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Atmospheric Environment 45 (2011) 6845e6851

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Atmospheric Environment

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Integrated modeling for forecasting weather and air quality:A call for fully coupled approaches

Georg Grell a,*, Alexander Baklanov b

a Earth Systems Research Laboratory of the National Oceanic and Atmospheric Administration (NOAA), and Cooperative Institutefor Research in Environmental Sciences (CIRES), Boulder, CO 80305-3337, USAbDanish Meteorological Institute (DMI), Lyngbyvej 100, DK-2100 Copenhagen, Denmark

a r t i c l e i n f o

Article history:Received 2 August 2010Received in revised form26 December 2010Accepted 7 November 2011

Keywords:MeteorologyNumerical weather predictionAir quality forecastingChemical weather predictionAtmospheric chemistryAerosolsCloudsRadiationClimateTwo-way interactionsFeedback mechanismsOnline integrated air quality e meteorologymodeling systemsAtmospheric chemical transport modelingChemical data assimilation

* Corresponding author.E-mail address: Georg.A.Grell@noaa.gov (G. Grell)

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.01.017

a b s t r a c t

This paper discusses some of the differences between online and offline approaches for both air qualityforecasting and numerical weather prediction, and argues in favor of an eventual migration to integratedmodeling systems that allow two-way interactions of physical and chemical processes. Recent studies areused that directly compared online and offline simulations to discuss possible shortcomings for both airquality and weather forecasting. The disadvantages of offline approaches are easy to show for air qualityforecasting. On the other hand, a positive impact on short to medium range weather forecasts that issignificant enough to justify an implementation at operational weather forecasting centers ismore difficultto prove, and may initially only come through an improvement of the meteorological data assimilation.Eventually though, a migration to an integrated modeling system will provide new opportunities forweather prediction modelers as well. The simulation of chemical species will allow identification ofshortcomings in currently used forecast models as well as lead to better use of meteorological dataassimilation.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

The prediction and simulation of the coupled evolution ofatmospheric transport and chemistry will remain one of the morechallenging tasks in environmentalmodeling over the next decades.Many of the current environmental challenges in weather, climate,and air quality involve strongly coupled systems. Field experimentsand satellite measurements have shown that chemistry-atmo-sphere feedbacks exist among the Earth systems including theatmosphere (e.g., Kaufman and Fraser, 1997; Rosenfeld, 1999;Rosenfeld and Woodley, 1999; Givati and Rosenfeld, 2004; Boerset al., 2006; Lau and Kim, 2006; Rosenfeld et al., 2007, 2008). It iswell accepted that weather is of decisive importance for air quality,or for the aerial transport of hazardous materials. It is also

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recognized that chemical species will influence the weather bychanging the atmospheric radiation budget aswell as through cloudformation. Until recently however, because of the complexity andthe lack of appropriate computer power, air chemistry and weatherforecasts have developed as separate disciplines, leading to thedevelopment of separate modeling systems that are only looselycoupled (offline). In Numerical Weather Prediction (NWP), thedramatic increase in computer power enables us to use higherresolution to explicitly resolve fronts, convective systems, localwind systems, and clouds, or to increase the complexity of thenumerical models. Additionally we can now directly couple airquality forecast models with numerical weather prediction modelsto produce a unified modeling system e online e that allows two-way interactions. Online modeling systems have been developedand used by the research community since the 1990s. The earliestonline approach for the simulation of air quality and chemicalcomposition may have been a model developed by Jacobson (1994,1997a,b), and Jacobson et al. (1996). The Novosibirsk school of

Fig. 1. Energy power spectrum from a WRF forecast with 10-km horizontal resolution(dashed black line) and analytic results from Lindborg (1999).

