Environmental Fluid Mechanics 4: 157–196, 2004.© 2004 Kluwer Academic Publishers. Printed in the Netherlands.

157

Intercomparison of Two Models, ETA and RAMS,with TRACT Field Campaign Data

S. TRINI CASTELLIa,∗, S. MORELLIb, D. ANFOSSIa, J. CARVALHOc andS. ZAULI SAJANId

aNational Research Council, Institute of Atmospheric Sciences and Climate, Torino, ItalybUniversity of Modena and Reggio Emilia, Department of Physics, Modena, ItalycLutheran University of Brazil Canoas, RS, BrazildRegional Agency for Environmental Protection, Modena (present affiliation)

Received 28 January 2003; accepted in revised form 15 September 2003

Abstract. In this work a model intercomparison between RAMS and ETA models is carried out,with the aim of evaluating the quality and accuracy of these mesoscale models in reproducing thetime evolution of the meteorology in real complex terrain. This is of great importance not only formeteorological forecast but also for air quality assessment. Numerical simulations are performed toreproduce the mean variables’ fields and to compare them with measurements collected during thefield campaign TRACT. The domain covers the Rhine valley and surrounding mountainous regionand we consider a time period of two days. Results from simulations are compared to observationsrelative to ground stations and radiosoundings. A qualitative analysis is joined to a quantitativeestimation of some reference statistical indexes. Both RAMS and ETA models performances aresatisfactory when compared to the measured data and also their relative agreement is good. Themean variable fields are reproduced with a satisfactory degree of reliability, even if the simulatedprofiles are not able to describe the largest fluctuations of the variables. At the surface stations, thebest agreement between predictions and observations is obtained for the wind velocity, while thequality of the results is lower for temperature and humidity.

Key words: complex terrain, mesoscale, model intercomparison, model validation, numerical simu-lations

1. Introduction

In complex terrain the mesoscale and local scale circulations, related to the pres-ence of main and lateral valleys and ridges, resulting in features such as air stagna-tion regions in the lee of obstacles, separation of the flow and differentially heatedvalley walls, are superimposed over the large scale circulation. Consequently, flowconditions (wind speed and direction) exhibit large variations both in the horizontaland in the vertical directions. Hence, the reconstruction of the 3-D wind field andits time evolution is, generally, a complicated task and plays an important rolefor many applications, like limited area meteorological forecasts or air pollutiontransport studies. Therefore, it is important to perform studies aimed at estimating∗Corresponding author, E-mail trini@to.infn.it

158 S. TRINI CASTELLI ET AL.

the quality and accuracy of circulation models’ simulations in real complex terrain.Interesting and important examples of this kind of studies in the U.S.A. can befound in Cox et al. [1] or in the Monograph edited by the American MeteorologicalSociety [2].

Usually, mesoscale models are validated in situations of peculiar meteorologicalinterest, such as strong winds, heavy precipitation events, fronts and so on. Here,our main interest is to verify the models’ ability to correctly reproduce the meanflow fields to drive dispersion models in air pollution transport studies. In thesecases, conditions of moderate wind, high pressure and nocturnal stability are mostimportant together with the complexity of the topography. Moreover, in this kindof applications a proper simulation of Planetary Boundary Layer (PBL) variablesis more relevant.

The aim of this paper is to present and discuss the results of a model intercom-parison carried out on the basis of extensive measurements performed in SouthernGermany during the TRACT Campaign [3, 4]. Two widely used mesoscale modelsproduced the simulations: ETA [5] and RAMS [6]. These two particular modelsare routinely used by our two groups for research studies. One of the goals we areprecisely interested in is to compare the performances of the models in the commonand standard configuration we are using them. Thus, the possible compatibilitybetween the two configurations is determined and limited by the versions availableto us, RAMS 3b and ETA hydrostatic. We wanted to verify if the two models, runon the same case and made as compatible as possible, were able to reproduce themain features of the flow despite of their differences.

The TRACT field campaign was part of the TRACT project (TRAnsport ofpollutants over Complex Terrain), which was a sub-project of EUROTRAC withinthe European EUREKA initiative. Two goals out of the various aims of the exper-iment, which are particularly related to the present work, were: i) To measure thehorizontal and vertical transport and turbulent diffusion at the mesoscale in com-plex terrain and ii) to perform numerical simulations of the situations documentedin the experimental phase. The central part of the investigated area was the RhineValley (South-Western Germany) and the total area included a part of France to theWest and of Switzerland to the South. The TRACT area is a rather complex region(Figure 1) characterised by the presence of valleys (such as the Rhine Valley) andmountains (like the Black Forest, the Vosges and the Swabian Alps).

A brief outline of the TRACT campaign is presented in Section 2; the two mod-els are briefly summarized in Section 3; the configuration of the two models for thepresent study is described in Section 4 and some details of the result comparisonsare introduced in Section 5. Finally, Section 6 presents the results of the comparisonand the related discussion.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 159

2. TRACT Campaign

The TRACT campaign took place from the 7 to 23 September 1992. During thisperiod, three intensive measuring phases (IMP) were organised, lasting 25, 37 and31 h, respectively. Besides the measurements routinely performed in the TRACTarea, temporary stations for screen level measurements and vertical soundings plussome aircraft flights were available during these IMPs only. Thus, data useful forthe present analysis include vertical profiles of wind, temperature and humiditycarried out at various radiosonde stations and temporal trend of the same quantitiesrecorded at screen level in many ground-based meteorological stations. Typicalradiosounding ascent frequency was eight per day. For a detailed presentationand discussion of TRACT campaign and its main results, the reader is referredto Fiedler [3], Zimmermann [4] and to the special TRACT issue of AtmosphericEnvironment [7] (Vol. 32, No. 7, 1998).

The second IMP, lasting from 05.00 UTC of September 16 to 18.00 UTC ofSeptember 17, is considered in the present analysis. In this period the generalweather conditions were characterised by a high-pressure system moving towardthe East, with a very weak pressure gradient [8]. On September 16, the centre of thesystem was localised over the English Channel, whereas on September 17 it movedover Denmark. In the Northern TRACT area, on the 16th, some stratocumulusclouds, associated with a cold front that was moving from Northern Europe towardscentral Europe, were observed. This cold front crossed the TRACT area during thenight between the 16th and the 17th. On the 16th, after the disappearance of fog insome regions of the TRACT area, the day was sunny with maximum near-groundtemperature of about 25 ◦C in the valleys and slightly less on the mountainous re-gions. The near-ground wind speed varied between 1 and 5 m s−1. Wind direction,both near ground and aloft, was approximately from the west-northwest. Whenthe centre of the high-pressure system moved northwards, the wind direction wasfrom the northwest. On the 17th, part of the Rhine Valley was covered by fog thatdisappeared around noon. In the other regions of the TRACT area, all day wassunny, with temperatures between 23 and 25 ◦C. Due to the Eastward movement ofthe high-pressure system, the near-ground wind blew from the east for all the day.

