P2.76 TWONEWCONTRAILDETECTIONMETHODSFORTHECOMPILATIONOFAGLOBALCLIMATOLOGYOFCONTRAILOCCURRENCE

DavidP.Duda,KonstantinKhlopenkov

ScienceSystemsandApplications,Inc.(SSA)),Hampton,Virginia

PatrickMinnisNASALangleyResearchCenter,Hampton,Virginia

1. INTRODUCTIONOne of the recommendations of the FederalAviation Administration’s (FAA) Aviation‐ClimateChangeResearchInitiative(ACCRI)isthedevelopmentof a global climatology of linear contrail occurrencedetectableviasatelliteremotesensingmethods.Suchacontrailclimatologyisnecessaryforthevalidationofa new generation of atmospheric models thatrepresentcontrailformationexplicitly.Wepresent twonewautomated contrail detectionalgorithms (CDAs) that can be used to develop thisclimatology. These CDAs are part of a contrailclimatology being developed by NASA LangleyResearch Center that includes concurrent contrailmicrophysicalandradiativepropertiessuchascontrailoptical depth, effective particle size and solar andbroadbandlongwaveradiativeforcingthatarederivedusingtheCERESclouddetectionandretrievalanalyses(Minnisetal.,2008,2009).2. MODIFIEDMANNSTEINETAL.CDAThe first algorithm is a modified version of thetechniquedescribedbyMannsteinetal.(1999),whichdetects linear contrails in multi‐spectral thermalinfrared (IR) satellite imageryusingonly twochannels(11&12µm)fromtheAdvancedVeryHighResolutionRadiometer (AVHRR). This method requires only thebrightness temperatures (BT) from the IR channels,with no other ancillary data, and can be applied toboth day and night scenes. It uses a scene‐invariantthresholdtodetectcloudedgesproducedbycontrails,and3binarymaskstodetermineifthedetectedlinearfeaturesaretrulycontrails.However,thesemasksarenotalwayssufficienttoremoveallnon‐edgefeatures.Toreducethenumberoffalsepositivedetectionsduetolowercloudstreetsandsurface________________________________________Corresponding author address: David P. Duda, Science Systems and Applications, Inc., NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681-2199; e-mail: david.p.duda@nasa.gov

features,weaddobservations fromother IR radiancechannels available on the MODerate‐resolutionImaging Spectroradiometer (MODIS) on Terra andAqua. The new modified method uses additionalmasks derived from the added thermal infraredchannels to screen out linear cloud features thatappearascontrailsintheoriginalmethod.The modified CDA follows the same overall dataflow as the original Mannstein et al. CDA. The IRbrightness temperature measurements are firstnormalized to allow the data to be compared withscene‐invariant thresholds. The brightnesstemperatures are first inverted to allow the BT andBTDdatatobetreatedwiththesameprocedures.(Forexample, T12.0 is the inverted BT for the 12.0 µmmeasurements.) Both the inverted BT and thebrightness temperature difference (here, BTD1 is theBT difference between 10.8 and 12.0 µmmeasurements) data are then smoothed with arotationally symmetric Gaussian 5×5 pixel lowpasskernel resulting in the images

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T12.0 and

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BTD1 . Thelocal standard deviation of the BT and BTD data arecomputed over the region of the Gauss kernel (forexample,

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STD(12.0µm) = (T12.0 ‐ T12.0)2 ) (1)by filteringthesquareof thedifferencesbetweentheoriginal and smoothed images again with the samekernel. The difference between the original and thesmoothed images was normalized with the localstandarddeviationtogetthenormalizedimage:

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N12.0 =(T12.0 ‐ T12.0)

(STD(12.0) + 0.1K) (2)

where0.1Kisaddedtothelocalstandarddeviationtolimit the sensitivity of the normalization over veryhomogeneousregions. InadditiontoT12.0andBTD1used in the original CDA, themodified CDA also usesthefollowingBTdata:

