AEROSOL OPTICAL THICKNESS RETRIEVAL OVER LAND AND WATER USING SCIAMACHY/GOME DATA

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1 AEROSOL OPTICAL THICKNESS RETRIEVAL OVER LAND AND WATER USING SCIAMACHY/GOME DATA J. Kumierczyk-Michulec 1,2, G. de Leeuw 1 1 TNO Phyic and Electronic Laboratory, P.O. Box 96864, 2509 JG The Hague, The Netherland 2 On leave: Intitute of Oceanology, Polih Academy of Science, Sopot, Poland ABSTRACT An algorithm for the retrieval of the aerool optical thickne over land and over water from SCIAMACHY (SCanning Imaging Aborption SpectroMeter for Atmopheric ChartographY) i preented. Becaue calibrated data are not yet available for the SCIAMACHY channel ued by the algorithm, the concept were teted with GOME (Global Ozone Monitoring Experiment) data. The cloud fraction in the GOME pixel ha been determined uing the FRESCO (Fat Retrieval Scheme for Cloud from the Oxygen A Band) algorithm. Surface contribution to the TOA reflectance are determined from the GOME urface reflectance databae [1]. The aerool retrieval algorithm ue Look Up Table (LUT) that were created uing the radiative tranfer model 6S. The algorithm allow for the retrieval of aerool type characterized by Ångtrom coefficient in the range from -0.1 to 2.8. Comparion of the reult with AERONET un photometer data for 12 ite in Europe and Africa how very good agreement. 1. INTRODUCTION Satellite are bet uited to determine the patial ditribution of aerool over large area. Uually, intrument ued for aerool retrieval are deigned for thi purpoe, uch a ATSR-2 (Along Track Scanning Radiometer 2) or MERIS (MEdium Reolution Imaging Spectrometer). In thi paper we explore the ue of intrument with high pectral reolution, deigned for accurate ga phae retrieval, uch a GOME (Global Ozone Monitoring Experiment), and SCIAMACHY (SCanning Imaging Aborption SpectroMeter for Atmopheric ChartographY). Becaue calibrated SCIAMACHY data are not yet available, therefore the algorithm wa teted with GOME data. A eriou diadvantage i the large GOME pixel ize reulting in a very low probability of clear ky pixel which limit the uefulne of GOME for aerool retrieval. Hence thi tudy mut be regarded a a preparation for the retrieval of aerool propertie from SCIAMACHY. 2. THEORY The reflectance at the top of atmophere above a lambertian homogeneou urface with reflectance urf viewed by a atellite enor and illuminated by the un can be decribed a follow [2] atm TOA( θ, θ v, ϕ) = Tg ( θ, θ v ) urf + T θ ) T ( θ v ) 1 S { ( } (1) The independent parameter in Eq.1 are defined a: θ, ϕ zenith and azimuth angle of the direct unlight, θ v, ϕ v view zenith and azimuth angle from a pacecraft toward the Earth urface, ϕ =ϕ - ϕ v, TOA the total apparent reflectance meaured at the atellite level, atm the atmopheric reflectance, S the pherical albedo of the atmophere i.e. the normalized irradiance backcattered by the atmophere when the input irradiance at the bottom i iotropic, urf the urface reflectance (land or water), T g the gaeou tranmiion, T (θ ), T (θ v ) the total tranmiion of the atmophere on the path between, repectively, the un and the urface, and the urface and the enor. All component in Eq. 1 were calculated uing the radiative tranfer model 6S [3]. In order to tet our algorithm with GOME data, the SCIAMACHY algorithm wa lightly modified. We could tet our algorithm for UV/VIS band but we were not able to ue the IR band of SCIAMACHY. The chematic decription of the aerool retrieval algorithm uing the GOME data i preented in Fig.1. The aerool retrieval algorithm ue the meaured TOA reflectance, averaged over a 1nm wide wavelength window, and centered at 380 nm, 440 nm, 463 nm, 495 urf Proc. of the 2004 Enviat & ERS Sympoium, Salzburg, Autria 6-10 September 2004 (ESA SP-572, April 2005)

