data for the year 1991 and the corresponding wind speed at the. 10-m level of .... pebbles of the regs and the serirs depends on the distance to the mountains ...
JOURNAL OF GEOPHYSICALRESEARCH,VOL. 104,NO. D24, PAGES31,207-31,215, DECEMBER27, 1999
Determination of the wind speedthresholdfor the emissionof desertdust usingsatelliteremote sensingin the thermal infrared Olivier ChometteandMichel Legrand Laboratoired'OptiqueAtmosph6rique, Villeneuved'Ascq,France
Batrice
Marticorena
LaboratoireInteruniversitaire desSyst•mesAtmosph6riques, Cr6teil,France
Abstract. The InfraredDifferenceDustIndex(IDDI), derivedfromimagesobtainedfrom the Meteosat10.5- to 12.5-gm channel,describesthe dustdistributionover the Saharan-
Sahelianregion.ThisIDDI, associated withthe 10-mwindspeedreanalyses fromthe EuropeanCentrefor Medium-RangeWeatherForecasts(ECMWF), revealswhetheror not theobserved dustis associated withemissionfroman underlyingsource.Thisresultallows oneto determine thewindspeedthresholds for dustemissionfromtargetslocatedin the western,central,andeasternSaharan-Sahelian region,by meansof satelliteremotesensing. Thresholdvaluesdetermined for seventargetsarepresented. A comparison is carriedout
betweensuchvaluesanddirectdeterminations obtainedthroughthedescription of thesoil textureandsurfaceroughness of thesetargets.The agreement betweenthesequite
independent determinations isconclusive, withanaverage difference of0.3rns-• andarms
differenceof 0.35 rn s-•.
1. Introduction
Thesemodelsof soil erosionand resultingdustemissiondiffer significantly in their sophistication.They were tested by In the first step of its cycle, desertdust is emitted from the comparing their simulated dust emissions with satellite source areas as a result of aeolian action on the erodible soils. The emissionprocessis not a simplelinear one,in whichthe dust observationsof the process[Marticorena et al., 1997a, 1999]. The presenceof desert dust over land is revealed, in the particlesareemittedin proportionto the kineticenergyor to the middle of the day, by a decreaseof the thermalinfraredradiance momentum of the airflow close to the surface. Instead, a outgoing to space[Legrand, 1990]. This effect is mappedin minimum wind shearstressis necessaryto break the mechanical terms of the Infrared Difference Dust Index (IDDI) derived equilibrium of the soil, to initiate soil erosion, and to result in dustemission.Thereforedustemissionwill startonly if the wind througha suitablealgorithmappliedto the IR imagesof the satelliteMeteosat[Legrandet al., 1994]. Owing to velocitynear the surfaceexceedssomethresholdvalues,as stated geostationary Meteosat's location over the Gulf of Guinea, the data concern by R.A. Bagnoldasearly as 1941 [Bagnold,1941]. mostly dust generated in the Saharaand in the Sahel. Early attemptsat simulatingdust emissionfrom desertareas The comparison between the modelswere carriedout for the withoutany wind speedthresholdof erosionresultedin a poor agreementwith the available experimentaldata, in large part westernSahara,northof 16øNand westof 12øE,usingthe IDDI wind speedat the because of the absence of this threshold [Jousseaume,1990, data for the year 1991 and the corresponding 10-m level of the reanalyses from the European Centre for 1993;Genthon,1992]. On the otherhand,modelsincludingsuch Medium-Range Weather Forecasts (ECMWF, Shinfield Park, a thresholdhave beenusedin the recentyears.Westphalet al. [1987] studiedthe dynamicsand microphysicsof Saharandust Reading, England). These tests [Marticorena et al., 1999] storm witha windspeed threshold of 5.2m s'1ata height of 10 indicatethat the agreementbetweensimulatedand observeddust m. Tegenand Fung [ 1994, 1995] useda uniformthresholdof 6.5 emissionrangesfrom very poor for a model withoutthresholdto modeland excellentwith a varying m s'1at 10-mheight overbareunvegetated soilsin theirstudiesgoodwith a single-threshold threshold model such as the MB scheme.A consistencyindex on mineral dust. Marticorena et al. [1997a] computedwind speedthresholdsat 10-m height for squarecells løxl ø in the measuringthe fractional agreementbetween simulationsand
westernSahara,on the basis of the physicaldescriptionand modeling of the emission processes [Marticorena and Bergametti, 1995]. This model, afterwardsreferredto as the MB scheme,involvesa thresholdwhich dependson soil textureand
surface roughness andwhichranges from6 to20m s'1.