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atmospheric modeling (Marchuk, 1982) started using the onlinecoupling approach for atmospheric environment modeling in the1980s (Penenko and Aloyan, 1985; Baklanov, 1988). The earliestrecognition of the importance of online chemistry for NWP modelsmay have been given by the European Centre for Medium RangeWeather Forecasts (ECMWF, Hollingsworth et al., 2008). Climatemodeling centers have gone to an Earth systemmodeling approachthat includes atmospheric chemistry and oceans. However, NWPcenters, as well as entities responsible for air quality forecasting, areonly now beginning to discuss whether an online approach isimportant enough to justify the extra cost (IFS, 2006; Baklanov,2010), and invest in additional computer power as well as addi-tional man power. For overviews of online and offline modelingsystems used for air quality forecasting, the reader is referred toZhang (2008) and Baklanov et al. (2008a, 2010). For references ofmodeling papers on potential impacts of aerosol feedbacks thereader is referred to (Lu and Seinfeld, 2005; Jacobson et al., 2007;Chapman et al., 2008; Korsholm, 2009; Grell et al., 2010; Zhang etal., 2010a,b; Aouizerats et al., 2010). In this article we will argue infavor of integrating weather and chemistry for both NWP as well asair quality and chemical composition forecasting.

Fig. 2. Dynamically small and dynamically large systems. Dependence on horizontallength scale and Rossby radius of deformation from Ooyama (1982) and Frank (1983).

2. Air quality forecasting: online versus offline

The more common offline approach only allows one-waycoupling of the meteorology e sampled at fixed time intervals.Initially ameteorologicalmodel is run independently of the chemicaltransport model to produce the meteorological fields that serve totransport and modify the chemistry fields. For these meteorologyonly simulations, the assimilation and modeling systems e notonly for the forecast model, but also for the sophisticated analysissystems e quite often need chemical input data, which is usuallysupplied by climatology. The output from the meteorological model,is subsequently used to drive the transport in the chemistry simu-lation. The meteorological fields from the weather predictionmodelare often only snapshot values and have to be interpolated in timeand space by the chemical transport model.

Although this methodology has computational advantages(discussed later), the separation of meteorology and chemistry canalso lead to a loss of potentially important information aboutatmospheric processes that often have a time scale much smallerthan the meteorological model output frequency (e.g., wind speedand directional changes, PBL height variations, cloud formation,and rainfall). This may be especially important in future air qualityprediction systems, since horizontal grid-sizes on the order of 1 kmmay be required to match the operational models.

To easily assess the amount of information that is lost, a powerspectrum of the wind fields may be determined. Fig. 1 shows anexample of energy spectra from a simulation with the WeatherResearch and Forecast (WRF) model (Skamarock, 2005) during theBow echo And Mesoscale convective vortices field Experiment(BAMEX). With a horizontal resolution of dx¼ 10 km, the powerspectrum shows the shortest possible waves at a 20-km wave-length. However, the model solution starts to deteriorate at about7dx, which is called the effective resolution (effr). This resolutiondepends on the model properties and numerical treatments. UsingFig. 1, for transport processes a corresponding time resolution (effT)may be on the order of O(U/L)�1. Using an advective speed of about10 m s�1 we may get effT w (effr/4)/10 w 30 min. A couplinginterval of 60 min would in this case correspond to an effectivehorizontal resolution of 14dx. Anything with resolution higher than14dxwould simply be cut out completely. Vertical transport may beeven more critical, as is discussed below. While these are undis-puted errors introduced by the offline approach, the importance of

these errors depends on the importance of the neglected scales forthe chemical transport.

An additional consideration must therefore be the importanceof the small scale systems that are not being properly resolved orsimulated. An interesting way to look at this issue is shown in Fig. 2.This figure was originally shown by Ooyama (1982), and again byFrank (1983). Ooyama (1982) examined scaling considerations interms of tropical cyclone development and evolution, Frank (1983)discussed these in more general terms for mesoscale convectivesystems.

The importance for offline versus online consideration here isthat systems with long length scales relative to the Rossby radius ofdeformation (dynamically large systems) are strongly rotationallyconstrained. The vertical velocity helps to transition from onebalanced state to the next. For increasingly shorter length scales,the rotational constraint diminishes, and the divergent componentof the wind may become dominant. Systems with length scalessmaller than the Rossby radius of deformation may also be calleddynamically small. The effective time resolution of the modelingsystem must be sufficient enough to resolve the wind fields

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appropriately, if these systems are to be simulated with someaccuracy. In chemical transport modeling, this part should beaccomplished by the meteorological model. The transport in anonline approach is automatically tied to the model resolution timeand space scales, whereas in the offline approach a consciousdecision must be made on what are the model resolvable time andspace scales. The coupling frequency must be based on thoseresolvable scales. If these resolvable scales are dynamically small,then the coupling needs to be more frequent.