3. Numerical Models

3.1. ETA MODEL

The ETA model is a prognostic model derived from HIBU (HydrometeorologicalInstitute and Belgrade University) model. The development of HIBU model startedin 1972 by F. Mesinger and Z. Janjic. Comprehensive testing, tuning and fur-ther development of the ETA model have been carried out at the NCEP (NationalCenters for Enviromental Prediction), Washington, U.S.A., where it has becomeoperational. It belongs to the Limited Area Model class and, in particular, to thesub-synoptic scale models. The model used for the simulations of this study is

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a three-dimensional, hydrostatic, primitive equation model described in detail inBlack [9], Lazic and Telenta [10] and Janjic [11–13]. The vertical coordinate, theso-called η coordinate, which represents a generalisation of the usual σ coordinate,is a main characteristic of the model and gives it the name. It was introduced byMesinger [14] to minimize the problems associated with steeply sloping coordinatesurfaces. It is defined as:

η = p − pt

ps − pt

ηs ηs = prf (zs) − pt

prf (0) − pt

,

where p is the pressure, z is the geometric height and the subscripts t and s

denote the values at the top of the model atmosphere and at the Earth’s surfacerespectively. Here prf is a suitable defined reference pressure as a function of z(e.g., from a polytropic atmosphere). This coordinate system makes the η surfacesquasi- horizontal everywhere.

The model topography is represented as discrete steps whose tops coincideexactly with model interfaces [5]. The prognostic variables (wind components,temperatures and specific humidity) are defined in the middle of the layers. Thehorizontal discretisation is performed on the semi-staggered Arakawa E grid anda transformed longitude-latitude framework is used in the model. The geodeticcoordinates are rotated in order to prevent the convergence of the meridians overthe integration domain. The physical package is based on the Mellor and Yamada2.5 [16] turbulence scheme, the Mellor–Yamada level 2 scheme for the surfacelayer [16], surface processes designed following Miyakoda and Sirutis [17] andMiyakoda et al. [18], Betts–Miller–Janjic shallow and deep convection schemes[12] and the Geophysical Fluid Dynamics Laboratory (GFDL) radiation scheme.The model physics package has been described in more detail by Janjic [12]. ETAis a widely used limited area model which has been extensively tested, showingvery good performances (e.g., [12, 19, 20]); the model has also been used forsimulations over Europe (e.g., [21]) and the Alpine region in the framework ofthe Mesoscale Alpine Programme (e.g., [22]).

3.2. RAMS

The Regional Atmospheric Modeling System (RAMS, version 3b) is a prognosticmodel, which was developed from a mesoscale model [23] and a cloud model[24] at the Colorado State University. It is designed to simulate a large range ofatmospheric flows in a large spectrum of scales, from the local and regional to thesynoptic scale. The model includes a large number of options for the description ofphysical processes in the atmosphere.

Based on the 2-way grid interactive procedures of Clark and Farley [25], RAMShas the capability to nest from large-scale area to smaller scale. The non-hydrostaticoption, alternative to the hydrostatic one, allows representation of all meteorolo-gically relevant spatial scales. Other characteristics of the model are a nudging

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 161

system, different options of numerical schemes, several top and lateral boundaryconditions, a set of physical parameterisations, a model for soil and vegetationtemperature and moisture. The vertical structure of the grid is based on the σz

terrain-influenced coordinate system, where the top of the model domain H isexactly flat and the bottom follows the terrain, so that:

σz = Hz − zg

H − zg

,

where zg is the terrain height. The grid stagger used in RAMS is the standardArakawa C grid [26]. The major components of RAMS are the atmospheric model,performing the actual simulations, a data analysis package, preparing the initialdata for the model from meteorological data, a post-processing model visualisationand an analysis package. RAMS has been widely used by many researchers indifferent applications, so that it is not possible to cite an exhaustive list of therelated works. Here we just recall for instance Tremback et al. [27]; Pielke et al.[6]; Walko and Pielke [28]; Martilli and Graziani [29]; Cai and Steyn [30]; Dalu etal. [31]; Kanda et al. [32]; and, among our works, Trini Castelli and Anfossi [33];Sansigolo Kerr et al. [34]; Trini Castelli et al. [35]; Carvalho et al. [36].

4. Outline of the Simulations

4.1. CONFIGURATION OF ETA MODEL

For the present simulation a one-way nesting procedure was used to obtain a hori-zontal resolution of nearly 4 km for the inner higher resolution run. The one-waynesting was adopted, because the ETA model is hydrostatic and published papers[25, 37] show that the improvements of the results, produced from the interactive(two-way) nesting with respect to parasitic (one-way) nesting, are more dramaticwith non-hydrostatic models.

The horizontal resolution of the outer grid (grid 1) was 0.2×0.2 transformed de-grees (nearly 30 km) with an integration domain of 8×8 transformed degrees. Suit-ably interpolated ECMWF (European Centre for Medium Range Weather Fore-casts) analyses at 0.5◦ horizontal resolution were used to initialise the integrationand to update boundary conditions every 6 h. The orographic heights were obtainedfrom the U.S. NAVY 10′ data set. The simulation starts at 15/09/1992 00 UTC andends 72 h later with basic integration time step equal to 72 s.

The inner grid (grid 2) had horizontal resolution equal to 0.025 transformeddegrees (nearly 4 km), the integration domain covered 4 × 4 transformed degrees,in order to apply the boundary conditions far enough from the region of interest[38]. Regarding the nesting procedure, the initial and boundary conditions of theinner higher resolution run were evaluated by interpolation from the outer lowerresolution run with an updating time-interval of 3 h. The topography was derivedfrom the EROS/DEM data set (resolution of 30′′ × 30′′). The simulation starts on

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the 16th at 00 UTC, in order to take advantage of the better resolution of the coarsergrid run with respect to ECMWF analyses. The integration time step was 10 s. Thevertical domain stretched from the sea level to 100 hPa, with 38 layers for bothruns. The depths of the layers slowly increased as higher levels were approached,so that the vertical resolution was higher near the bottom of the domain. The depthsranged approximately from 26 m to 1200 m at the domain top. The centre of theintegration domain was 8◦ E, 48◦ N for both runs.