Copyfight1999by the AmericanGeophysical Union. Papernumber1999JD900756. 0148-0227/99/1999JD900756509.00
observations reaches values of 7, 61, and 79% for these three
categories,respectively.
So a varying thresholdmodel is proven to be the most accuratein simulatingthe dustemission.This advantageis due to a comprehensive characterization of the sourceareas,takinginto accountthe physicaldescriptionof the erodedsurfaceand of its underlyingsoil. On the otherhand,prior to applyingthis model, an extensiveset of informationon the physicalcharacteristics of the arid areasof the westernSaharahad to be collectedas input data.Sucha task,concerningpropertiesat very smallscales,over a regioncovering4.5 million km2, hasnot beenan easyone.This
31,207
31,208
CHOME•ITE ET AL.: DESERT DUST EMISSION
difficultyconstitutes a majorhindranceto a globalextensionof the model.
In thispaper,we investigatean alternativetechniqueusingthe satelliteobservations to estimatedirectlythe valueof the erosion wind speedthreshold.In principle,this mustbe feasiblewith the IDDI, since this dust index was successfullyapplied to the validationand intercomparison of the variousmodelsof dust
where the effective friction velocity ratio f, smaller than unity, dependson the roughness lengthsof the obstacles,Z0, and of the smootherodible soil, z0. Equation (3) was validated with field measurements[Marticorena et al., 1997b]. Applying equation (1) defines the correspondingwind speedthreshold, Ut(Z), at heightZ:
emission.
After a brief surveyin section2, relatingto the physicsof dust
Ut(Z) =
emission and its use in the MB scheme, section 3 is dedicated to
Ut (Dp,zo,Zo) Z k
In
Z0
.
(4)
the satelliteremotesensingof dust over the sourceareas.The The erosionthreshold is a minimumfor a particlesizeDe of methoddesigned for retrievingthewindspeedthreshold fromthe 80 gm or so, for any groundsurface,with or without obstacles. IDDI imagesis presented in section4. In section5, resultsare Particles arenotliftedif theyaretoolarge(De > 2000 gm). On givenanddiscussed for a setof SaharanandSaheliantargets,and theotherhand,fine particles (De < 20 gm) aresubjected to high thesesatellitedeterminations are comparedto the valuesderived cohesion forces, preventing them from being moved by directlyfromthe soiltextureand surfaceroughness [Marticorena aerodynamicalforces only [Shao et al., 1993]. These particles et al., 1997a]. generally exist only associatedin larger soil aggregates.The particlesof intermediatesize which can be lifted by the wind, follow quasi-horizontaltrajectoriesdownwind, moving down to 2. Soil Erosion and Dust Emission the surface.This process,referred to as saltation,results in 2.1. Physical Background collisions whichbreakthe aggregates andexpelfine fragments
The physicsof aeolian soil erosion and dust emissionis describedin detail by Marticorena [ 1995] and by Marticorena and Bergametti[ 1995]. It is brieflysummarized below. In its motion,the lowestpart of the atmosphere, the so-called atmosphericboundary layer, exerts a friction stresson the underlying surface. This process involves a transfer of atmosphericmomentumto the Earth'ssurfaceand resultsin a slowingdownof the airflow.In adiabaticconditions, the wind in the boundarylayer over rough ground is characterizedby a logarithmicprofile:
able to be kept in suspension in the atmosphere. This is the sandblasting process, whichresultsin dustmobilization[Gillette, 1978; Gomeset ai., 1990; Shao et al., 1993]. As soonas the erosionthreshold is exceeded, the saltation andthesandblasting producedust,insofaras thereare aggregates in the soil. So the soil erosion threshold is also a dust emission threshold.