Examples of effects of different coupling intervals are given inGrell (2008), Grell et al. (2004), and Korsholm et al. (2009). Grell(2008) (G8) used WRF-Chem (Grell et al., 2005) in online modeand in offline mode with various coupling intervals. The model wasrun once a day over a two-week test period (15 runs, each for a 24-hforecast), to look at an average of cases typical of summertimeweather patterns. Results showed that root-mean-square errorsand biases were increased significantly when comparing predic-tions of peak 8-h average ozone concentrations to observations. Thehorizontal resolution was 12 km, with an effective resolution ofabout 85 km. For this resolution the differences were significant forhourly coupling intervals (up to an 8-ppb increase in bias), butmuch smaller for coupling intervals of 30 min.

Explicitly resolved convective systems are shown to havea particularly severe influence on the vertical redistribution ofmass.This was shown by G8 and also by Grell et al. (2004) (G4), whichused MM5-Chem (Grell et al., 2000) for cloud resolving simulations(dx¼ 3 km) in the US to test the dependence of the chemicaltransport modeling results on the coupling interval. Fig. 3 displaysthe percentage of the variability of the vertical velocity that iscaptured as a function of horizontal resolution and outputfrequency. Here 100% is defined as the sum of the spectral power forall frequencies. For an output interval of 1 h, less than 40% of thetotal variability is captured by both the original 3-km resolution runas well as the 9-km results. For the cloud-resolving simulation, itappears that the meteorological output interval must be less than10 min to capture more than 85% of the variability of the verticalvelocity. On the other hand, for the vertical velocities averaged to

Fig. 3. From G4, percentage of variability of the vertical velocity that can be capturedas a function of the meteorological sampling interval and the horizontal resolution.Variability was derived with power spectra calculated using vertical velocities thatwere representative (through averaging) of 3-km, 9-km (dotted), and 27-km (dashed)horizontal resolution at one particular grid point. Percentage is a fraction of 1, andoutput frequency is in minutes.

27-km horizontal resolution, results look somewhat better, espe-cially at the 30-min output interval, where more than 90% of thevariability is captured on the coupling interval.

G4 represented a study of amesoscale convective systemmovingthrough the domain and can therefore be considered an extremechallenge for an offline approach. The difference of the resulting COmixing ratios for this caseewhen averaged over a large part of theirmodeling domain ewas very large for 60- and 30-minute couplingintervals. Similar effects should be expected with other rapidlyevolving circulation systems. This is of particular importance forunbalancedflowregimes,where the divergent component becomessignificant. The vertical redistribution of the constituents may wellbe determinedmore by the nature of these circulation systems thanby the larger scale flow. As we go to even smaller scales, this effectwill be increased, since more and more of these types of circulationsystemswill be resolved. Themost noticeable influencewill be seenin simulations that use large domains to prevent air from quicklyflushing beyond their perimeters.

An additional modeling artifact, as the model resolutionapproaches cloud-resolving scales (horizontal grid spacing less than5 km), is that almost all vertical transport e with the exception ofsmall-scale turbulence e comes from resolved, explicit verticalmotion fields and not from convective parameterizations. Theseexplicit vertical motion fields usually exhibit very large variabilityon short time and space scales. Explicitly resolved convectivesystems may have a particularly severe effect on the vertical redis-tribution of mass, since no convective parameterizations can beused to accommodate the amount of mass flux that happens in themeteorological simulation; all mass transport has to come from themodel predicted vertical velocity fields. In this case it is not onlyimportant to capture most of the variability, but also most of thepeaks (positive and negative) of the vertical velocity fields, espe-cially in low levels. For a constituent that has its sources and highestconcentration in the PBL, both upward motion out of the PBL anddownward motion into the PBL will decrease its concentration inthe boundary layer. Ifwe can trust the capabilities of high-resolutioncloud-resolving NWP models to predict the complex flows in theatmosphere with reasonable accuracy in the future, online simula-tions may be required for most applications. Although the accuracyof the simulated vertical winds cannot be guaranteed, capturingmost of the variability in the vertical velocity field is still essential inorder to properly simulate the vertical redistribution of tracer.

