Top PDF Intercomparison of Aerosol Optical Depth from Brewer Ozone spectrophotometers and CIMEL sunphotometers measurements

Intercomparison of Aerosol Optical Depth from Brewer Ozone spectrophotometers and CIMEL sunphotometers measurements

Intercomparison of Aerosol Optical Depth from Brewer Ozone spectrophotometers and CIMEL sunphotometers measurements

440 nm from the CIMEL are shifted to 320 nm in using the α coefficient between 440 and 870 nm of the CIMEL. Fig- ure 4 shows the scatter plot of the 7386 AODs from Brewer #150 and the CIMEL sunphotometer at El Arenosillo. It can be observed that there is a good linear agreement be- tween AODs from the two instruments: the correlation coef- ficient, the slope and the intercept of the regression line are 0.82, 0.9356±0.0149 and 0.0001±0.003, respectively. If we remove the 11 outliers (see the red points with blue cross on Fig. 4), the comparison is clearly improved: the corre- lation coefficient, the slope and the intercept of the regres- sion line are 0.95, 0.9467±0.0124 and −0.0022±0.0027, respectively. No explanation for these 11 points is found yet. For more details on selection of outliers, see Sect. 4. The calibration of the CIMEL could be one of the reason to explain the discrepancy of the slope of the regression line compared to the one at the three other sites (Uccle, Toronto and Norrk¨oping) here the slope is very close to 1 (Toledano, 2005). The Angstr¨om’s coefficient α used at El Arenosillo is further from 320 nm than the one used at the three other sites and this could be another explanation. Further investigation is needed to understand where the outliers are coming from and why the slope is not so good at El Arenosillo compared to Uccle, Toronto and Norrk¨oping.
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Retrieval of aerosol optical depth in the visible range with a Brewer spectrophotometer in Athens

Retrieval of aerosol optical depth in the visible range with a Brewer spectrophotometer in Athens

The present work mainly focuses on two subsets of the full series recorded by Brewer #001 since 2004. The most recent subset includes an intensive campaign (13–25 May 2014, i.e. Julian days 133–145) specifically arranged for the pur- pose of comparing the Brewer and the Cimel on a short- term basis and quantifying some instrumental effects (e.g. sensitivity dependence on temperature) with maximum ac- curacy. For example, the Brewer optics were cleaned every day of the campaign to ensure maximum accuracy. Further- more, the Brewer was often checked to correctly point to the sun and an ad-hoc schedule was designed to provide as many DS measurements in n2 mode as possible, resulting in more than 8000 samples, among which 170 were nearly simultaneous (i.e. within ±1 min) with the Cimel. During the intensive campaign, the sky conditions were generally favourable with only few clouds. The AOD at 440 nm from AERONET ranged from 0.04 to 0.39 and the Ångström co- efficient obtained in the range 380–500 nm varied from 0.3 to 1.8. Those values reflect the different origin of air masses, including three episodes of advection of mineral dust from the Sahara desert (i.e. days 134, 139–140 and 144–145), as
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Intercomparison of spectroradiometers and Sun photometers for the determination of the aerosol optical depth during the VELETA-2002 field campaign

Intercomparison of spectroradiometers and Sun photometers for the determination of the aerosol optical depth during the VELETA-2002 field campaign

morning and afternoon data we have selected those with lower aerosol optical depth (AOD at 670 nm less than 0.09). Finally, three morning and one afternoon Langley plots were selected. For the selected cases the correlation coef- ficients were always greater than 0.995. The calibration coefficients applied were the averages of these four cases. In all circumstances the selected Langley plots presented calibration coefficients for all the channels that differed from the average value by less than 3 times the standard deviation. Table 6 shows the retrieved calibration coeffi- cients, including the average value and the standard error of the mean. The last column shows this error in percent. It is important to say that this column divided by 100 gives an estimate of the maximum error associated to the aerosol optical depth in each channel. It is evident that the errors were close to 0.01 with better results in the longer wavelengths, results that were in the range of those of AERONET [Holben et al., 1998; Eck et al., 1999]. It must be said that during the periods used for the Langley calibration the total columnar ozone do not vary more than 5% according to the analyses of the measurements performed by a Brewer MKIII at Armilla main station.
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Comparison of UV irradiances from Aura/Ozone Monitoring Instrument (OMI) with Brewer measurements at El Arenosillo (Spain) – Part 2: Analysis of site aerosol influence

