PREDICTION OF STABILITY AND MIXING HEIGHT IN THE … · Prognoza stabilnosti i visine sloja mijeπanja na orografski kompleksnom podruËju AMELA JERI»EVI∆, KORNELIJA ©POLER »ANI∆

1. INTRODUCTION

Air pollution models are useful tools for eva-luating emission rates and quantifying adversepollutant effects in certain region. Air pollu-tion models can be Lagrangian, Eulerian orGaussian. The Gaussian dispersion models arestill often used in practice. Stability and mix-ing height (MH) are two important dispersionparameters for those models. Ideally, hourlyinput parameters in dispersion models shouldbe calculated from measurements. Unfortu-

nately, radio sounding, which provides datafor stability and MH calculations, in Croatia isperformed only in Zagreb and Zadar twice aday. In practice, Numerical Weather Predic-tion (NWP) models are used to provide themeteorological input required by air pollutiondiffusion models.

The basic purpose of this study was to find thebest methods to calculate mixing height andstability from the limited-area NWP modelALADIN.

Hrvatski meteoroloπki Ëasopis, 39, 3∑14, 2004.

PREDICTION OF STABILITY AND MIXING HEIGHT IN THE

COMPLEX OROGRAPHY

Prognoza stabilnosti i visine sloja mijeπanja na orografski kompleksnom

podruËju

AMELA JERI»EVI∆, KORNELIJA ©POLER »ANI∆ and SONJA VIDI»

Meteorological and Hydrological ServiceGriË 3, 10000 Zagreb, Croatia

[email protected]@cirus.dhz.hr

Primljeno 5. listopada 2004, u konaËnom obliku 3. svibnja 2005.

Abstract: The aim of this study is to investigate the possibilities of using the numerical weatherprediction (NWP) model ALADIN to calculate input parameters, stability and mixing height(MH), for the Gaussian dispersion model. Since dispersion parameters were not a part of theroutine model output, the methods for their calculation were tested. The model characteristicswere analysed on the complex orography of the Rijeka area, the most developed industrialpart of the Croatian coast. This was also done for the episode of elevated SO2 concentrationsdue to fumigation processes. It was shown that the ALADIN model could be used for calcula-tions of the dispersion parameters. The model unreliability in 2 m temperature prediction re-lates to days with fog or mizzle and there is also an indication of night-time 2 m temperatureunderestimation.

Key words: Numerical Weather Prediction model, stability classes, mixing height

Saæetak: Analizirana su svojstva i moguÊnosti numeriËkog prognostiËkog modela ALADIN uproraËunu ulaznih parametara, stabilnosti i visine sloja mijeπanja (VSM), za gausovski disper-zijski model. BuduÊi da operativni izlaz modela ne ukljuËuje disperzijske parametre, bilo jenuæno pronaÊi najpogodniju metodu za njihov proraËun. Karakteristike modela analizirane suna orografski kompleksnu podruËju Rijeke, industrijski najrazvijenijem dijelu hrvatske obale.ProuËavane su i moguÊnosti primjene modela u sluËaju poveÊanja koncentracije SO2 zbog fu-migacije. Osim u danima s maglom ili rosuljom kada model precjenjuje temperaturu na 2 m, anoÊu je uglavnom podcjenjuje, pokazano je da se ALADIN moæe koristiti u proraËunima dis-perzijskih parametara.

KljuËne rijeËi: numeriËki prognostiËki model, klase stabilnosti, visina sloja mijeπanja

UDK: 551.551.2Izvorni znanstveni rad

HMC-39.qxd 11/11/05 18:30 Page 3

Pasquill stability classes describe the intensityof turbulence by dividing it into six categories(from extremely unstable to moderately sta-ble). Those categories are required as a com-mon input to the Gaussian dispersion model.The International Atomic Energy Agency(IAEA) safety guide (1980) summarises themethods for the determination of stabilityclasses and it is frequently used (e.g. Synodi-nou et al., 1996; Embaby et al., 2002; Essa etal., 2003). According to available NWP modeloutput data, two methods were selected: onebased only on the vertical temperature gradi-ent (VTG) and the other on VTG and meanwind speed.

