Mannstein, H.

Wendling, P.

Italian

Institute of Transport Research:Publications

Contrail observations from space using NOAA-AVHRR dataHermann MannsteinInstitut fur Physik der AtmosphareDLR OberpfaenhofenD-82234 Weling, GermanyAbstractThe infrared channels of the Advanced Very High Resolution Radiometer (AVHRR)onboard of the weather satellites of the NOAA series allow the detection of contrails.An automated detection scheme is described and tested against computer aided visualclassications by two experts. The algorithm seems to identify contrails within thesatellite data with a skill comparable to the human observers.Clusters of contrails within the satellite images are connected to outline regionswhere the atmospheric properties are favourable for the existance of observable con-trails. Air trac data shows, that over Middle Europe at least in the main ight levelsmost of these regions should be marked by detectable contrails. The mean areal cover-age of these regions is estimated to be in the range of 10% to 20%, the cloud coverageby detected contrails was 0.9% in 60 AVHRR scenes covering Central Europe.1. IntroductionWhether there is a relevant climatic impact of contrails is still an open question. Highand optical thin cirrus clouds and also aged contrails may have a positive correlation to theradiation budget of the combined surface and atmosphere system. An increase of coverageby contrails may thus lead to warmer surface temperatures while all other cloud types leadto surface cooling. The global mean coverage by contrails is not known. Bakan [1] derivedfrom visual inspection of AVHRR ('Advanced Very High Resolution Radiometer') data thecontrail coverage of the North Atlantic region and found values up to 2% depending onseason and location.Fig. 1: NOAA-12, T(Ch.5), May 4th, 1995. 07:43UT.Fig. 2: NOAA-12, T(Ch.4)- T( Ch. 5), May 4th, 1995.07:43 UT.Fig. 3: Regional standarddeviation of T5.1Fig. 4: Normalizedtemperature Ch.5.Fig. 5: Large scalemaximum gradient of T5.Fig. 6: rst guess Mask fordetection of contrails.Local area coverage (LAC) images recorded by the AVHRR onboard of the weathersatellites of the NOAA series show often linear structures which can be identied as contrailswithout any doubt. Best suited to identify contrails are the channels in the thermal infraredpart of the spectrum i.e. channel 4 (10.3 to 11.3 m) and channel 5 (11.5 to 12.5 m)and the dierence of the blackbody temperatures derived from this channels [2, 1]. Figs. 1und 2 show an extreme case of contrail coverge over Denmark, southern Sweden, northernGermany and the western part of Poland. This small part of an AVHRR scene is used inthe following to illustrate the contrail detection algorithm.2. Contrail Detection Algorithm2.1. DescriptionInput data for the detection algorithm are the equivalent blackbody temperature derivedfrom channel 5 (T5) and the temperature dierence between channel 4 and 5 (TD). To avoidinterference with artefacts produced by remapping we use the data in the original satelliteprojection. Figs. 1 and 2 show an example of this data. Both datasets are ltered with anhighpass kernel and normalized with the regional standard deviation STD5 and STDD(Fig.3).T5 is inverted. From the resulting normalized images N5 and ND (Fig. 4), the large scalemaximum gradient of T5 (G5) (Fig. 5) and TD we derive a mask shown in Fig 6, whichmarks all pixel fullling each of the following requirements: N5 +ND > 1:5 (This value depends on the type of normalisation) G5 < 2:  STD5 + 1 to avoid the interpretation of edges as lines TD > 0:2The xed parameters this requirements where derived by an evolutionary algorithmwhichmaximized the correlation of the resulting mask with the visual analysis of some test cases.The sum of the normalized imagesN5+ND is input for further ltering. We convolve thedata with a line lter of 19 x 19 pixel in 16 dierent directions. Because of the normalizationof the input data, a single threshold is sucient to isolate connected regions, which aretreated as seperate objects. Each of this objects is checked against the mask describedabove. To be regarded as a contrail, the remaining pixel in each object have to fulll thefollowing criteria:Fig. 7: Sum of normalizedimages convolved with linedetection kernel for 45.Fig. 8: contrails derived forthe direction 45.Fig. 9: Result from thecontrail detection scheme. Ten or more pixel form the object the length has to be more then 16 pixel distances the correlation of the coordinates must be greater then 0.975Using morphological functions we ll