Copernicus AtmosphereMonitoringService

Report

CHIMERE regional forecasting system and performance

June-July-August 2015

ISSUED BY:Meteo-FranceINERIS

Date: 14/10/2015

REF.: CAMS_0200_CHIMERE

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

2CHIMERE fact sheet.............................................................................................................................4

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

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

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

2.4Assimilation and forecast system: synthesis of main characteristics.............................................5

3CHIMERE background information......................................................................................................5

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

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

3.1.2Forcings and boundary values................................................................................................6

3.1.3Dynamical core......................................................................................................................7

3.1.4Physical parametrizations......................................................................................................7

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

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

3.2.1Optimal interpolation.............................................................................................................8

3.2.2Ensemble Kalman Filter (EnKF)..............................................................................................8

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

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

ANNEX: Verification report for June-July-August 2015........................................................................11

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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 CHIMERE 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 and the new “e-reporting” streams. As for the pastthree years, the verification statistics are based on the use of only representative sites selected fromthe objective classification proposed by Joly and Peuch (Atmos. Env. 2012).

During this quarter, the meteorological conditions of this summer 2015 were particularly challengingfor forecasts, with a succession of periods characterized by hot days with fresh periods. As aconsequence the CHIMERE performance has changed compared to previous periods with differentmagnitude according to the pollutant considered. For ozone, the impact is a decrease of theCHIMERE performances even larger than for the Ensemble. For PM10, even if such meteorologicalconditions may lead to a significant contribution of SOA which is still a difficult challenge for model toassess, the scores are stable compared to last year and better than for the previous quarter. For NO2,the CHIMERE performances have improved whatever the previous period considered.

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2 CHIMERE 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 9:00 UTC

O3, NO2, CO, SO2,PM2.5, PM10, NO, NH3, NMVOC, PANs,Birch pollen at surface during season

0-96h, hourly

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

O3 and PM10 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 CHIMERE 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 CHIMERE forecasts and analyses were provided on time is:

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

Availability 89 % 65 % 58 % 35 % 19 %

CHIMERE analyses were missing on 02(P) and 06(P) June 2015, on 07(P) and 18(P) July 2015, from 06to 08(P), on 10(P), 22(P), 27(P) and 28(P) August 2015.

Availability of CHIMERE forecasts was incomplete on 01(P), 02(P), 06(P), from 11 to 14(P), from 20 to22(P), from 25 to 30(P) June 2015, from 01 to 19(P), from 21 to 31(P) July 2015, from 01 to 04(P),from 06 to 23(P) and from 25 to 31(P) August 2015.

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During this quarter, 89 % of the production is achieved but most of the forecasting is out of theschedule. To explain this delay, we experienced some trouble in the summer production due tolowering of the computing resources (loss of cpu) followed by instabilities of the cluster.

2.4 Assimilation and forecast system: synthesis of maincharacteristics

Assimilation and Forecast SystemHorizontal resolution 0.15°x0.1°Vertical resolution Variable, 8 levels from the surface up to 500 hPaGas phase chemistry MELCHIOR2, comprising 44 species and 120

reactions (Derognat, 2003)Heterogeneous chemistryAerosol size distribution 9 bins from 10 nm to 40 μmInorganic aerosols Primary particle material, nitrate, sulphate,

ammoniumSecondary organic aerosols Biogenic, anthropogenicAqueous phase chemistryDry deposition/sedimentation Classical resistance approachMineral dust Dusts are considered Sea Salt Inert sea saltBoundary values Values provided by MACC globalInitial values 24h forecast from the day beforeAnthropogenic emissions MACC-TNO inventory 2011 Biogenic emissionsForecast SystemMeteorological driver 00:00 UTC operational IFS forecast from the day

beforeAssimilation System (not yet activated for daily operations)Assimilation method Optimal Interpolation, Ensemble Kalman filterObservations Surface ozone (rural) and PM10Frequency of assimilation Every hour over the day beforeMeteorological driver 00:00 UTC operational IFS forecast for the day

before

3 CHIMERE background information3.1 Forward modelThe CHIMERE multi-scale model is primarily designed to produce daily forecasts of ozone, aerosolsand other pollutants and make long-term simulations for emission control scenarios. CHIMERE runsover a range of spatial scale from the regional scale (several thousand kilometres) to the urban scale(100-200 Km) with resolutions from 1-2 Km to 100 Km. On this server, documentation and sourcecodes are proposed for the complete multi-scale model. However most data are valid only for Europeand should be revisited for applications on other continents.