The MH represents the vertical extent overwhich turbulence decays and laminar flow be-comes a dominant process. There are differentways to estimate MH (Seibert et al., 2000).The method using vertical turbulent diffusioncoefficient for momentum, KM, can be applied(Jacobsen et al., 1995) on the High ResolutionLimited Area Model (HIRLAM) output. The‘mechanical’ MH is defined as the lowest levelwhere KM is less than 1 m2s-1 and KM is deter-mined by the Blackadar (1979) method fromthe Richardson number. Another method isbased on friction velocity, u*, from theECMWF model (Wotawa et al., 1996) andMH is computed as MH=c1u*/f where the pro-portionality factor c1=0.07 is chosen, and f isthe Coriolis parameter. The most frequentlyapplied method for MH calculations is themethod of the bulk Richardson number, RiB(e.g. Sørensen, 1996). It is the ratio of the ver-tical potential temperature gradient and windshear, and it is based on the assumption thatturbulence vanishes at some previously de-fined critical value RiBc. The height of the levelat which RiB reaches RiBc is taken as MH. TheRiBc used in practice deviates from the theo-retical value 0.25 (e.g. Taylor, 1931; Stull,

1988; Grisogono, 1994). Different authors em-ploy different values of RiBc, Hanna (1969) has0.33∑0.56 and Sørensen et al. (1996) take RiBcin the interval 0.14∑0.24. Here, based on meas-urement analyses, the model was tested fordifferent RiBc values in the interval 0.1∑0.3.

This work will show that predicted stability andMH have seasonal and daily variations and thatthe ALADIN model can be used as a dataprovider for the Gaussian dispersion model.

2. METHODS AND INPUT DATA

Stability classes describe the intensity of at-mospheric turbulence in six categories: A - ex-tremely unstable, B - moderately unstable, C -slightly unstable, D - neutral, E - slightly stableand F - moderately stable. For the stability cal-culation from the NWP model two methodsare employed. One method (M1) determinesstability based on the VTG between two mo-del levels, while the other method (M2), be-sides VTG, includes the mean wind speed inthe layer. The M1 and M2 properties for sta-bility determination are represented in Table1 and Table 2, respectively. The modifiedPasquill method, developed by LonËar in 1974(according to Cividini and ©inik, 1987), is usedfor stability calculation from measurements.The stability classes calculated from measure-ments are then compared with the stability de-termined from the NWP model. The Pasquillmethod (Pasquill, 1961) works with 2m tempe-rature, wind speed data at 10 m, insolation dur-ing the day and cloud cover during the night.Further, the modified Pasquill method also in-cludes meteorological phenomena (hail, fog,thunder) and treats differently the cold andwarm part of the year. This method is com-monly used for dispersion studies in the Croa-tian Meteorological and Hydrological Service.

4 Hrvatski meteoroloπki Ëasopis, 39, 2004.

Table 1. Method M1 for Pasquill stability classes determination based on VTG (modified Table V in IAEA(1980)) A-extremely unstable, B-moderately unstable, C-slightly unstable, D-neutral, E-slightly stable,F-moderately stable.

Tablica 1. Metoda M1 za odreivanje Pasquillovih klasa stabilnosti na temelju VTG (modificirana Tablica Vu IAEA (1980)) A-ekstremno nestabilno, B-jako nestabilno, C-slabo nestabilno, D-neutralno, E-slabo stabil-no, F-jako stabilno.

z

T(K/100 m)

z

T-1.9 -1.9< -1.7 -1.7< -1.5 -1.5< 0.5 -0.5< 1.5 >1.5

Pasquill stability

class A B C D E F

z

T

z

T

z

T

z

T

z

T

HMC-39.qxd 11/11/05 18:30 Page 4

Stability class with T/ z (K/100 m), measured between 20 i 120 m height, of:

wind

speed

v (ms-1) z

T -1.5 -1.5 <

z

T -1.2 -1.2 <

z

T -0.9 -0.9 <

z

T -0.7 - 0.7 <

z

T 0 0 <

z

T2

z

T>2

v <1 A A B C D F F

1 v < 2 A B B C D F F

2 v < 3 A B C D D E F

3 v < 5 B B C D D D E

5 v < 7 C C D D D D E

7 v D D D D D D D

The MH is the fundamental parameter in dis-persion modelling. The bulk Richardsonmethod is the most frequently used procedureto derive MH from the NWP model. Thismethod is based on the bulk Richardson num-ber, a dimensionless parameter, which is theratio of the main turbulence productionsources, buoyant and mechanical:

