- Email: [email protected]
A preliminary study on the correlation between TOMS aerosol index and ground-based measured aerosol optical depth
Atmospheric Environment 35 (2001) 5093–5098
A preliminary study on the correlation between TOMS aerosol index and ground-based measured aerosol optical depth Francesco Espositoa, Giulia Paveseb,*, Carmine Serioa a
INFM Unita" di Napoli, Gruppo collegato di Potenza, Contrada Macchia Romana, 85100 – Potenza (I), Italy b Istituto di Metodologie Avanzate di Analisi Ambientale, CNR, c.da S.Loja 850, Tito Scalo (PZ), Italy Received 15 September 2000; received in revised form 20 January 2001; accepted 24 April 2001
Abstract The aim of this work is to study the correlation between ground-based measured aerosol optical depth (AOD) and TOMS Aerosol Index. For this reason, two AOD data-sets have been analysed. The first set of measurements has been obtained in a desert plateau in Namibia during July 1998, while the second one has been collected in Tito Scalo (Italy), a very small industrial zone surrounded by a large rural area, in June–July 2000. The AOD has been computed in the spectral range 400–870 nm with a resolution of 3 nm by measuring the direct solar irradiance. The used spectroradiometer is an Optical Spectrum Analyser, equipped with a continuously rotating diffraction grating. Successively, a correlation between the Earth Probe TOMS Aerosol Index, whose definition uses backscattered radiances at 331 and 360 nm, and the AOD in the visible range was searched for. A satisfying correlation was found, whose Pearson correlation coefficient R2 values range from 0.64 to 0.91. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Spectrophotoradiometer; Desert aerosol; Anthropogenic aerosol
1. Introduction Nowadays, it is well known that the aerosol particulate, both stratospheric and tropospheric, plays a central role in the Earth-atmosphere system. Actually, the scattering and the absorption of solar and thermal radiation affect the Earth radiative budget in a way depending on the aerosol intimate structure and composition. Obviously, the aerosol’s time evolution and residence in the atmosphere are also important variables. Undoubtedly, satellites constitute one of the most useful ways of monitoring this atmospheric component all along the whole planet. In order to have a better comprehension of the satellite data, it is useful to have ancillary data, such as ground-based *Corresponding author. Tel.: +39-0971-427240; fax +390971-427271. E-mail address: [email protected] (G. Pavese).
measurements of the aerosol optical depth (AOD), and study a possible correlation between them. This correlation allows a validation of the satellite-retrieved data. Moreover, as shown by Hsu et al. (JGR 1999), this relationship represents a powerful tool because, for regions with atmospheric aerosol having homogeneous characteristic over a wide area, it is possible to estimate the AOD, starting from aerosol index (AI) values. In this work, two data series of AOD, obtained by measuring the solar direct radiation with a spectrophotoradiometer during two measurement campaigns, are analysed. The first campaign was held in a desert area of Namibia (Africa) from 7 July to 10 July 1998, while the second was performed during June–July, 2000 in a very small industrial place, surrounded by a rural area located in southern Italy. Once the AOD was obtained, a possible correlation between them and the corresponding, satellite-derived, TOMS AI was searched.
1352-2310/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 3 2 3 - 5
5094
F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
2. Ground-based measurements procedure An Optical Spectrum Analyser, based on a scanning monochromator with a diffraction grating continuously rotating at 50 revolutions per second, is the core of our experimental equipment. This apparatus allows us to measure, in cloud-free conditions, the direct solar irradiance as a function of the wavelength, IðlÞ; in the spectral range 400–870 nm with a resolution of 3 nm. The radiation collecting system is a lens with a 24 cm focal length and a field of view of about 11, assuring us that the signal is not affected by any diffuse radiation. We reduce the random noise on our data recording each measurement by averaging it over 64 spectra. The wellknown Lambert–Beer–Bouguer law (Paltridge and Platt, 1976) describes our physical situation with a good approximation: Il ¼ I0l etl mr ;
ð1Þ
where I0 ðlÞ is the monochromatic extra-atmospheric solar radiation, mr is the relative air-mass and tl is the total atmospheric optical depth. The application of the usual Langley-plot procedure allows us to estimate I0 ðlÞ and thus tl : From this, after successive computations, we obtain the AOD in the visible spectral range. This AOD includes the Chappuis ozone absorption band, but it excludes all the other selective absorption spectral regions due to gases, where we cannot apply the described procedure. More details on our AOD calculation can be found in Amato et al. (1996).