CHIMERE proposes many different options for simulations which make it also a powerful researchtool for testing parameterizations The chemical mechanism (MELCHIOR) is adapted from the originalEMEP mechanism. Photolytic rates are attenuated using liquid water or relative humidity Boundarylayer turbulence is represented as a diffusion (Troen and Mahrt, 1986, BLM) Vertical wind is

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diagnosed through a bottom-up mass balance scheme. Dry deposition is as in Wesely (1989). Wetdeposition is included Six aerosol sizes represented as bins in the model. Aerosol thermodynamicequilibrium is achieved using the ISORROPIA model. Several aqueous-phase reactions consideredSecondary organic aerosols formation considered Advection is performed by the PPM (PiecewiseParabolic Method) 3d order scheme for slow species.

The numerical time solver is the TWOSTEP method. Its use is relatively simple provided input data iscorrectly provided. It can run with several vertical resolutions, and with a wide range of complexity. Itcan run with several chemical mechanisms, simplified or more complete, with or without aerosols.

CHIMERE is a parallel model that has been tested on machines ranging from desktop PCs running theGNU/Linux operating system, to massively parallel supercomputers (HPCD at ECMWF). CHIMERE is a French national CNRS tool meaning that source code and documentation are freely available on a dedicated web site, training courses are organized twice a year for users. More than 120 users, from 30 institutes, are registered on the model e-mail list.

3.1.1 Model geometryCHIMERE is an eulerian deterministic model, using variable resolution in time and space (forcartesian grids).

The model uses any number of vertical layers, described in hybrid sigma-p coordinates. The model runs over the GEMS-MACC domain with a 0.1° resolution and 8 vertical levels extending from the surface up to 500 hPa.

3.1.2 Forcings and boundary valuesThe model is off-line and has to be forced for meteorology and boundary conditions.

Two interfaces has been develop to connect CHIMERE with the chemical boundary conditions deliverby the Mozart global forecasts. The first one dedicated to gaseous species is active in MACC since theboundary conditions are provided operationally. The second one for aerosols (Sea salt, dust, blackcarbon...) is not used yet waiting for the daily provision of boundary conditions for particulate matter.

3.1.2.1 MeteorologyCHIMERE can use many meteorological models and interfaces are provided for the following models:MM5, WRF, IFS/ECMWF. For most studies done with CHIMERE, the MM5 model was used forced bythe National Centers for Environmental Prediction (NCEP) global meteorological data. MM5 wasconfigured with the PBL option MRF (Option 5), based on the Troen and Mahrt (1986)parameterization (the most consistent with the CHIMERE mixing formulation). The Schultz (Option 8)microphysics parameterization has also been tested with CHIMERE and is recommended.

Within MACC, CHIMERE is directly forced by the IFS forecasts from the daily operational productsdelivered at 00 UTC.

3.1.2.2 ChemistryBoundary conditions can be either "external" or given by a coarse resolution CHIMERE simulation. Incase of "external" forcing, the model is provided with several databases: The LMDz-INCA model[Hauglustaine et al., 2005] for gas-phase chemical species. The global aerosol model GOCART for mineral aerosols [Chin et al., 2004] or the CHIMERE-DUSToutputs. The 3-hourly GRG global forecast is used to provide boundary conditions for a set ofpollutants in MACC.

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3.1.2.3 LanduseThe proposed domain interface is based on the Global Land Cover Facility (GLCF: http://glcf.umiacs.umd.edu/data/landcover 1kmx1km resolution database from the University ofMaryland, following the methodology of Hansen et al. (2000, J. Remote Sensing).

3.1.2.4 Surface emissionsThe model provides an interface combining several emissions sources such as EMEP (Yearly totals),IER (Time variations), TNO (Aerosol emissions), UK Dept of Environment (VOC speciation, Passant,2002). The MACC emissions (TNO) are used in CHIMERE for MACC.

3.1.3 Dynamical coreThree advection schemes are implemented: The Parabolic Piecewise Method (PPM, a three-orderhorizontal scheme, after Colella and Woodward, 1984), the Godunov scheme (Van Leer, 1979) andthe simple upwind first-order scheme.

3.1.4 Physical parametrizations3.1.4.1 Turbulence and convectionVertical turbulent mixing takes place only in the boundary-layer. The formulation uses K-diffusion following the parameterization of [Troen and Mahrt, 1986], without counter-gradient term.

