Copernicus AtmosphereMonitoringService

Report

MOCAGE regional forecasting system and performance

June-July-August 2015

ISSUED BY:Meteo-France

Date: 15/10/2015

REF.: CAMS_0200_MOCAGE

Table of Contents1Executive Summary..............................................................................................................................3

2MOCAGE fact sheet.............................................................................................................................4

2.1Products portfolio.........................................................................................................................4

2.2Performance statistics...................................................................................................................4

2.3Availability statistics......................................................................................................................4

2.4Assimilation and forecast system: synthesis of main characteristics.............................................4

3MOCAGE background information.......................................................................................................5

3.1Forward model..............................................................................................................................5

3.1.1Model geometry....................................................................................................................6

3.1.2Forcings and boundary conditions.........................................................................................6

3.1.3Dynamical core......................................................................................................................6

3.1.4Physical parametrizations......................................................................................................6

3.1.5Chemistry and aerosols..........................................................................................................7

3.2Assimilation system......................................................................................................................7

3.3Development plans for the next months......................................................................................8

References.............................................................................................................................................8

ANNEX: Verification report for June-July-August 2015........................................................................10

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1 Executive SummaryThe Copernicus Atmosphere Monitoring Service (CAMS, www.copernicus-atmosphere.eu) isestablishing the core global and regional atmospheric environmental service delivered as acomponent of Europe's Copernicus program. Based on the developments achieved during theprecursor MACC (Monitoring Atmospheric Composition and Climate) projects, the regionalforecasting service provides daily 4-days forecasts of the main air pollutants ozone, NO2, and PM10,from 7 state-of-the-art atmospheric chemistry models and from the median ensemble calculatedfrom the 7 model forecasts.

This report documents the MOCAGE regional forecasting system and its statistical performanceagainst in-situ surface observations for the quarter that covers June, July and August 2015.Verification is done using the up-to-date methods described in the MACC-II dossiers coveringquarters #15 and #16. In this dossier, the dataset of surface observations used for verification iscollected from the EEA/EIONET NRT database. During the present phase of implementation of the “e-reporting” stream at the EEA, Meteo-France has got access to the most complete set of observationsby downloading data from both the EEA/EIONET NRT and the new “e-reporting” streams. As for thepast three years, the verification statistics are based on the use of only representative sites selectedfrom the objective classification proposed by Joly and Peuch (Atmos. Env. 2012).

The meteorological conditions of this summer 2015 were particularly challenging for ozone forecasts,with a succession of periods characterized by hot days with fresh periods. Ozone performance forMOCAGE were accordingly not as good as during Summer 2014. However, MOCAGE performsreasonably well for ozone daily maximum forecasts. NO2 scores are similar to last Summer, althoughslightly better for correlation. Performance for PM10 remains poor but it is expected to improve withthe implementation of secondary aerosols in the coming months.

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2 MOCAGE fact sheet2.1 Products portfolio

Name Description Freq. Available for users at Species Time spanFRC Forecast at

surface,50m,250m,500m,1000m,2000m,3000m, 5000m above ground

Daily 4:30 UTC O3, NO2, CO, SO2,PM2.5, PM10, NO, NMVOC, PANs,Birch pollen at surface during season

0-96h, hourly

ANA Analysis at the surface Daily 11:00 UTC for the daybefore

O3 , NO2 0-24h of the day before, hourly

2.2 Performance statisticsSee annex

2.3 Availability statisticsThe statistics below describe the ratio of days for which the MOCAGE model outputs were availableon time to be included in the ensemble fields (analyses and forecasts) that are computed at Météo-France. They are based on the following timeliness requirements: 11:30 UTC for the analysis, 5:00UTC for the 0-24h forecast, 6:00 UTC for the 25-48h forecast, 6:45 UTC for the 49-72h forecast and7:30 UTC for the 73-96h forecast.

The following labels are used referring to the reason of the problem causing unavailability:

(P) if the failure comes from the individual regional model production chain

(T) if this is related to a failure of the data transmission from the partners to Météo-France centralsite

(C) if this is a failure due to the central processing at Météo-France (MF)

Quarter June/July/August 2015

The ratio of days on which MOCAGE forecasts and analyses were provided on time is:

Terms Analyses 0-24h frc 25-48h frc 49-72h frc 73-96h frc

Availability 100 % 100 % 99 % 98 % 98 %

MOCAGE analyses were delivered on time everyday in June/July/August 2015.

