Retrieval of aerosol optical thickness over land surfaces from top-of-atmosphere radiance

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D9, 4260, doi: /2001jd002018, 2003 Retrieval of aerosol optical thickness over land surfaces from top-of-atmosphere radiance W. von Hoyningen-Huene, M. Freitag, and J. B. Burrows Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany Received 18 December 2001; revised 18 July 2002; accepted 2 January 2003; published 2 May [1] Aerosol remote sensing requires techniques enabling the determination of aerosol optical thickness (AOT) over land surfaces, because the most important sources (continental aerosols, anthropogenic aerosols, biomass burning, desert dust, volcano eruptions and others) are on continents. Here a retrieval method for the AOT over land surfaces from top-of-atmosphere (TOA) radiance using nadir looking instruments of the ocean color type (like Ocean Color and Temperature Sensor (OCTS), Sea viewing Wide Field Sensor (SeaWiFS), Moderate resolution Imaging Sensor (MODIS) or Medium Resolution Imaging Sensor (MERIS)) is presented. It is scheduled as an off-line procedure for the ENVISAT radiometers SCIAMACHY and MERIS. The method is based on lookup tables (LUT) between the AOT and the aerosol reflectance for wavelength <0.67 mm. The aerosol reflectance is obtained from TOA reflectance accounting for Rayleigh path reflectance and the apparent spectral surface reflectance. Over land the surface reflectance is estimated by a mixing model of bare soil and green vegetation spectra, tuned by the normalized differential vegetation index (NDVI) of the satellite scene. The method has been tested and validated with SeaWiFS data and with aerosol properties of the closure experiment LACE-98 (Lindenberg Aerosol Charactrization Experiment). For short wave channels ( mm) an agreement between the retrieved and ground-based data of 20% is achieved. Thus the method enables the investigation of AOT over land, yielding the regional turbidity situation as well as the identification of aerosol sources like large cities, large fire plumes, haze, small scale dynamical events and also thin cirrus clouds. INDEX TERMS: 0320 Atmospheric Composition and Structure: Cloud physics and chemistry; 0360 Atmospheric Composition and Structure: Transmission and scattering of radiation; 3359 Meteorology and Atmospheric Dynamics: Radiative processes; 3362 Meteorology and Atmospheric Dynamics: Stratosphere/troposphere interactions; KEYWORDS: atmospheric aerosol, optical thickness, satellite retrieval, land Citation: von Hoyningen-Huene, W., M. Freitag, and J. B. Burrows, Retrieval of aerosol optical thickness over land surfaces from top-of-atmosphere radiance, J. Geophys. Res., 108(D9), 4260, doi: /2001jd002018, Introduction [2] Aerosol exhibits high spatial and temporal variability in the atmosphere. This is caused by the different sources, the variability of its physico-chemical composition and its interaction with the humidity. Therefore aerosol investigation for climate research and environmental control require the identification of source regions, their strength and aerosol type, the determination of the variable turbidity state of regions and information on the transformation of aerosol types at long range transports of aerosols on regional and global scales. This can be retrieved from with space-borne measurements, if adequate methods are available, yielding also global data. [3] The present techniques for the retrieval of aerosol parameters from space borne measurements are mostly restricted to ocean surfaces using NIR channels. There the relatively low spectral surface reflectance of the ocean can Copyright 2003 by the American Geophysical Union /03/2001JD be neglected [cf. Husar et al., 1997; Moulin et al., 1997; Nakajima and Higurashi, 1997; Tanré etal., 1999; Deuzé et al., 1999; Goloub et al., 1999; Geogdzhayev et al., 2002]. Over land the absorbing aerosol index (AAI) and estimates of the optical depth in the UV from TOMS yield information about the existence of strongly absorbing aerosols over land and ocean [cf. J. R. Herman et al., 1997a, 1997b; Torres et al., 1998]. The transfer of the AAI to quantitative aerosol parameters as an aerosol optical thickness requires several model assumptions [cf. Torres et al., 1998, 2002]. [4] Over land the procedures for a nadir retrieval of the aerosol optical thickness fails in the NIR, because of the high surface reflectance and their high variability. However in the SW range (<0.67 m (except of snow and desert ground) the spectral surface reflectance has values in the range of 0.05 and lower, decreasing with shorter wavelength. Thus a retrieval of aerosol properties over land requires separation techniques to consider the variable contribution of the surface reflectance. [5] Such has been attempted for POLDER observation using polarization information [cf. M. Herman et al., 1997a, AAC 2-1