3. Earth-probe TOMS data description The total ozone mapping spectrometers (TOMS) are instruments working since 1978 on different platforms, as Nimbus 7 (1978), Meteor 3 (1991), ADEOS (1996) and Earth Probe (1996). The data used in this work come from Earth Probe platform and they were taken from the World Wide Web. TOMS data are uniformly gridded level 3 data products (longitude resolution 1.251, latitude resolution 11). These instruments were launched with the purpose of monitoring the ozone content by studying the backscattered radiances in the UV region. Besides, these radiances contain information on both scattering and absorption aerosol properties. An AI for Earth Probe TOMS is defined as in the following, Hsu et al. (1999) AI ¼ 100½log10 ðI331 =I360 Þmeas log10 ðI331 =I360 Þcalc : ð2Þ Here the subscript meas indicates the back-scattered radiance at a fixed wavelength and the subscript calc indicates the radiance calculated with a radiative transfer model describing a pure Rayleigh atmosphere.
According to relation (3), the AI assumes positive values in the presence of absorbing aerosols and negative values when the aerosols are non-absorbing. Nevertheless, as shown by Torres et al. (1998) in an analysis of simulated data, if a weakly absorbing particles layer is not far from the Earth’s surface, the aerosol scattering properties can dominate the absorption characteristics, giving negative values for the AI, as if the particulate matter were non-absorbing.
4. Ground-based measurements analysis The analysed AOD data-set were obtained during two measurements campaign, performed in two very different places: the first data were collected from 7 July to 10 July 1998 in the Gambsberg area, Namibia (16.051E, 23.201S), situated at 1800 m a.s.l. This site is a plateau characterised by scattered vegetation on a gravel surface and it is also very far from the major roads. The second data-set was recorded from 21 June 2000 to 6 July 2000 in Tito Scalo, Italy (15.731E, 40.61N, 800 m a.s.l.), a place characterised by small factories and surrounded by a large rural area. Solar irradiance spectra were collected throughout the day, the Namibian one with a time resolution of about 15 min, while the Italian one having a time resolution of about 5 min. As an example, in Fig. 1 the spectral dependence of the AOD for both sites is shown. From this figure it is clear that desert aerosol optical thickness is smaller than the industrial one. Some further information on the atmospheric particulate matter can be inferred from the estimation of the well-known Angstrom parameters a and b: tal ¼ bðl=l0 Þa ;
ð3Þ
where tal is the monochromatic aerosol’s optical thickness, l the wavelength in mm and l0 ¼ 1 mm. Usually, as in Hsu et al. (1999), a and b are calculated by means of the AOD ratio method, firstly developed by Volz (1987), which uses different couples of wavelengths at a time. In our case, we have the values of tal over a wide spectral range and this allows us to apply a best-fit procedure, provided that we have the log–log calculation of relation (4), as in Cuomo et al. (1993). This simple procedure points out a first, rough difference between the two kinds of particulate matter we are dealing with: the desert aerosols are characterised by smaller dimensions and lower concentration than the rural ones. In fact, in the first case we have a mean value of the a parameter, aave ¼ 2:111; while in the second case we have aave ¼ 0:8945 and the corresponding values for b are bave ¼ 0:028 and bave ¼ 0:172; respectively.
F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
Fig. 1. The AOD as a wavelength function for a day of measurements in Gambsberg and the other in Tito Scalo. The continuous line is the data fit.