Availability of MOCAGE forecasts was incomplete on 30(P) June 2015, on 17(P) and on 30(P) July2015, but the first 24h forecasts were delivered on time everyday. On these three days, the failure todeliver the 96h forecasts on time were due to synchronizing deficiencies (after HPC change, at theend of pollen season, or between post-processing and model run) on the operational chain. Theseproblems were fixed afterwards.

2.4 Assimilation and forecast system: synthesis of maincharacteristics

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Assimilation and Forecast SystemHorizontal resolution 0.2° regular lat-lon gridVertical resolution 47 layers up to 5 hPa

Lowest layer thickness about 40 mAbout 8 layers below 2 km

Gas phase chemistry RACM (tropospheric) and REPROBUS (stratospheric)

Heterogeneous chemistry Only reactions on Polar Stratospheric Clouds (stratosphere) yet

Aerosol size distribution BinsInorganic aerosols Not implemented in current MACC versionSecondary organic aerosols Not implemented in current MACC versionAqueous phase chemistry Aqueous reactions for sulphate productionDry deposition/sedimentation Resistance approach (Michou et al., 2004) for

gases, (Nho-kim et al., 2005) for aerosolMineral dust Included: see evaluation in (Sic et al. 2014)Sea Salt Included: see evaluation in (Sic et al. 2014)Boundary values MOCAGE global domain (2°) for all the chemical

species with optional relaxation towards G-RG values for species : ozone, CO, HCHO, NO, NO2, HNO3, PAN, CH4, ethane, isoprene

Initial values 24h forecast from the day beforeAnthropogenic emissions TNO (2009) inventory binned at 0.2° resolutionBiogenic emissions Fixed monthly biogenic emission, based upon

Simpson approach.Forecast SystemMeteorological driver 12:00 UTC operational IFS forecast for the day

beforeAssimilation System Assimilation method 3d-varObservations Ozone and nitrogen dioxide in situ data from EEA

(other compounds not yet assimilated)

3 MOCAGE background information3.1 Forward modelThe MOCAGE 3D multi-scale Chemistry and Transport Model has been designed for both researchand operational applications in the field of environmental modelling. Since 2000, MOCAGE allows tocover a wide range of topical issues ranging from chemical weather forecasting, tracking andbacktracking of accidental point source releases, trans-boundary pollution assessment, assimilationof remote sensing measurements of atmospheric composition, to studies of the impact ofanthropogenic emissions of pollutants on climate change, with over 40 references in theinternational refereed literature. For this, MOCAGE offers a flexible structure that allows to adapt themodel CPU/MEM requirements and parameterizations to the different applications. MOCAGE hasbeen run daily since 5 years and in 2004, Météo-France joined the partnership consortium andoperational platform "Prév'Air" (http://www.prevair.org, Rouil et al., 2009) in charge of the pollutionmonitoring and forecasting over France.

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3.1.1 Model geometryMOCAGE considers simultaneously the troposphere and stratosphere at the planetary scale and overlimited-area sub-domains at higher horizontal resolution, the model providing (by default) its owntime-dependent chemical boundary conditions. For MACC-II, MOCAGE configuration comprises aglobal domain (2°) and the new MACC-II regional domain (0.5°, 25°W-45°E and 30°N-70°N,corresponding to an extension of 20° in longitude and 5° in latitude compared to MACC domain).Only the products on the MACC-II domain are used for the regional services. In the vertical, 47 hybrid(,P) levels go from the surface up to 5 hPa, with approximately 8 levels in the Planetary BoundaryLayer (ie below 2km), 16 in the free troposphere and 24 in the stratosphere. The thickness of thelowest layer is about 40 m.

3.1.2 Forcings and boundary conditions3.1.2.1 MeteorologyThe MOCAGE configuration that has been developed and operated since the MACC project runs inoff-line mode, forced by IFS meteorological analyses or forecasts. For the daily production, the IFSdaily operational forecast are used: 0-108h (since the extension to 96h of the MACC-II forecast) threehourly forecasts of horizontal winds, humidity and surface pressure are taken from the 1200 suite.

