Copernicus AtmosphereMonitoringService

Report

MATCH regional forecasting system and performance

June-July-August 2015

ISSUED BY:Meteo-FranceSMHI

Date: 14/10/2015

REF.: CAMS_0200_MATCH

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

2MATCH 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

3MATCH 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......................................................................................7

References.............................................................................................................................................7

ANNEX: Verification report for June-July-August 2015..........................................................................9

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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 MATCH 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. During this quarter, theunderestimation of ozone and too little bias of PM10 by MATCH remains the same from earlierquarters. We expect recovery of better scores with a new MATCH setup with revised chemistry thatso far seems promising. The schedule for implementation is before the end of 2015.

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2 MATCH fact sheet2.1 Products portfolio

Name Description Freq. Available for users at

Species Time span

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

Daily 4:30 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, hourly assimilation

10:30 UTC for the day before

O3, NO2, CO, PM2.5, PM10

0-24h, hourly

2.2 Performance statisticsSee annex

2.3 Availability statisticsThe statistics below describe the ratio of days for which the MATCH 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 MATCH forecasts and analyses were provided on time is:

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

Availability 100 % 95 % 95 % 95 % 95 %

MATCH analyses were delivered on time everyday during June/July/August 2015.

Availability of MATCH forecasts was incomplete from 16 to 20(P) July 2015.

During this quarter, forecasts were missing due to that the MATCH system was designed to switch ofthe birch pollen forecasts at 15 July. An unforeseen down stream error then occurred in the postprocessing aborting the GRIB2 output. This happened in the middle of the summer vacation periodand was simply fixed by putting the end of pollen forecasts some weeks forward.

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2.4 Assimilation and forecast system: synthesis of maincharacteristics

Assimilation and Forecast SystemHorizontal resolution 0.2° (since 10 January 2011)Vertical resolution 52 levelsGas phase chemistry Based on EMEP (Simpson et al., 1993), with

modifications for isoprene production (Carter, 1996; Langner et al., 1998).

Heterogeneous chemistry Equilibrium reactions for NH3-H2SO4-SO4Aerosol size distribution 0.02-0.1, 0.1-1, 1-2.5, 2.5-10Inorganic aerosols Sulphate, Nitrate, Ammonium (not included in

the output during the period).Secondary organic aerosols NoAqueous phase chemistry SO2 oxidation by H2O2 and O3Dry deposition/sedimentation Resistance approach/size dependent

sedimentation velocity, in link with PBL parameterisations

Mineral dust YesSea Salt YesBoundary values MOZART IFS forecast for the day before (zero

boundaries for sea-salt).Initial values MATCH 24h chemical forecasts from the day

beforeAnthropogenic emissions TNO (2009) inventory with 0.25°x0.125°

resolution (0.5°x0.25° resolution for shipping emissions)

Biogenic emissions Isoprene, Simpson et al., 1993Forecast SystemMeteorological driver 12:00 UTC operational IFS forecast for the day

before (0.2°, 78 levels)Assimilation SystemAssimilation method 3DvarObservations NRT, AIRS, OMIFrequency of assimilation Performed once a day for the previous day and

hour by hourMeteorological driver IFS forecast and analyses for the same day (0.2°,

78 levels)

3 MATCH background information

3.1 Forward modelThe Multi-scale Atmospheric Transport and Chemistry model (MATCH) is an of-line model with aflexible design, accommodating diferent weather data forcings on diferent resolutions andprojections, and a range of alternative schemes for deposition and chemistry.

In MACC MATCH is forced by weather data from ECMWF MARS archive. The photochemical schemeis based on Simpson et al. (1993).

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3.1.1 Model geometryThe model geometry is taken from the input weather data. The vertical resolution is identical to theECMWF operational model and thus in hybrid vertical coordinates. The horizontal geometry isdefined when retrieving the weather data from the MARS system (currently a lat-long grid with 0.2degree resolution). For the air quality simulations we presently use the lowest 52 layers of theECMWF model.

The domain covered is 28.8° W to 45.8° E and 29.2° N to 70.0° N. The grid is an Arakawa C-grid withstaggered wind components.

3.1.2 Forcings and boundary conditions3.1.2.1 MeteorologyForcing meteorology is the IFS model retrieved on 0.2 degree resolution with analysis 12Z the day before the first forecast date.