3.1.4.2 DepositionDry deposition is considered for model gas species i and is parameterized as a downward flux F(d,i)= -v(d,i) c(i) out of the lowest model layer with c(i) being the concentration of species i. The deposition velocity is, as commonly, described through a resistance analogy [Wesely, 1989]. The wet deposition follows the scheme proposed by [Loosmore, 2004]

3.1.5 Chemistry and aerosolsCHIMERE offers the option to include different gas phase chemical mechanisms. The original, complete scheme [Lattuati, 1997], hereafter called MELCHIOR1, describes more than 300 reactions of 80 gaseous species.

The hydrocarbon degradation is fairly similar to the EMEP gas phase mechanism [Simpson, 1992]. Adaptations are made in particular for low NOx conditions and NOx-nitrate chemistry. All rate constants are updated according to [Atkinson, 1997] and [De More, 1997]. Heterogeneous formation of HONO from deposition of NO2 on wet surfaces is now considered, usingthe formulation of [Aumont, 2003]. In order to reduce the computing time a reduced mechanism with 44 species and about 120 reactions is derived from MELCHIOR [Derognat, 2003], following the concept of chemical operators [Carter, 1990]. This reduced mechanism is called MELCHIOR2 hereafter.

MACC CHIMERE version consists in the baseline gas-phase version with MELCHIOR2 chemistry, together with a sectional aerosol module. This module accounts for 7 species (primary particle material, nitrate, sulfate, ammonium, biogenic secondary organic aerosol SOA, anthropogenic SOA and water). Potentially, Chloride et Sodium can be included (high computing time). In its initial version the module uses 6 bins from 10 nm to 40 μm. Now the module moves to 8 bins from 10 nm to 10 μm.

3.2 Assimilation systemDuring a first stage, waiting for the development of the ensemble kalman filter, we will use anoptimal interpolation method to assimilate daily concentration values for correcting the raw

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forecasts of CHIMERE. This method has been widely evaluated and validated in the Prev’Air systemfor ozone and PM10.

3.2.1 Optimal interpolationThe analysis method is designed to assess, as accurately as possible, near–real time surfaceconcentration fields of ozone and PM10. CHIMERE analysis is carried out over France and sincesummer 2009 over Europe. The observations retrieved in near–real time are used in combinationwith D-1 daily maxima for ozone, D-1 daily mean for PM10 and D-1 hourly values for both. We useone of the methods proposed by Blond et al [2003], based on the kriging of the differences betweensimulated and observed values, often called innovations in meteorology. Kriging methods have theadvantage of providing spatial interpolations that necessitate few assumptions and give robustresults. Few assumptions are needed in kriging methods; a sensitivity analysis on the krigingparameters has been performed, enabling to select the most appropriate parameters.

The choice of the measurement sites is a crucial stage in the analysis procedure: the monitoringstations selected must deliver concentrations representative of the gridded concentrations. Ruralstations are selected in priority, then suburban stations and urban stations, provided that theinfluence of local sources of pollution and local meteorology is minor. At a given location s, theanalyzed concentration is calculated from the following equation:

kbk

p

kkkba sZsYswsZsZ

0

1

where Za(s) refers to the analyzed concentration at site s; Zb(s) refers to the corresponding simulatedvalue; Yo(sk) is the measured concentration at site sk and wk(sk) are the weights derived from thekriging constraints (see Blond et al. [2003] for more details about the method). Innovations Y o(sk) -Zb(sk) are estimated at each monitoring site sk. The kriging method used here is ‘‘exact’’: at themeasurement sites, the analyzed concentration is equal to the observed concentration.

3.2.2 Ensemble Kalman Filter (EnKF)The ensemble Kalman Filter is now coupled to the CHIMERE model. It allows to assimilate (separatelyat the moment) ozone measurements from ground based stations and from the IASI instrument on board the METOP platform.

An advanced sequential data assimilation method (EnKF) has been set-up for the purpose of 3D dataassimilation. We use ensembles, generated by using Monte Carlo methods, to calculate spatially andtemporally varying forecast-error covariances for the purpose of performing data assimilation.

3.3 Development plans for the next months

A new version of CHIMERE will be implemented in the coming months to improve the computingtime and to insert fire emissions in the forecasts. The operational download of the observation fileprocessed by MeteoFrance will be set-up and used to produce the analyses.

ReferencesBeekmann M., and Derognat C.: Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the atmospheric pollution over the Paris area (ESQUIF) campaign, J. Geophys. Res., 108(D17), 8559, doi:10.1029/2003JD003391, 2003.