Comparison of UV irradiances from Aura/Ozone Monitoring Instrument (OMI) with Brewer measurements at El Arenosillo (Spain) – Part 2: Analysis of site aerosol influence

In this framework, our work is focused on the com- parison between OMI UV irradiance products (erythemal irradiance (UVER), and spectral irradiances at 305, 310 and 324 nm) and the ground-based UV measurements us- ing a well-calibrated Brewer spectroradiometer located at El Arenosillo (37.1 ◦ N, 6.7 ◦ W, 20 m a.s.l.), representative of the SW of Spain. In a companion paper (Ant´on et al., 2010) the effect of several factors as clouds, aerosols, ozone and the solar elevation on OMI-Brewer UV bias were analysed and compared with previous TOMS-Brewer results (Ant´on et al., 2007). In the previous works, as in Ant´on et al. (2010), the aerosol effect was studied in terms of aerosol extinction optical depth (AOD) measured at visible 440 nm wavelength. The comparison results showed that un- der moderate-high aerosol load (AOD(440 nm) > 0.25) the OMI bias is about 19–15% for UVER and spectral UV irra- diances. Under cloud-free and low aerosol load conditions (AOD(440 nm) < 0.1) the OMI bias was smaller ∼11% for UVER, and similar spectral results. These studies had not attempted correcting OMI UV data for aerosol absorption, because no information about aerosol absorption properties at UV wavelengths was available at our site.
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Detailed Aerosol Optical Depth Intercomparison between Brewer and Li-Cor 1800 Spectroradiometers and a Cimel Sun Photometer

Detailed Aerosol Optical Depth Intercomparison between Brewer and Li-Cor 1800 Spectroradiometers and a Cimel Sun Photometer

Aerosol optical depth (AOD) using different instruments during three short and intensive campaigns carried out from 1999 to 2001 at El Arenosillo in Huelva, Spain, are presented and compared. The specific aim of this study is to determine the level of agreement between three different instruments running in operational conditions. This activity, however, is part of a broader objective to recover an extended data series of AOD in the UV range obtained from a Brewer spectroradiometer. This instrument may be used to obtain AOD at the same five UV wavelengths used during normal operation for ozone content determi- nation. As part of the validation of the Brewer AOD data, a Cimel sun photometer and another spectror- adiometer, a Li-Cor 1800, were used. A detailed comparison of these three instruments is carried out by means of near-simultaneous measurements, with particular emphasis on examining diurnal AOD variability. Absolute AOD uncertainties range from 0.02 for the Cimel to 0.08 for the Brewer, with intermediate values for the Li-Cor 1800. All data during the comparison are in reasonable agreement, when taking into account the different performance characteristics of each instrument. The comparison also demonstrates current deficiencies in the Brewer data and thus the difficulty to determine AOD values with low errors.
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Complex experiment on stydying the microphysical, chemical, and optical propertires of aerosol particles and estimating the contribution of atmospheric aerosol to Earth radiation budget

Complex experiment on stydying the microphysical, chemical, and optical propertires of aerosol particles and estimating the contribution of atmospheric aerosol to Earth radiation budget

The comparison of numerical calculation results and values of downward solar radi- ation fluxes on the Earth’s surface measured in the clear-sky atmosphere during sum- mer periods of 2010–2012 in a background region of boreal zone of Siberia shows that relative differences between the model and experimental values of the direct and total radiation, on the average, do not exceed 1 and 3 %, respectively, when instrumental er-

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An improved dust emission model – Part 2: Evaluation in the Community  Earth System Model, with implications for the use of dust source functions