3.1.2.2 ChemistryMATCH is initialised with fixed boundary conditions, based on climatology, for most of the 70 speciesincluded in the model. Some of the fixed boundaries are replaced by dynamical boundaries from theglobal CTM MOZART at intervals of every 3 hour. At present dynamic boundaries are taken for O3,CO, HCHO, NO, NO2, SO2, HNO3, PAN, CH4, C5H8, OXYLENE, sulphate and C2H6. The model topboundary is defined as the mean of the horizontal boundaries at the model top (due to empty globalboundaries in the internal of the domain). The dynamic boundary fields are redistributed in thevertical in a mass-conservative way to fit into the vertical hybrid coordinates used by ECMWF.

The procedures of the handling the boundary conditions imply that the model will run withacceptable boundaries even when the dynamic boundaries from MOZART are missing.

3.1.2.3 LanduseLand-use information is based on 4 classes, sea, low vegetation, forest and no vegetation.

3.1.2.4 Surface emissionsAt percent the TNO-MACC emissions in 11 SNAP classes for year 2009 are used. Ship emissions fromEMEP are added.

3.1.3 Dynamical coreMass conservative transport schemes are used for advection and turbulent transport. The advectionis formulated as a Bott-like scheme (Bott [1989], Robertson et al. [2007]). A second order transportscheme is used in the horizontal as well as the vertical. The vertical difusion is described by animplicit mass conservative first order scheme where the exchange coefficients for neutral and stableconditions are parameterized following Holtslag and Moeng (1991). In the convective case theturbulent Courant number is directly determined from the turn-over time in the ABL.

Part of the dynamical core is the initialization and adjustment of the horizontal wind components.This is a very important step to ensure mass conservative transport. The initialization is based on aprocedure proposed by Heimann and Keeling (1989), where the horizontal winds are adjusted bymeans of the diference between the input surface pressure tendency and the calculated pressuretendency assumed to be an error in the divergent part of the wind field.

3.1.4 Physical parametrizations3.1.4.1 Turbulence and convectionBoundary layer parameterization is based on surface heat and water vapour fluxes as described vanUlden and Holtslag (1985) for land surfaces, and Burridge and Gadd (1977) for sea surfaces. The

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boundary layer height is calculated from formulations proposed by Zilitinkevich and Moronov (1996)for the neutral and stable case and from Holtslag et al. (1995) for the convective case. Theseparameterizations drive the formulations for dry deposition and vertical difusion.

3.1.4.2 Deposition

3.1.5 Chemistry and aerosolsThe photochemistry scheme is based on the EMEP chemistry scheme (Simpson et al., 1993), withsome updates where a modified production scheme for isoprene is the most notable based on theso-called Carter-1 mechanism (Carter, 1996; Langner et al., 1998).

3.2 Assimilation systemThe model for data assimilation is an integrated part of the MATCH modelling system (Robertson etal., 1999, Langner et al., 1998). The data assimilation scheme as such is a variational spectral scheme(Kahnert, 2008) implying t hat the background covariance matrices are modelled in spectral space.The limitation then is that covariance stru ctures are describe as isotropic and homogeneous. Theadvantage though is that the background error matrix becomes block diagonal and there are no scaleseparations as the covariance between spectral components are explicitly ha ndled. The blockdiagonal elements are the covariance between wave components at model layers and chemicalcompou nds.

Modelling the background error covariance matrices are the central part in data assimilation. This isconducted by means of the so called NMC approach (Parish and Derber, 1992). The CTM model(MATCH) is run for a three month per iod for photo chemistry and aerosols with analysed andforecasted ECMWF weather data. The diferences a re assumed to mimic the background errors, andthe statistics in spectral space are generated for diferent combinations of the model compounds:

O3, NO2, NO

SO2

CO

PM2.5, PM10

3.3 Development plans for the next months

Setup will be made to comply with the demand to use IFS 00UTC forecast for the analysis part. Aversion upgrade of the MATCH model with revised photo-chemistry will be made that is intended toreduce the negative bias of ozone and improve PM10 calculations. The version upgrade will alsoaddress identified overestimates of sea salt emissions.

A module for mineral dust emissions has been under development and validation is in progress. Thevalidated emission module will be brought to an update of the operational suite. The value will bethat mineral dust emissions, especially from desert areas, will complement the global boundaries,and the objective is to improve simulations of PM10 in the southern Europe.

ReferencesCarter, W.P.L, Condensed atmospheric photo oxidation mechanism for isoprene. Atmosph. Env. 30,

4275-4290, 1996.