Thehorizontal (mass)flux of soilparticles, G, is proportional to the surfacefraction,E, of erodiblesoil. It is calculated by summingthe varioussizesof soil particlescontributingto saltation,weightedwith the (mass)sizedistributionof the soils,
(dM/dLnDp). Beyondthe thresholdU[, G increases steeply U(Z) =
U
Z
ln•,
(1)
according toa power lawin U'3[Bagnold, 1941 ]. Theresulting
vertical(mass)flux of dust,F, is proportional to G [Shaoet al., 1993],theproportionality factoratdepending on thesoilcontent where k is the Karman constant (k=0.4), Z0 is the ground of aggregates of fine particles,particularythe clay content, roughness lengthandU' is thewindfrictionvelocity related to accordingto an empirical relationship[Marticorena and the friction stressx through u
Bergametti,1995]. =
,
(2)
Pa
2.2. Model Input Data
Theinputparameters needed bytheMB scheme areLF,z0,Z0,
E, (dM/dLnDp),and at. Specificmethodswere developed to wherep• is the air density. theseparameters necessary for usingthe scheme on a In arid regionsthe unprotecteddry soil is subjectedto aeolian determine erosion.A particle of soil can be drawn up by the wind friction large scale. The parametersof concernare defined over a 1ø stress,providedthis force overbalancesthe addedeffectsof the latitudex 1o longitudegrid andmappedoverthewesternSahara. particle weight and of the interparticlecohesionforce. The limit The general approach used is summarized below from of the equilibriumbetweenthesethree componentsdefinesthe Marticorena[ 1995] andMarticorenaet al. [ 1997a]. 1. The adiabatichypothesis beingassumed andthe roughness erosion thresholdof the particle, either in terms of the wind length Zobeing known, U' canbecomputed fromthewindspeed friction stressor of the corresponding wind friction velocity U;•. The formulation of the threshold u•' relevant to a "smooth" U at heightZ throughequation(l). The wind field at the 10-m is usedfor U. erodiblesurface(i.e., free of any solid obstacle),as a functionof heightfromtheECMWF reanalyses the (spherical) particlediameterDe, wasderivedfrom lversen 2. The roughnesslengthZ0 andthe fractionof erodiblesoil, E, fromthe densityof obstacles, theirmeanheight, and White's [1982] results by Marticorena and Bergameni aredetermined the western [ 1995]. However,naturalarid surfacesare often sprinkledwith a and their shape.The typical surfacesconstituting into fourclasses according to the presence variety of small-scaleobstaclessuchas gravels,pebbles,stones, Saharaareclassified and rocks,as well as elementsof vegetation,absorbinga part of (or absence)of vegetationand inert obstacles.On the basis of ratesfor eachtypeof obstacle the atmosphericmomentumand opposingthe soil erosion.The variousmeanheightsandcovering resulting erosion threshold U;• is therefore increased as a and of the values of z0 for a smooth surface, 40 different roughness categories aredistinguished. functionof the obstacle'sroughness,accordingto the relation
U?=fu•( Dp•) , ( Zo,zo )
(3)
3. The parametersrelated to the soil erodibility are (dM/dLnD•,), Zo,andat.Theseparameters aredetermined through a coupledmineralogical-granulometric description of the soil in
CHOMETTEET AL.' DESERTDUSTEMISSION
31,209
>
20.0
17.0 15.0 14.0 13.0
12.0 11.0 10.0
9.5 8,5 8,0 7.75
7.3
7,2 7.0
6.8 6.5 non
source ,
Plate 1. Thresholdwind velocitydefinedat 10 m [Marticorenaet al., 1997a].