An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions

“correct” value of b is highly uncertain, in part because the parameterization of Fecan et al. (1999) is based on wind tun- nel studies. Implementing this small-scale parameterization into a climate model scales it up by many orders of mag- nitude, potentially producing physically unrealistic results. Furthermore, the inhibition of dust emission by soil moisture depends on the moisture content of the top layer of soil par- ticles (McKenna Neuman and Nickling, 1989), which is in direct contact with the surface air. In contrast, the top soil layer of hydrology models in climate models usually has a thickness of multiple centimeters and thus responds differ- ently to precipitation and changes in atmospheric humidity, which are important in determining the dust emission thresh- old (Ravi et al., 2004, 2006). The “correct” value of b in a climate model is therefore likely to depend substantially on the model methodology, and in particular on the specifics of the model’s hydrology module. Since the choice of b is thus ambiguous, we investigated the sensitivity of our results to the particular value of b by running simulations with a wide range of values (Table S1 in the Supplement). We empha- size that other models using the K14 scheme should do a similar sensitivity study to avoid an unrealistic influence of the model’s hydrology on the dust emission fluxes. Because we found that the simplest case of not using a tuning con- stant (i.e., b = 1) produces the best overall results for all four model configurations, we used b = 1 for the results reported in Sect. 3. But note that the wide range of values of b that we tested all produced results qualitatively similar to those presented here (see Table S1 in the Supplement).
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Analysis of aerosol optical depth evaluation in polar regions and associated uncertainties

Analysis of aerosol optical depth evaluation in polar regions and associated uncertainties

The AOD was computed with a software package de- veloped by us based on the algorithms used in the new AERONET processing version 2 (Smirnov, 2004). Figure 1 shows the mean AOD in each spectral channel, as well as the standard deviation, maximum and minimum. The mean AOD (500 nm) was 0.1, just on the limit between clear and haze conditions in the arctic (Tomasi et al., 2007). Fig- ure 2 represents a breakdown in the percentages of the rela- tive contribution of each atmospheric component to the total optical depth (TOD) for each wavelength. This analysis is useful in helping to understand the importance of each error source. Thus, in the ultraviolet range even a small error in the Rayleigh OD can result in large AOD error. Other algorithms were subsequently introduced in the data processing to study their contribution to the uncertainty in the AOD. These will be analyzed in the following sections.
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Optical depths of semi-transparent cirrus clouds over oceans from CALIPSO infrared radiometer and lidar measurements, and an evaluation of the lidar multiple scattering factor

Optical depths of semi-transparent cirrus clouds over oceans from CALIPSO infrared radiometer and lidar measurements, and an evaluation of the lidar multiple scattering factor

Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Fla- mant, P., Fu, Q., Hoff, R., Kittaka, C., Kubar, T. L., LeTreut, H., McCormick, M. P., Megie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: a global 3-D view of aerosols and clouds, B. Am. Meteorol. Soc., 91, 1211–1229, 2010. Yang, P., Wei., H., Huang, H. L., Baum, B. A., Hu, Y. X., Kattawar, G. W., Mishchenko, M. I.,

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Changes in atmospheric aerosol loading retrieved from space-based measurements during the past decade

Changes in atmospheric aerosol loading retrieved from space-based measurements during the past decade

Table 3 lists the relative percentage errors to the ac- tual trend at the selected AERONET stations. Except for the Mongu station (because of an almost negligible ac- tual trend), the stations influenced by industrial or biomass burning aerosols (i.e., Avignon, Beijing, GSFC, Ispra, MD_Science_Center, Shirahama, and Skukuza) have larger relative errors (−156 to 399.2 %) than the stations (Ban- izoumbou, Dakar, Ouagadougou, SEDE_BOKER, and So- lar_Village) near desert regions (−63.6 to +59.3 %). At the Mauna_Loa (in free troposphere and open ocean) and Sevil- leta stations (rural region), the relative errors range from −100.0 % to 0.0 %. These errors demonstrate that the trend estimate from different/limited temporal sampling can be dif- ferent to the actual trend. In addition, Fig. 5 shows the com- parisons between the AERONET-resampled trends. The un- certainty is more serious over the regions where industrial or biomass burning aerosols are dominant, and thereby it is possible to derive different or contradictory trends to the ac- tual trend. Generally, the AERONET-resampled trends are underestimated by 24 to 36 %. This knowledge is important for understanding the difference in the AOT trends from the different/limited temporal sampling of the satellite data.
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The Collection 6 MODIS aerosol products over land and ocean

