air pollution viii, c.a. brebbia, h. power & j.w.s longhurst … · 52 air pollution viii was...

Five months quasi-operational forecasting of

atmospheric constituents - comparison of

results to data from official monitoring

networks

S. Tilmes, J. Rifimann, I. Jacobsen, J. Zimmermann

Deutscher Wetterdienst, Germany.

Abstract

From May to September 1999 quasi-operational forecasts of atmospherictrace gases for Central Europe were performed at the Deutscher Wetter-dienst (DWD) using a comprehensive model system developed in of a co-operation between the DWD and several universities. In parallel to theforecasting an operational real-time verification of model results was estab-lished using the measurements of the environmental agencies of Germanyand its Federal States at about 360 sites. Subsequently an evaluation of themodel performance for the complete summer season was carried out. Theaim of this was twofold. On the one hand the focus lies on the climatologi-cal properties of the modelling system. On the other hand, monitoring theunderlying observations is essential for the assessment of the overall inter-pretation.

1 Introduction

The main objective of the German "Tropospheric Research Program" (TFS,funded by the German government) was to enhance the understanding ofthe formation of photochemical oxidants in the troposphere. Especially,it was expected to improve the forecasting quality of chemistry transportmodels (CTMs) for the troposphere. Thereupon research groups from theDWD and the Universities of Cologne, Karlsruhe, and Stuttgart built upthe TFS model network to set-up and test a comprehensive modelling sys-tem for air quality forecasting. The aim of the research project at the DWD

Air Pollution VIII, C.A. Brebbia, H. Power & J.W.S Longhurst (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-822-8

52 Air Pollution VIII

was to demonstrate that a daily real-time 48 hour forecast of atmospherictrace constituents for Central Europe with this modelling system is feasi-ble until afternoon of the first day of the forecast. This was shown day byday from May to September 1999. Accompanying this activities a secondproject was concerned with the development of methods for the verificationof the forecasts and the evaluation of the modelling system using routine airquality data. In the next section we will describe the state of the modellingsystem during the summer 1999. This will be followed by a presentation ofthe operational results and their verification. Examples for the evaluationof the model system with data from the whole season and the assessmentof the observation data will be given afterwards. Finally we will summariseand give an outlook on our further plans.

2 The forecasting system

Figure 1 gives an overview over the state of the modelling system as it wasin operation in the testing phase in summer 1999. The modelling domaincovers Central Europe, from Ireland to Poland and from Italy to South-ern Scandinavia. The horizontal resolution is 0.1875°, about 21 km, with109 x 109 gridpoints. In the vertical there are 36 layers used for the meteo-rological forecast and 16 for the CTM with a coarser resolution in the uppertroposphere and stratosphere. In both models the lowest layer is about 35m thick and the model top is at 20 hPa. A terrain following cr-coordinateis used.

2.1 The weather prediction part

The nonhydrostatic Lokal-Modell (LM) of the DWD was used to provide themeteorological input to the system (Doms and Schattler [1]). The forecastswere driven by the analysis of the Europa-Modell (EM) and by boundarydata provided by EM forecasts. The LM ran on a CRAY T3E. For the futureit is planned to use data from the new global model GME of the DWD asinitial and boundary data or to utilize the interpolated operational weatherforecast by the LM.

2.2 The emission forecasting part

At the University of Stuttgart the CAREAIR-ECM was developed to pre-dict emission factors with the desired spatial and temporal resolution forall the species calculated by the CTM (Friedrich et al. [2]). In 1999 cli-matological data were used to describe the dependency of the emissions onthe meteorology. Additionally, during this summer the biogenic emissionswere calculated by the CTM itself. The ECM was operated on a SGI work-station. The next steps in the development of the system will include theconsideration of the current weather forecast and the operation of a modulefor the biogenic emissions.


Air Pollution VIII 53

STATE OF THE FORECASTING SYSTEM(modifications foroperational testsin summer 1999)

EMdata meteorological boundary data

LM

meteorological forecasts v

PPCpreprocessor formeteorological input,

CTMchemistry

preprocessor for transportforecast

photolysis ratesEEM

data flow—{•':• •» input for different module—^- forecast outputDWD: Deutscher Wetterdienst

EL RAD emission modelEuropa-ModellInst. f. Energiewirtschaft;Univ. StuttgartInst. f. Geophys. + Meteorol.;Univ. KolnGlobal-ModellLokal-ModellEmissions-Modell CAREAIR-ECMCTM preprocessorchemistry transport modelpost-processor

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modelling ofanthropogenicemissionswithoutactualmeteorology

PSTverification,

products

routinetesting of theforecastingsystem,build up ofoperationalvisualizationand verification

Figure 1: Sketch of the modelling system in summer 1999.

2.3 The chemistry transport forecasting part

The EURAD-CTM from the University of Cologne was applied to calculatethe distribution of the atmospheric trace substances (Hass [3]). As chem-istry scheme the RADM2 mechanism was used. Transport was calculatedby a fourth order Bott algorithm. In order to meet the requirements ofthe LM the CTM was extended by a nonhydrostatic option and the op-portunity to utilize a rotated geographical coordinate system. Chemicalinitial data were provided by the results of the previous day 24 hour fore-cast. The operational runs with the CTM were carried out on a CRAY C90.