4.2. CONFIGURATION OF RAMS

For the present simulation RAMS version 3b model was configured with threenested grids, as in a previous work [36]. The outer grid had 20 × 20 points and a16 km horizontal resolution, the intermediate grid had 38 × 38 points and a 4 kmhorizontal resolution, and the inner grid had 62 × 62 points and a 1 km horizontalresolution. A stretched grid with forty levels was used in the vertical. The lowestresolution close to the surface was 50 m and it increased up to a maximum of 500 mfor the highest levels, till the domain top at 15 km. The simulation time step wasset to 30 s for the outer grid, 10 s for the intermediate grid and 3.3 s for the innergrid. Also RAMS was initialised using the ECMWF gridded 0.5◦ analysis fields.New analysis data at each 6 h were used to nudge the lateral boundary limits of theouter grid. The 2.5 level scheme of Mellor and Yamada [16] was adopted for thevertical diffusion parameterisation, together with a Smagorinsky-type [39] para-meterisation for the horizontal diffusivities. Landuse data with a 500 m resolutionand topography data with a 1000 m resolution for the TRACT area were suppliedto RAMS. For the soil model, the initial values of moisture and temperature inthe ground were obtained from the TRACT experiment. For this application themoisture processes were considered as passive water vapour only. The simulationstarts at 00 UTC on 15th September 1992 and ends 72 h later.

The main characteristics of the present ETA and RAMS configuration are com-pared in Table I.

5. Details of the Result Comparisons

Figure 1 shows the simulated area of the larger grid (grid 1) in RAMS, pointingout the orographic characteristics of the site, and the position of the nested grids2 and 3. The Rhine Valley, situated between the Black Forest and the Vosges, isoriented along the SW-NE direction and has an average height of 100–110 m a.s.l.and a typical width of about 50 km. It constitutes the main structure. Secondarystructures, like valleys and mountains, are also visible. The higher mountains (Bav-arian Alps, about 1900 m a.s.l.) lie in the South-Eastern corner of grid 1. Withinthe Rhine Valley, excluding the inhabited areas, land use is prominently of agri-cultural and forest type. The conifer forest covers most of the mountainous areas.Different mountain chains and valleys, characterised by different horizontal and

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 163

Table 1. RAMS and ETA configuration.

Model parameter RAMS ETA

Basic equation Non-hydrostatic Hydrostatic

Horizontal grid Arakawa C-grid Arakawa E-grid

Initialisation ECMWF analysis ECMWF analysis

Time differencing(integration)

Hybrid(combination of leapfrogand forward-backward)

Split-explicit

Advection 2-nd order leapfrog; Euler-backward

2-nd order forward Janjic [13]

Horizontal coordinate system Rotated polar Rotated lat/lon

Stereographic Coordinate

Vertical coordinate system Sigma Eta

Lateral boundary conditions Klemp–Wilhelmson for thenormal velocity component;zero gradient inflow andradiative outflow for the othervariables.

Determined values at inflowboundary points. Tangentialvelocity extrapolated from theinterior at outflow boundarypoints. Blending at the secondoutermost row.

Top boundary conditions Rigid lid: Vertical velocityforced to zero

ptop = constantdη/dt = 0

Surface parameterisations Similarity theory surfacelayer;

Mellor–Yamada level2 scheme;

Multilayer soil; Two-layer soil;

Land use

Subgrid diffusionparameterisations

Mellor–Yamada 2.5turbulent kinetic energyclosure scheme

Mellor–Yamada 2.5turbulent kinetic energyClosure scheme

Deep convectionparameterisations

Modified Kuo Betts–Miller–Janjic

Radiation parameterisations Long- and shortwavescheme with cloudeffects and withatmospheric tendencies

GFDL

Resolved conditionsparameterisations

Bulk microphysicsParametrization withliquid and up to five ice species

164 S. TRINI CASTELLI ET AL.

Figure 1. Simulation domain and orography with locations of measuring stations (circles forradiosounding sites: R2, R9, R11 and R12, squares for surface stations: S102, S137, S188 andS193) and main cities (asterisks).

vertical scales, with different solar exposures, variable during the day, certainlycause secondary circulations that superimpose on those of synoptic origin. It isworthwhile to point out that, even in the finest resolution grid (grid 3 of RAMS),simulations could not resolve the flow with a detail greater than about 2 km.

Figure 1 (see also Tables II and IX) also indicates the positions of the four tem-porary vertical sounding stations (Bruchsal, R2, Musbach, R9, Oberbronn, R11,and Offenburg, R12) and of the four ground level stations (Karlsruhe, S102, Müh-lacker, S137, Sinsheim, S188, and Strassburg–Entzheim, S193) considered in thepresent intercomparison. They are all included into the ETA and RAMS simu-lation domain (grid 2) and they were chosen because the area limited by themcovers the central part of the region and their locations account for different oro-graphic characteristics. Bruchsal is located in the flat area of the Rhine Valley,whereas Musbach is located in the mountainous region, Oberbronn and Offenburg

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 165

are respectively on the West and East slope sides of the mountains. Karlsruhe andStrassburg-Entzheim are placed inside the valley, northeast and southwest respect-ively, while Mühlacker and Sinsheim lay in the northeast slope of the valley. Thus,these stations are representative of two interesting circulation situation, within thevalley and over the ridges. In Figure 1 the locations of the main towns in the studiedregion are also marked.

The vertical profiles of the horizontal wind (u,v), potential temperature (ϑ) andspecific humidity (q), measured every three hours at Bruchsal, Musbach, Ober-bronn and every 6 h at Offenburg, were compared to the same quantities simulatedby ETA and RAMS. The heights are referred to the mean sea level. For the statist-ical analysis, all the models’ levels included between the ground and the maximumheight reached by each radiosounding were accounted for. In the case of the RAMSsimulations, the simulated profiles have been horizontally interpolated on the meas-uring stations with a bilinear formula and the level nearest to the model level wasconsidered. Since the ETA model coordinate surfaces are quasi horizontal anddifferent from the sigma surfaces used in RAMS, some difference arose in thedata processing. For all stations, the ETA simulated profile at a near grid point,having also approximately the same altitude, was considered. Their coordinatesare, respectively, 8.50◦ E, 49.15◦ N, 106 m for Bruchsal, 8.49◦ E, 48.5◦ N, 700 mfor Musbach, 7.62◦ E, 48.92◦ N, 256 m for Oberbronn and 7.89◦ E, 48.45◦ N,172 m for Offenburg. For the statistical comparison, observations were interpol-ated at the same levels of the model. To point out the behaviour of the modelsin critical regions, like close to the surface, we performed the statistical analysisalso dividing the altitude range in six subranges. To have a large set of data, foreach subrange we considered all the four sites together and all the different hoursavailable. To complete the statistics and to provide better information about thegeneral trend of the simulations and observations agreement, we also estimatedthe cumulative frequency distributions of all the correspondent couples of data,observed and simulated, for the different variables.

The time variations of the quantities (u, v, T and q), measured at the four groundlevel stations (S102, S137, S188 and S193), were also compared to the ETA andRAMS simulation results. Even in this comparison the simulated data were inter-polated from the four nearest grid points with a bilinear formula, on a hourly timestep for RAMS and on a 3 h interval for ETA.