3.1.2.2 ChemistryChemical initial values are provided by MOCAGE 24h forecast from the day before. The use ofMOCAGE global domain helps introducing smoothly, on the horizontal as well as on the vertical,these chemical boundary conditions into the regional MACC domain. A model option exists to relaxthe global MOCAGE domain towards the three-hourly C-IFS values for available species: ozone, CO,HCHO, NO, NO2, HNO3, PAN, CH4, ethane, isoprene. Reactivating this option is under consideration.

3.1.2.3 Surface emissionsSurface emissions are pre-processed using the SUMO2 pre-processor. MACC-II/TNO inventory of 2009 is used.

3.1.3 Dynamical coreThe dynamical forcings from IFS (hydrostatic winds, temperature, humidity and pressure) feed theadvection scheme, as well as the physical and chemical parameterizations. Forcings are read-in every3 hours, and are linearly interpolated to yield hourly values, which is the time-step for advection;smaller time-steps are used for physical processes and chemistry, but the meteorological variablesare kept constant over each hour. MOCAGE is based upon a semi-lagrangian advection scheme(Williamson and Rasch, 1989), using a cubic polynomial interpolation in all three directions.Evaluation of transport in MOCAGE using Radon-222 experiments can be found in (Josse et al., 2004).

Concerning physical and chemical parameterizations, an operator splitting approach is used.Parameterizations are called alternatively in forward and reverse order, with the objective to reducesystematic errors. Several options are available within MOCAGE; we briefly mention here the optionsused for MACC-II.

3.1.4 Physical parametrizations

3.1.4.1 Turbulence and convectionFor sub-gridscale transport processes, vertical diffusion is treated following Louis (1979) andtransport by convection is from Bechtold et al. (2001). Scavenging within convective clouds isfollowing Mari et al. (2000), allowing to compute wet removal processes directly within the

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convective transport parameterisation. Wet deposition in stratiform clouds and below clouds followsGiorgi and Chameides (1986).

3.1.4.2 DepositionA description of MOCAGE surface exchanges module is presented in Michou and Peuch (2002), aswell as in Michou et al. (2004). The dry deposition parameterization relies on a fairly classical surfaceresistance approach (Wesely, 1989), but with a refined treatment of the stomatal resistance, similarto the one used in Météo-France NWP models: see description of the ISBA original approach in(Noilhan and Planton, 1989). Sedimentation of aerosol follows (Nho-Kim et al., 2004).

3.1.5 Chemistry and aerosolsThe MOCAGE configuration for MACC comprises 118 species and over 300 reactions and photolysis.It is a merge of reactions of the RACM scheme (Stockwell et al., 1997) with the reactions relevant tothe stratospheric chemistry of REPROBUS (Lefèvre et al., 1994). Evaluation at the intercontinentalscale can be found in Bousserez et al. (2008); evaluation for air quality applications are discussed inDufour et al. (2004). Aqueous chemistry for the formation of sulphate is represented, following(Ménegoz et al., 2009). Detailed heterogeneous chemistry on Polar Stratospheric Clouds (types I, II) isaccounted for, as described in Lefèvre et al. (1994). Other heterogeneous chemistry processes arecurrently not included.

Photolysis are taken into account using a multi-entry look-up table computed off-line with the TUVsoftware version 4.6 (Madronich, 1987). Photolysis depends on month (including monthly aerosolclimatologies), solar zenith angle, ozone column above each cell (as the model extends to the mid-stratosphere, it is actually the ozone profile computed by MOCAGE which is used at every timestep),altitude and surface albedo in the UV. They are computed for clear-sky conditions and the impact ofcloudiness on photolysis rates is applied afterwards.

The aerosol module of MOCAGE includes the species: dusts, black carbon, sea salts and organiccarbon. Thus, currently not all aerosol species (secondary inorganic and organic aerosols) areaccounted for in this version. A low bias is expected for PM10 and PM2.5. Details on MOCAGEaerosol simulation evaluation can be found in Martet et al. (2009) and Martet (PhD, 2008) for dusts,in Nho-Kim et al. (2005) for black carbon, and in Sic et al. (2015) for the latest version of MOCAGEprimary aerosol module. The representation of secondary aerosol species is an on-goingdevelopment at MF-CNRM.