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Heimann, M. and Keeling, C.D., A three-dimensional model of CO2 tramsport based on observedwinds. Model description and simulated trace experiment. In aspects of Climate Variability in thePacific and Western Americas (ed. D.H Peterson). American Geophysical Union, Washington, DC,pp. 237-275, 1989.

Holtslag. A.A.M., Meigaard, E. van and De Rooy, W.C., A comparison of boundary layer difusionschemes in unstable conditions over land. Boundary Layer Met., 76, 69-95, 1995.

Kahnert, M., Variational data analysis of aerosol species in a regional CTM: Background error covariance constraint and aerosol optical observations operators. Tellus, 60B, 753-770, 2008.

Langner, J., Bergström, R. and Pleijel, K., European scale modeling of sulphur, oxidized nitrogen andphotochemical oxidants. Model dependent development av evaluation for the 1994 growingseason. SMHI report, RMK No. 82, Swedish Met. And Hydrol. Inst., Norrköping, Sweden, 1998.

Parish, D.F. and Derber, J.C. The national Meteorolocial Center’s spectral statistical interpolation analysis system. Mon. Wea. Red. 120, 1747-1763, 1992.

Robertson, L. , Langner, J. and Engardt, M., An Eulerian limited-area atmospheric transport model. J.Appl. Met. 38, 190-210, 1999.

Simpson, D. Andersson-Sköld, Y. and Jenkin, M.E., Updating the chemical scheme for the EMEP MSC-W oxidant model: current status. EMEP MSC-W Nore 2/93, 1993.

van Ulden, A.P and Holtslag, A.A.M., Estimation of atmosphericv boundary layer parametrers fortdifusion applications. J. Climate. Appl. Met., 24, 1196-1207, 1975.

Zilitinkevich, S. and Mornom, D.V., A multi-limit formulation for the equilibrium depth of a stablestratified boundary layer. Max-Planck-Institute for Meteorology. Report No. 185, ISSN 0397-1060,30 pp., 1996.

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

There are a general underestimation of ozone and the the large RMSE in the daily maximum valuesare attributed to this. The underestimation of ozone levels have been a weakness of the MATCHsetup for some time now. The underestimation is however less during the morning hours (06-12UTC), a feature that MATCH shares with the ensemble, where also the correlation is better but notat the level of the ensemble. The reason for the deficiency of the MATCH model is not entirely clear.More about coming update in the summary.

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

The NO2 modified mean bias exhibits the inverse of the ozone bias with an underestimation of NO2during the mornings hours (06-12) and less bias at night time. The RMSE also shows a repetitivenature for each forecasted day with minimum values at early morning and early afternoon. Themorning peak in RMSE and noon minima almost overlap the period of minimum in modified meanbias that appears a bit confusing and we are not able catch a good explanation of this part of thescores. The correlations are of the order 0.1 less than for the ensemble and slowly decreasing byincreasing forecast lead time. Comparing scores from earlier periods the NO2 scores looks verysimilar and are as well close to the ensemble scores over the time. The modified mean bias and theRMSE variations are remarkably persistent from day to day.

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

The modified mean bias and the RMSE are for the MATCH model less than for the ensemble (eventhough the RMSE diference is small), and there is a trend of less bias in the MATCH results withforecast lead time. One could, however, expect the scored bias to be too small with respect to thatSOA is missing in the present MATCH setup. The RMSE exhibits a small variation around 13 ug/m3both for MATCH and the ensemble. The correlation is not that impressive for neither the ensembleand MATCH where the MATCH model for sure needs improvements. The RMSE for daily maximumshows a decreasing trend for the MATCH model and is comparable with the ensemble by the end ofthis period.

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Analysis of MATCH 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.

For ozone the MATCH setup has given too low levels for several quarters and a thorough review ofthe setup has been done and under evaluations with significant improvements.

The scores for NO2 is very much the same over the quarters and also similar to the scores for theensemble.

The PM10 is for the shown scores somewhat better for the MATCH model than for the ensembleexcept for the correlation.

Improvements from the new setup under way are also seen for PM10 as shown below. Still the biasmay be a too good with respect to missing SOA in the PM calculations.

A new revised scheme is under testing that significantly reduce the negative bias of ozone, andimproves the ozone variability to the same amplitude as shown from observations. Scores for NO2will however not be changes to any degree.

In terms of PM10 new scheme under implementation shows improvements in terms of correlationand variability while the estimates will still be too close to the observations with regards to that SOAis not included. It remains to be shown that later planed inclusion of SOA will improve the scores.

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