(1)

where f is the geopotential height, q is poten-tial temperature and u,v are wind components(e.g., Sørensen et al., 1996).

This method assumes that the transition oftwo different flow conditions, from turbulentto laminar and vice versa can be determinedby the critical value of the bulk Richardsonnumber, RiBc.

Starting from the first, lowest model level, thecalculated RiB number is compared with thepreviously defined critical value RiBc. The levelat which the critical value is reached is as-sumed to be MH.

The methods for stability and MH determina-tion were applied to four 15-day periods re-presentative for winter (25 January 2002∑8February 2002), spring (7∑21 May 2002), sum-mer (7∑21 August 2002) and autumn (7∑21October 2002.) at eight selected grid pointscorresponding to Ljubljana, Rijeka-town, Ri-jeka-airport, Udine, Zadar, Zagreb-airport,

Zagreb-Maksimir and Zagreb-GriË. Thesespecific locations differ according to their geo-graphical and topographical characteristicsand they are used to analyse the spatial vari-ability of stability and MH. The stability clas-ses and MH were also analysed for a pollutionepisode, which occurred in the Rijeka(j=45.3° N, l=14.4° E) area on 3∑5 February2002, when SO2 exceeded its critical value.Furthermore, with the modified Pasquillmethod, the stability classes were determinedfrom measured data at climatological terms(0700, 1400 and 2100 LST), at Rijeka, for theperiod 1981∑2001.

3. RESULTS AND DISCUSSION

3.1. Stability and MH

The comparison between the observed andmodelled stability classes for the different locations is shown in Figure 1. The observedstability classes frequencies at different loca-tions, determined with the modified Pasquillmethod, are small. They are 5 ∑ 10 % for classesC, D and E between Zagreb, which is a conti-nental location, and Zadar, which is on thecoast. For these locations, the spatial diffe-rence for classes A, B and F is less than 5%.Note that for Rijeka, which is on the northerncoast, the stability frequencies are more simi-lar to Zagreb than to Zadar. The stability de-termined with M2 shows better agreementwith the observed stability than the one deter-mined with M1. Generally, M1 tends to pro-duce more stable classes and underestimate

5A. JeriËeviÊ, K. ©poler »aniÊ and S. VidiË: Prediction of stability and mixing height in thecomplex orography

Table 2. Method M2 for Pasquill stability classes determination based on VTG and mean wind speed in thelayer (modified Table VII in IAEA (1980) and reference therein: Vogt, K.J. et al.,1971: Ausbreitung undAblagerung, Kernforschungsanlage Jülich, FRG Report Jül-807-ST)

Tablica 2. Metoda M2 za odreivanje Pasquillovih klasa stabilnosti na temelju VTG i srednjeg vjetra u sloju(modificirana tablica VII u IAEA (1980) s referencom: Vogt, K.J. et al., 1971: Ausbreitung und Ablagerung,Kernforschungsanlage Jülich, FRG Report Jül-807-ST)

22)()( jj

iBvu

R

HMC-39.qxd 11/11/05 18:30 Page 5

the lability in the atmosphere. On the otherhand, M2, besides vertical temperature varia-tions, includes the mean wind in the layer, re-lated to the mechanical turbulence productionterm, and thus the stability representation ismore realistic. In Figure 2, daily and seasonalvariations of stability for the same four loca-tions are represented. During the day, insta-bility classes dominate mainly due to an inten-sified VTG. Stable and neutral situations, oc-curring during night-time, are usually connect-ed with a negative surface VTG. There ismore spread in the unstable classes daily inter-val during the longer spring and summer dura-tion of the day. Although a greater spread wasexpected during summer, the calculation hasshown it in spring. The reason could be themore frequent cyclones that dominated theweather conditions at the beginning of thestudied summer period, which increased pre-cipitation especially in the Adriatic region.Climatologically, May 2002 was very warm andAugust was very wet compared with 30-year