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Bessagnet B., L. Menut, G. Aymoz, H. Chepfer and R. Vautard, Modelling dust emissions and transport within Europe: the Ukraine March 2007 event, J. Geophys. Res., 113, D15202, doi:10.1029/2007JD009541, 2008.

Bessagnet B., L.Menut, G.Curci, A.Hodzic, B.Guillaume, C.Liousse, S.Moukhtar, B.Pun, C.Seigneur, M.Schulz, Regional modeling of carbonaceous aerosols over Europe - Focus on Secondary Organic Aerosols, J. Atmos. Chem., in press, 2009.

Boynard, A., Beekmann, M., Foret, G., Ung, A., Szopa, S., Schmechtig, C. and Coman, A.: Assessment of regional ozone model uncertainty with a modelling ensemble using an explicit error representation, Atm. Env., 45, 784-793, 2011.

Burgers, G., Van Leeuwen, P.J. and Evensen, G.: Analysis Scheme in the Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719-1724, 1998.

Coman, A., Foret, G., Beekmann, M., Eremenko, M., Dufour, G., Gaubert, B., Ung, A., Schmechtig, C., Flaud, J.-M., and G. Bergametti, Assimilation of IASI partial tropospheric columns with an Ensemble Kalman Filter over Europe, 26943-26997, 11, ACPD, 2011.

de Meij A., Gzella, A., Cuvelier, C., Thunis, P., Bessagnet, B., Vinuesa, J.F., Menut, L., Kelder H., The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations, Atmos. Chem. Phys. , in press, 2009.

Desroziers, G., L. Berre, B. Chapnik and P. Poli, Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. R. Meteorol. Soc., 131, 3385–3396 doi: 10.1256/qj.05.108, 2005.

Eremenko, M., Dufour, G., Foret, G., Keim, C., Orphal, J., Beekmann, M., Bergametti, G., and Flaud, J.-M.: Tropospheric ozone distributions over Europe during the heat wave in July 2007 observed from infrared nadir spectra recorded by IASI, Geophys. Res. Lett., 35, L18805,doi:10.1029/2008GL034803, 2008.

Evensen, G., Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlomethods to forecast error statistics, J. Geophys. Res., 99 (C5), 10143-10162, 1994.

Evensen, G.: Sampling strategies and square root analysis schemes for the EnKF, Ocean Dynamics, 54,539-560, DOI 10.1007/s10236-004-0099-2, 2004.

Evensen, G.: Data assimilation: The Ensemble Kalman Filter, Springer-Verlag Berlin Heidelberg, 2007.

Flemming, J., van Loon, M., Stern, R., 2004. Data assimilation for CTM based on optimum interpolation and KALMAN filter. In: Borrego, C., Incecik, S. (Eds.), Air Pollution Modeling and its Application, vol. XVI. Kluwer Academic/Plenum Publishers, New York.

Flemming, J., A., Inness, H., Flentje, V., Huijnen, P., Moinat, M. G., Schultz, and O. Stein, Coupling global chemistry transport models to ECMWF’s integrated forecast system, Geosci. Model Dev., 2, 253-265, 2009.

Galmarini S., Bianconib R., Klug W. et al.: Ensemble dispersion forecasting – part I: Concept, approachand indicators, Atmos. Environ., 38, 4607– 4617, 2004.

Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I. and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181-3210, 2006.

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Hanea, R., Velders, G. and Heemink, Data assimilation of ground level ozone in Europe with a kalman filter and chemistry transport model, J. Geophys. Res., 109, 5183-5198, 2004.

Honoré C., L. Rouil, R. Vautard, M. Beekmann, B. Bessagnet, A. Dufour, C. Elichegaray , J.-M. Flaud, L. Malherbe, F. Meleux, L. Menut, D. Martin, A. Peuch, V.-H. Peuch, N. Poisson, Predictability of European air quality: the assessment of three years of operational forecasts and analyses by the PREV'AIR system, J. Geophys. Res., 113, D04301, doi: 10.1029/2007JD008761, 2008.

Houtekamer, P.L. and Mitchell, H.L.: Data assimilation using an Ensemble Kalman Filter technique, Mon. Weather Rev.,126, 796-811, 1998.

Mallet, V., and Sportisse B.: Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling,J. Geophys. Res., 111, D01302, doi:10.1029/2005JD006149, 2006.

Maybeck, P.: Stochastic models, estimation, and control, Academic Press, London, 1979.