3.2 Assimilation systemAny assimilation algorithm can be seen as a sequence of elementary operations or elementarycomponents that can exchange data (Lagarde et al., 2001). Based on this idea, CERFACS hasdeveloped a coupling software PALM software (www.cerfacs.fr/~palm), that manages the dynamiclaunching of the components of assimilation systems (forecast model, algebra operators, I/O ofobservational data,...) and the parallel data exchanges.

MACC-III operations use the assimilation system based upon MOCAGE and PALM, which has beendeveloped and evaluated during the ASSET European project (Geer et al., 2006; Lahoz et al., 2007).This system is particularly versatile, as both the CTM degree of sophistication (for instance, thenumber of chemical tracers involved, the physical or chemical parameterisations, the horizontal andvertical geometries,…) and the data assimilation technique used via PALM can be changed easily.Current available options are 3D-VAR, 3D-FGAT and incremental 4D-VAR methods to assimilateprofile and column data for key measured atmospheric constituents, by means of a genericobservation operator component. As a first approximation, background error standard deviations areprescribed as proportional to background amounts. In order to spread assimilation increments

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spatially, background error correlations are modelled using a generalized diffusion operator (Weaverand Courtier 2001). Several data assimilation experiments have been published with MOCAGE, bothfor the stratosphere and troposphere (see for instance Pradier et al., 2006 or El Amraoui et al., 2008).

In MACC-II, a 3D-VAR technique was implemented. The first version of the MOCAGE assimilationsuite (from July 1st 2010 on and for R-EVA re-analyses for 2008 and 2009) considered only surfaceozone. The assimilation window is 1h every hour. The assimilation has been extended to surface NO 2

for the EVA re-analysis for 2011 and now for the ENS daily production.

3.3 Development plans for the next months

The next 6 months will be devoted to the a major update of the NRT MOCAGE chains, in order tofurther improve the operational status of the chains, and also to improve model performance. Weplan to introduce the chemical and aerosol boundary conditions from global CAMS. The emissioninventory over Europe will be updated to be the MACC-III TNO 2011 inventory.

The formation of secondary inorganic aerosols will be introduced in the model, based on researchthat has led to the interface of the ISORROPIA module in MOCAGE. Past verification of MOCAGE haveindeed shown a strong negative bias of particulate matter at the surface, and overall relatively lowperformances for aerosols, that the formation of secondary aerosols should improve. This workshould also enable the development of the assimilation of PM10 surface observations, and to theproduction of MOCAGE PM10 analyses.

ReferencesBechtold, P., E. Bazile, F. Guichard, P. Mascart and E. Richard, A mass flux convection scheme forregional and global models, Quart. J. Roy. Meteor. Soc., 127, 869-886, 2001.Blond, N., and R. Vautard, Three-dimensional ozone analyses and their use for short-term ozoneforecasts, J. Geophys. Res., 109, D17303, doi:10.1029/2004JD004515, 2004.Bousserez, N., J.-L. Attié, V.-H. Peuch, M. Michou, G. Pfister, D. Edwards, M. Avery, G. Sachse, E.Browell and E. Ferrare, Evaluation of MOCAGE chemistry and transport model during theICARTT/ITOP experiment, J. Geophys. Res., 112 (D120S42), doi: 10.1029/ 2006JD007595, 2007.Dufour, A., M. Amodei, G. Ancellet and V.-H. Peuch, Observed and modelled "chemical weather"during ESCOMPTE, Atmos. Res., 74 (1-4), 161-189, 2004.El Amraoui, L., N. Semane, V.-H. Peuch and M. L. Santee, Investigation of dynamical and chemicalprocesses in the polar stratospheric vortex during the unusually cold winter 2004/2005, Geophys.Res. Lett., 35, L03803, doi:10.1029/2007GL031251, 2008.Geer, A.J. , W.A. Lahoz, S. Bekki, N. Bormann, Q. Errera, H.J. Eskes, D. Fonteyn, D.R. Jackson, M.N.Juckes, S. Massart, V.-H. Peuch, S. Rharmili and A. Segers, The ASSET intercomparison of ozoneanalyses : method and first results, Atmos. Chem. Phys., 6, 5445-5474, 2006.Giorgi, F. and W. L. Chameides, Rainout Lifetimes of Highly Soluble Aerosols and Gases as InferredFrom Simulations With a General Circulation Model, J. Geophys. Res., 91(D13), 367-376, 1986.Josse B., Simon P. and V.-H. Peuch, Rn-222 global simulations with the multiscale CTM MOCAGE,Tellus, 56B, 339-356, 2004.Lagarde, T., Piacentini, A. and O. Thual, A new representation of data assimilation methods: the PALMflow charting approach, Q.J.R.M.S., 127, 189-207, 2001.Lahoz, W.A., A.J. Geer, S. Bekki, N. Bormann, S. Ceccherini, H. Elbern, Q. Errera, H.J. Eskes, D. Fonteyn,D.R. Jackson, B. Khattatov, S. Massart, V.-H. Peuch, S. Rharmili, M. Ridolfi, A. Segers, O. Talagrand, H.E.Thornton, A.F. Vik et T. Von Clarman, The Assimilation of Envisat data (ASSET) project, Atmos. Chem.Phys., 7, 1773-1796, 2007.