To quantify the goodness of the simulations the following statistical indexeswere computed: fractional bias (FB), Root-Mean-Square Error (RMSE) and Root-Mean-Square Vector Error (RMSVE), defined as follows:

FB = 2x0 − xp

xo + xp

RMSE =[

1

N�(x0 − xp)

2

]1/2

166 S. TRINI CASTELLI ET AL.

RMSVE ={

1

N�

[(u0 − up

)2 + (v0 − vp

)2]}1/2

,

where the ‘o’ subscript refers to the observed value and ‘p’ to the predicted one, xis the considered variable. RMSE is used to compare scalar quantities (temperatureand specific humidity), whereas RMSVE [1] is used for the vectorial quantities(wind).

The statistical indexes have been computed, for each profile, on the resultingseries of observed and prescribed data.

6. Results and Discussion

From a qualitative point of view the mean structure of the flow and the diurnal-nocturnal cycle of the boundary layer are captured by both models, together withthe most relevant local flow structures, like the flow channelling and air stratifica-tion inside the valleys, the down-slope and down-valley winds (with reference toRAMS simulations, see, for instance, Carvalho et al. [36], Figure 6). In the follow-ing we focus our discussion on the comparison between the two model predictionsand observations (vertical profiles and measurements at the surface).

Before starting the presentation and discussion of the results of the presentwork, it is worth considering the following: i) Because of the smoothed nature ofsimulation results, the finer structure in the observed profiles (large variations, insome cases, at different adjacent heights), cannot be captured (see [40]); ii) modeloutputs are volume averages defined by the horizontal and vertical grid resolutions�x×�y×�z of the domain and refer to mean quantities, while the correspondingobservations are instantaneous and single-point values that may significantly differfrom the averages [41]; furthermore, the sites are generally non-uniform at the gridscale and in complex terrain the orography used in the simulations is generallysmoother than the real orography and this may cause that the measuring point andsimulation grid cell have significantly different heights; iii) observed meteorolo-gical variables are affected by fundamental stochastic uncertainty; according tomany authors (e.g., [42–44]), a variability of hourly averaged wind speed of theorder of 1–2 ms−1 over distances of a few kilometres can be observed even on flathomogeneous terrain, due to mesoscale turbulence fluctuations; iv) we mentionthat also instrumentation and averaging errors, unavoidable in any measurements,affect the comparisons [45].

6.1. VERTICAL PROFILES

Figures 2 and 3 show the comparison among the observed u, v, ϑ and q profiles(diamonds) at Bruchsal and those simulated by ETA (dashed line) and RAMS(solid line). They refer to, respectively, 14.00 UTC of 16 September and 05.00 UTCof 17 September. Figures 4 and 5, 6 and 7 show the same comparison for Musbach

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 167

Table II. Radiosounding stations.

Location Code Latitude Longitude Height

(degrees N) (degrees E) (m m.s.l.)

Bruchsal R2 49.13 8.56 110

Musbach R9 48.50 8.48 695

Oberbronn R11 48.94 7.61 274

Offenburg R12 48.44 7.96 150

and Oberbronn. For the graphical presentation of the results, we selected these threelocations since Offenburg has less frequent measured profiles and because they arelocated over a critical part of the region, inside the valley (R2), upslope (R11) andover the hill (R9), and at three different altitudes, respectively 110, 695 and 274 ma.s.l. The intention is to describe the spatial variability of the meteorological fieldsand to evaluate the agreement between observed and simulated data by means ofthe comparison of the variable profiles at different times.

Looking at these Figures, and considering also the other comparisons not presen-ted here, suggests the following considerations. The general shape of observedprofiles is correctly reproduced by the simulation results. Typically, temperatureprofiles compare well with observations; almost the same can be said for the windcomponent profiles, while humidity profiles show the largest differences, even ifthe overall shape is correct. The wind speed at higher levels are correctly sim-ulated, confirming other model comparison results, e.g., Walko and Pielke [28]and Yamada and Henmi [46], but differing with respect to some other previousresults from model comparison, e.g., Gross [40] and Alpert and Tsidulko [47].Some differences between ETA and RAMS results can be noticed, particularly inthe wind and humidity profiles. As anticipated, the simulations, whose smoothingdepends on the grid resolution, do not resolve the strong fluctuations of the ob-served profiles. In fact, model values are averaged over the grid volume, thus arenot able to reproduce variations on smaller scales, which are instead detected bythe instruments. Furthermore, it is to remember that observed profiles representinstantaneous measurements. Recalling that no observed profiles were employedfor the initialisation and temporal nudging of the simulated fields, both simula-tions appear to be accurate in reproducing the general shape of the mean variableprofiles.

Tables III–V show the statistical indexes computed for each profile. In par-ticular, Tables III-1, III-2 and III-3 concern the wind velocity profiles, respect-ively referring to the Bruchsal, Musbach, and Oberbronn radiosounding stations.Tables IV-1, IV-2 and IV-3 refer to potential temperature profiles and Tables V-1,V-2 and V-3 are relative to specific humidity profiles. Regarding the wind velocityprofiles (Tables III-1, III-2 and III-3), the values of RMSVE attained by the RAMS

168 S. TRINI CASTELLI ET AL.

Figure 2. Radiosounding Station R2, Bruchsal: 16/09/1992, 14 UTC. Wind velocity compon-ents u (a) and v (b), potential temperature θ (c) and specific humidity q (d). Observed data:Diamonds; RAMS: Solid line; ETA: Dashed line.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 169

Figure 3. Radiosounding Station R2, Bruchsal: 17/09/1992, 05 UTC. Wind velocity compon-ents u (a) and v (b), potential temperature θ (c) and specific humidity q (d). Observed data:Diamonds; RAMS: Solid line; ETA: Dashed line.

170 S. TRINI CASTELLI ET AL.

Figure 4. Radiosounding Station R9, Musbach: 16/09/1992, 14 UTC. Wind velocity compon-ents u (a) and v (b), potential temperature θ (c) and specific humidity q (d). Observed data:Diamonds; RAMS: Solid line; ETA: Dashed line.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 171

Figure 5. Radiosounding Station R9, Musbach: 17/09/1992, 05 UTC. Wind velocity compon-ents u (a) and v (b), potential temperature θ (c) and specific humidity q (d). Observed data:Diamonds; RAMS: Solid line; ETA: Dashed line.

172 S. TRINI CASTELLI ET AL.

Figure 6. Radiosounding Station R11, Oberbronn: 16/09/1992, 14 UTC. Wind velocity com-ponents u (a) and v (b), potential temperature θ (c) and specific humidity q (d). Observed data:Diamonds; RAMS: Solid line; ETA: Dashed line.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 173

Figure 7. Radiosounding Station R11, Oberbronn: 17/09/1992, 05 UTC. Wind velocity com-ponents u (a) and v (b), potential temperature θ (c) and specific humidity q (d). Observed data:Diamonds; RAMS: Solid line; ETA: Dashed line.