A different example of the effect of coupling intervals on resultsis given by Korsholm et al. (2009). Fig. 4 shows an example of onlineversus offline runs of the coupled Enviro-HIRLAMmodel (Baklanovet al., 2008b; Korsholm et al., 2008). The horizontal resolution wasdx¼ 40 km. During off-line runs advection was updated withrelevant meteorological fields every 0.5, 1, 2, 4, 6, 12 and 24 h (usingconstant input in between updates). Simulations of the ETEX-1(Nodop et al., 1998) release were conducted and a comparisonwithobservations was used to calculate statistical quantities at specificstations. As the offline coupling interval increases, so does the error,which reaches considerable magnitudes when the couplinginterval is between two and four hours. A false (not in observations)peak preceding the plume existed and is indicative of mesoscaleinfluences during plume development in the model. Notice that thefirst peak did not contribute to the statistical scores because theobservations are zero. As the coupling interval was increased, themain (second) peak remained unaffected while the amplitude ofthe first peak gradually increased.

3. Online chemistry for NWP

While advantages of onlinemodeling for AQ forecasting are easyto argue, the pros and cons of online modeling for NWP are much

Fig. 4. Measured and modeled time development of concentration (ngm�3) at ETEXstations DK02 (a) and F15 (b) for the online and offline simulations with couplingintervals 10 (online), 30, 60, 120, 240 and 360 min (Korsholm et al., 2009).

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less explored. It is well known that chemical species will influenceboth atmospheric radiation as well as microphysics. Among thespecies that may be most important for NWP are greenhouse gases,Ozone, and aerosols such as natural aerosols (e.g., sea salt, dust,volcanic aerosols) and primary and secondary particles of anthro-pogenic origin. Some aerosol particle components warm the air byabsorbing solar radiation (e.g., black carbon (BC), iron, aluminium,polycyclic, and nitrated aromatic compounds) and thermal-IR (e.g.,dust), whereas others (water, sulphate, nitrate, most organiccompounds) cool the air by backscattering incidents short-waveradiation to space. However, much less known is how large thesensitivity to these species is for NWP on different time and spacescales. Additionally, any new additions or modifications can only beallowed to go into operation if an improvement can be noticed.Although this improvement may be the addition of previously notparameterized physical (or chemical) processes, this is only justi-fied if evaluation results are not worsened. NWP models at oper-ational centers are highly tuned to produce the best possibleforecast of the meteorological fields. Changes introduced throughadding chemistry in the forecast models may well e at first e

reduce the skill of the forecasts.Research and rigorous evaluations with long test-bed data sets

may be necessary to retune the expanded modeling systems.Experiments with research models have already shown a signifi-cant impact of aerosols on weather forecasts. This should be

expected, since Aerosol Optical Depth (AOD) can reach values of 1or higher, which corresponds to a significant reduction in solarradiation that reaches the ground. In addition Black Carbon (BC) canlead to significant warming through absorption in layers above thesurface. In order for this to have a positive impact on the weatherforecasts, it is nevertheless essential to be able to forecast AOD aswell as the vertical profiles of the aerosols or other importantchemical species with accuracy that is significantly better thanwhat climatology would predict. It may well be very difficult toimprove upon good quality climatology. An exception is usuallyareas with strong signals where AOD is the largest. An examplehere may be dust events or wild fires.

Fig. 5 shows an example of a 24-h forecast using a cloudresolving (dx¼ 2 km) WRF-Chem simulation during a period withvery intense wildfires in Alaska (July 2004). In this figure Grell et al.(2010) compare 24-h forecasts (with and without wild firesincluded in the model runs) with the observed sounding fromFairbanks, Alaska. The forecast with the effect of fires included leadsto a much improved simulation (cooler near the ground, warmerand dryer between 1 km and 5 km). The resulting CAPE is alsomuch closer to the observed, and even the winds show improve-ment. Grell et al. (2010) also show a significant impact on cloud andprecipitation forecasts through the interaction of the microphysicswith cloud condensation nuclei, where precipitation amounts weredecreased during the first 12 h of the simulation (larger number ofsmaller cloud droplets), but then increased significantly later in theforecasts.