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Fig. 2. Gambsberg areaFin the upper figure is shown the correlation between t (550) and t (480), and in the lower figure the correlation between t (550) and t (620).
the linear relation: 5. 5 correlation between TOMS AI and ground-based AOD
tal1 ¼ ktal2
In order to study a correlation between the TOMS AI and the ground-based measured aerosol optical thickness, it would be better to use tal values corresponding to l as near as possible to the AI wavelengths that are, in this case, l0 ¼ 331 nm and l00 ¼ 360 nm. For the Namibia case study, the lower wavelength bound for the ground-based observations was 400 nm, while for the Tito Scalo case it was 460 nm. Moreover, the relative error associated to these first data is very high (about 40%), due to a lower Signal to Noise Ratio in the first part of the spectrum. This error is 10% for l ¼ 480 nm but less than 4% for wavelengths greater than 500 nm. Although our AOD is obtained for wavelengths that differ from those of the AI, we expect to find a correlation between these quantities because AODs measured at different wavelengths are correlated with each other. This suggests that a correlation between AI and AOD in the UV range could imply a correlation with AOD measured in the visible range. Recalling Eq. (4) and fixing two wavelengths l1 and l2, the corresponding AOD tal1 and tal2 are linked by
with k ¼ ðl1 =l2 Þa : Experimental studies (Korotaev et al., 1993) showed that the nearer l1 and l2 are, the higher will be the correlation between tal1 and tal2 ; while low systematic errors on tal will lead the intercept of the line regression close to zero. We verified the existence of the correlation between AODs measured at different wavelengths for all days of our two data-sets. As an example, the data obtained on 7 July 1998 (Gambsberg) and 21 June 2000 (Tito Scalo) are reported in Figs. 2 and 3 where the couples of wavelengths used for the correlation are, l1 ¼ 480 nm l2 ¼ 550 nm and l1 ¼ 620 nm l2 ¼ 550 nm, respectively. The correlation coefficient values of the AOD measured at two different wavelengths are in general satisfying, except for some days of measurements in Tito Scalo, where we found values of R2 ranging from 0.98 to 0.67. This is caused by the different Signal to Noise Ratio, which, for these wavelengths, is always lower than for Namibia case. Moreover, the intercept of the line regression assumes absolute values from 0.0038 to 0.04, in agreement with the results obtained by Korotaev
ð4Þ
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F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
Fig. 3. Tito Scalo–the same as in Fig. 2.
et al. (1993). This enables us to search for a linear dependence of the AOD from the TOMS AI. In Fig. 4, we report on both graphs the AI on the xaxis and ta430 and ta480 ; respectively, on the y-axis for the data collected in Namibia, while the continuous line represents the linear regression fit to the data. It has to be noticed that each point represents a measure taken in a different day at the time of the satellite overpass. In order to limit the time-dependent uncertainty, each ground-based datum is obtained by averaging three measurements carried out within 715 min from the satellite overpass time. In Fig. 5, we have a similar graph for data collected in Italy, but at a different couple of wavelengths (l ¼ 480 nm and l ¼ 550 nm). The number of measurements at our disposal is not very large: in particular, we did not use some data collected in Tito Scalo because of the passage of high cirrus that introduced a high uncertainty on the retrieved AOD. Nevertheless, the results appear satisfactory, especially for Tito Scalo data-set where, for ta480 ; we have a regression linear coefficient, R2 ¼ 0:91: As a further check, a test confirming the existence of the correlation was done: in fact, using the AODs measured 1 h after the Earth Probe overpass the correlation disappeared.
Fig. 4. Gambsberg areaFin the upper figure is shown the correlation between t (430) and the EP TOMS AI, in the lower figure the correlation between t (550) and the same AI. Each point correspond to a different day of measurement.