2 AAC 2-2 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS 1997b] and from ATSR-2 data by utilizing the dual-view technique [cf. Veefkind et al., 1999]. The multiviewing technique has been shown as a powerful tool in aerosol remote sensing and is extended by MISR to up to 9 viewing geometries for the same ground target. Recently also studies are made to use AVHRR data together with a detailed surface reflectance database [cf. Knapp and Stowe, 2002]. Also modern multichannel space-borne radiometers such as SeaWiFS, MODIS, MISR, MERIS, SCIAMACHY and AATSR provide sufficient spectral information in the short-wave range (<0.55 mm) to enable the separation of land surface and aerosol scattering also for nadir scanning instruments. A summary of the state in aerosol retrieval and the problems connected is given by Kaufman et al. [1997a, 1997b]. Finally Kaufman et al. [2000] concluded that polar orbiting satellites will be able to estimate the daily aerosol properties. [6] With the present study we present a retrieval technique using a separation technique between aerosol and land surface properties in the short-wave visible channels of the SeaWiFS radiometer ( (0.670) nm), cf. section 2. It is scheduled to be used as off-line procedure for the aerosol retrieval over Europe with the ENVISAT radiometers SCIAMACHY and MERIS. This technique is a special variant of the dark target method [cf. Kaufman et al., 1997a, 1997b] with a dynamical estimation of the surface reflectance. The land surface reflectance here is estimated by a linear mixing model of vegetation and nonvegetation spectra, tuned by the normalized differential vegetation index (NDVI, see equation (11)). By the elimination of surface reflectance and Rayleigh path radiance from TOA reflectance an aerosol reflectance can be defined, for which lookup tables (LUT) for the retrieval of the aerosol optical thickness (AOT) are generated using radiative transfer model (RTM) calculations. [7] The RTM requires accurate knowledge of the optical aerosol properties e.g. the spectral slope of the aerosol optical thickness, mainly of the aerosol phase function and the single scattering albedo etc. and the surface reflectance, cf. section 4. These input parameters for the RTM are taken from the LACE-98 experiment (Lindenberg Aerosol Characterization Experiment 1998 [Ansmann et al., 2002]). One goal of the LACE-98 experiment was to achieve closure between radiances and fluxes obtained from measurements made by ground-based, aircraft and satellite instrumentation. For this purpose SeaWiFS TOA-radiances, aircraft measurements of surface spectral reflectance and ground-based sky brightness and derived aerosol optical thickness have been used. Phase functions and single scattering albedo are obtained from ground-based Sun and sky radiometer measurements, applying the CIRATRA (Coupled Inversion Radiative Transfer) retrieval algorithm [von Hoyningen-Huene and Posse, 1997]. The spectral ground reflectance are taken from aircraft radiometer measurements with a CASI instrument [Olbert, 1998]. Thus the closure between ground based data and the satellite data is achieved only by experimental data, giving the input data for the RTM of the LUT. [8] The studies have resulted in the method presented below, which describes an approach to retrieve the spectral aerosol optical thickness over heterogeneous land surfaces. The focus of this contribution is on the results from LACE- 98 and its application, cf. section 5, but the method has also been shown to work successfully on INDOEX data [von Hoyningen-Huene et al., 2002] and for pollution episodes at the U.S. East Coast. 2. Method 2.1. Retrieval Approach for SeaWiFS Data [9] The method is described now in detail for the use of SeaWiFS data. SeaWiFS measures the upwelling or top of the atmosphere (TOA) radiance L(l) in 8 channels, and the solar extraterrestrial irradiance E 0 (l) at the following wavelengths, lambda: 0.412, 0.443, 0.490, 0.510, 0.555, 0.670, and mm. [10] The retrieval of aerosol optical thickness is based on lookup tables (LUT) describing relationship between the measured L(l) and the aerosol optical thickness d A (l). This requires an adequate set of LUT taking into account all factors which influence the radiative transfer in the atmosphere: i.e. solar elevation, illumination and observation geometry, Rayleigh scattering, surface reflectance for the different vegetation cover, the surface elevation with its surface pressure conditions, and finally the aerosol parameters: aerosol phase function, aerosol optical thickness etc. [11] The logical flow and mathematical manipulation of the data within the retrieval algorithm is described in Figure 1 and comprises the following steps: [12] 1. Normalization of the L(l) to the solar illumination conditions for each wavelength l to generate the TOAreflectance r TOA (l): r TOA ðlþ ¼ p LðlÞ E 0 ðlþ M 0 M 0 is the airmass factor for the solar elevation, E 0 (l) the extraterrestrial irradiance and L(l) the measured TOAradiance of the satellite sensor as provided by the level 1 data after running the calibration procedure. [13] 2. Subtraction of the Rayleigh path reflectance including multiple scattering from the TOA-reflectance for each illumination and observation geometry, yielding a corrected reflectance ^r(l). ð1þ ^rðlþ ¼ r TOA ðlþ r Ray ðl; q; p; M 0 ; M S Þ ð2þ r Ray (l, q, p, M 0, M S ) is the normalized Rayleigh path reflectance inclusive multiple scattering for the scattering angle q, the pressure p, the airmass factors for the illumi- Figure 1. (opposite) Scheme of the steps and the interaction used in the retrieval procedure for the aerosol optical thickness over land and ocean surfaces: 1. the calculation of the TOA reflectance (equation (1)), 2. the discrimination between land and ocean surface using TOA reflectance levels in channel 8 (0.870 mm), 3. determination of the Rayleigh path reflectance using a digital elevation model, 4. determination of the aerosol reflectance subtracting the surface reflectance by equation (12), 5. Application of the LUT of section 4. and the constraints in section 2.2.