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Lefèvre, F., Brasseur, G. P., Folkins, I., Smith, A. K. and P. Simon, Chemistry of the 1991-1992stratospheric winter: three-dimensional model simulations, J. Geophys. Res., 99 (D4), 8183-8195,2004.Louis J.F., A parametric model of vertical eddy fluxes in the atmosphere, B. Layer Meteor., 17, 197-202, 1979.Madronich S., Photodissociation in the atmosphere, 1. Actinic flux and the effects of groundreflections and clouds, J. Geophys. Res., 92, 9740-9752, 1987.Mari,C., Jacob, D.J. and Betchold,P., Transport and scavenging of soluble gases in a deep convectivecloud, J. Geophys. Res., 105, D17, 22, 255–22267, 2000.Martet, M., Introduction des différentes composantes de l’aérosol dans le modèle MOCAGE (inFrench), PhD from Université Paul Sabatier Toulouse (26/06/2008).Martet, M., V.-H. Peuch, B. Laurent, B. Marticorena and G. Bergametti, evaluation of long-rangetransport and deposition of desert dust with the CTM Mocage, Tellus, 61B, 449-463, 2009.Ménégoz, M., D. Salas y Melia, M. Legrand, H. Teyssèdre, M. Michou, V.-H. Peuch, M. Martet, B. Josseand I. Etchevers-Dombrowski, Equilibrium of sinks and sources of sulphate over Europe: comparisonbetween a six-year simulation and EMEP observations, Atmos. Chem. Phys., 9, 4505-4519, 2009.Michou M., P. Laville, D. Serça, A. Fotiadi, P. Bouchou and V.-H. Peuch, Measured and modeled drydeposition velocities over the ESCOMPTE area, Atmos. Res., 74 (1-4), 89-116, 2004.Michou M. et V.-H. Peuch, Surface exchanges in the multi-scale chemistry and transport modelMOCAGE, Water Sci. Rev., 15 special issue, 173-203, 2002.Nho-Kim, E.-Y., V.-H. Peuch and S. N. Oh, Estimation of the global distribution of Black Carbonaerosols with MOCAGE, the CTM of Météo-France, J. Korean Meteor. Soc., 41(4), 587-598, 2005.Nho-Kim, E.-Y., M. Michou and V.-H. Peuch, Parameterization of size dependent particle drydeposition velocities for global modeling, Atmos. Env., 38 (13), 1933-1942 , 2004.Noilhan, J. and S. Planton, A simple parameterization of land surface processes for meteorologicalmodels, Mon. Wea. Rev., 117, 536-549, 1989.Pradier, S., J.-L. Attié, M. Chong , J. Escobar, V.-H. Peuch, J.-F. Lamarque, B. Khattatov and D. Edwards,Evaluation of 2001 springtime CO transport over West Africa using MOPITT CO measurementsassimilated in a global chemistry transport model, Tellus, 58B , 163-176, 2006.Rouil, L., C. Honoré, R. Vautard, M. Beekmann, B. Bessagnet, L. Malherbe, F. Méleux, A. Dufour, C.Elichegaray, J.-M. Flaud, L. Menut, D. Martin, A. Peuch, V.-H. Peuch and N. Poisson, PREV'AIR: anoperational forecasting and mapping system for air quality in Europe, Bull. Am. Meteor. Soc., 90(1),73-83, doi:10.1175/2008BAMS2390.1, 2008.Sič, B., L. El Amraoui, V. Marécal, B. Josse, J. Arteta, J. Guth, M. Joly, and P. Hamer, Modelling ofprimary aerosols in the chemical transport model MOCAGE: development and evaluation of aerosolphysical parameterizations, Geosci. Mod. Dev., 8, 381-408, 2015.Stockwell, W.R. et al., A new mechanism for regional atmospheric chemistry modelling, J. Geophys.Res., 102, 25847-25879, 1997.Wesely, M. L., Parameterization of surface resistance to gaseous dry deposition in regional numericalmodels, Atmos. Env., 16, 1293-1304, 1989.Williamson, D. L. and P. J. Rash, Two-dimensional semi-lagrangian transport with shape-preservinginterpolation, Mon. Wea. Rev., 117, 102-129, 1989.

