünzen K.H., M. Haller (Eds) (2011): Evaluation of Integrated Tools. MEGAPOLI Scientific Report 11-03, MEGAPOLI-29-REP-2011-03, 51p, 978-87-92731-07-4




MEGAPOLI

Megacities: Emissions, urban, regional and Global Atmos-

pheric POLlution and climate effects, and Integrated tools for

assessment and mitigation

EC FP7 Collaborative Project

2008-2011

Theme 6: Environment (including climate change) Sub-Area: ENV-2007.1.1.2.1:

Megacities and regional hot-spots air quality and climate

MEGAPOLI Project web-site http://www.megapoli.info

MEGAPOLI Project Office Danish Meteorological Institute (DMI) Lyngbyvej 100, DK-2100 Copenhagen, Denmark

E-mail: [email protected] Phone: +45-3915-7441 Fax: +45-3915-7400

MEGAPOLI Project Partners

• DMI - Danish Meteorological Institute (Denmark) - Contact Persons: Prof. Alexander Baklanov (co-ordinator), Dr. Alexander Mahura (manager)

• FORTH - Foundation for Research and Technology, Hellas and University of Patras (Greece) - Prof. Spyros Pandis (vice-coordinator)

• MPIC - Max Planck Institute for Chemistry (Germany) - Dr. Mark Lawrence (vice-coordinator)

• ARIANET Consulting (Italy) – Dr. Sandro Finardi • AUTH - Aristotle University Thessaloniki (Greece)

- Prof. Nicolas Moussiopoulos • CNRS - Centre National de Recherche Scientifique

(incl. LISA, LaMP, LSCE, GAME, LGGE) (France) – Dr. Matthias Beekmann

• FMI - Finnish Meteorological Institute (Finland) – Prof. Jaakko Kukkonen

• JRC - Joint Research Center (Italy) – Dr. Stefano Galmarini

• ICTP - International Centre for Theoretical Physics (Italy) - Prof. Filippo Giorgi

• KCL - King's College London (UK) – Prof. Sue Grimmond

• NERSC - Nansen Environmental and Remote Sensing Center (Norway) – Dr. Igor Esau

• NILU - Norwegian Institute for Air Research (Norway) – Dr. Andreas Stohl

• PSI - Paul Scherrer Institute (Switzerland) – Prof. Urs Baltensperger

• TNO-Built Environment and Geosciences (The Netherlands) – Prof. Peter Builtjes

• MetO - UK MetOffice (UK) – Dr. Bill Collins • UHam - University of Hamburg (Germany) – Prof.

Heinke Schluenzen • UHel - University of Helsinki (Finland) – Prof.

Markku Kulmala • UH-CAIR - University of Hertfordshire, Centre for

Atmospheric and Instrumentation Research (UK) – Prof. Ranjeet Sokhi

• USTUTT - University of Stuttgart (Germany) – Prof. Rainer Friedrich

• WMO - World Meteorological Organization (Switzerland) – Dr. Liisa Jalkanen

• CUNI - Charles University Prague (Czech Repub-lic) – Dr. Tomas Halenka

• IfT - Institute of Tropospheric Research (Ger-many) – Prof. Alfred Wiedensohler

• UCam - Centre for Atmospheric Science, Univer-sity of Cambridge (UK) – Prof. John Pyle

Work Packages

WP1: Emissions (H. Denier van der Gon, P. Builtjes)

WP2: Megacity features (S. Grimmond, I. Esau)

WP3: Megacity plume case study (M. Beekmann, U. Baltensperger)

WP4: Megacity air quality (N. Moussiopoulos)

WP5: Regional and global atmospheric composition (J. Kukkonen, A. Stohl)

WP6: Regional and global climate impacts (W. Collins, F. Giorgii)

WP7: Integrated tools and implementation (R. Sokhi, H. Schlünzen)

WP8: Mitigation, policy options and impact assessment (R. Friedrich, D. van den Hout)

WP9: Dissemination and Coordination (A. Baklanov, M. Lawrence, S. Pandis)

Documents

MEGAPOLI Scientific Report 11-03 Evaluation of Integrated Toolsmegapoli.dmi.dk/publ/MEGAPOLI_sr11-03.pdf · 2011-05-25 · MEGAPOLI Scientific Report 11-03 Evaluation of Integrated