The Collection 6 MODIS aerosol products over land and ocean

on a global basis, the MODIS over land AOD is well correlated with, and matches the ground truth SP data to within expected uncertainty. However, these studies also show regional situations with much poorer accuracy. Sometimes this degradation of accuracy occurs at high AOD, where model assumptions dominate the error. Such locations in- clude South America during the biomass burning season where the slope between

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Regional and monthly and clear-sky aerosol direct radiative effect (and forcing) derived from the GlobAEROSOL-AATSR satellite aerosol product

Regional and monthly and clear-sky aerosol direct radiative effect (and forcing) derived from the GlobAEROSOL-AATSR satellite aerosol product

T. F., Boucher, O., Chin, M., Collins, W., Dentener, F., Diehl, T., Easter, R., Feichter, J., Fill- more, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Herzog, M., Horowitz, L., Isaksen, I., Iversen, T., Kirkev ˚ag, A., Kloster, S., Koch, D., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J. F., Lesins, G., Liu, X., Lohmann, U., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, O., Stier, P., Takemura, T., and Tie, X.: An AeroCom initial

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Intercomparison of ground-based ozone and NO2 measurements during the MANTRA 2004 campaign

Intercomparison of ground-based ozone and NO2 measurements during the MANTRA 2004 campaign

and solar lines fit as interfering species. The data analysis is described in detail in Fu et al. (2007). The uncertainty in the retrieved PARIS-IR results for ozone include contributions from the spectral noise, interfering molecules, uncertainties in the viewing geometry, and uncertainties in atmospheric temperature profiles. This value is estimated to be 2.7%. For both instruments, the a priori ozone profile was taken from ozonesondes flown during the campaign. For the U of T FTS, the pressure and temperature profiles are taken from the National Centers for Environmental Prediction weather model (NCEP) (McPherson, 1994) and the U.S. Standard Atmosphere. For PARIS-IR, the pressure and temperature profiles are taken from NCEP and the Mass-Spectrometer- Incoherent-Scatter model (MSIS-2000) (Picone et al., 2002). The columns from the DU FTS were generated using an optimal estimation retrieval based on the DU RADCO code (RADiation COde) which is used as the spectral for- ward model, and from which the ray-tracing algothrithm is adapted (Blatherwick et al., 1989; Fogal, 1994). The model atmosphere (pressure, temperature, and ozone) was constructed based on radiosonde data from sonde flights flown as part of the campaign. The HITRAN 2004 spectral database was used. The spectra analyzed for ozone are all from the longer wavelength channel (987.45–987.55 cm −1 ), as the other channel (1438–2154 cm −1 ) is dominated by wa- ter vapour at ground level. H 2 O and CO 2 are fit as interfering
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Estimation of aerosol water and chemical composition from AERONET Sun–sky radiometer measurements at Cabauw,  the Netherlands

Estimation of aerosol water and chemical composition from AERONET Sun–sky radiometer measurements at Cabauw, the Netherlands

