Copernicus AtmosphereMonitoringService

Report

SILAM regional forecasting system and performance

June-July-August 2015

ISSUED BY:Meteo-FranceFMI

Date: 14/10/2015

REF.: CAMS_0200_SILAM

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

2SILAM 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

3SILAM background information...........................................................................................................5

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

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

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

3.1.3Transport core........................................................................................................................6

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

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

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

3.3Development plans for the next months......................................................................................7

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 SILAM regional forecasting system and its statistical performance againstin-situ surface observations for the quarter that covers June, July and August 2015. Verification isdone using the up-to-date methods described in the MACC-II dossiers covering quarters #15 and #16.In this dossier, the dataset of surface observations used for verification is collected from theEEA/EIONET NRT database. During the present phase of implementation of the “e-reporting” streamat the EEA, Meteo-France has got access to the most complete set of observations by downloadingdata from both the EEA/EIONET NRT and the new “e-reporting” streams. As for the past three years,the verification statistics are based on the use of only representative sites selected from the objectiveclassification 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.

The current quarter is the first summer of SILAM v.5.4 in operations – and the first summer with themodel running with 0.1 degree resolution. However, the current suite is run with the old depositionscheme it succeeded from v.5.2. With that scheme, SILAM shows noticeable over-estimation ofozone in Southern Europe. This tendency has stayed in the current suite and is a plausibleexplanation for the substantial RMSE of the maximum daily concentrations and modified mean bias.

With NO2, the only significant issue is a certain decorrelation of night-time fields from theobservations, as well as a limited growth of bias for that part of the day. A potential reason for that isa comparatively long life time of NO2 in SILAM, which has been noticed in several model inter-comparisons.

Scores for PM10 confirmed the necessity to close the aerosol budget, in particular, introducing thesecondary organic aerosols. The module has been developed and is currently under evaluation andtuning, its introduction is planned later.

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2 SILAM 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 3:00 UTC O3, NO2, CO, SO2,PM2.5, PM10,Birch pollen at surface during season

0-96h, hourly

ANA Analysis at the surface Daily 9:00 UTC for the day before

O3, NO2, SO2 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 SILAM model outputs were available ontime 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 SILAM forecasts and analyses were provided on time is:

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

Availability 99 % 98 % 98 % 98 % 98 %

SILAM analyses were missing on 4(P) June 2015.

Availability of SILAM forecasts was incomplete on 4(P) June and on 14(P) August 2015.

During this quarter, one issue was connected with too late IFS meteorological data arrival, which,exacerbated with technical maintenance of the FMI supercomputer, broke down both forecast andanalysis. We also faced one break due to a model bug that affected the forecast of 14 August.

2.4 Assimilation and forecast system: synthesis of maincharacteristics

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Assimilation and Forecast SystemHorizontal resolution 0.1° regular lat-lon gridVertical resolution 69 layers for meteorological pre-processor (IFS

hybrid levels 69 to 137, covering the troposphere), 9 layers for chemistry and vertical sub-grid-scale mixing calculations

Gas phase chemistry CBM-4 gas-phase transformation, inorganic heterogeneous chemistry scheme (Sofiev, 2000)

Heterogeneous chemistry Sofiev (2000)Aerosol size distribution BinsInorganic aerosols SO4, NO3, NH4, Primary BC, OC, sea saltSecondary organic aerosols Not implemented in current MACC versionAqueous phase chemistry SO2 oxidation, nitrate formation (Sofiev, 2000)Dry deposition/sedimentation Resistance approach (Wesely et al., 1989) for

gases, (Kouznetsov & Sofiev, 2012) for aerosolMineral dust Not implemented in current MACC versionSea Salt Updated source term Sofiev et al (2011)Boundary values G-RG values for all available speciesInitial values 24h forecast from the day beforeAnthropogenic emissions MACC-2009 inventory binned at 0.1° resolutionBiogenic emissions Dynamic biogenic emissions, based upon

Poupkou et al. (2010).Forecast SystemMeteorological driver 12:00 UTC operational IFS forecast for the day

before (up to +84)Assimilation SystemAssimilation method Operational intermittent 3d-var for analysis;