arid regions[Chatenetet al., 1996].This description was classifiedaccordingto five types.Thenthe regionalorographic established bydrysieving anda chemical analysis of a setof 26 contextis analyzed,resultingin sortingeverysquaredegreecell to its neighbors with respectto the surface samples collected in several aridandsemi-arid regions (Sahara, by comparison with eachlandscape unit (e.g.,the sizeof the Sahel,California, andSpain).All thesamples areverifiedto be featuresassociated of theregsandtheserirsdepends on thedistance to the describable asa mixturein variousproportions of fourreference pebbles
populations characterized by both their own lognormalmountainswhere these elements originate). This orographic analysisis supportedand completedby geologicaland granulometric distribution andtheirmineralogical composition
analyses (e.g., considering the relationsbetween (referred to asaluminosilicated silt,finesand,coarse sand,and climatological salts). In addition, ananalysis of wetsedimentation allowed usto the grains'mineralogyand size near the surfaceand the natureof the substratum or considering the current measure theclaycontent tobeassigned toeachofthese reference geological and past precipitation rates, in relation to the fraction of fine components andhence theircharacteristic ctvalues. grains near the surface, or to the vegetation height and density.) Combining thesefour reference soils,eightsoil typeswere derived, representing everyerodible soilin thewestern Sahara, At this point, the informationis still qualitative.It is by usingthequantitative references provided by direct characterized by theirowngranulometric distribution (madeof quantified observations and measurements performed in the region under up to threelognormal modes), mineralogy, andct values.In consideration [e.g., Mainguet and Callot, 1979; Callot, 1988, addition, theroughness lengthz0isestimated tobeDp,,/30,where
andextended to thewhole Dp,•is themedian diameter of thecoarse lognormal mode of 1992],usedas"pointsof calibration" everysoil type.
In summary, thedataneeded for large-scale useof theMB scheme arethewindspeedat the 10-mheight,thecovering rate
andmeanheightof theobstacles, andthemineralogical typeof
mappedarea.
The resultingmapis characterized by up to five typesof surfacefeatures,eachone beinga fractionalcoveragefor each
1øx1ogridcell.Eachtypeof surface featureis definedaccording to itserodiblesoiltypeandroughness category (i.e.,valuesforz0,
Zo,E, (dM/dLnDp), andct).Therefore thegridcellsgenerally by a uniquewind friction velocity describing thestateof thearidsurfaces is foundin specific soil cannotbe represented maps,in association withhigh-resolution satellite images when threshold. Instead,thethreshold mapshownin Plate1 is determined asa necessary. Thebasisforthisgeneral information consists of the topographic mapsof theFrenchNationalGeographic Institute mapof windspeedat the 10-m heightat whichthe modelvertical fluxF isequal toa weak value of 10'•3gcm'2 (IGN),fromwhichthemainlandscape unitscanbeidentified and simulated
the erodible soils. Relevant information on these parameters
,31,210
CHOMETI"E
ET AL.' DESERT DUST EMISSION
s'• (belowwhichemission is considered negligible). Sucha thresholdstemsclearlyfrom the dustproductivityof the surfaces, determinedprimarily by the c• valuesfound in a grid cell and dependingalso on the corresponding fractionalcoverageof the cell. Consequently,thesethresholdvaluesshouldnot be confused with the theoreticalvaluesgivenby equation(4), exceptfor those homogeneous cells madeup of a singletype of surfacefeature, even though one can expect some agreementbetween the threshold values in Plate 1 and the lowest theoretical threshold of
3. The atmosphericelements,cloudsand dust, are separated from the surfaceinformationby subtracting the originalcorrected imagesfrom the RI, which resultsin differenceimages(Dis). On theseimages,cloudsand dustcan be observedagainsta smooth backgroundresemblingthe oceansurface. 4. The cloudsare identified in the Dis using an algorithm derivedfrom the spatialcoherencemethodconcept[Coakleyand Bretherton, 1982]. The pixels identified as cloudy are masked, while the countsof the clearpixelsrepresentthe basicIDDI data.
the corresponding surfacetypecomponents foundin eachcell.
3.3. RelationBetweenIDDI and Visibility 3. Satellite
Observation
of Saharan
Dust
3.1. RemoteSensingin the Thermal Infrared The presenceof dustin the atmosphere resultsin radiativeand nonradiativeinteracting processes[Cautenet et al., 1992]. A major radiativeeffect in the shortwaveis the extinctionof the downwellingsolarflux, resultingin a daytimeshift of the energy balance at Earth's surface. In the arid regions where dust is generated,the thermalresponseof the dry unvegetatedsoil to a solar irradiance drop is a strong fall of the (skin) surface temperature, peakingnearmidday,involvinga strongdecreaseof the thermal infrared emissionby the surface.In addition, this weakerground-emittedradianceis reducedeven more during its transferthroughthe dust layer, which is much colder than the surface.