It is important to highlight the difference of the correlation sign for the test cases: we found a positive slope for Namibia data (Fig. 4) and a negative slope for the Italian ones (Fig. 5): this is clearly due to the different natures of the aerosols in the two sites. The obtained results are consistent with these shown by Hsu et al. (1999) and by Torres et al. (1998). In the first paper the authors analyse different dust data-sets coming from several places (Sahel, Senegal, Cape Verde and Barbados) and they always find a positive regression linear coefficient. On the other hand, Torres et al. (1998) study the dependence of the aerosol optical thickness (l ¼ 380 nm) from the AI by applying a theoretical model comprehensive of the main aerosol models which can be absorbing, as dust, or non-absorbing, as sulphate. The only model that gives a negative slope for the correlation is the small-particle sulphate aerosol, which is representative of tropospheric aerosols of anthropogenic origin. This kind of particulate matter is consistent with the environmental conditions in Tito Scalo. According to this theoretical model, if the absorbing aerosols layer is located near the Earth’s surface, the
F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
Fig. 5. Tito Scalo–the same as in Fig. 4.
absorbing properties can be detected from the platform only for large values of the optical depths, as in Hsu et al. (1999). This explains the negative sign of the AI for our desert measurements. In the case of Italian data we have both negative values of the AI, as it should be for sulphate particles, and positive ones, thus revealing the presence of nonabsorbing and absorbing particles, as it could be in a large rural area with small factories. Although preliminary, we would like to point out an important application of this study. The availability of ground-based measurements over a seasonal period is useful in order to obtain, for more than one wavelength, the relationship describing the dependence of the AOD on the AI allowing to monitor the aerosol content, starting from the AI values.
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visible spectral range. Firstly, we observed the higher values of the AOD measured in Italy, if compared with the desert AOD, as expected by environmental considerations. The spectral distance between the couple of wavelengths used for the AI estimation (l0 ¼ 331 nm, l00 ¼ 360 nm) and that of our measurements (measured spectra start from 400 nm in desert test-case and 460 nm in the other), pushed us to verify the existence of a correlation between the aerosol optical thickness measured at two different couples of wavelengths (l1 ¼ 480 nm, l2 ¼ 550 nm and l1 ¼ 550 nm, l2 ¼ 620 nm). The results are satisfying for all days of measurements, though the different Signal to Noise Ratio of the spectra influences the values of the linear correlation coefficient R2 : In a successive step, we searched for a correlation between measured optical depths and TOMS AI: for desert measurements a positive linear correlation coefficient has been found, while, for the Italian area, a negative one has been obtained. These behaviours are in agreement with previous papers, both theoretical and experimental, as in Torres et al. (1998) and Hsu et al. (1999). For absorbing particles, the desert data correspond to negative values of the AI and this is probably due to the low mean height of the aerosols layer. The second data-set behaves as a small-particles sulphate model of non-absorbing aerosols, where the AI assumes both negative and positive values, showing the presence of anthropogenic and rural particles. Due to the limited number of measurements analysed, we obtained preliminary results suggesting us, for the future, a way to continuously monitor the AOD for more than one wavelength, starting from AI values.
Acknowledgements We would like to thank Prof. Christian Wiedner from Max Planck Institute of Nuclear Physics in Heidelberg and Prof. G. Auriemma from Universit"a della Basilicata who partially supported our campaign in Namibia. The campaign in Tito Scalo was partly supported by ASI.
References
6. Conclusions Two campaigns of direct solar irradiance observations (Namibia and Italy) were carried out in order to find a relationship between ground-based measured AOD and Earth Probe TOMS AI, which is defined positive for absorbing particles and negative for non-absorbing ones. A high resolution spectroradiometer has been used for direct solar irradiance measurements in the
Amato, U., Di Bello, D., Esposito, F., Serio, C., Pavese, G., Romano, F., 1996. Intercomparing the Twomey method with a multimodal lognormal approach to retrieve the aerosol size distribution. Journal of Geophysical Research 101 (D4), 19267–19275. Cuomo, V., Esposito, F., Pavese, G., Serio, C., 1993. Determining angstroms turbidity coefficients: an analysis with a wide-range grating spectrometer. Aerosol Science and Technology 18, 59–69. Hsu, N.C., Herman, J.R., Torres, O., Holben, B.N., Tanr!e, D., Eck, T.F., Smirnov, A., Chatenet, B., Lavenu, F., 1999.