174 S. TRINI CASTELLI ET AL.

simulations are, as an average, slightly better than those of ETA; the opposite holdstrue for FB values that, in the ETA simulations, show a tendency to compensate thedifferences between prescribed and observed data. Examining the FB time trendindicates that both models tend to overestimate the wind velocity during daytime(approximately from 08.00 UTC to 17.00 UTC) and to underestimate it during nihttime (17.00 UTC–08.00 UTC). This behaviour is more marked at the lower altitudestation. We notice that, on average, Bruchsal radiosoundings reach heights lowerthan at the other two stations, so that the PBL values have a larger weight on thecorresponding Bruchsal statistics. The best RMSVE value is 1.15 m s−1 for theRAMS 14.00 UTC profile of 17 September at Bruchsal and the worst is 5.47 forthe ETA 20.00 UTC profile of 16 September again at Bruchsal. The best and worstFB values are found both for RAMS profiles at Bruchsal, respectively −0.005 at11.00 UTC of 17 September and 0.87 at 23.00 UTC of 16 September. Concerningthe potential temperature profiles (Tables IV-1, IV-2 and IV-3), it can be noticedthat RAMS underestimates observed values at all the three stations during all theexamined period, in particular during daytime, while ETA tends to underestimateduring daytime and to overestimate during night time. The best RMSE value is0.47 K for the ETA 14.00 UTC profile of 17 September at Oberbronn and theworst one is 2.75 K for the RAMS 08.00 UTC profile of 17 September at Bruchsal.In the case of the humidity (Tables V-1, V-2 and V-3), ETA and RAMS profilesat Musbach produced comparable results and RAMS slightly overcame ETA atBruchsal and Oberbronn. These indexes indicate no regular tendency except thoserelative to the ETA simulation of Bruchsal and Oberbronn radiosoundings, whereall the FB are positive, thus indicating a slight model underprediction. The bestRMSE value is 0.48 g kg−1 for the RAMS 17.00 UTC profile of 16 September atMusbach and 14:00 of 17 September at Oberbronn, while the worst one is 1.71 gkg−1 for the ETA 20.00 UTC profile of 16 September at Bruchsal.

To evaluate how the two models perform as a function of the height, in Tables 6to 8 we report the statistical indexes estimated in six separated vertical subranges,considering together all the four radiosounding stations, Offenburg included, atall the hours where measurements were available. We considered the couples ofdata, observed with simulated, at the simulations vertical levels, which explainsthe different numbers of data used in RAMS and ETA. Wind velocity, in Table VI,has the best values of RMSVE between 1500 m and 3000 m, both for RAMS andfor ETA. The best FB for RAMS is over 6000 m, where however the number of datais the lowest, while ETA’s best FB is in the range between 600 m and 1500 m. BothRAMS and ETA potential temperature RMSEs, instead, improve going towardshigher levels, while the correspondent FBs fluctuate around low values. The betteragreement among the two models and measurements at the higher levels is expec-ted, since at these heights the effect of the surface and of the underlying orographyis not felt anymore. In the specific humidity, as far as RMSE is concerned, the twomodels perform differently: ETA improves its performance with the height, whileRAMS presents a slightly worse result between 600 m and 3000 m and then largely

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 175

Table III-1. Bruchsal station: Wind velocity statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (m s−1) (m s−1) (m s−1) (m s−1)

16/09 05 2.13 0.04 2.28 −0.04

16/09 08 2.09 −0.05 2.29 0.02

16/09 11 3.09 −0.39 3.77 −0.32

16/09 14 2.97 −0.47 3.48 −0.67

16/09 17 4.14 0.23 4.91 0.16

16/09 20 3.87 0.56 5.47 0.64

16/09 23 4.22 0.87 4.74 0.78

17/09 02 2.38 0.56 1.72 0.23

17/09 05 3.25 0.51 2.17 0.29

17/09 08 2.16 0.16 2.60 0.12

17/09 11 1.60 −0.005 2.72 −0.08

17/09 14 1.15 −0.11 2.08 −0.22

17/09 17 1.56 −0.11 1.60 −0.10

RAMS solid line, ETA dashed lines

improves over 3000 m. FB approaches zero between 600 and 1500 m for ETA andbetween 1500 and 3000 m for RAMS. The worst FB value is above 6000 m forboth models. The worse agreement among the two models and specific humiditymeasurements at the higher levels is likely related to the fact that in that layerhumidity generally attains very low values.

In Figures 8 the cumulative frequency distributions (c.f.d.) are depicted for thewind velocity components, u (a) and v (b), the potential temperature (c) and thespecific humidity (d). All the pairs of data of RAMS and ETA with their cor-respondent observations at the fours radiosounding stations, at all the levels andhours, are included. These graphs allow summarizing the results discussed above.The agreement between the models and measurements is good for the u compon-ent, in particular for RAMS. In the case of the v component, ETA underestimates

176 S. TRINI CASTELLI ET AL.

Table III-2. Musbach station: Wind velocity statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (m s−1) (m s−1) (m s−1) (m s−1)

16/09 05 3.44 −0.09 4.06 −0.20

16/09 08 3.37 −0.21 4.53 −0.23

16/09 11 3.56 −0.23 2.92 −0.10

16/09 14 2.35 −0.13 2.27 −0.10

16/09 17 2.26 −0.08 2.26 −0.04

16/09 20 2.53 −0.01 3.27 0.03

16/09 23 2.41 0.07 2.64 0.10

17/09 02 2.98 0.08 3.77 0.05

17/09 05 2.29 0.06 2.89 0.02

17/09 08 1.88 −0.10 2.87 −0.13

17/09 11 1.50 −0.10 2.40 −0.21

17/09 14 2.44 0.26 2.42 0.04

17/09 17 2.51 0.20 4.06 0.11

RAMS solid line, ETA dashed lines

the highest negative velocities, i.e., North to South winds, while RAMS tends toslightly overestimate the South-North winds. ETA, in general, correctly reproducesthe measured potential temperature c.f.d, while RAMS shows a good agreementfor the temperature range greater than 305 K and clearly underestimates the lowertemperature values.