average values (DHMZ, 2003). Neutral classesare frequent during whole day in this autumnperiod, especially for the Rijeka and Zadarcoastal stations, shown in Figure 2(d). Thereason is increased precipitation decreasingthe temperature gradients, which is usual forthis time of year. For Zagreb-Maksimir andZagreb-GriË, the spatial difference in stabilityis most intense in winter, with periods of sta-ble classes lasting the whole day. (Fig. 2(a)).This stable, stagnation periods are characteris-tic of winter anticyclone situations in the con-tinental part of Croatia.


RIJEKA

0

10

20

30

40

50

60

70

A B C D E Fstability classes

fre

qu

en

cy

(%

)

ZAGREB-MAKSIMIR

0

10

20

30

40

50

60

70


fre

qu

en

cy

(%

)

ZADAR

0

10

20

30

40

50

60

70


fre

qu

en

cy

(%

)

ZAGREB-GRI

0

10

20

30

40

50

60

70


fre

qu

en

cy

(%

)

Pasquill method M1 M2

Figure 1. Comparison between the observed stability classes, determined with the modified Pasquill method,and data in climatological terms (0700, 1400 and 2100 LST) in the period 1981∑2001 at the Rijeka, Zadar, Za-greb-Maksimir and Zagreb-GriË meteorological stations and stability classes determined with the M1 and M2methods from modelled data for the same locations. The classes go from extremely unstable, A, to moderatelystable, F.

Slika 1. Usporedba klasa stabilnosti odreenih modificiranom Pasquillovom metodom iz meteoroloπkih po-dataka u klimatoloπkim terminima (0700, 1400 i 2100 LST) u razdoblju 1981∑2001. na meteoroloπkim postajamaRijeka, Zadar, Zagreb-Maksimir i Zagreb-GriË klasama stabilnosti odreenim metodama M1 i M2 iz podatakamodela za te lokacije. Podjela klasa stabilnosti je od ekstremno nestabilno, A, do umjereno stabilno, F.

HMC-39.qxd 11/11/05 18:30 Page 6


Figure 2. The daily and seasonal course of stability classes determined with M2 from the ALADIN model forRijeka, Zadar, Zagreb-Maksimir and Zagreb-GriË, Croatia, for (a) winter, (b) spring, (c) summer and (d) au-tumn.

Slika 2. Dnevni i sezonski hod klasa stabilnosti odreenih pomoÊu metode M2 iz modela ALADIN za Ri-jeku, Zadar, Zagreb-Maksimir i Zagreb-GriË, Hrvatska za (a) zimsko, (b) proljetno, (c) ljetno i (d) jesenskorazdoblje.

(a) (b)

HMC-39.qxd 11/11/05 18:30 Page 7


(c) (d)

Figure 2. continued.

Slika 2. nastavak.

HMC-39.qxd 11/11/05 18:30 Page 8


Winter

0

300

600

900

1200

1500

1800

0 3 6 9 12 15 18 21 24

hours

MH

(m

)

Figure 3. The daily and seasonal course of the averaged mixing height (MH) estimated from the ALADINmodel with the bulk Richardson number method with RiBc = 0.1 for eight different locations in the model thatcorrespond to: Rijeka-airport, Rijeka-town, Zadar, Udine, Ljubljana, Zagreb-airport, Zagreb-GriË and Za-greb-Maksimir.

Slika 3. Dnevni i sezonski hod srednje visine sloja mijeπanja (VSM) proraËunate iz modela ALADIN bulk-Richardsonovom metodom uz RiBc = 0.1 na osam razliËitih lokacija u modelu koje odgovaraju: Rijeka-aero-drom, Rijeka-grad, Zadar, Udine, Ljubljana, Zagreb-aerodrom, Zagreb-GriË i Zagreb-Maksimir.