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Comparisons of the TOMS aerosol index with Sunphotometer aerosol optical thickness: results and applications. Journal of Geophysical Research 104 (D6), 6269– 6279. Korotaev, G.K., Sakerin, S.M., Ignatov, A.M., Stowe, L., McLean, P., 1993. Sun-photometer observations of aerosol optical thickness over the North Atlantic from a Soviet research vessel for validation of satellite measurements. Journal of Atmospheric and Oceanic Technology 10, 725–735.
Paltridge, G.W., Platt, C.M., 1976. In: Radiative Process in Meteorology and Climatology. Elsevier, New York, pp. 69–70. Torres, O., Bhartia, P.K., Herman, J.R., Ahmad, Z., Gleason, J., 1998. Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: theoretical basis. Journal of Geophysical Research 103 (D14), 17099–17110. Volz, F.E., 1987. Measurements of the skylight scattering function. Applied Optics 26, 4098–4105.
A preliminary study on the correlation between TOMS aerosol index and ground-based measured aerosol optical depth Francesco Espositoa, Giulia Paveseb,*, Carmine Serioa a
INFM Unita" di Napoli, Gruppo collegato di Potenza, Contrada Macchia Romana, 85100 – Potenza (I), Italy b Istituto di Metodologie Avanzate di Analisi Ambientale, CNR, c.da S.Loja 850, Tito Scalo (PZ), Italy Received 15 September 2000; received in revised form 20 January 2001; accepted 24 April 2001
Abstract The aim of this work is to study the correlation between ground-based measured aerosol optical depth (AOD) and TOMS Aerosol Index. For this reason, two AOD data-sets have been analysed. The first set of measurements has been obtained in a desert plateau in Namibia during July 1998, while the second one has been collected in Tito Scalo (Italy), a very small industrial zone surrounded by a large rural area, in June–July 2000. The AOD has been computed in the spectral range 400–870 nm with a resolution of 3 nm by measuring the direct solar irradiance. The used spectroradiometer is an Optical Spectrum Analyser, equipped with a continuously rotating diffraction grating. Successively, a correlation between the Earth Probe TOMS Aerosol Index, whose definition uses backscattered radiances at 331 and 360 nm, and the AOD in the visible range was searched for. A satisfying correlation was found, whose Pearson correlation coefficient R2 values range from 0.64 to 0.91. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Spectrophotoradiometer; Desert aerosol; Anthropogenic aerosol
1. Introduction Nowadays, it is well known that the aerosol particulate, both stratospheric and tropospheric, plays a central role in the Earth-atmosphere system. Actually, the scattering and the absorption of solar and thermal radiation affect the Earth radiative budget in a way depending on the aerosol intimate structure and composition. Obviously, the aerosol’s time evolution and residence in the atmosphere are also important variables. Undoubtedly, satellites constitute one of the most useful ways of monitoring this atmospheric component all along the whole planet. In order to have a better comprehension of the satellite data, it is useful to have ancillary data, such as ground-based *Corresponding author. Tel.: +39-0971-427240; fax +390971-427271. E-mail address: [email protected] (G. Pavese).
measurements of the aerosol optical depth (AOD), and study a possible correlation between them. This correlation allows a validation of the satellite-retrieved data. Moreover, as shown by Hsu et al. (JGR 1999), this relationship represents a powerful tool because, for regions with atmospheric aerosol having homogeneous characteristic over a wide area, it is possible to estimate the AOD, starting from aerosol index (AI) values. In this work, two data series of AOD, obtained by measuring the solar direct radiation with a spectrophotoradiometer during two measurement campaigns, are analysed. The first campaign was held in a desert area of Namibia (Africa) from 7 July to 10 July 1998, while the second was performed during June–July, 2000 in a very small industrial place, surrounded by a rural area located in southern Italy. Once the AOD was obtained, a possible correlation between them and the corresponding, satellite-derived, TOMS AI was searched.