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ANNEX: Verification report for June-July-August 2015This verification report covers the period June/July/August 2015. The MOCAGE skill scores aresuccessively presented for three pollutants: ozone, NO2 and PM10. The skill is shown for the entireforecast horizon from 0 to 96h (hourly values), allowing to evaluate the entire diurnal cycle and theevolution of performance from day 0 to day 3. The forecasts cover a large European domain (25°W-45°E, 30°N-70°N). The statistical scores that are reported are the root-mean-square error, themodified mean bias and the correlation.

Since June 2014, the surface observation dataset used for verification has been collected from theEuropean Environmental Agency(EEA)/EIONET near-real-time (NRT) dataflow. During MACC, MACC-IIand MACC-III, work was done with EEA to increase the number of countries that provide their data inNRT to the EEA. There were some technical issues on data formats and availability times of the EEAdataset, that have been mostly solved during MACC-II. From the beginning of 2015, the EEA has beendeveloping a new Up-To-Date “e-reporting” stream that is intended to replace the present one insome months. During the present transition phase, both reporting streams coexist and somecountries report their NRT data through the one of them of both.

The observations from EEA/EIONET are downloaded and are stored in an operational database atMeteo-France. Since June 2015, the observations from the “e-reporting” have been added andMeteo-France has set up a procedure to avoid the duplicated observations that come from the twostreams. This double download allows to get access to the most complete set of NRT observations.Some other ad hoc treatments of the observations are operated at Meteo-France, in order to correctsome data inconsistencies that have been identified, such as permanent zero concentrations valuesat some stations. Inconsistencies for CO units remain, which makes the CO concentration valuesunusable.

As in MACC-II and MACC-III, the observations are selected in order to take into account the typologyof sites, following the work that has been carried out in MACC [Joly and Peuch, 2012] to build anobjective classification of sites, based on the past measurements available in Airbase (EEA) (seeMACC D_R-ENS_5.1 for more details). This objective approach is necessary because there is nouniform and reliable metadata currently for all regions and countries, which have all differentapproaches to this documentation. Verification is thus restricted to the sites that have a sufficientspatial representativeness with respect to the model resolution (10-20 km). The statistical approachusing only representative sites -according to the objective classification- is clearly the way forward (asit does not also thin too much the NRT data available), leading to a general significant improvementof the overall skill scores (see MACC-II D_102.1_1/D106.1_1 for more details). Filtering stations onthe EEA/EIONET NRT data leads to a mean numbers of: ~500 sites for ozone, ~400 sites for NO2, ~300sites for PM10 and ~150 sites for PM2.5. Since the amount of observations available is satisfactoryfor PM2.5, it is planned to report verification of PM2.5 forecasts soon.