Also the specific humidity distribution confirms the result of the previous statist-ical analysis (see Tables III–VIII): RAMS performance is satisfactory, while ETAproduces values lower than those observed and it is not able to catch the highestvalues. In contrast to RAMS, no landuse data and no values of moisture and tem-perature in the ground were supplied to ETA model. In fact, a rather conventionalformulation was contained for the surface processes (see Section 3.1) in the usedversion. Besides the other characteristics of the model configuration, this aspect can

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 177

Table III-3. Oberbronn station: Wind velocity statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (m s−1) (m s−1) (m s−1) (m s1)

16/09 05 2.53 −0.04 3.27 −0.08

16/09 08 2.97 −0.05 3.34 −0.05

16/09 11 2.62 −0.04 3.02 −0.02

16/09 14 2.11 −0.04 2.24 0.01

16/09 17 2.92 0.10 2.69 0.10

16/09 20 3.18 0.07 3.54 0.15

16/09 23 2.94 0.10 3.21 0.15

17/09 05 3.04 0.11 2.42 −0.01

17/09 08 2.43 0.07 2.83 −0.11

17/09 11 1.77 0.01 2.76 −0.15

17/09 14 2.19 0.03 2.54 −0.08

17/09 17 2.65 0.01 3.96 0.12

RAMS solid line, ETA dashed lines

play as a decisive limit for the correct simulation of humidity fields. At present, inthe versions of NCEP mesoscale ETA model following the used one, a series ofadvances related to land-surface schemes have been implemented.

Considering all the limitations of the comparison between simulations and meas-urements, the agreement shown by the radiosounding profiles, in Figures (2–8),and the values of the statistical indexes, Tables III–VIII, show that both models canyield reliable reconstruction of the 3-D wind, temperature and humidity fields incomplex terrain.

One of the most important differences between the two models is the hydrostaticagainst non-hydrostatic options respectively adopted in ETA and RAMS. Since theobserved meteorological scenario was correctly and compatibly described by thetwo models and also the specific measurements were simulated with analogousquality, we can state that the results of this case-study do not show significant

178 S. TRINI CASTELLI ET AL.

Table IV-1. Bruchsal station: Potential temperature statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (K) (K) (K) (K)

16/09 05 1.31 0.002 1.63 −0.0004

16/09 08 1.51 0.003 1.20 0.0008

16/09 11 1.15 0.001 1.05 0.0001

16/09 14 1.05 0.001 0.93 0.0009

16/09 17 1.34 0.002 1.41 0.0028

16/09 20 1.40 −0.001 2.13 −0.0029

16/09 23 1.33 0.0002 2.38 −0.0042

17/09 02 1.61 0.001 1.73 −0.0018

17/09 05 1.45 0.002 1.36 −0.0013

17/09 08 2.75 0.008 2.00 0.0035

17/09 11 1.34 0.001 0.97 0.0012

17/09 14 0.75 0.0009 0.89 0.0012

17/09 17 0.86 0.0005 0.75 0.00003

RAMS solid line, ETA dashed lines

differences related to the hydrostatic or non-hydrostatic options in the reproduc-tion of the mean variable fields. While on coarse grids (some tens of km) thetwo options are expected to give similar results, the hydrostatic assumption is amore questionable approximation when solving deep convection and significanttopographic features with high grid resolution. During the days we have consideredin this work, high pressure systems and daily convective conditions dominated, butno phenomena of deep convection were observed and analysed (i.e., see [8, 48]).The orography of TRACT region was complex but not extremely steep and thealtitude of the considered measuring stations ranged from about 100 to 250 m witha peak in Musbach of 695 m. For these reasons probably the simulations were notso sensitive to the hydrostatic/non-hydrostatic assumption, even at the resolutionof 4 km.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 179

Table IV-2. Musbach station: Potential temperature statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (K) (K) (K) (K)

16/09 05 1.27 0.002 1.47 0.0009

16/09 08 1.55 0.003 1.86 0.0026

16/09 11 1.24 0.002 1.49 0.0009

16/09 14 1.83 0.004 1.85 0.0043

16/09 17 1.20 0.002 1.03 0.0019

16/09 20 1.05 0.001 1.87 −0.0001

16/09 23 1.65 0.0002 2.19 −0.0022

17/09 02 1.14 0.001 1.25 −0.0031

17/09 05 2.34 0.006 1.66 0.0017

17/09 08 2.28 0.006 1.45 0.0030

RAMS solid line, ETA dashed lines

6.2. SURFACE DATA

Figures 9–10 display the comparison between the time variations of wind velocitycomponents, temperature and specific humidity observed at two of the four groundlevel stations and those prescribed by RAMS and ETA. The simulation results werehorizontally interpolated on the measuring location, using the nearest four RAMSand ETA grid points. In the vertical, RAMS wind velocity data were estimated atthe relative measuring height with a logarithmic interpolation of the two valuesrelative respectively to the first grid level (about 23 m) and to the roughness height,where the wind velocity is assumed to vanish (no-slip condition). For temperatureand humidity it was considered directly the value at the first RAMS grid level,23 m. Estimating the values of the variables at 10 m and 2 m on the basis of thesurface fluxes and similarity functions, produced negligible differences. In ETA,since the first available vertical level is generally found about 50 m above the alti-tude of the station, the calculation of the 10 m values was based on the integration

180 S. TRINI CASTELLI ET AL.

Table IV-3. Oberbronn station: Potential temperature statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (K) (K) (K) (K)

16/09 05 1.27 0.002 1.06 0.0008

16/09 08 1.21 0.001 1.06 0.0015

16/09 11 0.94 0.0005 0.77 0.0014

16/09 14 0.73 0.0007 0.68 0.0010

16/09 17 0.85 −0.00009 1.04 −0.0007

16/09 20 0.98 −0.0007 1.37 −0.0011

16/09 23 1.31 0.0005 1.55 −0.0011

17/09 05 1.37 0.003 1.16 −0.0001

17/09 08 1.46 0.002 1.11 0.001

17/09 11 1.46 −0.0001 0.86 0.001

17/09 14 0.91 −0.0009 0.47 0.0002

17/09 17 0.74 −0.0006 0.92 −0.0002

RAMS solid line, ETA dashed lines

of the momentum fluxes in the model surface layer, using the model’s lowest layerwinds. Analogously, temperature and humidity values have been interpolated at2 m by an integration of the fluxes in the model surface layer.

The time period covers two days, 16th and 17th September, in all the stationsapart for S102, where measurements were collected for a shorter period.

In order to assess the accuracy obtained by the nested grids, in Figures 9a–dobservations at the S193 station are compared to ETA predictions on its grid 2 andto the RAMS predictions on its three nested grids (reported in the figures as: grid 1,solid line, grid 2, dotted line, and grid 3, dashed line). The RAMS curves producedon grids 2 and 3 superpose almost perfectly, while some differences appear with re-spect to the curve on grid 1, remarkably for the u wind component. In this case, themost pronounced time variations are better caught on the finer grids, characterisedalso by a smaller integration time step, than on the coarsest one. While for wind

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 181

Table V-1. Bruchsal station: Specific Humidity statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (g kg−1) (g kg−1) (g kg−1) (g kg−1)

16/09 05 0.91 0.10 1.34 0.08

16/09 08 0.60 −0.06 0.92 0.06

16/09 11 0.68 −0.02 0.68 0.09

16/09 14 0.85 0.08 1.19 0.20

16/09 17 0.78 0.12 1.38 0.21

16/09 20 1.06 0.01 1.71 0.10

16/09 23 1.43 −0.13 1.15 0.03

17/09 02 1.54 −0.10 1.12 0.04

17/09 05 1.07 −0.004 1.53 0.20

17/09 08 0.91 −0.03 1.21 0.09

17/09 11 0.63 −0.04 0.74 0.11

17/09 14 0.49 −0.04 0.64 0.06

17/09 17 0.90 −0.09 1.12 0.12

RAMS solid line, ETA dashed lines

and temperature ETA and RAMS results compare well, in the case of humiditythey show an opposite trend. In particular RAMS overestimates the observationsgenerally during night time whereas ETA always underestimates.