Summer

0

300

600

900

1200

1500

1800

0 3 6 9 12 15 18 21 24

hours

MH

(m

)

Spring

0

300

600

900

1200

1500

1800

0 3 6 9 12 15 18 21 24

hours

MH

(m

)

Autumn

0300600900

120015001800

0 3 6 9 12 15 18 21 24

hours

MH

(m

)

Ljubljana Rijeka-airport Rijeka-town

Udine Zadar Zagreb-airport

Zagreb-Gri Zagreb-Maksimir

Figure 4. The daily and seasonal course of MH estimated from the ALADIN model with the bulk Richard-son number method with RiBc = 0.1 for Rijeka, Croatia.

Slika 4. Dnevni i sezonski hod VSM-a proraËunate iz modela ALADIN bulk-Richardsonovom metodom uzRiBc = 0.1 za Rijeku, Hrvatska.

HMC-39.qxd 11/11/05 18:30 Page 9

The spatial and seasonal variation of MH de-termined from the NWP model is also repre-sented. Figure 3 shows the seasonally aver-aged MH calculated with RiBc = 0.1 for all chosenlocations. In the winter period, the averageMH did not exceed 600 m. Long-lasting stableconditions suppressed turbulence and the ave-rage MH shows only a slight shift at noonhours. In spring, the atmospheric processesbecame more intense, increasing instabilityand consequently making the turbulentboundary layer thicker. This can be seen in thedaily course of the predicted MH when the ave-rage maximum at times reached 1600 m. Insummer, the buoyancy term is the dominantturbulence generator and the average MH insummer has maximum values ranging from800 to 1800 m. Although it is reasonable to ex-pect higher MH values during summer, the be-ginning of this analysed summer periodshowed lower MH values (Fig. 4). This is be-cause the convective processes were disturbedby an unstable synoptical situation and fre-quent cyclone formations that influenced theweather situation. In autumn, MH was againlower, depending on the cold air mass breachfrequency and stagnating periods. It is obviousthat the determined MH varies spatially,showing a larger spread during the colder partof the year.

Figure 4 presents the daily and seasonalcourse of MH calculated from the ALADINmodel with RiBc=0.1 for Rijeka. The optimalcritical value RiBc=0.1 for the Rijeka area wasfound based on a comparison study (notshown here). The ALADIN has seasonal anddaily variations of MH with low values duringstable boundary layer (SBL) conditions and inthe colder part of the year (winter and au-tumn) and higher MH in convective boundarylayer (CBL) conditions and during spring andsummer.

Stability describes turbulence intensity in theatmosphere, and MH determines its verticalrange. Hence, these two parameters jointlygive an interpretation of the dispersionprocesses in the boundary layer. It is reason-ably to expect a coincidence between MH andstability, having the smaller MH for stableclasses and the higher for unstable ones.Therefore, Figures 2 and 4 show good agree-ment between stability and MH and their cor-responding daily and seasonal variations. Note

that there is a seasonal MH variation for thecorresponding stability class. For example, thedifference between the summer and winterMH, that corresponds to class A - extremelyunstable - is around 1000 m.

3.2. SO2 episode in Rijeka

A pollution episode occurred in Rijeka on 3 February 2002 when the concentration of SO2increased approximately by 300% reaching ameasured daily concentration of 225.5 µg m-3.This high daily concentration continued dur-ing the next two days and on 4 February 2002a warning was sent out to the public not to ex-pose outdoors it being a health risk. The dailyaveraged SO2 concentrations from 1 to 15February 2002 are represented in Figure 5.This significant increase in daily concentrationsis obvious for the period 3∑5 February 2002.The refinery and other industrial sources ofSO2 claimed that they did not increase emis-sions in this period and city traffic was also ofusual intensity. If this is accurate, these higherconcentrations are the consequence of specificmeteorological conditions. Rijeka is a coastalindustrial city and also the most polluted partof Croatia. It is situated on the coast withmountains right behind it and with two localcirculations: the sea breeze and mountainslope circulation which superpose and mayproduce higher pollution in this complex area.The synoptical situation of that winter periodwas characterized by a surface anticyclonecentred over the continental part of Croatia,and a NW and W warm flow from the Atlanticocean at higher altitudes, originating from theIsland Cyclone, as shown in Figure 6 for 25 January 2002 at 1200 UTC, and this situa-tion remained during the most of the analysed


SO2

Rijeka

February, 2002

0

100

200

300

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15days

co

nc

en

tra

tio

ns

(µg

m-3

)

Figure 5. The daily averaged SO2 concentrationsmeasured in Rijeka in the period from 1 to 15 Feb-ruary 2002.