2 nm, 555 nm and 670 nm, e.g. where aborption by gae other than ozone i negligible. The calibration procedure of the raw GOME TOA radiance i decribed in detail by [1]. The altitude correction of land pixel wa done uing the information about the ground preure that wa found from the ETOPO-5 databae [4]. The olar and viewing angle were averaged over the GOME ground pixel. Pixel with olar zenith angle larger than 60 degree were dicarded. Next, the GOME data were corrected for the preence of cloud. The cloud fraction i determined uing the FRESCO (Fat Retrieval Scheme for Cloud from the Oxygen A Band) algorithm [5, 6]. The FRESCO method allow to derive the effective cloud fraction and cloud preure, uing the oxygen A band. For further retrieval only data are ued for which the cloud fraction i le than 0.06 (6%). Thi criterion reult in rejection of more than 80% of data. The validation of the reult with cloud-free AERONET [7] level 2.0 data how that thi aumption i reaonable, in ome cae may be even too trict. In the next tep, the GOME data are corrected for ozone aborption. Reflectance at TOA meaured by GOME (380, 440, 463, 495, 555, 670 nm) Cloud creening FRESCO algorithm Correction for ozone aborption Surface correction GOME urface reflectance databae LUT of aerool/atmopheric/rayleigh reflectance for a given atellite and olar geometry 3 predominantly aerool type - maritime, <-0.1, 0.65> - continental, <0.65, 1,32> - urban, <1.32, 2.8> The bet fit with aerool model AOD Fig. 1 Schematic decription of the aerool retrieval algorithm for the GOME data over land and water. uing the GOME urface reflectance databae [1]. The GOME databae include information of the urface reflectance for a given wavelength and month of the year, with a patial reolution of 1 x 1 deg. Next, uing Look Up Table (LUT) the aerool optical thickne i calculated. The LUT that are created uing the radiative tranfer model 6S [3] include information about the Rayleigh reflectance, a well a the aerool and atmopheric reflectance for three main aerool type: maritime, continental and urban [7]. However, the algorithm allow to retrieve aerool mixture characterized by Ångtrom coefficient in the range from -0.1 to 2.8, i.e. the value oberved by the AERONET ground-baed meaurement [8]. The main idea of the aerool retrieval algorithm i that after correction for the urface contribution to the TOA reflectance, the olution of Eq.1 i earched in three domain of poible olution: maritime, continental and urban, applying a biection procedure [9]. We aume that for predominantly maritime aerool, the Ångtrom coefficient α <-0.1; 0.65>, for predominantly continental aerool α <0.65; 1.32>, and for predominantly urban aerool α <1.32; 2.8>. Thi ditinction i baed on earlier tudie on the relation between the aerool compoition, and the value of the Ångtrom coefficient [10, 11, 12, 13]. The threhold value of the aerool optical thickne at 380 nm and 440 nm are aumed to be 1.5, and 1 for the other wavelength. 3. VALIDATION To tet the accuracy of the GOME aerool algorithm, the retrieved aerool optical thickne value were compared with collocated un photometer meaurement (Level 2.0), available from the AERONET web-page (//aeronet.gfc.naa.gov). Validation wa done for Validation for 2000 wa done for only a few ite with imilar reult. Comparion of the GOME derived aerool optical thickne wa made for everal ite, repreenting different type of urface, and aerool (Table 1). To avoid cloud contamination in the GOME aerool retrieval, all data for which the cloud fraction wa higher than 6% were removed. Comparion were made for the ame ite, day, and hour. The lit of ite i preented in Table 1. The urface correction i.e. the etimation of the urface contribution to the TOA reflectance wa done

3 Table 1. Lit of ite for inter-comparion between the GOME retrieval and the AERONET un photometer data January-December 1997 Site Location Period Banizombou 13N, 2E January to October 1997 Bondoukoui 11N, 3W January to December 1997 Barbado 13N, 59W January to Capo Verde 16N, 22W January to December 1997 AOD(440) GOME y = x R 2 = N= AOD(440) AERONET Fig. 2. Comparion of the GOME retrieved aerool optical thickne and the AERONET un photometer meaurement at 440 nm for the ite lited in Table1. Rame Head 50N, 4W April to July 1997 Bidi Bahn 14N, 2W January to September 1997 Dakar 14N, 16W January to July 1997 GSFC 39N, 76W February to Lille 50N, 3E June to September 1997 Ipra 45N, 8E Augut to Bermuda 32 N, 64W December 1997 Aire Adour 43N, 0E February to The GOME retrieved aerool optical thickne i compared with the AERONET meaurement at 440 nm for all ite lited in Table 1, in the catter plot (ee Fig. 2). The error bar of the GOME data were obtained from averaging over an area of 0.5 deg x 0.5 deg. The high correlation give confidence that the aerool retrieval algorithm work well. An example of the patial ditribution of the aerool optical thickne over the region of the North Atlantic and Europe (i.e. latitude from 5 0 N to 70 0 N and longitude from 80 0 W to 70 0 E) i preented in Fig. 3. Fig. 3. Comparion of the AOD retrieved from GOME data during the month January, February, November and December The large GOME pixel imply a large fraction of cloud contaminated pixel, reulting in only few data uitable for aerool retrieval. Therefore the data have been combined to repreent a longer period, in thi cae the European winter i.e. January, February, November, December (JFND). The grey colour repreent no data. The reaon of thi large grey area for the latitude above 45 deg, i the high cloud contamination a well a the aumption that only data correponding to olar zenith angle up to 60 degree hould be taken into account.