Other examples of indirect effects of urban plume aerosols onmeteorological fields have been shown based on simulations byEnviro-HIRLAM (Baklanov et al., 2008b; Korsholm et al., 2008).Korsholm (2009) showed that aerosol feedbacks through thesecond indirect effect induce considerable changes in meteoro-logical fields and large changes in chemical composition, inparticular NO2, in a case of convective cloud cover and littleprecipitation. The effects of urban aerosols on the urban boundarylayer height could be so significant that they are comparable to theeffects of the urban heat island in specific meteorological condi-tions for a stable boundary layer (Baklanov et al., 2008b). InKorsholm et al. (submitted for publication), monthly averagedchanges in surface temperature due to aerosol indirect effects ofprimary aerosol emissions in Western Europe are analyzed andvalidated versus measurement data. It shows that modifications ofcloud properties due to anthropogenic aerosols may take placethrough modification of cloud reflectance and precipitationdevelopment, referred to as the first and second aerosol indirecteffects respectively. It is shown that their characteristic time scale isthe same as that of the clouds and significant differences may existfor various types of clouds, so they can be important not only inclimate but also in NWP models. By comparing model runs withand without the indirect effects it was found that a monthly aver-aged signal in surface temperature of about 0.5 �C exists. Inparticular the indirect effects led to stronger convection andheavier precipitation in some places and suppression of precipita-tion in other places. Comparison to temperature and dew pointmeasurement data showed that root-mean-square error and biasdecreased near the surface, when averaged over all availablemeasurement stations.

While these intense aerosol events can be shown to lead toimprovements, this may be muchmore difficult to accomplish withweaker aerosol signals, although differences still exist. In a casestudy by Zhang (2008) for US anthropogenic emissions, it wasshown that including only the direct aerosol effects leads toa temperature change about 0.2 �C and the water vapor mixingratio increases more than 3% at/near the surface. However, a posi-tive impact on meteorological data assimilation may be more

Fig. 5. Predicted (blue) and observed (black) Skewt diagrams for 24-h simulationswith WRF-Chem including the effects of fires (b) and excluding the effects of fires (a).Also shown is observed (dashed red) and modeled (dashed magenta) ConvectiveAvailable Potential Energy (CAPE). For the runs with fire, the CAPE lines are overlayingeach other.

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obvious and easier to prove (Hollingsworth et al., 2008). Given thatthe modeling system is able to beat climatology and persistence inevaluation scores of aerosols and GH Gases, assimilation of satelliteradiances as well as retrieval of satellite data should improve. Workis currently in progress at several operational centers to test thishypothesis.

4. Final remarks

Although we may continue to develop and run modelingsystems of Earth system components separately, a scientificperspective would argue for an eventual migration to integrated

modeling systems that allow two-way interaction of physical andchemical components of chemical weather forecasting systems.While this may be the obvious approach for air quality forecasting,more research and discussion may be needed for NWP. Addition-ally, better and more complete representations of physical andchemical processes and interactions in both air quality andnumerical weather prediction models are needed.

The focus on online integrated systems is timely, since recentresearch has shown that meteorology/climate and chemistryfeedbacks are important in the context of many research areas andapplications, including NWP, climate modeling, air quality fore-casting, climate change, and Earth system modeling. Potentialimpacts of aerosol feedbacks include: a reduction of downwardsolar radiation; changes in surface temperature as well as tropo-spheric temperature (through absorption effects), resulting inchanges of wind speed, relative humidity, and atmosphericstability; a decrease in cloud drop size and an increase in dropnumber by serving as cloud condensation nuclei; an increase inliquid water content, cloud cover, and lifetime of low-level clouds,and suppression or enhancement of precipitation. Traditionally,aerosol feedbacks have been neglected in NWP and AQ modeling,due to an historical separation between the meteorological and airquality communities, and the necessity to make approximationsrequired by limited computing resources. Although our under-standing of the underlying interaction mechanisms is still limited,we now realize that thesemechanismsmay, however, be importanton a wide range of temporal and spatial scales, from days todecades and from global to local.