The usage of the observation dataset is twofold: for verification of the forecasts and also forassimilation in the regional models. To be used for data assimilation, downloading the observationsat 7h UTC is a reasonable compromise between the amount of data and the desired early time ofproduction of the analyses (before 12h UTC). However, the number of observations at the end of theday decreases rapidly, due to the fact that some countries do not report observations to the EEAduring the night. For forecast verification, observations are thus downloaded later, at 23h UTC, whichleads to a more homogeneous distribution over the day. Similarly to forecast verification, Meteo-France plans to set up procedures for verification of the NRT analyses. To get prepared, Meteo-France has set up a sorting of observations, so that some stations are not distributed for assimilation,

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but kept for future verification scores of NRT analyses. The verification of NRT analyses is planned tobe reported from next quarter.

Figure 1: coverage of surface observations selected as representative for verification (for O3, NO2,PM10 and PM2.5), collected from the EEA.

The following figures present, for each pollutant (ozone, NO2, PM10):

- in the upper-left panel, the root-mean square error of daily maximum (for ozone and NO 2) or ofdaily mean (PM10) for the first-day forecasts with regards to surface observations, for every quartersince DJF2014/2015, a target reference value is indicated as an orange line,

- in the upper-right panel, the root-mean square error of pollutant concentration forecasts withregards to surface observations as a function of forecast term,

- in the lower-left panel, the modified mean bias of pollutant concentration forecasts with regards tosurface observations as a function of forecast term,

- in the lower-right panel, the correlation of pollutant concentration forecasts with surface observations as a function of forecast term.

The graphics show the performance of MOCAGE (black curves) and of the ENSEMBLE (blue curves).

Joly, M. and V.-H. Peuch, 2012: Objective Classification of air quality monitoring sites over Europe,Atmos. Env., 47, 111-123.

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MOCAGE: ozone skill scores against data from representative sites, period June/July/August 2015

The MOCAGE RMSE reaches minimum values in the afternoon. The values are higher than duringSummer 2014, particularly during the end of the night. The RMSE of daily maximum for MOCAGE isbetter than the ENSEMBLE, for which the RMSE is minimum before the ozone concentration peak.The RMSE of daily maximum fails to reach the reference target.

The MOCAGE MMB is positive, and is minimal (close to 0) in the afternoon. The correlation is alsomaximum in the afternoon, and overall better than during Summer 2014 (by ~0.03). The MOCAGEcorrelation is lower than the ENSEMBLE by ~0.1.

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MOCAGE: NO2 skill scores against data from representative sites, period June/July/August 2015

MOCAGE NO2 RMSE and MMB are close to the ENSEMBLE. The bias is always negative. Both scoresare also close to the performance of Summer 2014.

MOCAGE NO2 correlation is not as good as the ENSEMBLE, but it is better than in Summer 2014 by~0.02.

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MOCAGE: PM10 skill scores against data from representative sites, period June/July/August 2015

MOCAGE PM10 mean bias is still negative and similar to Summer 2014. The RMSE is higher than theENSEMBLE. The scores do not show strong diurnal variations. Still, the RMSE values remainreasonable since the PM10 concentrations are rather low during this season.

The correlation is very poor, oscillating between 0 and 0.1.

These rather poor performances are attributed to the lack of secondary aerosols.

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Analysis of MOCAGE performance for quarter June/July/August 2015

The meteorological conditions of this summer 2015 were particular with a succession of periodscharacterized by hot days with fresh periods. Such situations are complicated for Air Quality modelswith several transitions of good air quality with high levels of pollution.

These conditions and the high daily ozone maximum concentrations may explain the degradation ofMOCAGE ozone RMSE and of ozone bias. However, MOCAGE performs well for the ozone dailymaximum, even better than the ENSEMBLE, but fails to reach the reference target for RMSE. TheMOCAGE correlation has improved since Summer 2014.

NO2 scores for MOCAGE are similar than in Summer 2014, except for the correlation, which is better.Further improvement may be possible after implementation of TNO MACC-III emission inventory for2011.

PM10 : The bias, RMSE and correlation are similar to Summer 2014. MOCAGE shows anunderestimation of PMs that can be attributed to the MOCAGE configuration which only includesprimary aerosol so far. To address this issue, inclusion of secondary inorganic aerosols is planned inthe coming months.

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