3 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-3

4 AAC 2-4 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS nation M 0 and observation M S. Over land the surface elevation has to be taken into account by using a digital elevation model (DEM) and accommodating the field of view of the satellite sensor. The pressure at the elevation z (km) used for the determination of the Rayleigh path reflectance assuming a given pressure at the sea level p 0 is given by the parameterized barometric equation. " # 28:97 g 0:75 z pz ðþ¼p 0 exp 8:315 T Surf g 0:75 z Here is g the gravity acceleration (9.807 m/s 2 ) and z the surface height above sea level in km. [14] The contribution of the Rayleigh scattering increases with decreasing wavelength and requires a high accuracy in the short-wave channels. For low surface reflectance r Surf (l) 0.15, which are expected for surfaces fully or partly covered with vegetation in the short-wave region, the multiple scattering is a nonlinear function, which can be readily parameterized [Deepak et al., 1980]. For high surface reflectance, the Rayleigh path reflectance has to be calculated for the particular reflectance case. [15] 3. The separation of aerosol and surface scattering. The residual value of the corrected reflectance ^r(l) contains the combined effect of aerosol scattering and surface reflectance r Surf (l). To separate these parameters, constraints, i.e. additional information, are required and are described in the following section. Kaufman et al. [1997a, 1997b] use for the separation the restriction on dark targets, i.e. also vegetation surfaces. The targets will be identified by the NDVI. Then there empirical relations between the surface reflectance of a channel at 2.2 mm and that of the wavelength in the SW range are used. [16] However this information of the 2.2 mm channel is for SeaWiFS and other ocean color radiometers not available. The information on vegetation cover and the surface reflectance in the SW channels must be estimated on another way. In the present contribution the estimation of the spectral surface reflectance is made by a linear mixing model of a green vegetation and a bare soil spectrum, tuned by the NDVI as an indicator for the vegetation cover, cf. section 2.1, equation (11). This yields an apparent spectral surface reflectance for the single scene observed by the satellite sensor. Since the vegetation and the soil spectrum are characterized by their typical spectral decreases to shorter wavelength, the apparent decrease and the connected reflectance is obtained by the mixing of both types. [17] Considering further constraints (item 5) on the spectral smoothness of the AOT the separation can be made with success, yielding a wavelength dependent aerosol reflectance (refined reflectance). ð3þ ~rðlþ ¼^rðlÞ w o ðlþt R ðl; M S Þr Surf ðl; z O ; z S Þ ð4þ [18] t R (l, M S ) is the transmission for a Rayleigh atmosphere for the zenith distance z S of the the satellite from the observed ground target and z O - the zenith distance for the Sun, w o (l) - the single scattering albedo and r Surf (l, z O, z S ) - the surface reflectance for the illumination and observation geometry, given by r Surf (l, z O, z S )=r Surf (l) cos(z O ) cos(z S ). [19] These refinements made reduce the amount of LUT s, especially, if one defines accuracy slots for the retrieved aerosol optical thickness. All LUT s produced from this second refinement step can be parameterized well by polynomials of second order and starting at 0, suitable for a fast processing of the satellite data. [20] 4. Definition of input parameters for the calculation of the LUT. As mentioned, they were taken from experimental data from LACE-98, cf. section 4. The LUT s for the aerosol reflectance ~r(l) are calculated for a given phase function and the single scattering albedo. The largest influence on the aerosol reflectance is determined by the aerosol phase function, which should be appropriate for the aerosol type or types to be retrieved in a selected region. [21] 5. Finally the application of the constraints, cf. section 2.2 in an iterative procedure minimizes the RMSD (root mean square deviation) of the spectral aerosol optical thickness, which has a smooth spectral dependence according to the Angström power law. This procedure yields the aerosol optical thickness at several short-wave SeaWiFS channels and an estimation of the Angström turbidity parameters. As a by-product the spectral surface reflectance for the used short-wave channels is also obtained. The logical procedure, described above, yields the spectral AOT over land surfaces for the SW channels of SeaWiFS Constraints Used in the Retrieval of Aerosol Optical Thickness [22] As mentioned above, the separation of aerosol scattering and land surface reflectance requires additional information. The constraints used in this method are (1) to require that the wavelength dependence of the aerosol optical thickness is a smooth nonlinear function of wavelength defined by the Angström power law, (2) a weighting parameter to ensure the convergence during iteration, and (3) the linear mixing of the surface spectral reflectance from vegetation with that of the Earth. These are described in more detail as follows. [23] 1. The spectral aerosol optical thickness has a smooth spectral behavior following an Angström power law ~ d A (l) =b {l} a. b = d A (l =1.0mm), the turbidity coefficient and a the spectral slope. For this purpose, in contrary to most approaches [cf. Eck et al., 1999], here the Angström parameters a and b are calculated by the last square fit of the Angström power law with the retrievals for all used spectral channels, e.g. 6 channels over land and 8 channels over oceans. With this is and a ¼ P N i¼1 ln d A ðl i Þ ln d A ln li ln l h 2 i ð5þ ln l i ln l P N i¼1 b ¼ exp ln d A þ ln l a with N - the number of spectral channels used and the averaged logarithms of the spectral AOT and wavelength as ln d A ¼ 1 P N N i¼1 ln d Aðl i Þ and ln l ¼ 1 P N N i¼1 ln l i: This gives a much more stable estimation of the Angström parameters then a two wavelength estimation, especially, if one has ð6þ

5 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-5 errors in the first iterations for the AOT of the single channels. Since reference wavelength for the SeaWiFS retrieval is mm, the Angström power law is transformed to l a d A ðlþ ¼ d A ðl ¼ 0:412 mmþ0:412 mm : The main purpose of the use of the Angström power law is to ensure the smoothness of the AOT spectrum. In the first iteration step the surface reflectance obtained by equations (10) and (12) is used. In the opposite to the surface reflectance mostly the spectral AOT decreases with increasing wavelength. The Angström parameters are constrained as follows: [24] The spectral slope a is determined in the first iteration from the retrieved spectrum of the AOT. It is defined to lie within the limits 0.5 a 2.0. These boundaries are selected from extreme spectral conditions for the AOT, found in ground-based measurements. If the retrieved spectral slope it is outside this limit it is set to to the climatologic average of a = 1.3 This constraint corresponds to the range of the alpha obtained for the majority of atmospheric aerosol types from Sun photometer measurements [Holben et al., 2001] and models [cf. d Almeida et al., 1991]. [25] The smoothness is estimated from the RMSD determined from the individual estimates d A (l) and the value represented by the Angström power law for d A (l). RMSD ¼ 1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi d A ðlþ N 2 d A ðlþ ; ð7þ where N is the number of channels used. For insufficient smoothness of the spectral aerosol optical thickness, the spectral surface reflectance has to be modified iteratively. Convergence of solution is assumed to have been achieved when RMSD [26] 2. The weighting parameter to facilitate convergence. The aerosol scattering in the SeaWiFS channel 1 (0.412 mm) dominates over the land surface reflectance. In addition atmospheric transmission decreases with wavelength. Thus the retrieved aerosol optical thickness in this channel has lower relative errors from an inaccurate determination of the surface reflectance. This is expressed in a spectral weighting factor w(l) applied in the iterative modification of surface spectral reflectance. This factor damps oscillations of the surface spectral reflectance thereby fulfilling the smoothness criterion for the spectral aerosol optical thickness and facilitates a relatively rapid convergence. The weighting parameters have been determined empirically from the set of closure experiments ACE-2 and LACE-98, where all relevant radiative parameters needed have been measured. The criterion for the fixing of the weighting factors was the rapid convergence by the individual iteration steps to the known optical thickness from ground-based measurements during the closure experiments. It is fixed with a value of 0.15 for channel 1 at mm and increases to 0.30 for channel 5 at mm or channel 6 and is used in all cases up to now. For the i-th iteration step r Surf (l, i) is given by r Surf ;i ðlþ ¼ r Surf ;i 1 ðlþwðlþð1 i ðlþþ ð8þ w(l) is a weighting factor taking into account the spectral variability of the surface reflectance and i (l) is the relative deviation of the AOT from the smooth behavior, defined by the Angström power law term given by i ðlþ ¼ d AðlÞ d A ðlþ d A ðlþ [27] 3. Linear mixing of the vegetation and ground surface spectral reflectance. Both vegetation and ground surface spectral reflectance in the short-wave region (0.5 mm) decrease to shorter wavelength. For this study it is assumed that the apparent spectral surface reflectance over land in the satellite scene is composed from surface parts covered with vegetation and such with bare soil. Thus the apparent surface reflectance r Surf,i=0 (l) is given by a weighted mixing the actual reflection spectrum used from spectra of green vegetation and bare soil using the NDVI for the satellite scene. r Mixing Surf ;i¼0 ðlþ ¼ C Veg r Veg l ð Þþ 1 C Veg rsoil ðlþ ð9þ ð10þ [28] C Veg = NDVI for NDVI 0 is the estimation for the vegetation cover fraction. For positive NDVI the relationship above whereas for negative values C Veg is set to 0. This assumption is similar to that made by also van der Meer and de Jong [2000] for LANDSAT TM data. However there the fraction of different soil and vegetation types is there not derived from the NDVI. This mixing model transfers the large variability of the high surface reflectance in the NIR into the range of the shortwave channels (<0.67 mm) with its low surface reflectances and lower variability. The NIR channels and the SW channels are correlated over this mixing rule, similar as the approach of Kaufman et al. [1997a, 1997b], which is using empirical correlations between the SW channels and a channel of 2.2 mm. [29] The NDVI is derived for SeaWiFS from the TOAreflectance of the channels 8 (0.865 mm) and 6 (0.670 mm). NDVI ¼ ^r ð l 8Þ ^rðl 6 Þ ^rðl 8 Þþ^rðl 6 Þ ð11þ The surface spectra used for the mixing are presented in Figure 3. [30] To adapt the level of the surface reflectance to that required within the satellite scene a scaling factor is introduced. with r Surf ðlþ ¼ F r Mixing ð Þ Surf ;i¼0 l F ¼ rtoa Surf ð0:67mmþ ð0:67mmþ r Mixing Surf ð12þ ð13þ Here r TOA Surf (0.67 mm) is determined from TOA reflectance, subtracting Rayleigh path radiance and an estimation for the aerosol reflectance obtained for the channel 1 (0.412 mm) under the assumption of a black surface. The transfer of this estimation to the wavelength of 0.67 mm is made by the Angström power law assumption and an spectral slope of a = 1.0. The value from the mixing rule r Mixing Surf (0.67 mm) is taken from equation (10). This scaling factor contributes much to a stabilization of the solutions and reduces the