3 Routine operation of the system

The LM run set up on the routine 00 UTC run of the EM which was availableat about 4:15 UTC. Afterwards the LM data were extracted from the database and processed to the format desired by the CTM. Anthropogenic andespecially biogenic emissions were calculated using this preprocessed meteo-rological data. At about 8:00 UTC the CTM calculation started producinga 48 hour forecast until about 14:15 UTC. Consequently the prediction ofthe maximum ozone concentrations for the following day were available atabout 13:30 UTC. The forecasts of the LM, ECM, and CTM were archivedroutinely during the operation of the model chain. With the current set-up about 0.9 GByte output was produced for each daily 48 hour run. Thecomplete model chain was controlled by a script running on the CRAY C90.

3.1 Routine products

Accompanying the model chain a second script running on the workstationcontrolled the preparation and distribution of products. This included thegeneration of plots and MPEG films of the forecasted meteorology and theground-level ozone field. These were distributed to the network partnersvia ftp. Additional output was generated for special purposes. For examplemeteorological and chemical initial and boundary data were extracted anddelivered via ftp for subsequent forecasts with the KAMM/DRAIS modelsystem at the University of Karlsruhe (Nester et al. [4]). Another examplewas the preparation of data and plots as a supporting tool for the routineobservation programme at the meteorological observatory Hohenpeifienbergwhich is operated by the DWD as part of a GAW global station.

3.2 Routine verification

The forecasts were verified on a routine basis. Meteorological parameterswere verified against the analyses of the operational weather prediction sys-tem which has a horizontal resolution of about 7 km. The main focus wasput on the verification of the chemical forecasts. For this purpose we re-ceived once a day data from the routine monitoring networks of the Germanenvironmental protection agency (UBA) and the corresponding institutionsof the Federal States. These data consisted of half hour averaged ground-level ozone values from more than 360 sites all over Germany. The datapresented in this paper are of preliminary status.

Figure 2 gives an example for the verification of the 14 hour forecastvalid for September 10 14:00 UTC 1999. Ozone mixing ratios are shownon the left (filled contours of the model results for the lowest model layer,filled circles for the observed data) and the differences between forecasts in-terpolated to the station locations and observations on the right hand side.Below a statistical evaluation of the data is presented including a frequencydistribution of the deviations modelled minus observed data. For that time



Figure 2: Operational verification for September 10 14 UTC 1999.

no data were available for Bavaria with the exception of observations fromtwo UBA stations. In this verification no quality control was performed onthe observations nor any preselection of sites was done. Thus, the resultsmust be interpreted taking into account that to a great extend the sites areonly of limited spatial representativeness and that the inhomogeneity in thespatial distribution of the sites has effects on the calculated scores.



Verifcation period: September 6- 16,1999observations: thin; frcst. from today: thick; frcst. from yesterday: dashed

10 11 12 13 14 15 16

Figure 3: Spatial verification for the period September 6-16 1999.

4 Evaluation of the model performance

Up to now the evaluation of the model performance within this five monthof operation concentrated on ground level ozone data. This was mainly dueto the lack of other routinely available data of appropriate quality. Since48 hour forecasts were performed, two model time series can be constructedand compared. The time series of the first day (the forecast "from today",forecast length 1-24 hours) and the one of the second day (the forecast"from yesterday", forecast length 25 - 48 hours).

4.1 Spatial statistics

Figure 3 shows time series of the spatial statistical measures. The selectedtime range is September 6-16 1999. The interesting feature of this episode



is its pronounced photosmog character, which is unusually late in the yearand is following a summer with rather low ozone mixing ratios. In the firstrow the number of observation sites entering the statistics is shown. For eachhour there were data from at least 270 sites available. The next three rowspresent the mean values, the 98 % percentile, and the standard deviationfor the two forecasted time series and the observed time series. Bias (modelminus observations), root mean square error (rmse), and spatial correlationcoefficient can be seen in the lower rows of the figure.

The beginning and the intensity of the ozone episode was well forecastedby the model as can be seen from the mean and 98 % percentile values.Only at the end of the episode (September 15 - 16) the model showedsome over predict ion. About ten German sites can be classified as remote.Those sites are in general situated on elevated mountainous terrain. Due tothe coarse horizontal resolution the model orography is mostly smoother.Thus, for these stations it is questionable to compare the observations todata from the lowest model layer. The effect is an underestimation of the98 % percentile values during night time. Another effect of the coarsehorizontal resolution is that the model cannot reproduce the very locallyinfluenced ozone concentrations which are observed at traffic related sites.This effect is also visible mainly during the night-time hours when at thesestations most of the ozone is titrated to N02- This is the reason why thenight-time spatial standard deviation in the observations is much largerthan in the model forecasts. This is also valid for the scores of the bias,rmse, and correlation. On the other hand the figure shows an overall goodmodel performance for the times of the daily maximum concentrations inthe afternoon.