At the other stations the comparison, presented for S137 in Figures 10a–d, refersto grid 2 for both models. At all the stations, on average, the simulations are ableto satisfactorily match the trend of the observed wind data (as shown, for instancein Figures 9a, b and 10a, b). In the case of temperature (Figures 9c and 10c), theshape of the curves and the range of values are well reproduced, but a shift in timeappears, particularly at S193 station. A similar behaviour occurs for the specifichumidity (Figures 9d and 10d), where models seem not to be fully able to predictthe observed ranges, probably because of the large spatial variability typical of thesurface moisture.

182 S. TRINI CASTELLI ET AL.

Table V-2. Musbach station: Specific humidity statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (g kg−1) (g kg−1) (g kg−1) (g kg−1)

16/09 05 0.93 −0.10 0.60 0.01

16/09 08 0.64 −0.01 1.01 0.11

16/09 11 0.84 0.03 1.32 0.18

16/09 14 0.70 0.05 1.20 0.18

16/09 17 0.48 −0.02 1.14 0.16

16/09 20 0.98 −0.05 0.99 0.005

16/09 23 1.54 −0.12 1.27 −0.08

17/09 02 1.66 −0.20 1.24 −0.08

17/09 05 1.65 −0.15 1.33 −0.02

17/09 08 1.06 −0.07 1.46 0.04

RAMS solid line, ETA dashed lines

To interpret these results, we have to consider that mesoscale simulations aregenerally very sensitive to initial and boundary conditions (see, for instance [47]).In our case, observed surface data have not been used in RAMS initialisation, nospecific land surface model has been used in ETA, the topography elevation on thegrid is more smoothed than the real terrain height and the values at the surfaceare obtained by interpolation from higher levels. In the case of RAMS, it wasdemonstrated that initial values of moisture in the soil, which is a poorly knownand measured quantity, have a strong influence on the surface energy budget (see[28]). Also this aspect can affect the simulation of temperature and humidity at thesurface. Then, to simulate the subgrid-scale phenomena, in particular the variationsof the surface properties influencing the local meteorology, a much smaller gridresolution should be used. Accounting for these limitations, the results demonstratethe reliability of the model in reproducing the trend of the different meteorologicalfields, even at the surface.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 183

Table V-3. Oberbronn station: Specific humidity statistical indexes comparison.

RAMS ETA

Day Hour RMSVE FB RMSVE FB

(UTC) (g kg−1) (g kg−1) (g kg−1) (g kg−1)

16/09 05 0.72 −0.07 1.25 0.07

16/09 08 0.66 0.002 0.95 0.12

16/09 11 0.62 0.08 0.78 0.13

16/09 14 0.59 0.09 0.90 0.19

16/09 17 0.77 0.11 0.83 0.11

16/09 20 0.84 0.08 0.85 0.11

16/09 23 0.68 0.05 0.90 0.20

17/09 05 0.50 −0.01 0.76 0.17

17/09 08 0.61 −0.02 0.82 0.17

17/09 11 0.51 0.02 1.36 0.26

17/09 14 0.48 0.04 1.03 0.20

17/09 17 0.98 0.05 1.63 0.26

RAMS solid line, ETA dashed lines

Table VI. Sub-range statistics, four radiosounding stations, all times available from obser-vations. Wind velocity.

Wind velocity ms−1

RAMS ETA

z levels n. data RMSVE FB n. data RMSVE FB

< 300 m 213 2.66 −0.087 99 3.18 −0.054

300 < z < 600 m 125 2.89 0.042 63 3.46 −0.100

600 < z < 1500 m 173 2.55 −0.022 158 2.81 0.0179

1500<z<3000 m 123 2.43 0.049 165 2.74 0.082

3000<z<6000 m 185 3.63 0.054 162 3.74 −0.037

z > 6000 m 121 4.50 0.003 72 3.52 −0.039

184 S. TRINI CASTELLI ET AL.

Table VII. Sub-range statistics, four radiosounding stations, all times available fromobservations. Potential temperature.

Potential temperature (K)

RAMS ETA

z levels n. data RMSVE FB n. data RMSVE FB

< 300 m 209 1.83 −0.001 96 2.37 −0.004

300 < z < 600 m 121 1.55 0.001 62 1.55 −0.003

600 < z < 1500 m 172 1.84 0.004 166 1.48 0.002

1500 < z < 3000 m 133 1.28 0.002 174 0.93 0.001

3000 < z < 6000 m 214 1.01 0.001 212 0.92 0.001

z > 6000 m 133 0.99 0.001 77 0.85 0.001

Table VIII. Sub-range statistics, four radiosounding stations, all times available fromobservations. Specific humidity.

Specific humidity (g kg−1)

RAMS ETA

z levels n. data RMSVE FB n. data RMSVE FB

< 300 m 209 0.90 0.007 96 1.96 0.207

300 < z < 600 m 121 0.90 0.005 62 1.54 0.145

600 < z < 1500 m 172 1.25 −0.003 166 1.40 0.087

1500 < z < 3000 m 133 1.08 −0.120 174 0.93 0.001

3000 < z < 6000 m 214 0.62 −0.156 212 0.42 0.102

z > 6000 m 133 0.14 0.386 77 0.15 0.505

Table IX. Ground level stations.

Location Code Latitude Longitude Height

(degrres N) (degrees E) (m m.s.l.)

Karlsruhe S102 49.03 8.37 112

Mühlacker S137 48.97 8.87 244

Sinsheim S188 49.25 8.88 169

Strassbourg–Entzheim S193 48.55 7.64 150

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 185

The model evaluation has been carried out for the ground based stations aswell. Consequently the same statistical indexes used for the vertical profiles havebeen calculated for these temporal series and are shown in Tables X–XII. Theanalysis of these index values confirms almost all the results found for the pro-files, namely that RAMS performs slightly better than ETA in the simulation ofwind and humidity and ETA is preferable for the temperature. Predictions presentthe best agreement with observations for the wind velocity, while the quality ofthe statistics is generally lower for temperature and humidity. The best statisticsis related to stations S137 (the highest location) for both models in the case ofwind velocity, to station S102 for temperature for both models, and vice versa tostations S102 for RAMS and S137 for ETA for the specific humidity. In general,the worst result occurs at station S193, in particular for temperature and humidity.Considering that the models do not account for the presence of the cities, whichcertainly modify the subgrid structure of the flow, we point out that station S193 islocated in a dense urban area and is influenced by the close presence of the citiesof Strasbourg, Offenburg and also Fribourg. Moreover, two important orographicfeatures, the Vosges and the Black Forest, are not far from S193 location and caninduce on it very local perturbations, which are difficult to be correctly reproducedby mesoscale modelling.