Rodgers, C.D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific, Series on Atmospheric, Oceanic and Planetary Physics, 2, Hackensack, N. J., 2000.

Rouil L., C. Honore, R. Vautard, M. Beekmann, B. Bessagnet, L. Malherbe, F. Meleux, A. Dufour, C. Elichegaray, J.-M. Flaud, L. Menut, D. Martin, A. Peuch, V.-H. Peuch, N. Poisson, PREV'AIR : an operational forecasting and mapping system for air quality in Europe, Bull. Am. Meteor. Soc., doi: 10.1175/2008BAMS2390.1, 2009.

Sakov, P. and Oke, P.R.: Implications of the form of the ensemble transformation in the ensemble square root filters, Monthly Weather Review, 136, 1042-1053, 2008.

Schwinger, J., and H. Elbern, Chemical state estimation for the middle atmosphere by four-dimensional variational data assimilation: A posteriori validation of error statistics in observation space, J. Geophys. Res., 115, doi:10.1029/2009JD013115, 2010.

Szopa S., G. Foret, L. Menut, A. Cozic, Impact of large scale circulation on European summer surface ozone: consequences for modeling, Atmospheric Environment, 43(6), Pages 1189-1195, doi:10.1016/j.atmosenv.2008.10.039, 2009.

Valari M. and L. Menut, Does increase in air quality models resolution bring surface ozone concentrations closer to reality?, J. Atmos. Ocean. Tech., doi: 10.1175/2008JTECH A1123.1, 2008.

Vivanco M. G., Palomino I., Vautard R., Bessagnet R., Martin F., Menut L., Jimenez S., Multi-year assessment of photochemical air quality simulation over Spain, Env. Mod. and Software, doi:10.1016/j.envsoft.2008.05.004, 2008.

Visschedijk, A.J.H., Zandveld, P.Y.J., and Denier van der Gon, H.A.C.A., High resolution gridded European database for the EU Integrate Project GEMS, TNO-report 2007-A-R0233/B.

Whitaker, J.S. and Hamill, T.M.: Ensemble Data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913-1924, 2002.

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ANNEX: Verification report for June-July-August 2015This verification report covers the period June/July/August 2015. The CHIMERE 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 or 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 CHIMERE (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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CHIMERE: ozone skill scores against data from representative sites, period June/July/August 2015

The general behaviour of CHIMERE shows performances lower than the Ensemble, but thedifferences between both are not that large. Furthermore, it is worth noting that RMSE for the dailymaximum are quite very close. Scores show a little decrease of the CHIMERE performances withtime.

Compared to previous year, the CHIMERE minimum RMSE which occurred when ozone is maximumhas increased by 3-4 µg/m3. All scores depict higher differences highlighting a decrease of theCHIMERE performances during this summer.

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

CHIMERE NO2 RMSE is very close to the Ensemble NO2 RMSE but differences increase for the dailymaximum of RMSE, which occurs at the end of the night. The MMB indicates a higherunderestimation of the NO2 concentrations in CHIMERE than in the Ensemble, once again maximumat the end of the night. Correlation are decreasing slowly for both forecasts. Compared to theprevious quarter, we can see that for the daily maximum the RMSE are similar for both. The scoresare almost the same than one year before.

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

CHIMERE has a better RMSE than the ENSEMBLE for PM10 but the underestimation seems to behigher in CHIMERE. The CHIMERE correlation is lower than the Ensemble and quite poor. It mightresult from the low concentration which occurred during summer and the difficulties models have tocompute the spatial and temporal variabilities of SOA, which should be a major contributor to PM.Compared to previous quarter, CHIMERE has increased its RMSE. Compared to previous year, scoresare similar except for MMB which has significantly decreased.

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Analysis of CHIMERE 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.

It is certainly the main reason why the results, especially for ozone, are lower than one year ago. Thedaily fluctuations of concentration with transition from low level to high level is difficult to capturefor models. And as the summer 2014 was more quiet in terms of ozone episodes, it is certainly themain reason. The decrease of the performances is true also for the Ensemble but at a lesser extentthan for CHIMERE.

For NO2 and to a certain extent PM10, the CHIMERE RMSE depicts a positive evolution with a slightimprovement of scores. Such evolution can be due to the implementation of new emission inventoryand also to the CHIMERE version 2014 which has new settings for the aerosol module.

For PM10, the pretty good scores are a good news as the summer meteorological conditions shouldhave led to significant contribution of SOA in the aerosol composition which is still a challenging partof the PM to correctly reproduce by models.

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