In summary, the main advantages of online versus offlinemodeling approaches are the following:

(1) Online coupling� The online approach represents the atmosphere more real-istically, since in reality the processes are all intertwined.The errors introduced by the offline approach for air qualityforecasting can be quite substantial as the resolution isincreased.

� For air quality forecasting, the online approach is numeri-cally more consistent. No interpolation in time or space isrequired, although some time interpolation could be addedto gain a computational advantage. Physical parameteriza-tions as well as atmospheric transport are the same. This isespecially significant for studies of the aerosol indirect effector when aqueous phase processes are of importance. Feed-back mechanisms can be considered.

� For weather forecasting, inclusion of online chemistry maydirectly improve the medium range forecasts (1 to 5 days). Itmay also indirectly improve the forecasts through improvingthe assimilation of meteorological data.

� The needed closer interaction between atmospheric physi-cists and chemists will lead to improvements in both theNWP as well as the atmospheric chemistry modelingapproaches.

(2) Offline coupling� Low computational cost, esp. if meteorological output isalready available from a forecast run or observations. This isof particular interest for regulatory agencies that need toperform many simulations with different chemicalassumptions (such as emissions input). This is also ofinterest on coarser resolutions.

� There exists more flexibility in specifying ensembles withlower computational cost in an offline approach. This isprobably most significant for regulatory agencies, but alsofor emergency response, where a multitude of ensemblescan quickly be run.

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Operationally, the possibly faster turnaround of the offlineapproach is attractive, yet this should be weighed against the errorsintroduced by this approach. For NWP centers, an additional attrac-tiveness of the online approach is its possible usefulness for meteo-rological data assimilation, where the retrieval of satellite data anddirect assimilation of radiances will likely improve e assuming thatthe modeling system can beat climatology when forecastingconcentrations of aerosols and radiatively active gases. For AQ fore-casting, the online approach does not need meteo- pre/post-processors resulting in the reduction of IO operations. On even finerresolution, the additional computational effort for IOmay well offsetthe cost of running the meteorology in lockstep with the chemistry.

AQ and NWP communities should work more closely together.National weather centers are advised to include chemistry/aerosolinteractions into NWP systems extending their forecasts to thechemical weather as well. Centers that are responsible for AQforecasting should seriously consider online modeling as a neces-sary part of their suite of forecasts. Additional advantages will arisefrom cross evaluations for both disciplines. Chemical species willallow identification of short comings in currently used forecastmodels as well as lead to better use of meteorological data assim-ilation. Other outcomes from such collaboration and the onlinecoupling may include benefits for: (i) meteorological weatherforecasting (e.g., in urban areas, severe weather events, fog, andvisibility, UV-radiation and solar energy, etc.), (ii) chemicalweather/air quality and bio-meteorology forecasting, (iii) seasonaland decadal air quality/climate prediction, (v) global and regionalprojections of the climate/Earth system.

While we strongly argue in favor of integrated modeling, it isclear that for some applications offline approaches are here to stay.These may include but are not limited to regulatory emission workor very simplified methods to produce ensemble forecasts quickly,for example in the case of accidental releases.

Finally, the most uncertain issue regarding the inclusion ofchemistry in NWP models is whether an integrated model canimprove upon a good climatology of the most important chemicalspecies. To go one step further, the integrated model must also beable to beat persistence forecasts of these species. Considering themany uncertainties in AQ forecasting (such as emission invento-ries), this may not be easy. More research and studies are needed toshow that models with online chemistry are able to perform wellenough to meet these standards.

Acknowledgement

This study was supported by the COST Action 728, and EC FP7Project MEGAPOLI, and support from the US National WeatherService National Air Quality Forecast Capability. The authors aregrateful to a number of COST728, MEGAPOLI and their WRF-Chemand Enviro-HIRLAM team colleagues, who participated in theabove-mentioned studies, for productive collaboration anddiscussions. We would also like to thank Rainer Bleck and JohnBrown for reviewing the manuscript as well as Ann Reiser forproviding editing assistance.

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