Slika 5. Dnevne osrednjene koncentracije SO2 izmje-rene u Rijeci u razdoblju od 1. do 15. veljaËe 2002.

HMC-39.qxd 11/11/05 18:30 Page 10


Figure 6. The synoptical situation at 850 hPa overEurope on 25 January 2002 at 1200 UTC (from theEuropäische Wetterbericht, 2002)

Slika 6. SinoptiËka situacija na 850 hPa plohi nadEuropom 25. sijeËnja 2002. u 1200 UTC (iz Euro-päische Wetterbericht, 2002).

Figure 7. The time representation of the vertical stability classes determined from the ALADIN model withthe M2 method for 2, 3 and 6 February 2002 in Rijeka, Croatia.

Slika 7. Vremenski prikaz vertikalnih klasa stabilnosti proraËunatih iz modela ALADIN metodom M2 za 2,3. i 6. veljaËe 2002. u Rijeci.

layer between levels layer between levels

1 2 - 17 m 10 1141 - 1395 m2 17 - 65 m 11 1395 - 1674 m3 65 - 141 m 12 1674 - 1977 m4 141 - 244 m 13 1977 - 2306 m5 244 - 373 m 14 2306 - 2660 m6 373 - 528 m 15 2660 - 3041 m7 528 - 707 m 16 3041 - 3449 m8 707 - 912 m 17 3449 - 3884 m9 912 - 1141 m 18 3884 - 4348 m

winter period. The weather was stable. It wasa long-lasting stagnation period characterizedby fog and low stratified cloudiness and thesurface wind was week.

In the Rijeka area, the highest emissionsources are approximately 200 m above theground and obviously they were above MHsince there was no increase in the daily SO2concentration before 3 February 2002. Thisled to an accumulation of pollutants in the ele-vated stable layer. This long-lasting stagnationperiod was broken the day before a smog in-crease, when weather conditions changed. Itwas sunny and calm on 2 February 2002, andthe buoyant processes elevated MH whichreached the stable polluted layer. When the MHreaches a stable layer intensive mixing occurs,this process is known as fumigation, and thepollutants are drawn into the turbulent layer.

HMC-39.qxd 11/11/05 18:30 Page 11

This is probably the main reason for the mea-sured increased SO2 ground concentrations on3 February 2002. The stagnation period endedwith the Genoa Cyclone approaching on 6 February 2002, which brought rain andstronger winds, so the SO2 concentrations de-creased.

The prognostic values of MH from 25 Januaryto 5 February 2002 are low (see Figure 4) havingdaily maxima less then 400 m. On 6 February2002, with the weather change, MH was high-er. However, we can conclude that this pre-dicted MH was too high, even with RiBc=0.1,to describe this fumigation process. The calcu-lated stability also confirms that ALADINover-predicted the near-surface stability. Thevertical representation of the predicted stabili-ty is shown in Figure 7 for 2, 3 and 6 February2002. These specific days were selected be-cause they were characterized with differentatmospheric conditions. On 2 February 2002 itwas sunny and calm and the buoyant proces-ses are well described in the model, having un-stable classes during the day. However, thenext day was also calm but cloudy and there

was fog, and the model representation wassimilar as the day before. This continued until6 February. As long as there was fog, the modelshowed unstable conditions. On 6 February,the model gave nearly-neutral conditionsthrough all vertical layers, and that day it wasraining.