T6.8–watervaporchannel(6.8µm)BTBTD2=(T8.6–T12.0),BTD3=(T8.6–T13.3),BTD4=(BTD1+BTD2).ThesumofthenormalizedimagesN=N12.0+NBTD1+NBTD2was thenconvolvedwitha line filterof19×19pixels in 16 different directions, and the individualconnected regions resulting from the filtering areconsideredaspossiblecontrailobjects. Theseobjectsare then checkedagainst 6binarymasks to check forcontrails. The first three masks are similar to theoriginalCDA:A.N=NBTD4+NBTD2+N6.8+N12.0 +NBTD1>3.45K.B.BTD1>0.2K.C.Gradient(12.0µm)<2×STD(12.0µm)+1K.The threshold in condition A selects pixels having ahigher‐than‐average normalized brightness, which istypicalofthincirrusintheBTDimagery.TherelativelylowBTD1thresholdwasretainedfromtheoriginalCDAto allow detection of possible contrails over loweropaqueclouds. ConditionC compares the large‐scalemaximumgradientfor12.0µmina15×15pixelvicinityof a possible contrail pixel with the local standarddeviation to eliminate features like coastlines whichalsoappearaslinesinthenormalized12.0µmimages.Three additional binary masks are included in themodifiedCDA:D.(Gr(6.8)–Gr(12.0)/(Gr(12.0)+0.03)>‐0.85.E.NBTD3>0.0K.F.Gradient(BTD3)<1.5×STD(BTD3)+0.2K.Allthreemasksareusedtoreducethenumberoffalsepositive detections of lower level cloud streets andother edge features in the IR imagery. Condition Dcompares the large‐scale maximum gradient for the6.8‐µm water vapor channel with the gradient fromthe12.0‐µmchannel. Thewatervaporchannel tendstobesmoothexceptinregionswithhighclouds,whilethe 12.0‐µmchannel shows cloudiness at all levels inthe atmosphere. Thus, condition D removes regionswhere only low clouds are likely in the imagery.Condition E tends tomask regions with lower cloudsmorethanareaswithhigherclouds,whileconditionFissimilarinfunctiontoconditionC.Figure 1 shows an example of the modified CDAalong with the original CDA and BTD1 image. The

modifications in the contrail detection algorithmeliminate several false detections of contrails in thelowerhalfoftheimagery.3. KHLOPENKOVCDAThe second algorithm is based primarily on imageanalysis and pattern recognition techniques. It usesthe BTDs of the MODIS IR imagery producing thehighestcontrast forthecontrails inaparticular image(seeFigure2,left). Thecoreofthealgorithmconsistsof several stages of correlation analysis that usepredefined linear patterns to match contrails withdifferentwidths and orientations. In the first stage, a5×5‐pixel masking is applied to a reduced imageproduced by the 2×2 box averaging of the original.This yields thedirectionalityand strengthofpotentialcontrail fragments. Next, a more precise correlationwith 16x16 patterns is applied to the original image,butonlytothepixelswithahighratingafter thefirststageandonlywiththepatternsalignedcloselytothedirectionsdetectedat thefirststage.Severalpatternswith different directions, widths, and subpixel offsetsare tested to produce the highest correlation. Thetwo‐stageapproachprovidesforhighsensitivitytoanylinear features on the image while achieving areasonable performance. The result from thiscorrelation analysis is shown in Figure 2, right. Themap of likely contrail nodes (pixels with highcorrelation) obtained from the initial analysis thenundergoesageometryanalysis.Atfirst,thenodesarechecked to produce the optimal link pairs. The left‐hand side of Fig. 3 shows themap by using differentcolors to indicate the different directions of thedetected links. Based on the geometry of the linkedpairs, the line segments are then generated withweightsassignedaccordingtotheamountandweightsof the included nodes. To discriminate contrails fromcommon cirrus streamers, the algorithm analyses thedensity of the detected contrails that follow similardirectionswithina small area.The line segments thatfollow this local direction are assigned lowerweightsso that the algorithm suppresses the detected linesegments that are likely to correspond to cirrusstreamers.Finally,thelinesegmentsaremergedandextendedby joiningchainsof links into lines,merging lines thataretooclosetogether,andextendinglinesbyincludingweaker nodes too. Lineswith lowweight and limitedlength are discarded. The result is shown in Figure 3,right. The output contrail mask has different weightsfor each detected contrail indicating a combinedconfidence level of the detection. This allows some

flexibility for the end user when deciding whether astricterormoreliberaldetectionisneeded.Fig.4showsthefinalcontrailmaskandFig.5showsthe results from the modified Mannstein et al. CDA.Bothmethodsshowcomparablecontraildetections.4. WAYPOINTDATAAircraft emissions data for 2006 (Wilkerson et al.,2010)wereusedtoprovidethelocationofcommercialaircraft around the globe at 1‐hour resolution. Toprovideastandardgeographicgridforthecontrailandaircraftdata,alllocationsofthedetectedcontrailsandthe commercial aircraft flight tracks derived from thewaypointdatawerere‐griddedtoaLambertAzimuthalEqual‐Areaprojection.Asapreliminary testof thenewCDAs, thecontrailmasks produced by the modified Mannstein et al.algorithmhavebeencomparedtotheglobalwaypointdata provided by the FAA. The waypoint data havebeenusedasa reference todistinguish thickdiffusedcontrails from natural cirrus clouds. The bearings ofthe waypoint data‐based flight tracks have beencompared with the orientation of the detectedcontrails to determine which detected contrails aremost likely false detections (Fig. 6). Future plans toimprove the matching of detected contrails withwaypoint data include using wind data from GEOS‐4[Goddard Earth Observing System (GEOS) DataAssimilationSystem–Version4]analysestoadvectthewaypointflighttracksanddevelopingsimpleanalyticalmodelstosimulatethegrowthanddecayofcontrails.These models will be superimposed on the advectedwaypointflighttrackstodeterminethetracksthataremostlikelytomatchwiththedetectedcontrails.The contrail detection masks developed from theCDAs form the first step in the contrail climatologyprocess. The contrail masks will then be integratedinto the NASA LaRC CERES cloud product retrievalsystem (Minnis et al., 2008, 2009) to derive contrailoptical properties for a global contrail climatology.Figure 7 shows an example of contrail optical depthandlongwaveradiativeforcingcomputedforasampleMODISimage.5. SUMMARYANDFUTUREWORKTwonewcontraildetectionalgorithms(CDAs)havebeen developed to detect linear persistent contrailsfrom 1‐km resolution thermal infrared imagery fromMODIS.Bothretrievalsdetectfewerbutmorereliablecontrails than theoriginalMannsteinet al. CDA. TheKhlopenkov CDA is an independent contrail detection