interrupted contrails. The results for each directionare added. The whole algorithm is then applied to the same dataset sampled every secondelement and line to detect wider contrails. Fig. 9 shows the result.2.2. Verication: Comparison with visual classicationFor the comparison we used AVHRR Data of the noon overpasses for the months of Julyand October 1990. The data covers Middle Europe. Due to the changeable satellite positionthe width of the dataset and the spatial resolution varies. The data has been classied bytwo observers using an interactive computer program, which allows to mark and store linesof variable width within an image. The result of these two classications is given in Fig. 10.Fig. 11 shows the result of the automated classication in comparison to the mean value ofthe two human observers. The variability within the two datasets is comparable.3. Regions of potential contrailsOver Middle Europe we detect contrails usually in clusters. The typical width of theseclusters is in the order of 100 km, the shape is often elongated. We conclude, that theseclusters mark regions where the state of the atmosphere within the cruising levels is suitablefor the formation of persistent contrails. The atmospheric conditions for contrail formationare extensively described in [3]. For the persistence of contrails a steady lifting of the airparcel seems to be an additional requirement.Under unchanged atmospheric conditions, the areal extent of these regions sets an upperlimit to contrail coverage in case of increased air trac. Due to the uncertainties in thehumidity measurement of radiosondes in this altitudes, and the subsynoptic scale of thisregions of potential contrails, a direct determination from standard observational data seemsto be not feasible.Due to the dense air trac over Middle Europe, we can assume that most of the regionsof potential contrails are also marked by them. The scheduled ights from May 4th 1995from 1.5 h before the satellite overpass at 7:43 UT are schown in Fig. 12. The ightroutesvisual classification C.visual classification B.Fig. 10: Comparison of twoindependent visual classicationsAutomated classificationVisual classificationFig. 11: Comparison of the automatedcontrail detection scheme to the meanvalue of the two visual classicationsFig. 12: Air trac over Middle Europe May 4th 1995, 06:13 - 07:43 UTC, only ightsabove ight level 200 (20000 feet). A windspeed of 30 kn from NNE is assumed for thewhole area.are shifted to simulate a windspeed of 40 kn from NNE, which was measured on this day overnorthern Germany in the 300 hPa level To estimate the minimum extent of those regionswe use the morphological image processing operators \erode" and \dilate" with a circulartemplate of 25 pixels radius. The result is shown in grey in Fig 9: The regions wherethe algorithm found contrails are connected, while those regions without contrails within adistance of 25 pixels are left blank. The preliminary inspection of 60 AVHRR scenes fromJuly and October 1990 over Central Europe showed that this regions cover about 10%, whilethe coverage with contrails is 0.9%. These numbers are preliminary, but longer time seriesare now being processed, which will lead to statistically signicant data.4. ConclusionsAn algorithm was developed, which allows the automated detection of contrails from AVHRRLAC data. As the AVHRR is own on the operational meteorological satellites of the NOAAseries since 1979, and the LAC data are received and archived at numerous receiving stationsaround the world, this algorithmmakes it possible to produce worldwide statistics on contrailcoverage. In regions with dense air trac it is also possible to estimate the regions of potentialcontrail.Acknowledgements: Parts of this work were supported by the BMBF (German FederalMinistry for Education, Science, Research and Technology) within the DLR-BMBF project\Schadstoe in der Luftfahrt". I wish to express my thanks to EUROCONTROL for the\ight proles" and to Caroline Vogt for handling this data.References[1] S. Bakan, M. Betancor, V. Gayler, and H. Grassl. Contrail frequency over Europe fromNOAA-satellite images. Annales Geophysicae, 12:962{968, 1994.[2] Thomas F. Lee. Jet contrail identication using the AVHRR infrared split window.Journal of Applied Meteorology, 28:993{995, 1989.[3] U. Schumann. On conditions for contrail formation from aircraft exhaust. MeteorologischeZeitschrift, N.F., 5:4{23, 1996.

Contrail Observations from Space Using NOAA-AVHRR Data

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