With reference to the RMSE values, it has to be also born in mind that largevalues of this parameter, especially for the temperature trend, could be attributed toa non-perfect temporal coincidence between observed and predicted data. If thereis a temporal shift, even a good correspondence between the two trends, yields abad RMSE value. This is suggested by the curves in Figures 9c (S193) and 10c(S137). This kind of tendency appeared even in other works. For instance, Gross[40], comparing the Project WIND (phase I) simulations performed with FITNAHmodel, found that the simulated temperatures were about 5 K higher than thoseobserved. Alpert and Tsidulko [47] found that, due to the different exposure, attwo nearby stations (whose separating distance was about one grid length only),the observed temperatures differed widely, whereas the model predictions weresimilar.

7. Conclusions

The aim of this work was to present and discuss the results of an intercomparisonbetween two mesoscale models, RAMS version 3b [6] and ETA [5], against fieldexperimental data in complex terrain. We recall that this study was mainly focusedon verifying the models’ ability to correctly reproduce the mean flow fields to drivedispersion models in air pollution transport studies, rather than on studying themeteorological episode.

The measured data set from TRACT Campaign [3, 4], performed in SouthernGermany in 1992, was chosen. The two models were configured as similar aspossible, compatibly with the versions available, and initialised with the ECMWF

186 S. TRINI CASTELLI ET AL.

Figure 8. Cumulative Frequency Distribution of the data at the four radiosounding stationsR2, R9, R11 and R12. Wind velocity components u (a) and v (b), potential temperature θ (c)and specific humidity q (d). Observed data: Solid line; RAMS: Dashed line; ETA: Dotted line.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 187

Figure 8. (Continued.)

188 S. TRINI CASTELLI ET AL.

Figure 9. Surface Station S193, Strass–Entzheim. Time variation of the horizontal wind ve-locity components, u (a) and v (b), temperature (c) and specific humidity (d) at the surface.Observed data: diamonds; RAMS: solid line (grid 1), dotted line (grid 2) and dashed line(grid 3); ETA (grid 2) long-dashed line.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 189

Figure 9. (Continued.)

190 S. TRINI CASTELLI ET AL.

Figure 10. Surface Station S137, Muehlacker. Time variation of the horizontal wind velocitycomponents, u (a) and v (b), temperature (c) and specific humidity (d) at the surface. Observeddata: Diamonds; RAMS: Solid line; ETA; Long-dashed line.

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 191

Figure 10. (Continued.)

192 S. TRINI CASTELLI ET AL.

Table X. Ground Stations: Wind velocity statistical indexescomparison.

RAMS ETA

Station RMSVE FB RMSVE FB

(m s−1) (m s−1) (m s−1) (m s−1)

S102 1.77 0.13 1.77 −0.13

S137 1.30 −0.05 1.70 −0.11

S188 1.52 −0.04 3.62 −0.77

S193 1.68 0.15 1.75 1.38

Table XI. Ground Stations: Temperature statistical indexescomparison.

RAMS ETA

Station RMSVE FB RMSVE FB

(C) (C) (C) (C)

S102 1.86 0.07 1.08 0.001

S137 4.05 −0.14 4.28 −0.16

S188 3.32 −0.08 3.30 −0.13

S193 4.36 −0.16 4.81 −0.18

analyses. RAMS was used in its non-hydrostatic option, while ETA was run hy-drostatic. Simulations on three nested grids for RAMS and two nested grids forETA were performed to reproduce the flow during the second TRACT IMP (In-tensive Measurement Period), 16 and 17 September 1992. The meteorology duringthe campaign was characterised by a high pressure system, sunny weather andmoderate or low wind speeds.

As discussed in Walko and Pielke [28], the results presented are among manypossible outcomes produced by RAMS and ETA for the same case, due to theflexibility in setting up the model. The comparison of the simulation results withobserved data were shown in graphical form for the profiles measured at three outof four selected radiosounding stations, and for the temporal variation at two ofthe four surface stations. A statistical analysis was performed both for the fourradiosounding station data and for the four surface station ones. As general results,the mean variable fields were reproduced with a satisfactory degree of reliability,quantified by the statistical analysis, from both models. Besides the many differ-ences between the two models, no evident sensitivity to the non-hydrostatic optionwas revealed in the simulated circulation scenario during the examined period, asdiscussed in the paper. Some improvements in the agreement between predictions

INTERCOMPARISON OF TWO MODELS, ETA, RAMS, WITH TRACT FIELD CAMPAIGN DATA 193

Table XII. Ground Stations: Specific humidity statistical indexescomparison.

RAMS ETA

Station RMSVE FB RMSVE FB

(g kg−1) (g kg−1) (g kg−1) (g kg−1)

S102 1.23 −0.08 1.57 0.07

S137 1.98 −0.16 1.32 0.03

S188 1.58 −0.10 1.40 0.14

S193 2.17 −0.17 2.74 0.24

and observations appear on the finer grids with respect to the coarse one, thanks tothe nesting process. The simulated profiles did not catch the largest fluctuations ofthe variables, likely due to the averaging on grid resolution not sufficient to describethe smallest scales. A temporal shift in the daily cycle of the predicted temperatureand humidity with respect to the measurements was observed. The models appearto be less sensitive to the nocturnal variations and do not drop towards the nightlowest values. This kind of failure can be related to the grid resolution, not suf-ficient to simulate subgrid scale phenomena, in particular in complex terrain anda highly variable surface. Humidity occurred to be the most difficult variable topredict: this is related also to the difficulty of assigning a proper initial value, inparticular at the surface and in the soil, which compromises the surface energybudget. Considering other results in analogous studies, like in Pielke and Pearce[2] and in the special TRACT issue of Atmospheric Environment [7], RAMS andETA performances appear to be of comparable quality and reliability.

These results indicate that both the models are satisfactorily reliable for air-quality studies in fair weather conditions and over a not extremely complex terrain.

References

1. Cox, R., Bauer, B.L. and Smith, T.: 1998, A mesoscale model intercomparison, Bull. Am.Meteor. Soc. 79, 265–283.

2. Pielke, R.A. and Pearce, R.P., eds.: 1994, Mesoscale modeling of the atmosphere, Meteorol.Monogr. 25 (47), American Meteorological Society.

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