6 AAC 2-6 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Table 1. Average Surface Reflectance and Its Variability for Different Target Classes Derived From Aircraft Radiometer Measurements During LACE-98 lmm Object Class Average Surface Reflectance and Errors r Surf r Surf r Surf r Surf r Surf r Surf green vegetation bare soil regional variability over the land surfaces caused by different surface types in Europe. 3. Sensitivity and Error Analysis [31] For the application of the method described above it is of interest, how sensitive it is against disturbances of the not known or not correct estimated surface reflectance by the linear mixing model and what for errors result for the determination of the AOT. [32] For this reason the influence of the surface reflectance on the retrieval of the AOT is considered a) without (using TOA-reflectance directly) and b) with the refinements (using the aerosol reflectance) and a final error discussion is added, using data on experimental errors of the spectral surface reflectance. [33] At first error data for the surface reflectance, derived from the aircraft radiometer measurements will be presented. These are typical values, which are observed for 0.412, and mm. The estimation for the experimental errors r Surf we took from the variability of the reflectance within the different targets for the green vegetation and the bare soil classes of the aircraft radiometer data, presented in Table 1. They will be used in the further error discussion. [34] The following sensitivity investigations are made for the wavelength of the SeaWiFS channel 2 (0.412 mm), because this channel is used with the highest weight by the retrieval procedure over land. [35] In general LUT s describing the relationship between the TOA-radiance and the AOT are strongly dependent of the surface reflectance. An incorrect estimation would lead to the selection of wrong LUT, as it is demonstrated in Figure 2. For a given illumination and observation geometry by the satellite (solar illumination: zenith distance z o =37, a o = 194, satellite observation: zenith distance z S =25, azimuth a S = 149 ) in a simulation the surface reflectance r Surf is changed between 0 and [36] These different curves show, that a very accurate estimation of the surface reflectance is required, if the AOT should be estimated by LUT on the basis of the direct TOAradiance. In this case an error of 0.02 in the surface reflectance would lead to the selection of a wrong LUT resulting in an error of about 0.1 in the AOT. Since the mixing state is between the classes green vegetation and bare soil the expected error for the surface reflectance at mm is less than 0.01 and consequently the AOT can be derived with an accuracy of [37] If one is using, like in this procedure, the refinements by the calculation of the aerosol reflectance, a sensitivity and error analysis must consider two aspects: (1) the sensitivity of the LUT s describing the relationships between the aerosol reflectance and the AOT for the surface reflectance, and (2) the sensitivity of the determination of the aerosol reflectance from the TOA radiance. This is the main step correcting Rayleigh scattering and surface reflectance effects. [38] The sensitivity for the first aspect, the aerosol reflectance - AOT relationship, is calculated from aerosol reflectance with changes in the surface reflectance. Since in this step the surface reflectance of the correct value is subtracted, only small deviations remain. If the surface reflectance is changed ±0.02 the main relationship between aerosol reflectance and AOT remains, Figure 3 and the AOT can be obtained from this relationship. The error for the AOT is here 10% of the AOT. Also larger deviations let obtain the AOT with increasing errors. In this relation remains the error of AOT by choosing a wrong aerosol type. Consequently the main effect on the sensitivity comes from the second aspect. [39] For the second aspect the correction of the surface reflectance and the Rayleigh path radiance has to be considered. For this consideration we do not include surface elevation, so the Rayleigh path radiance for dark targets is a constant contribution for the same illumination-observation geometry. The variability comes only from the surface reflectance term: w o ðlþt R ðl; M S Þr Surf l ð Þcosðz O Þcosðz S Þ ð14þ [40] w o (l) - is the single scattering albedo, set here to 1.0, t R (l, M S ) - is the transmission of the Rayleigh atmosphere for the viewing geometry of the satellite (M S - air mass factor for the satellite zenith distance z S ), r Surf (l) -the surface reflectance self and z O and z S - the zenith distances for the Sun and the satellite. [41] Varying the surface reflectance around its correct value one obtains the changes in the aerosol reflectance, Figure 4. This effect depends on the illumination-observation geometry. For the discussion 3 cases of solar zenith distances z O and 2 cases for the satellite observation z S are presented: z O :30 - typical for midlatitude summer conditions, 60 - midlatitude winter conditions, 10 - lower latitude conditions, z S : 20 - smallest observation zenith distance of SeaWiFS in the center of the scan line, determined by the tilt angle used, 50 - largest value at the boarders of the scan line. Clearly one can see that longer atmospheric path s reduce the sensitivity of the aerosol reflectance against variations caused by the surface reflectance. Assuming here an error of 0.02 in the surface reflectance the error in the AOT depends on the zenith distances of the Sun and the satellite: It is <0.15 for low latitudes (with zenith distances of 10 ), <0.1 for summer values of the midlatitudes (with zenith distances of 30 ) and <0.07 for higher latitudes or winter conditions of the midlatitudes (with zenith distances of 60 ). These errors still decrease with larger scan angles of the satellite radiometer (SeaWiFS covers in one scan line a range of zenith angles of ). If one takes the expected error from the mixing model for the surface reflectance of 0.01, then the errors above are reduced to 0.07, 0.05 an 0.04 respectively. [42] This sensitivity study also shows, that the results of the retrieval can be improved, if an estimation of the surface reflectance can be used, which is close to the true value. For this reason the scaling factor (equation (13)) is introduced into the procedure, which stabilized the obtained