4dvar in research and re-analysis (pollen) modesObservations In-situ surface data operational; and vertically

integrated columns in research modeFrequency of assimilation HourlyMeteorological driver IFS (ECMWF)

3 SILAM background information

3.1 Forward modelThe air quality and emergency modelling system SILAM v.5.5 was introduced into operations25.2.2015 replacing the version 5.2 described in Kukkonen et al, 2012). It is an Eulerian chemicaltransport model (Sofiev et al, 2015). The model transport is based on advection scheme of Galperin(2000) and adaptive vertical diffusion algorithm of Sofiev (2002). Apart from the transport andphysico-chemical cores described below, SILAM includes a set of supplementary tools including ameteorological pre-processor, input-output converters, grid transformers, interpolation routines, etc.In the pre-operational forecasts, these enabled direct forcing of the model by the ECMWF IFSmeteorological fields. A system outlook can also be found also in at http://silam.fmi.fi.

The model v.5.5 got updated transport core, which is transitional to the new implementation madefor the forthcoming v.5.5. The new core conserves the mixing ratio rather than concentrations as thev.5.2. Scavenging scheme has been replaced with more mechanistic approach. A new dry deposition

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scheme for gases was implemented but not set into production due to substantial negative bias ofozone shown in the evaluation runs. The model became substantially faster, which allowed settingthe model “true” computational resolution to 0.1 degree and increasing the number of vertical layersto 9 (the model top was kept at ~6.7km)

3.1.1 Model geometryHorizontal computational grid:

number of grid cells: nx = 700 ny = 400

western-most longitude = 25.0 W, eastern-mot longitude = 44.9° E

southern-most latitude = 30.0 N, northern-most latitude = 74.1° N

resolution: dx = 0.1° dy = 0.1°

Vertical grids:

Following Sofiev (2002), SILAM uses multi-vertical approach with the meteorology-resolving gridcorresponding to the tropospheric part of the IFS vertical: hybrid levels from 45 to 91 and from 69 to137. The chemical transformations and vertical fluxes are computed on the basis of 9 thick staggeredlayers with the thickness increasing from 25 m for the lowest layer to 1000-2000 m in the freetroposphere. Within the thick layers, the sub-grid information is used to evaluate the weightedaverages of the high-resolution meteorological parameters.

3.1.2 Forcings and boundary conditions3.1.2.1 MeteorologyMeteorological forcing is the ECMWF IFS operational forecasts taken from the 12UTC forecast of theprevious day. Thus, the forecast length of the meteorology fields is from +12 hr till +84 hr. The meteofields are taken from the operational dissemination procedure of ECMWF in rotated lon-latcoordinates system (southern pole of the rotated grid is at (0E, 30S)) with 0.125° resolution.

3.1.2.2 ChemistryBoundary conditions are taken from the MOZART model. The full fields are imported every 3 hours;in-between, the linear interpolation is applied.

3.1.2.3 Landuse

3.1.2.4 Surface emissionsEmission fields are based on the MACC-2009 database for CO, SO2, NO2, NH3, PM2.5. and PM2.5-10.Wherever available, point-source information is used. In addition, the add-ons for all the species areincluded from the EMEP database: the complete set for anthropogenic VOCs, and natural and shipemissions for all compounds. Emissions of biogenic VOCs and sea salt are computed in thecorresponding dynamic modules, which are described below.

3.1.3 Transport coreThe SILAM Eulerian transport core (Sofiev et al, 2015) is based on the coupled developments:advection scheme of Galperin (2000) and vertical diffusion algorithm of Sofiev (2002) andKouznetsov & Sofiev, (2012). The methods are compatible in a sense that the both use the same setof variables to determine the sub-grid distribution of tracer mass. The approach, in particular, allowscomputing correct vertical exchange using high-resolution input data but low-resolution chemistryand diffusion grids. The later feature is used in the vertical setup with 9 thick layers.