4 However, depite of the large GOME pixel, the plume cloe to Africa, oberved alo by other atellite, i viible. The mean value of the aerool optical thickne inide the plume are in the range between 0.5 and 1. Outide the plume the value are much lower, around Thi plume i not a clear a it i often oberved from other atellite. One of the reaon could be that area with high AOD may be aigned a cloud contaminated and then removed. 4. CONCLUSIONS The GOME aerool retrieval algorithm work well, for cloud free pixel, uch a in the cae of comparion with level 2.0 un photometer data. Becaue of the high probability of cloud contamination, GOME i not uitable for monitoring aerool on a daily or even on a monthly bae. A oon a the SCIAMACHY data will be available the algorithm will alo be teted for the IR band. The pectral information from the IR channel can be ued to better dicriminate dut particle and for the elimination of cloud. Moreover the unique feature of SCIAMACHY-limb viewing geometry can be ued for tratopheric retrieval. 5. ACKNOWLEDGEMENTS We thank the PI invetigator: Didier Tanré, Brent Holben, Philippe Goloub and Giueppe Zibordi, and their taff for etablihing and maintaining the AERONET ite ued in thi invetigation. We acknowledge Piet Stamme and Martin de Graaf for kindly providing u with GOME data and the reult from FRESCO algorithm. We thank Darek Makimiuk for hi help in olving the oftware problem, Roman Mark for helpful dicuion, Marianne Degache and Robin Schoemaker for their upport. The algorithm wa developed a part of the EO-037 project of the National Data Uer Support Program of the Netherland Space Reearch Organization (SRON). 6. REFERENCES 1. Koelemeijer, R. B. A., J. F. de Haan, and P. Stamme, A databae of pectral urface reflectivity in the range nm derived from 5.5 year of GOME obervation, 2003, J. Geophy. Re., 107, doi: /2002jd Tanré, D, M.Herman, P.Y. Dechamp, and A. de Leffe, Atmopheric modeling for pace meaurement of ground reflectance, including bidirectional propertie, Appl. Opt., Vol.18, No 21, pp , Vermote E. F., Tanré, D., Deuzé J.L., Herman M., and J.-J. Morcrette, Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview, IEEE Tranaction on Geocience and Remote Sening, vol.35, No. 3, Haxby, W.F., G.D. Karner, J.L. La Brecque, and J.K.Weiel, Digital image of combined oceanic and continental data et and their ue in tectonic tudie, Eo tran. AGU, 64, , Koelemeijer, R. B. A., P. Stamme, J. W. Hovenier, and J. F. de Haan (2001), A fat method for retrieval of cloud parameter uing oxygen A band meaurement from GOME, J. Geophy. Re., 106, Koelemeijer, R. B. A., P. Stamme, J. W. Hovenier, and J. F. de Haan (2002), Global ditribution of effective cloud fraction and cloud top preure derived from oxygen A band pectra meaured by the Global Ozone Monitoring Experiment: Comparion to ISCCP data, J. Geophy. Re., 107, doi: jd McClatchey, R. A., H. -J. Bolle, K. Y. Kondratyev, J. H. Joeph, M. P. McCormick, E. Rachke, J. B. Pollack, D. Spänkuch, and C. Mateer (1984), A preliminary cloudle tandard atmophere for radiation computation, report, 53 pp., Intl. Radiat. Comm., Boulder, Colo. 8. Dubovik, O., B. Holben, T. F. Eck, A. Smirnov, Y.J. Kaufman, M. D. King, D. Tanré, and I. Slutker (2002), Variability of aborption and optical propertie of key aerool type oberved in worldwide location, J. Atmo. Sci., 59, Pre, W. H., S.A. Teukolky, W.T. Vetterling, B. P. Flannery (1992), Numerical Recipe in C. The art of cientific computing, Cambridge Univerity Pre, pp Kumierczyk-Michulec, J., O. Krueger, and R. Mark (1999), Aerool influence on the ea-viewing wide-field-of-view enor band: Extinction meaurement in a marine ummer atmophere over the Baltic Sea, J. Geophy. Re, 104(D12), 14,293-14, Kumierczyk-Michulec, J. and R. Mark (2000), The influence of ea-alt aerool on the atmopheric extinction over the Baltic and the North Sea, J. Aerool Sci., 31(11), Kumierczyk-Michulec, J., M. Schulz, S. Ruellan, O. Krueger, E. Plate, R. Mark, G. de Leeuw, and H. Cachier (2001), Aerool compoition and related optical

5 propertie in the marine boundary layer over the Baltic Sea, J. Aerool Sci., 32(8), pp Kumierczyk-Michulec, J., G. de Leeuw, and C. Roble Gonzalez (2002), Empirical relationhip between aerool ma concentration and the Ångtrom parameter, Geophy. Re. Let., 29(7), doi: /2001gl