The main reason for this is that ALADIN haslarge deviations from 2m temperature for dayswith fog. A seasonal comparison between ALADIN and the measured 2 m temperaturein Figure 8 confirms this. During spring andsummer, the measured and modelled tempe-rature curves fit, which is also the case for win-ter and autumn when there is no fog or mizzle.Nevertheless, when there is fog the measuredtemperature does not have a daily coursewhile the 2 m temperature in the model does.

The unrealistically predicted 2 m temperatureduring fog occurrence reproduces more turbu-lent conditions in the atmosphere with higherMH and more unstable classes.


Figure 8. The daily and seasonal comparison between the measured (black curve) and modelled (red curve) 2 m temperature in Rijeka, Croatia.

Slika 8. Dnevna i sezonska usporedba izmjerene (crna krivulja) i modelirane (crvena krivulja) temperaturena 2 m u Rijeci.

HMC-39.qxd 11/11/05 18:30 Page 12

4. CONCLUSION

The main goal of this research was to find themethods for stability and MH calculation fromthe NWP model ALADIN which were thenapplied and tested in different topographicalconditions and used as input parameters in theGaussian dispersion model.

Stability was calculated with two methods: one(M1) takes into account the VTG and the other(M2) both VTG and the mean wind in the lay-er. Comparing the results from the model withthe observed stability, calculated with themodified Pasquill method, it was found that M2is better. The seasonal and daily variations ofstability classes calculated from ALADIN withM2 have a realistic representation. This method,M2, is more used in practice (e.g. Ferenczi,2002). It is shown that the highest spatial vari-ations in stability between the continental andcoastal part are present during the colder partof the year (autumn and winter), which is alsoa climatological characteristic of Croatia(GajiÊ-»apka and ZaninoviÊ, 2004; ZaninoviÊand GajiÊ-»apka, 2005).

Here, the bulk Richardson method has beenused with RiBc= 0.1 and it has been confirmedthat it is practical for MH calculation fromNWP models. Empirically determined RiBc isdependent on seasonal variability and on localcharacteristics e.g. surface roughness (e.g.Teliπman and Grisogono, 2002). Further, thisvalue strongly depends on the NWP modelcharacteristics (resolution, parameterisationused etc.), which is confirmed by other authors (Zilitinkevich and Baklanov, 2002).Our results confirm that a detailed analysis isneeded when determining RiBc from the NWPmodel and that RiBc can vary from the theoreti-cal value of 0.25 (e.g. Stull, 1988). The ALADINmodel gives a realistic representation of dailyand seasonal MH variations and shows that itis sensitive in different orographycal condi-tions.

Comparing stability and MH, good agreementhas been found between the results. There arelower values of MH for stable classes (E, F)e.g. during the night, and higher MH for un-stable ones (A, B and C), e.g. during dailyconvection.

The dispersion parameters for the Rijeka areaare of special interest because this is an indus-trial region and its orography is complex. Un-

fortunately, here are only ground measure-ments in Rijeka, and thus the NWP model data are very important. The basic question iswhether pollution episodes can be predictedbased on the dispersion parameters, i.e. stabilityand MH, calculated from ALADIN? Natural-ly, this is important for emission control pur-poses, and concentration calculations wouldgive a straightforward answer. Nevertheless, itis possible to expect higher pollutant concen-trations in longer stagnation periods, having astable atmosphere and low mixing.

Some characteristics of the model have beenfound through this analysis and it has been shownthat in the days with fog or mizzle ALADINoverpredicts 2 m temperature. Further, thepredicted stability and MH also describe moreunstable conditions than the existing in dayswith fog and this should be taken into consi-deration.

The results have confirmed that the ALADINmodel could be used over complex orographyfor the MH and stability calculation needed inthe Gaussian dispersion models.

Acknowledgement: The authors are thankful toVesna –uriËiÊ and to Branko Grisogono onmany valuable suggestions. Many thanks to Na-da MatkoviÊ from Zavod za javno zdravstvoPrimorsko-goranske æupanije for the SO2 con-centration data. We are also grateful to our re-viewers. This study has been partly supported bythe Croatian Ministry of Science and Technolo-gy, under project numbers 0004001 and 0129011.

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