1352-2310/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 3 2 3 - 5
5094
F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
2. Ground-based measurements procedure An Optical Spectrum Analyser, based on a scanning monochromator with a diffraction grating continuously rotating at 50 revolutions per second, is the core of our experimental equipment. This apparatus allows us to measure, in cloud-free conditions, the direct solar irradiance as a function of the wavelength, IðlÞ; in the spectral range 400–870 nm with a resolution of 3 nm. The radiation collecting system is a lens with a 24 cm focal length and a field of view of about 11, assuring us that the signal is not affected by any diffuse radiation. We reduce the random noise on our data recording each measurement by averaging it over 64 spectra. The wellknown Lambert–Beer–Bouguer law (Paltridge and Platt, 1976) describes our physical situation with a good approximation: Il ¼ I0l etl mr ;
ð1Þ
where I0 ðlÞ is the monochromatic extra-atmospheric solar radiation, mr is the relative air-mass and tl is the total atmospheric optical depth. The application of the usual Langley-plot procedure allows us to estimate I0 ðlÞ and thus tl : From this, after successive computations, we obtain the AOD in the visible spectral range. This AOD includes the Chappuis ozone absorption band, but it excludes all the other selective absorption spectral regions due to gases, where we cannot apply the described procedure. More details on our AOD calculation can be found in Amato et al. (1996).
3. Earth-probe TOMS data description The total ozone mapping spectrometers (TOMS) are instruments working since 1978 on different platforms, as Nimbus 7 (1978), Meteor 3 (1991), ADEOS (1996) and Earth Probe (1996). The data used in this work come from Earth Probe platform and they were taken from the World Wide Web. TOMS data are uniformly gridded level 3 data products (longitude resolution 1.251, latitude resolution 11). These instruments were launched with the purpose of monitoring the ozone content by studying the backscattered radiances in the UV region. Besides, these radiances contain information on both scattering and absorption aerosol properties. An AI for Earth Probe TOMS is defined as in the following, Hsu et al. (1999) AI ¼ 100½log10 ðI331 =I360 Þmeas log10 ðI331 =I360 Þcalc : ð2Þ Here the subscript meas indicates the back-scattered radiance at a fixed wavelength and the subscript calc indicates the radiance calculated with a radiative transfer model describing a pure Rayleigh atmosphere.
According to relation (3), the AI assumes positive values in the presence of absorbing aerosols and negative values when the aerosols are non-absorbing. Nevertheless, as shown by Torres et al. (1998) in an analysis of simulated data, if a weakly absorbing particles layer is not far from the Earth’s surface, the aerosol scattering properties can dominate the absorption characteristics, giving negative values for the AI, as if the particulate matter were non-absorbing.
4. Ground-based measurements analysis The analysed AOD data-set were obtained during two measurements campaign, performed in two very different places: the first data were collected from 7 July to 10 July 1998 in the Gambsberg area, Namibia (16.051E, 23.201S), situated at 1800 m a.s.l. This site is a plateau characterised by scattered vegetation on a gravel surface and it is also very far from the major roads. The second data-set was recorded from 21 June 2000 to 6 July 2000 in Tito Scalo, Italy (15.731E, 40.61N, 800 m a.s.l.), a place characterised by small factories and surrounded by a large rural area. Solar irradiance spectra were collected throughout the day, the Namibian one with a time resolution of about 15 min, while the Italian one having a time resolution of about 5 min. As an example, in Fig. 1 the spectral dependence of the AOD for both sites is shown. From this figure it is clear that desert aerosol optical thickness is smaller than the industrial one. Some further information on the atmospheric particulate matter can be inferred from the estimation of the well-known Angstrom parameters a and b: tal ¼ bðl=l0 Þa ;
ð3Þ
where tal is the monochromatic aerosol’s optical thickness, l the wavelength in mm and l0 ¼ 1 mm. Usually, as in Hsu et al. (1999), a and b are calculated by means of the AOD ratio method, firstly developed by Volz (1987), which uses different couples of wavelengths at a time. In our case, we have the values of tal over a wide spectral range and this allows us to apply a best-fit procedure, provided that we have the log–log calculation of relation (4), as in Cuomo et al. (1993). This simple procedure points out a first, rough difference between the two kinds of particulate matter we are dealing with: the desert aerosols are characterised by smaller dimensions and lower concentration than the rural ones. In fact, in the first case we have a mean value of the a parameter, aave ¼ 2:111; while in the second case we have aave ¼ 0:8945 and the corresponding values for b are bave ¼ 0:028 and bave ¼ 0:172; respectively.