method that can be used as a check of themodifiedMannstein algorithm. Additional datasets includingflight trackdata fromtheACCRIwaypointdatasetarebeing used to improve the accuracy of the contraildetectionalgorithms. TheCDAs formthe first step inthe development of a satellite‐based contrailclimatology of contrail occurrence and opticalproperties.Futureplans include theuseofupper troposphericwind data from GEOS‐4 meteorological analyses toadvect the flight tracks determined from ACCRIwaypoint data. The advected flight tracks will becompared with contrail masks from the CDAs todetermine conditions where false positive detectionsare likely. In addition, CERES cloud product retrievalinformationmayalsobeusedtoreducethenumberoffalsepositivedetections.Efforts are currently underway to verify theaccuracyof themodifiedMannsteinetal.CDAandtocompile detection statistics of the CDA (includingprobability of detection, false alarm rate) by visualinspection of test cases. Once these tests arecompleted,weplan to process one year ofAqua andTerra MODIS imagery over Northern Hemisphere tocreate a contrail climatology, as well as a two‐yearcontrailclimatologyoverthecontiguousUnitedStates(CONUS).6. REFERENCESMannstein, H., R. Meyer, P. Wendling, 1999:

Operational detection of contrails from NOAA‐AVHRRdata. —Int. J.RemoteSensing,20,1641‐1660.

Minnis, P., and Co‐authors, 2008: Cloud detection innon‐polarregionsforCERESusingTRMMVIRSandTerra and Aqua MODIS data. IEEE Trans. Geosci.RemoteSens.,46,3857‐3884.

Minnis, P., and Co‐authors, 2009: CERES Edition‐2cloud property retrievals using TRMM VIRS andTerra and Aqua MODIS data. IEEE Trans. Geosci.RemoteSens.,submitted.

Wilkerson, J. T., M. Z. Jacobson, A. Malwitz, S.Baalsubramanian, R. Wayson, G. Fleming, A. D.Naiman,andS.K.Lele,2010:Analysisofemissiondata from global commercial aviation: 2004 and2006.Atmos.Chem.Phys.,10,6391‐6408.

Acknowledgements.ThisresearchissupportedbytheFAAACCRIprogram,andbyfundingfromtheAmericanRecoveryandReinvestmentAct(ARRA).

11–12µmBTD OriginalCDA ModifiedCDAFigure1.TerraMODISchannel31 (10.8µm)minuschannel32 (12.0µm)brightness temperaturedifference (BTD1) imageryoverNorthAtlanticOcean(1530UTC13March2006).Persistentcontrailsarevisibleintoppartofimagery.

Figure2.TerraMODISchannel29(8.6µm)minuschannel32(12.0µm)brightnesstemperaturedifference(BTD2)imagery(left)andpotentialcontrailfragments(nodes)determinedfromthecorrelationanalysis(right).

Figure3.Mapofoptimallinksbetweennodescodedbycolortoshowdifferentdirectionsofdetectedlinks(left)andarrayoflinesegmentsobtainedaftermergingandextending(right).

Figure4.KhlopenkovCDAcontrailmask(greenlines)overlaidTerraMODISBTD2image.

Figure5.ModifiedMannsteinetal.CDAcontrailmask(greenlines)overlaidTerraMODISBTD2image.

Figure 6. BTD1 image from TerraMODIS at 1345 UTC on 1 January 2006. ModifiedMannstein et al. CDA contrailmask isoverlaidinorange.CommercialjetflighttracksderivedfromACCRIwaypointdataappearasgreybands.Thewidthandcoloroftheflighttracksindicatetheiragerelativetooverpasstime.Oldertracksappearasdarkerandwiderbands.

Figure7.BTD1image(A),contrailmask(B),contrailvisibleopticaldepth(C),andcontrail longwaveradiativeforcinginWm‐2(D)computedforTerraMODISimagefrom1135UTCon13March2006.