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3.1.4 Physical parametrizations3.1.4.1 Turbulence and convectionDiffusion is parameterised following the first-order K-theory based closure. Horizontal diffusion isembedded into the advection routine, which itself has zero numerical viscosity, thus allowing fullcontrol over the diffusion fluxes. For simplicity, the horizontal components of eddy diffusivity areprescribed so that the width of an isolated puff increases in the cross-wind direction as 10% of thetravel distance of the puff. The vertical diffusivity parameterization follows (Genikhovich et al,2004).The procedure diagnoses all the similarity theory parameters using the profiles of the basicmeteorological quantities: wind, temperature and humidity. Output includes the value of eddydiffusivity for scalars at some reference height (taken to be 1m), which forms the basis for thediffusivity profile over the ABL and further over the free troposphere.

3.1.4.2 DepositionDry deposition parameterization follows the standard resistive analogy of Wesely (1989). Resistancesfor the aerosols are evaluated using the original (Kouznetsov & Sofiev, 2012) algorithm. Wetdeposition parameterization is based on the scavenging coefficient after Sofiev (2000) for gas speciesand Sofiev et al (2006) for aerosols.

3.1.5 Chemistry and aerosolsThe main gas-phase chemical mechanism is CBM-4. The heterogeneous scheme is an updatedversion of the DMAT model scheme (Sofiev, 2000). It incorporates the formations pathways ofsecondary inorganic aerosols.

Emission of two set of compounds is embedded into the dynamic simulations: biogenic VOC and seasalt. The bio-VOC computations follow the Poupkou et al. (2010) model and provide isoprene andmono-terpene emissions (currently, only isoprene emission is used in the CB-4 mechanism). The seasalt emission parameterization is the original development generally based on Sofiev et al (2011)with refinements and spume formation mechanism added in v5.2.

3.2 Assimilation systemThe embedded data assimilation is based on the 3- and 4-dimentional variational approach (4D-VAR).The adjoint formulations exist for all dynamic modules, linearized transformation scheme of sulphuroxide and for aerosol particles. The assimilation procedure has been tested for both initialising theconcentration fields and for refinement of the emission coefficients. The observation operators existfor in-situ observations and for the vertically integrated columns observed by the nadir-lookingsatellites.

For the near-real-time analyses, the previous-day observations are used in a 3D-VAR dataassimilation suite. The assimilated species are SO2, NO2 and O3. The setup is similar to the one usedin R-EVA reanalyses.

3.3 Development plans for the next months

The summer quarter was using the new suite SILAM v.5.4 with higher resolution and several physicaland technical updates outlined above. During summer, the operational work with large grids of 0.1degree resolution was stabilised, so that the next 3 months will be dedicated to achieve fullcompliance to CAMS specification, in particular, introducing PAN and NMVOC in the model output,and to introduce 0.1 degree resolution into the analysis production chain.

Further testing of the new dry deposition scheme will be continued, aiming at allowing thiscomponent of v.5.4 in the operational forecasts and analysis within autumn-winter period.

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ReferencesSofiev, M., Berger, U., Prank, M., Vira, J., Arteta, J., Belmonte, J., Bergmann, K.-C., Chéroux, F., Elbern,

H., Friese, E., Galan, C., Gehrig, R., Khvorostyanov, D., Kranenburg, R., Kumar, U., Marécal, V.,Meleux, F., Menut, L., Pessi, A.-M., Robertson, L., Ritenberga, O., Rodinkova, V., Saarto, A., Segers,A., Severova, E., Sauliene, I., Siljamo, P., Steensen, B. M., Teinemaa, E., Thibaudon, M., and Peuch,V.-H. (2015) MACC regional multi-model ensemble simulations of birch pollen dispersion inEurope, Atmos. Chem. Phys., 15, 8115-8130, doi:10.5194/acp-15-8115-2015, http://www.atmos-chem-phys.net/15/8115/2015/.

Sofiev, M., Vira, J., Kouznetsov, R., Prank, M., Soares, J., Genikhovich, E. (2015) Construction of anEulerian atmospheric dispersion model based on the advection algorithm of M.Galperin:dynamic cores v.4 and 5 of SILAM v.5.5, Geosci.Model Developm. Discuss., 8, 2905-2947,doi:10.5194/gmdd-8-2905-2015.