F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
Fig. 1. The AOD as a wavelength function for a day of measurements in Gambsberg and the other in Tito Scalo. The continuous line is the data fit.
5095
Fig. 2. Gambsberg areaFin the upper figure is shown the correlation between t (550) and t (480), and in the lower figure the correlation between t (550) and t (620).
the linear relation: 5. 5 correlation between TOMS AI and ground-based AOD
tal1 ¼ ktal2
In order to study a correlation between the TOMS AI and the ground-based measured aerosol optical thickness, it would be better to use tal values corresponding to l as near as possible to the AI wavelengths that are, in this case, l0 ¼ 331 nm and l00 ¼ 360 nm. For the Namibia case study, the lower wavelength bound for the ground-based observations was 400 nm, while for the Tito Scalo case it was 460 nm. Moreover, the relative error associated to these first data is very high (about 40%), due to a lower Signal to Noise Ratio in the first part of the spectrum. This error is 10% for l ¼ 480 nm but less than 4% for wavelengths greater than 500 nm. Although our AOD is obtained for wavelengths that differ from those of the AI, we expect to find a correlation between these quantities because AODs measured at different wavelengths are correlated with each other. This suggests that a correlation between AI and AOD in the UV range could imply a correlation with AOD measured in the visible range. Recalling Eq. (4) and fixing two wavelengths l1 and l2, the corresponding AOD tal1 and tal2 are linked by
with k ¼ ðl1 =l2 Þa : Experimental studies (Korotaev et al., 1993) showed that the nearer l1 and l2 are, the higher will be the correlation between tal1 and tal2 ; while low systematic errors on tal will lead the intercept of the line regression close to zero. We verified the existence of the correlation between AODs measured at different wavelengths for all days of our two data-sets. As an example, the data obtained on 7 July 1998 (Gambsberg) and 21 June 2000 (Tito Scalo) are reported in Figs. 2 and 3 where the couples of wavelengths used for the correlation are, l1 ¼ 480 nm l2 ¼ 550 nm and l1 ¼ 620 nm l2 ¼ 550 nm, respectively. The correlation coefficient values of the AOD measured at two different wavelengths are in general satisfying, except for some days of measurements in Tito Scalo, where we found values of R2 ranging from 0.98 to 0.67. This is caused by the different Signal to Noise Ratio, which, for these wavelengths, is always lower than for Namibia case. Moreover, the intercept of the line regression assumes absolute values from 0.0038 to 0.04, in agreement with the results obtained by Korotaev
ð4Þ
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F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
Fig. 3. Tito Scalo–the same as in Fig. 2.
et al. (1993). This enables us to search for a linear dependence of the AOD from the TOMS AI. In Fig. 4, we report on both graphs the AI on the xaxis and ta430 and ta480 ; respectively, on the y-axis for the data collected in Namibia, while the continuous line represents the linear regression fit to the data. It has to be noticed that each point represents a measure taken in a different day at the time of the satellite overpass. In order to limit the time-dependent uncertainty, each ground-based datum is obtained by averaging three measurements carried out within 715 min from the satellite overpass time. In Fig. 5, we have a similar graph for data collected in Italy, but at a different couple of wavelengths (l ¼ 480 nm and l ¼ 550 nm). The number of measurements at our disposal is not very large: in particular, we did not use some data collected in Tito Scalo because of the passage of high cirrus that introduced a high uncertainty on the retrieved AOD. Nevertheless, the results appear satisfactory, especially for Tito Scalo data-set where, for ta480 ; we have a regression linear coefficient, R2 ¼ 0:91: As a further check, a test confirming the existence of the correlation was done: in fact, using the AODs measured 1 h after the Earth Probe overpass the correlation disappeared.