7 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-7 Figure 2. LUT s for the relationship between TOA-radiance and AOT (for mm) of a given geometry (z O =37, a O = 194, z S =25, a S = 149 ) and changed surface reflectance. The different lines give surface reflectance steps of The reference was r Surf (l = mm) = An incorrect estimation of the surface reflectance has large influence on the retrieved AOT. solutions much and keeps the reflectance near the true value of the scene and in the range of low deviations around r Surf = 0. Thus we expect that the real errors on AOT caused by the surface reflectance are smaller than the given values. The given errors above are good estimations for the maximum error for the AOT at the wavelength of mm. These errors will increase with increasing wavelength, because the surface reflectance and its variablity is rising. [43] Further the application of the procedure with Sea- WiFS data showed, that over land surfaces the consideration of the cosine s of the surface illumination and observation is sufficient to cover zenith distances from 50 to 20 for observation and 30 to 70 for the solar illumination without accounting effects of a bi-directional surface reflectance. 4. Setup of Lookup Tables (LUT) [44] In this study, RTM calculations are performed to establish the LUT of aerosol reflectance - AOT relation.

8 AAC 2-8 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Figure 3. LUT s for the relationship between aerosol reflectance and AOT (for mm) of the same geometry as Figure 2 and changed surface reflectance. The different lines give surface reflectance steps of The reference was r Surf (l = mm) = This is made, using the radiative transfer code from Nakajima and Tanaka [1988], for the solar illumination and satellite observation geometry within the scene. Equal values have been obtained using other RTM, like GOME- TRAN or SCIATRAN [Rozanov et al., 1997, 2002]. Aerosol reflectance is strongly dependent on illumination and viewing geometry. Since the RTM includes multiple scattering, its effects are considered in the LUT. [45] The setup of LUT requires input parameters for the RTM. These are the aerosol optical thickness, the phase function and the single scattering albedo of the aerosol and the spectral surface reflectance. These parameters were determined from the ground-based measurements in LACE-98. [46] LACE-98 took place from 13th of July until the 13th of August 1998 in Lindenberg (52.22 N, E) Germany at the Meteorological Observatory of the German Weather Service (DWD) [cf. Ansmann et al., 2002]. This is a rural continental site typical for northern middle Europe Ground-Based Data [47] During LACE-98 combined radiation measurements of direct spectral solar radiation and sky radiance in the almucantar were performed. Since the required aerosol

9 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-9 Figure 4. Sensitivity of the aerosol reflectance for variations of the surface reflectance (for mm) for three solar zenith distances z O :30, 60, 10 and two satellite zenith distances z S :20, 50. parameters for the RTM are not directly measurable, they have to be inverted by the CIRATRA approach (Coupled Inversion RAdiation TRAnsfer) [von Hoyningen-Huene and Posse, 1997]. CIRATRA comprises the following: (1) an inversion of an optical equivalent aerosol size distribution from the measured spectral aerosol optical thickness and the angular aureole brightness distribution (part of the skybrightness function for scattering angles q 10 ), (2) the calculation of the aerosol phase function using a light scattering theory (Mie-theory for spherical particles, semiempirical scattering theory of Pollack and Cuzzi [1980] for nonspherical particles), and (3) radiative transfer calculations to obtain a calculated sky-brightness function to be compared with that measured. [48] Real part of refractive index is varied from 1.31 to 1.7 until a minimum RMSD (0.05) between the measured and the calculated sky-brightness function is obtained. The range of refractive indices varied represents realistic limits for tropospheric aerosol. [49] Initially Mie theory calculations of the phase function are used. If convergence is not obtained, the semiempirical theory of Pollack and Cuzzi [1980] is used to determine the phase function for nonspherical aerosols with an increased lateral scattering.