Vira, J., Sofiev, M. (2015) Assimilation of surface NO2 and O3 observations into the SILAM chemistrytransport model, Geosci. Model Dev., 8, 191–203, www.geosci-model-dev.net/8/191/2015/,doi:10.5194/gmd-8-191-2015.

Simpson, D., Andersson, C., Christensen, J.H., Engardt, M., Geels, C., Nyiri, A., Posch, M., Soares, J.,Sofiev, M., Wind, P., Langner, J. (2014), Impacts of climate and emission changes on nitrogendeposition in Europe: a multi-model study, ACP, 14, 13, 6995 - 7017, http://www.atmos-chem-phys.net/14/6995/2014/.

Langner J, Engardt M, Baklanov A, Christensen J.H, Gauss M, Geels C, Hedegaard G.B, Nuterman R,Simpson D, Soares J, Sofiev M, Wind P, Zakey A. (2012) A multi-model study of impacts of climatechange on surface ozone in Europe. Atmos. Chem. Phys., 12, 10423-10440, doi:10.5194/acp-12-10423-2012, 2012

Kouznetsov, R., Sofiev, M. (2012) A methodology for evaluation of vertical dispersion and drydeposition of atmospheric aerosols. JGR,117, DOI: 10.1029/2011JD016366.

J. Kukkonen, T. Balk, D. M. Schultz, A. Baklanov, T. Klein, A. I. Miranda, A. Monteiro, M. Hirtl, V.Tarvainen, M. Boy, V.-H. Peuch, A. Poupkou, I. Kioutsioukis, S. Finardi, M. Sofiev, R. Sokhi, K.Lehtinen, K. Karatzas, R. San José, M. Astitha, G. Kallos, M. Schaap, E. Reimer, H. Jakobs, and K.Eben (2012) Operational, regional-scale, chemical weather forecasting models in Europe. Atmos.Chem. Phys., 12, 1–87, 2012, www.atmos-chem-phys.net/12/1/2012/, doi:10.5194/acp-12-1-2012

Poupkou, A., Giannaros, T., Markakis, K., Kioutsioukis, I., Curci, G., Melas, D., Zerefos, C., A model forEuropean Biogenic Volatile Organic Compound emissions, 2010: Software development and firstvalidation, Env. Model. & Software, 25, 1845-1856.

Sofiev, M., Extended resistance analogy for construction of the vertical diffusion scheme fordispersion models, J. Geophys. Res., 107(D12), doi: 10.1029/2001JD001233, 2002.

Sofiev, M., Vankevich, R., Ermakova, T., Hakkarainen, J. (2013) Global mapping of maximum emissionheights and resulting vertical profiles of wildfire emissions. Atmos. Chem. Phys., 13, 7039-7052,doi. 10.5194/acp-13-7039-2013, http://www.atmos-chem-phys.net/13/7039/2013/.

Sofiev, M., Ermakova, T., and Vankevich, R. (2012) Evaluation of the smoke injection height from wild-land fires using remote sensing data, Atmos. Chem. Phys. 12, 1995-2006, www.atmos-chem-phys.net/12/1995/2012/, doi:10.5194/acp-12-1995-2012

Sofiev, M., Soares, J., Prank, M., de Leeuw, G., Kukkonen, J., 2011: A regional-to-global model ofemission and transport of sea salt particles in the atmosphere, J. Geophys. Res., 116, D21302,doi:10.1029/2010D014713.

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Vira, J. and M. Sofiev, 2012: On variational data assimilation for estimating the model initialconditions and emission fluxers for the short-term forecasting of SOx concentrations. Atmos.Env., in press.