Theseprocesses convergeto generatea strongdecreaseof the longwave radiance outgoing to space during daytime, a circumstance which enablesthe satelliteremotesensingof dustin the thermal infrared. Early resultscan be found in the work of Shenk and Curran [1974] (Nimbus/temperature-humidity infraredradiometer(THIR), 10.5-12.5 gm) and of Legrandet al. [ 1982, 1985] (MeteosatIR channel,10.5-12.5 gm). 3.2 Infrared
Difference
Dust Index
The Infrared Difference Dust Index (IDDI) is defined as the
decreaseof the thermalinfraredradiancedue to the presenceof dust in the atmosphereduring daytime. It is derived from Meteosat IR channel measurementsat 1130 UTC and expressed in radiometriccounts (a count correspondingto a radianceof
The parameters usuallyemployedto indicatethe presence of dustandto estimateits amountare the visibilityandthe aerosol optical depth.Thesedust indicatorshave been usedto checkthe validity of the IDDI as a dust indicator[Legrandet al., 1985,
1989;TanrdandLegrand,1991]. Visibilityhasbeenwidelyused in connection withdustmeasurements andstudies[e.g.,Bertrand et al., 1975;Middleton,1985;Ben Mohamedand Frangi, 1986; d'Almeida,1986; Ackermanand Cox, 1989; N'Tchayiet al., 1994, 1997]. In spiteof noticeableshortcomings, namely,(1) a limited quantitativeaccuracy,comparedwith the photometric aerosoloptical depth, and (2) a characterizationlimited to the atmospheric layerneighboringthe groundlevel, the advantage of
the visibilityparameter is to be routinelymeasured in the many stationsof the synopticmeteorologicalnetwork.Thereforeit is well suitedfor a comparison of climatologicalrelevancewith the IDDI.
In Figure1, the IDDI is comparedwith the visibilitymeasured at 39 stationsall year round in 1984. The stationslie in the western and central Sahel and in the south of Sahara(between 10øN-25øN and 15øW-20øE).The data were classifiedinto seven categoriesaccordingto the visibility values at 1200 UTC. The
meanand the medianhave been computedfor everycategory. Owing to the presenceof someoutliersin the data set,the mean is somewhat greaterthanthe median,so the latteris expectedto approximatemore closely the investigatedrelation. The IDDI
increases whenthe visibilitydecreases, as is predictible,and it reachesa valueof 10 countsfor a visibilityof 10 km. Abovethis limit of visibility,dustis consideredto be absent.
0.08 W m'2 sr'l for Meteosat-4). Imagesin the formatB2, obtainedby samplingone pixel out of six accordingto both dimensionsin the primefull resolutionimages,were providedby the EuropeanSpaceOperationalCentre(ESOC). Now thesedata are deliveredby Eumetsat,Darmstadt,Germany(informationcan be foundvia the Internetat http://www.eumetsat.de). The IDDI is processedthrough the following algorithm [Legrandet al., 1994]: 1. The raw images,preadjustedfor geographicalcoincidence, are correctedfor the daily variations of sensitivityof the IR channel,using the calibrationcoefficientsprovidedwith every
•
2o
,,:
16
•
12
•
4
image.
2. These original correctedimages are used to realize a referenceimage(RI) representingapproximatelyclear and dustfree conditionsof satellite viewing. As both dust and clouds
0
....
o
i
,5
....
i
....
1o
i
15
....
L ....
20
I
....
25
result in a decrease in the satellite-measured radiance, the RI is
createdby selectingthe highestradiometriclevel for everypixel, from a time seriesof originalimages.The optimallengthof the seriesis 15 days,a compromisereconcilingthe conflictinggoals of a goodeliminationof cloudsand dustand of a limited impact of the biasdue to seasonalor otherpossiblelong-termeffects.
Visil)ili•' (kin) Figure 1. Averages (solidsquares) andmedians(opensquares) of Infrared Difference Dust Index (IDDI) for seven classesof visibility: 0-2.5 km, 2.5-5 km, 5-7.5 km, 7.5-10 km, 10-15 km,
15-20km, and20-30 km, adaptedfromLegrandet al. [ 1994].
CHOMETI'E
ET AL.' DESERT DUST EMISSION
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