10 AAC 2-10 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Figure 5. Aerosol phase functions p(q) for the wavelength 0.87 mm, derived by the CIRATRA approach from skybrightness measurements during LACE-98. Clearly visible is the increased lateral scattering effect for the time between 8 and 10. August [50] CIRATRA requires the spectral aerosol optical thickness in the wavelength range of mm, and the skybrightness function (the normalized skyradiance in the almucantar) in the range of scattering angles between 3 and These can only be provided by the ground-based measurements, if totally cloud-free conditions are available and the solar elevation do not exceed over 30 - e.g. in the morning and evening hours, not directly during the over flight Aerosol Phase Function [51] During LACE-98 for all cloud-free sections of the morning and evening hours (h 0 30 ) angular sky-brightness measurements in the almucantar for the wavelength of 0.56 and 0.87 mm are performed together with measurements of the spectral aerosol optical thickness in the wavelength range of mm. ASP radiometers of the University of Bremen have been used for this tasks. The results are shown in Figure 5 and can be divided into two distinct periods: [52] During the period of 13 July to 6 August 1998 the Mie theory yields a phase function describing the measured sky-brightness function. The Angström parameter, characterizing the spectral slope of the spectral aerosol optical thickness was 1.2 a 1.7. [53] During the period of 8 10 August 1998 the phase function describing the observed sky-brightness requires the use of the semiempirical theory of Pollack and Cuzzi [1980] for nonspherical particles. The difference in the phase function is seen in Figure 5. In this case Mie-theory leads to a strong overestimation of the aerosol optical thickness by the retrieval from satellite data and no closure by Mietheory between the radiances could be achieved. The spectral aerosol optical thickness had a flatter slope and the Angström parameters were 0.2 a Single Scattering Albedo [54] The second parameter of interest and significance for an aerosol retrieval from TOA-radiances is the single scattering albedo. The slope of the comparison between measured flux and the calculated flux assuming a single scattering albedo of 1.0, is used to estimate the single scattering albedo. The method is described by von Hoyningen-Huene et al. [1996] and von Hoyningen-Huene et al. [1999]. This approach can be applied to broadband flux measurements, measured by CM-11 radiometers for total (global) flux and diffuse flux separately, as well as to spectral global fluxes, measured with a SP1A-spectral radiometer (Schulz & Partner). [55] The broadband values for the single scattering albedo w 0 varied within the period of the LACE-98 experiment between , see Table 2. The same level we find in the spectral single scattering albedo, cf. Figure 6, for the wavelength region mm, except within the s-r-tband of water vapor. For wavelength below 0.4 mm we find a decrease of w 0 (l) with decreasing wavelength, indicating

11 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-11 Table 2. Results of the Broadband Single Scattering Albedo From Total (Global) and Diffuse Radiation Measurements in Comparison With the Total and Diffuse Flux Calculations Without and With Consideration of Multiple Scattering (MS) Using the Phase Functions and the Aerosol Optical Thickness Measured Date N Global Radiation Single Scatt. Albedo Diffuse Radiation Without MS Diffuse Radiation With MS Average 0.94 ± ± ± 0.03 an increased aerosol absorption towards to the UV, cf. Figure 6. [56] As the single scattering albedo for the channels of SeaWiFS (all > 0.4 mm) is w 0 (l) > 0.9 then for this study it produces minor errors and in the LUT calculation could be neglected. It is planned for the analysis of SCIAMACHY data, which are extended down to mm, to take this into account as more UV measurements will be made Surface Reflectance [57] The spectral surface reflectance has been measured by a CASI radiometer (Compact Airborne Spectrographic Imager) [cf. Olbert, 1998] on board a Cesna-aircraft by the Institute of Space Sciences of the Free University of Berlin in a wavelength range of mm. These measurements have been classified into different subgroups of Figure 6. Spectral aerosol single scattering albedo w 0 (l), derived by spectral flux correlation during LACE-98. Clearly visible is the decrease of w 0 (l) below 0.4 mm wavelength. The region mm (except the water vapor absorption in the s-r-t- band) is comparable with the results of the broadband values.

12 AAC 2-12 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Figure 7. Used reflectance spectra, composed from CASI-radiometer measurements and extensions, taken from the CAMELEO database. For water surfaces of the Baltic Sea data from measurements of the HIRES radiometer (DLR, Institute of Space Sensor Techniques) have been used. The error bars give the standard deviation of the classified measurements of a spatial resolution of 0.2 m 0.2 m and are not representative for the kilometer-scale. surfaces e.g. lakes, forests, green vegetated fields, meadows, bare soils, yellow ripe corn. Since the spatial resolution of the CASI radiometer for the flight level is about 0.2 m 0.2 m the measurements have large standard deviations and area-averages have to be used for a satellite comparison with the SeaWiFS resolution in a kilometerscale. Averaged spectra of the main surface classes from these measurements are adequate for this scale and are presented in Figure 7. Since in a satellite scene, observed by SeaWiFS, these surfaces are not observed separately and the pixel contains a mixture of different surface types, an effective reflectance spectrum for each scene is observed. This comprises the main surface classes, and is described by the parameter r Surf, i=0 (l) in equation (10). [58] A second problem, connected with these measurements, was, that the spectral region had to be extrapolated to the wavelength of channel 1 (0.412 mm), because the CASI instrument only measured in the spectral region mm during the LACE-98 experiment. The most important region of mm, which is required for the SeaWiFS channels 1 6 is only partly observed. Here spectra of vegetation and soil measurements of the CAM- ELEO project (Changes in Arid Mediterranean Ecosystems on the Long term and Earth Observation) [Escadafal and Bohbot, 1999] are used, with a spectral extension from mm. These measurements give an exponential decrease with decreasing wavelength into the UV, continued until 0.37 mm, as well as for vegetation as for bare soil conditions. This behavior is scaled with the CASI measurements in the spectral range, where both data are available and used to extrapolate the CASI measurements from the LACE-98 experiment to mm. These extrapolated spectra averaged for a green vegetation and a typical bare soil for the LACE-98 experiment serve as the basis spectra for the reflectance spectrum, cf. Figure 7. [59] For the water surface of the Baltic Sea measurements made by the HiRES-ES radiometer (High Resolution Spectrometer [cf. Zimmermann, 1998]) of the DLR, Institute of