Fig. 4. Gambsberg areaFin the upper figure is shown the correlation between t (430) and the EP TOMS AI, in the lower figure the correlation between t (550) and the same AI. Each point correspond to a different day of measurement.
It is important to highlight the difference of the correlation sign for the test cases: we found a positive slope for Namibia data (Fig. 4) and a negative slope for the Italian ones (Fig. 5): this is clearly due to the different natures of the aerosols in the two sites. The obtained results are consistent with these shown by Hsu et al. (1999) and by Torres et al. (1998). In the first paper the authors analyse different dust data-sets coming from several places (Sahel, Senegal, Cape Verde and Barbados) and they always find a positive regression linear coefficient. On the other hand, Torres et al. (1998) study the dependence of the aerosol optical thickness (l ¼ 380 nm) from the AI by applying a theoretical model comprehensive of the main aerosol models which can be absorbing, as dust, or non-absorbing, as sulphate. The only model that gives a negative slope for the correlation is the small-particle sulphate aerosol, which is representative of tropospheric aerosols of anthropogenic origin. This kind of particulate matter is consistent with the environmental conditions in Tito Scalo. According to this theoretical model, if the absorbing aerosols layer is located near the Earth’s surface, the
F. Esposito et al. / Atmospheric Environment 35 (2001) 5093–5098
Fig. 5. Tito Scalo–the same as in Fig. 4.
absorbing properties can be detected from the platform only for large values of the optical depths, as in Hsu et al. (1999). This explains the negative sign of the AI for our desert measurements. In the case of Italian data we have both negative values of the AI, as it should be for sulphate particles, and positive ones, thus revealing the presence of nonabsorbing and absorbing particles, as it could be in a large rural area with small factories. Although preliminary, we would like to point out an important application of this study. The availability of ground-based measurements over a seasonal period is useful in order to obtain, for more than one wavelength, the relationship describing the dependence of the AOD on the AI allowing to monitor the aerosol content, starting from the AI values.
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visible spectral range. Firstly, we observed the higher values of the AOD measured in Italy, if compared with the desert AOD, as expected by environmental considerations. The spectral distance between the couple of wavelengths used for the AI estimation (l0 ¼ 331 nm, l00 ¼ 360 nm) and that of our measurements (measured spectra start from 400 nm in desert test-case and 460 nm in the other), pushed us to verify the existence of a correlation between the aerosol optical thickness measured at two different couples of wavelengths (l1 ¼ 480 nm, l2 ¼ 550 nm and l1 ¼ 550 nm, l2 ¼ 620 nm). The results are satisfying for all days of measurements, though the different Signal to Noise Ratio of the spectra influences the values of the linear correlation coefficient R2 : In a successive step, we searched for a correlation between measured optical depths and TOMS AI: for desert measurements a positive linear correlation coefficient has been found, while, for the Italian area, a negative one has been obtained. These behaviours are in agreement with previous papers, both theoretical and experimental, as in Torres et al. (1998) and Hsu et al. (1999). For absorbing particles, the desert data correspond to negative values of the AI and this is probably due to the low mean height of the aerosols layer. The second data-set behaves as a small-particles sulphate model of non-absorbing aerosols, where the AI assumes both negative and positive values, showing the presence of anthropogenic and rural particles. Due to the limited number of measurements analysed, we obtained preliminary results suggesting us, for the future, a way to continuously monitor the AOD for more than one wavelength, starting from AI values.
Acknowledgements We would like to thank Prof. Christian Wiedner from Max Planck Institute of Nuclear Physics in Heidelberg and Prof. G. Auriemma from Universit"a della Basilicata who partially supported our campaign in Namibia. The campaign in Tito Scalo was partly supported by ASI.
References
6. Conclusions Two campaigns of direct solar irradiance observations (Namibia and Italy) were carried out in order to find a relationship between ground-based measured AOD and Earth Probe TOMS AI, which is defined positive for absorbing particles and negative for non-absorbing ones. A high resolution spectroradiometer has been used for direct solar irradiance measurements in the
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