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

This is the first summer of the operational simulations with SILAM v.5.4. In general, the modelshowed quite similar performance with the testing years that were run prior to setting the modelinto operations. One has to also keep in mind that the “native” dry deposition scheme of v.5.4 wasnot included in the current release due to substantial negative bias for ozone. Therefore, the moduleof v.5.2 was kept in the configuration. With that module, the system shows positive bias, reflected inthe lower left panel. This bias is somewhat smaller than during the last year (peak < 0.4 vs ~0.45).The evening-time bias stayed at the level 0.05, which seems to be encouraging: the span of themodel error has reduced.

The daily max prediction is close to the accuracy target though slightly above it, presumably due toover-estimation of ozone in late-spring-early-summer, when the too low dry deposition was causingsubstantial over-estimation of the concentration.

One should also note some 5-10% of better correlation in comparison with last year – but alsosomewhat higher RMSE, which probably came together with the positive bias in early-summer.

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

For NO2, SILAM demonstrates good scores, which, similar to the last-year computations, are betterthan the ensemble during the middle part of the day. As a result, daily max error is smaller than theresult of ensemble in all quarters assessed. The error is also within the target limit. This largelyrelated to quite small bias of the model, which is less than a half of that of the ensemble – and gotsome 5-10% smaller in comparison with the last-year summer.

One can attribute part of this positive dynamics to higher model resolution: NO2, being largelytraffic-related pollutant, is sensitive to the model resolution. Essentially the only issue that has beensucceeded from v.5.2, is a substantial loss of correlation and growth of RMSE during night time. Thenew version is marginally better (some 5%) but robustness of this improvement with regard to, e.g.,collection of the observation sites and meteorological conditions of 2015 is unclear.

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

Scores for PM10 in summer 2015 were quite comparable with those of 2014. The model bias is stillquite substantial, which is an expected issue for PM10. Still, the RMSE is comfortably within thetarget limit for all quarters, and also close to the results of the ensemble.

RMSE stayed essentially the same as a year ago, including its diurnal pattern: the strongest under-estimation and, consequently, highest RMSE, are reported for late evening. The daytime error isalmost 25% lower. Correlation coefficient is also quite low, again night-time being the most-problematic.

An interesting feature of the current species: RMSE has a tendency to become better with theforecasting lead time, largely, owing to somewhat improving bias for long lead times. Conversely,correlation has a strong tendency to worsen and to loose its diurnal pattern – for ensemble evenmore pronounceable than for SILAM. Such behaviour might suggest natural emissions as a possiblereason: longer lead time might affect wind fields characteristics.

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Analysis of SILAM 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.

The current quarter is the first summer of SILAM v.5.4 in operations – and the first summer with themodel running with 0.1 degree resolution. A drawback of the current version is a delayedimplementation of the new dry deposition scheme for gases, which showed insufficient scores in themodel test runs. Therefore, the current suite is run with the old deposition scheme it succeededfrom v.5.2. One of the issues of the old scheme is that SILAM shows noticeable over-estimation ofozone in Southern Europe. Expectedly, this tendency has stayed in the current suite and seems to bea plausible explanation for the too high RMSE of the maximum daily concentrations and modifiedmean bias. It is therefore considered as a high priority task to improve the new scheme and set itinto the operational use. Noteworthy, already at the current stage, it provides better correlation incomparison with the current setup.

With NO2, the only significant issue is a certain decorrelation of night-time fields with theobservations, as well as a limited growth of bias. A potential reason for that is a comparatively longlife time of NO2 in SILAM, which has been noticed in several model inter-comparisons. It remainshowever a research question whether this longer lifetime is incorrect: some inter-comparisonsshowed that the background values of SILAM are actually closer (though indeed higher) to theobservations than those of some other models.

Scores for PM10 confirmed the necessity to close the aerosol budget, in particular, introducing thesecondary organic aerosols. The module has been developed and is currently under evaluation andtuning, its introduction is planned for phase 2. Smaller issues, which might however bring substantialimprovement is the dust emission within the model domain: despite the sources are limited they arein close proximity to the observation sites and thus can prove significant.

An interesting issue related rather to the observational technology than to the models is presence ofwater in aerosol particles. It can comprise tens of %, thus accounting for a significant part of thepresent bias. A dedicated study is being finalised and its results will find their way to the treatment ofaerosol output in SILAM.

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