13 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-13 Space Sensor Technique, Berlin have been used. Changes in the water quality can be considered by mixing this spectrum with a spectrum of clean ocean water. However a simple index, comparable to the NDVI, was not available, so the spectrum as shown in Figure 4 is used over sea water (Baltic Sea, Mediterranean Sea) adding a surface reflectance term by the Fresnel reflection of a wave surface. The surface spectra used as basic spectra for the aerosol retrieval are presented in Figure Lookup Tables [60] Assuming a green vegetation as surface, radiative transfer calculations have been performed to obtain the aerosol reflectance. The phase functions from the groundbased measurements have been used. The single scattering albedo of about w 0 > 0.9 has been neglected, since during the LACE-98 experiment it was near 1.0 and has no significant spectral feature (compare Table 1 and Figure 6). The radiative transfer calculations for the aerosol reflectance have been made, varying the aerosol optical thickness d A (l) from 0 to 0.6, typical for Germany. The possibility to misinterpret effects of subpixel clouds as aerosol effects increases with higher d A (l). [61] This radiative transfer calculations yield relationships between the aerosol optical thickness and the TOA reflectance for each channel of SeaWiFS. As one example TOA reflectance determined for the 10th August 1998 are presented in Figure 8 - a day exhibiting increased lateral scattering in the phase function, cf. Figure 5. The starting point of the individual curve of TOA-reflectance r TOA (l)for d A (l) = 0 is determined by the surface reflectance and the contribution of the Rayleigh scattering. [62] The concept for the retrieval used in this study originates from the parallel behavior of the AOT versus TOA reflectance for the different wavelength channels of SeaWiFS as shown in Figure 8. Subtraction of the surface reflectance and Rayleigh scattering yields the LUT or plot of AOT versus aerosol reflectance, shown in Figure 9. These curves start with 0 for d A (l) = 0, and are very similar in shape for all the spectral channels considered. The main variability in the curves comes from the different aerosol phase functions. The LUT s in Figure 9 present one day from the first period where Mie theory could be applied (Day 202, 21th July 98). The two other curves LUT represent days of the second period of the experiment, where an increased lateral scattering have been observed and the application of the semiempirical scattering theory of Pollack and Cuzzi [1980] (Day 221 and 222, 09th and 10th August 1998 have been required. In Figure 9 for two points of one curve the maximal errors of the error analysis in section 3 are inserted as error bars. [63] Combining the errors in both directions one obtains for the retrieved aerosol optical thickness a relative error of about 20%. This error is similar to the difference between satellite retrieved aerosol optical thickness and groundbased data, cf. Figure 10. [64] For water surfaces LUT s are calculated in the same manner. Here the water spectrum, shown in Figure 7, is used. The differences in the aerosol reflectance ~r(l) are negligible and merge within the curves from channels 1 5 of Figure 9. [65] Comparing the LUT s for different days, slight changes in the slope, produced by the different phase Figure 8. Lookup tables for the relationships between TOA-reflectance r TOA (l) and aerosol optical thickness using ground-based data from the 10. August functions, are observed. If phase functions are used, obtained with the semiempirical scattering theory of Pollack and Cuzzi [1980] the slope decreases compared with the application of the Mie theory. For such cases, Mie theory will overestimate the aerosol optical thickness depending on the degree of nonspherical particle scattering, expressed in the parameters of the theory of Pollack and Cuzzi [1980]. 5. Retrieval Results 5.1. Application Retrieval Method [66] The method, described in section 2, is part of a aerosol retrieval procedure for nadir scanning multiwavelength radiometers SCIAMACHY and MERIS on ENVI- SAT developed by the IUP of the University of Bremen, called BAER (Bremen AErosol Retrieval). For its test SeaWiFS local area cover (LAC) and global area cover (GAC) data are used. The procedure has two parts: the land part, subject of this paper, and the ocean part. [67] A threshold criterion is used to discriminate between ocean, land and clouds both using the TOA-reflectance in channel 8: a) sea surface: r TOA (l) < 0.08, b) land surface: 0.20 > r TOA (l) 0.08 and c) clouds: 0.2 < r TOA (l). The upper threshold for clouds is derived from the minimum cloud reflectance by Kokhanovsky [2001]. This threshold for the discrimination of clouds may be subject of change, especially if subpixel clouds and broken cloud fields occur and the scale of the pixel is changing from LAC to GAC data. Also for the higher aerosol optical thickness during INDOEX or for desert dust outbreaks from Africa this threshold had to be increased. [68] The land retrievals use the channels 1 6 of SeaWiFS ( mm) for the determination of the aerosol

14 AAC 2-14 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Figure 9. Lookup tables for the relationships between the aerosol reflectance (refined reflectance) ~r (l) and aerosol optical thickness using ground-based data from three days (202, ; 221, and 222, ) of the LACE-98 experiment. The filled marker represent the wavelength mm and the open markers mm. optical thickness. The channels 6 (0.670 mm) and 8 (0.865 mm) have been used for the determination of the NDVI and the mixing state of the vegetation - bare soil ratio for the initial surface reflectance. Because of the decreasing reflectance with decreasing wavelength, the channel 1 (0.412 mm) gives the best results for the aerosol optical thickness. This is working well over snow-free regions in different regions, like the whole of Europe, India, the eastern part of the United States, and Australia. [69] For desert conditions, if low NDVI exist and the effect of the bare soil with its higher reflectance become valid, modifications of the soil spectrum are planned using the redness index to get a better characterization of different soil types. For regions like central Europe, India, the eastern part of the United States, and Australia, such modifications have not been required, because the NDVI was high enough to cover the main part of the soil spectrum. However over desert surfaces with its higher reflectance or in winter time, if snow cover exist wrong AOT results will be obtained by this approach. [70] The ocean part uses all channels (1 8). However the best channels with low disturbance by the sea water reflectance are the NIR channels 7 and 8. Also the water reflectance will be modified to obtain a smooth spectral aerosol optical thickness in the same manner as the land surface spectrum. Here the initial reflectance spectrum is fixed by the water spectrum from Figure 7. [71] In both cases the aerosol reflectance is calculated to apply the LUT s. The overview of the whole procedure is presented with the method in Figure Test and Validation [72] The test and the validation is made mainly in Central Europe during the LACE-98 experiment. Additionally the

15 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-15 Figure 10. Intercomparison of retrieved aerosol optical thickness from SeaWiFS L1-radiances with ground-based measurements for the over-flight times during LACE-98. The grey solid line is the 1:1 relation, the dashed grey lines give the 20% borders, the thick dashed lines gives the linear fits through the data and the origin for the channels 1 (0.412 mm) and channel 5 (0.555 mm). method is applied meanwhile in different regions of the world. Thus also a period of pollution outbreaks from the East Coast of the United States in August 2001 is used for validation with AERONET data. [73] During the LACE-98 experiment five over-flights ( , , , and ) could be used for a complete closure and the test of the procedure and the intercomparison with ground-based measurements of the aerosol optical thickness. The others have been contaminated by clouds, so that no ground-based data in Lindenberg (52.21 N, E) and Kühlungsborn (54.11 N, E) with the spectral aerosol optical thickness and the angular sky brightness have been available. Additional validation data (only optical thickness) are obtained for more over-flights from Lindenberg, Kühlungsborn, Hohenpeienberg (47.80 N,11.02 E), Ispra (45.48 N, 8.37 E) and Venice (45.31 N, E), however the LUT have been taken from neighbor days, because no phase function could be determined. The coincidence of the retrieved aerosol optical thickness with ground-based data for two different channels one can see in Figure 10. The data points of the comparisons are mostly within the 20% error level (dashed lines) and show a sufficient correlation. The linear fits for the data and the origin gives d A Sat (0.412 mm) =