In Figure 3 a marked difference can be observed between the perfor-mance of the two forecast series. As mentioned above, the anthropogenicemission data are calculated using a climatological meteorology. Thus, mostof these differences are due to the different integration periods of the me-teorological forecasts by the LM. Further evaluation studies clearly showa pronounced negative influence on the mean model performance. This isvalid for the rmse and the correlation but not for the bias what indicates a"sane" model behaviour without significant drifts.

4.2 Temporal statistics

A second step in evaluating the model performance was the computation oftemporal statistics, the calculation of statistical measures for the observedand modelled time series at each single station location. Since most of thesites are under urban influence the ozone concentrations show a markeddiurnal variation. To avoid that this extenuates the statistical scores onlythe data for 14 - 15 UTC are evaluated to compute the scores presentedin Figure 4. The spatial distribution of the bias, rmse, and correlationcoefficient are shown accompanied by the frequency distributions for the


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complete suite of stations.The picture is a different one for each statistical measure. The model

performance is different for different regions in Germany. For example themodel shows large values for the rmse within the highly industrialised Rhein-Ruhr area in North-Rhine-Westphalia (NRW) whereas the performance isbetter for other — also industrialised — areas like the southern part ofHesse. For the southern part of Saxony the model shows less skill in termsof the correlation coefficient. There is a multitude of reasons for the differ-ent effects. The latter one might be due to the inhomogeneous terrain ofthe Erz Gebirge, but also due to the very limited representativeness of somesites which are to a large extend influenced by local emissions. This mightalso be a reason for the large errors in NRW. On the other hand the modelmight perform better e. g. in NRW with a better horizontal resolution es-pecially in view of a better differentiation of the anthropogenic emissions.Nevertheless, the problem of the representativeness of the German moni-toring sites is very serious. Most of the sites were set up to monitor wintersmog and, therefore, are often placed close to emission sources. Thus, it isnecessary to distinguish between different station classes, but this is beyondthe scope of this paper.

5 Conclusions and outlook

Our first intention was to show that it is possible to operate a state-of-the-art chemistry transport model for the routine forecasting of air pollution.This was proved over a five month period. Forecasts of atmospheric traceconstituents for the following day were available in time in the afternoonof the present day. Several products were developed and distributed todifferent users in a routine mode.

The second task was to evaluate the model performance in terms of averification of the ground level ozone forecasts. Especially the distributionof the daily maximum concentrations was well simulated. The model showedno significant drifts or biases during the five month period. Some deficienciescould be pointed out concerning for example different regions in Germanywhere the model performed worse. Reasons for this are beyond others thecoarse resolution and the missing coupling of meteorology and emissions.On the other hand it turned out that the monitoring of the observations isa prerequisite to the assessment of the model performance.

Plans for the future development of the system include new meteoro-logical initial and boundary data from the operational global model of theDWD and chemical data assimilation. As a basis for this more chemicalobservation data must be made available in real time on the European scalealso for the diurnal verification. This will include data from European ozonesondes and commercial airliners. It is also planned to increase the horizontalresolution within the next few years following the evolution in the numericalweather prediction.



The developed modelling system is a powerful tool for assessing the airquality in Europe. From a scientific point of view it is well suited to inves-tigate the climatological behaviour of the system and the long-term testingof the state-of-the-art chemistry transport models. The broad interest inthe outcome of the system documents the need for such a system and theefforts in evaluation guarantees a thorough assessment of the results. Thesystem is well suited for supporting the air quality monitoring over Europeand also the assessment of observation networks.

Acknowledgements

We want to thank the members of the model network group: the EUR ADgroup, University of Cologne, the IER, University of Stuttgart, and theIMK, University of Karlsruhe. We are grateful to the German environmen-tal protection agency (UBA) for providing the ozone data of its stationsand the networks of the Federal States via the UMEG, Karlsruhe. Thiswork was supported by the Bundesministerium fur Bildung, Wissenschaft,Forschung und Technologic of Germany under grants 07TFS10/LT1-A2 and07TFS10/LT1-C4.

References

[1] Doms, G. & Schattler, U. The nonhydrostatic limited-area model LM(Lokal-Modell) of DWD. Part I: Scientific Documentation, DeutscherWetterdienst (DWD), Offenbach, 1999.

[2] Friedrich, R., Heidegger, A. & Kudermann, F. Development of anemission calculation module as a part of a model network for regionalatmospheric modelling. Proceedings of EUROTRAC Symposium '98,eds. P. M. Borrel & P. Borrel, WITpress, Southampton, 247-250,1999.

[3] Hass, H. Description of the EURAD Chemistry-Transport-Model Ver-sion 2 (CTM2)j Mitteilungen aus dem Institut fur Geophysik undMeteorologie der Universitat zu Koln, eds. A. Ebel, F. M. Neubauer& P. Speth, No. 83, 1991.

[4] Nester, K., Fielder, F. & Panitz, H.-J. Simulation of mesoscale airpollution with the model system KAMM/DRAIS. Papers of the llthWorld Clean Air and Environment Congress, 13-18 September, 1998,Paper 10D-2, Volume 4, Durban, South Africa, 1998.


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