16 AAC 2-16 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Figure 11. Intercomparison of retrieved aerosol optical thickness from SeaWiFS L1-radiances with ground-based AERONET measurements at the east side of the United States and the over-flight times in the period 5 10 August The highest values with the large error bars may result from thin cloud coverage d A Ground (0.412 mm) for channel 1 and d A Sat (0.555 mm) = d A Ground (0.555 mm) for channel 5 with a RMSD of and respectively. [74] Another satellite ground intercomparison is made in August 2001 with data retrieved over the East Coast of the United States, using the mean LUT of the LACE-98 experiment. The period of 5 10 August 2001 has been characterized by pollution outbreaks from the United States to the Atlantic. Since in this region exist several AERONET instruments, each over-flight gave some validation points. This independent validation for channel 1 is presented in Figure 11. The linear fits for the data and the origin gives d A Sat (0.412 mm) = d A Ground (0.412 mm) with a RMSD of [75] In both cases this is a slight underestimation of the AOT by the satellite retrieval, especially for larger AOT. One reason for this could be, that the single scattering albedo is not considered in these retrievals. The consideration of the single scattering albedo corrects the results for higher AOT in the right direction, however for lower AOT too, where no correction would be needed. Differences between higher and lower AOT could have different reasons: 1. the changes could be caused by changes of the aerosol type, which is only determined at the site Lindenberg, 2. different mixing layer heights lead to a different influence of the single scattering albedo as it is discussed by Gordon [1997]. [76] The scene of the 10. August 1998 as one golden day of LACE-98 is presented as example for the regional change in the aerosol parameters, cf. Figure 13, showing the aerosol optical thickness for l = mm from North to the South of Germany. The color scale is focused on the linear range of an aerosol optical thickness of , black areas are pixels containing clouds. The scene is with the exception of the Alps and its North-East edge totally cloud free and the different land surface features such as mountains, forests, lakes etc. could be observed well in the radiance data. In this scene four ground-based sites (Kühlungsborn, Lindenberg, Hohenpeienberg and Ispra) could be used for the comparison. [77] The meteorological situation of the 10th August is given by a high pressure situation over the North Sea and N- Germany and most regions have been free of clouds. The Northern part of the scene was under clean marine air influence coming from North over Denmark forced by a low pressure system over the Balticum. The South-West is under the far-influence of a occluding low-pressure system over France and the Mediterranean leading to an increase of the air humidity and turbidity, cf. Figure 13. The increase in the turbidity in the scene follows approximately with the increase of the observed surface temperatures and lowering of the air pressure. [78] After running the procedure, the land surface features do not appear in the AOT within the error of the

17 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS AAC 2-17 Figure 12. Aerosol optical thickness for the wavelength mm (SeaWiFS channel 1) for the 9. August 1998, 12:27 UTC during the LACE-98 experiment in Germany. The meteorological overlay gives the 1020 hpa isobare and a weak cold front line from the DWD analysis for 12:00 UTC. technique. The method yields the aerosol reflectance free from errors resulting from changes in elevation and vegetation cover across the scene. The obtained aerosol optical thickness shows low turbidity in the North part of Germany with , as has been measured also during the day at Lindenberg (52.21 N, E) and Kühlungsborn (54.11 N, E). Further one can see an increased turbidity toward to the South of about in the aerosol optical thickness for l = mm. This increased turbidity was observed at the Meteorologisches Observatorium Hohenpeienberg (47.80 N,11.02 E) of the German Weather Service DWD (Deutscher Wetterdienst) and by the AERONET-instrument operated in Ispra (45.48 N, 8.37 E). The increase of the turbidity from N to SW is connected with a slight decrease of the air pressure, a temperature increase and a change in wind direction from indifferent directions in the North to south-easterly winds in the South. The meteorological record gives a dry warm front, plotted in Figure 13. It could be the change in the aerosol type from marine influence with flat spectral slopes to a more continental one with increased spectral slopes. As is shown in Figure 10 reasonable agreement is obtained between ground-based and satellite determination of AOT. [79] On a second view increased aerosol optical thickness can be found as plumes over large cities, as Berlin (52.5 N, 13.5 E), Munich (48.0 N, 11.5 E), Prague (50.0 N, 14.5 E) and Milan (45.5 N, 9.0 E) with differences of about Clearly also aerosol sources as large power plants and industrial regions can be recognized, especially in eastern Germany (Saxony) and in Silesia in Poland or the Po-valley in Italy. Also effects produced by the boundary layer dynamic can be seen in the aerosol optical thickness. Unless wide parts of the scene have been cloud-free, cloud-like structure are visible in the aerosol, indicating that the aerosol is involved in convection and other thermodynamic processes. So with the LAC (local area cover) resolution ( km for nadir observa-

18 AAC 2-18 VON HOYNINGEN-HUENE ET AL.: RETRIEVAL OF AEROSOL OPTICAL THICKNESS Figure 13. Aerosol optical thickness for the wavelength mm (SeaWiFS channel 1) for the 10. August 1998, 11:33 UTC during the LACE-98 experiment in Germany. The meteorological overlay is extracted from the DWD analysis for 12:00 UTC. tion) of SeaWiFS some wave structure and the drying of the aerosol by the descent into the Eger-valley in Northern Bohemia can be seen. [80] Interestingly in other scenes significant structures are observed. For example on the 9th of August 1998, Figure 12, Figure 13 the local structures of linear gravity waves or parallel aircraft contrails having an optical thickness of 0.05 are observed. This indicates the existence of thin Cirrus clouds. Also large cities like Berlin, Paris (48.7 N, 2.5 E), Munich or Milan can be recognized by increased AOT in this scene. 6. Conclusion [81] A retrieval method is described and tested using observations obtained during the closure experiment LACE-98. This retrieves the aerosol optical thickness over land surfaces having a mixture of vegetation and soil,