Kulmala, M., Asmi, A., Lappalainen, H. K., Baltensperger, U., Brenguier, J.-L., Facchini, M. C., Hansson, H.-C., Hov, Ø., O’Dowd, C. D., Pöschl, U., Wiedensohler, A., Boers, R., Boucher, O., de Leeuw, G., Denier van der Gon, H. A. C., Fe- ichter, J., Krejci, R., Laj, P., Lihavainen, H., Lohmann, U., Mc- Figgans, G., Mentel, T., Pilinis, C., Riipinen, I., Schulz, M., Stohl, A., Swietlicki, E., Vignati, E., Alves, C., Amann, M., Ammann, M., Arabas, S., Artaxo, P., Baars, H., Beddows, D. C. S., Bergström, R., Beukes, J. P., Bilde, M., Burkhart, J. F., Canonaco, F., Clegg, S. L., Coe, H., Crumeyrolle, S., D’Anna, B., Decesari, S., Gilardoni, S., Fischer, M., Fjaeraa, A. M., Foun- toukis, C., George, C., Gomes, L., Halloran, P., Hamburger, T., Harrison, R. M., Herrmann, H., Hoffmann, T., Hoose, C., Hu, M., Hyvärinen, A., Hõrrak, U., Iinuma, Y., Iversen, T., Josipovic, M., Kanakidou, M., Kiendler-Scharr, A., Kirkevåg, A., Kiss, G., Klimont, Z., Kolmonen, P., Komppula, M., Kristjánsson, J.-E., Laakso, L., Laaksonen, A., Labonnote, L., Lanz, V. A., Lehtinen, K. E. J., Rizzo, L. V., Makkonen, R., Manninen, H. E., McMeek- ing, G., Merikanto, J., Minikin, A., Mirme, S., Morgan, W. T., Nemitz, E., O’Donnell, D., Panwar, T. S., Pawlowska, H., Pet- zold, A., Pienaar, J. J., Pio, C., Plass-Duelmer, C., Prévôt, A. S. H., Pryor, S., Reddington, C. L., Roberts, G., Rosenfeld, D., Schwarz, J., Seland, Ø., Sellegri, K., Shen, X. J., Shiraiwa, M., Siebert, H., Sierau, B., Simpson, D., Sun, J. Y., Topping, D., Tunved, P., Vaattovaara, P., Vakkari, V., Veefkind, J. P., Viss- chedijk, A., Vuollekoski, H., Vuolo, R., Wehner, B., Wildt, J., Woodward, S., Worsnop, D. R., van Zadelhoff, G.-J., Zardini, A. A., Zhang, K., van Zyl, P. G., Kerminen, V.-M., S Carslaw, K., and Pandis, S. N.: General overview: European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) – integrating aerosol research from nano to global scales, Atmos. Chem. Phys., 11, 13061–13143, doi:10.5194/acp- 11-13061-2011, 2011.
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Aerosol single-scattering albedo and asymmetry parameter from MFRSR observations during the ARM Aerosol IOP 2003

Aerosol single-scattering albedo and asymmetry parameter from MFRSR observations during the ARM Aerosol IOP 2003

(Table 1). A ground-based CIMEL sun-sky scanning radiometer (part of AERONET) estimates aerosol microphysical and optical properties (e.g., Dubovik et al., 2002). For our comparison we use available cloud-screened data (level 1.5). The CIMEL ra- diometer was collocated with the AOS, the MFRSR and a normal incidence multifilter radiometer (NIMFR) to within a few hundred meters. Uncertainty of τ can affect sub-

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Retrieval of aerosol optical depth in vicinity of broken clouds from reflectance ratios: case study

Retrieval of aerosol optical depth in vicinity of broken clouds from reflectance ratios: case study

V´arnai and Marshak, 2009). The cloud contamination ef- fect comes from partly cloudy pixels which can be misclassi- fied as cloud-free. This effect is more pronounced for coarse resolution satellite observations performed over regions con- taining small clouds. For example, over the tropical western Atlantic Ocean, the population of misclassified partly-cloudy 1.1-km MISR pixels is found to be 12% and the correspond- ing cumulus-induced AOD increase is about 20% (Zhao et al., 2009). The cloud adjacency effect relates to enhancement (near illuminated cloud sides) and reduction (near shadowy cloud sides) in the reflectance R of cloud-free columns as a result of sunlight scattering by clouds adjacent to the pixel of interest. Since clouds have complex three-dimensional (3- D) geometry, a critical assessment of the cloud-induced en- hancement/reduction can be obtained by using 3-D radiative transfer calculations for observed or simulated cloud fields (e.g., Cahalan et al., 2001; Nikolaeva et al., 2005; Wen et al., 2006, 2007; Yang and Di Girolamo, 2008). The evidence of a large impact on the retrieved AOD due to the cloud adja- cency effect (e.g., Wen et al., 2006, 2007) has led to the de- velopment of techniques to address problems associated with cloud-induced enhancement. These problems may be ad- dressed by using three different but overlapping approaches. The first approach includes selection of clear pixels lo- cated far away from clouds/shadows, where the 3-D radia- tive effects of clouds are relatively small (e.g., Wen et al., 2006, 2007). Statistical analysis of the two-dimensional (2- D) horizontal distribution of visible reflectance provides a population of appropriate clear pixels as a function of the nearest cloud distance (d), which determines the range of a completely clear area from a clear pixel of interest and is specified as the distance from the clear pixel to the nearest cloudy pixel (Wen et al., 2006). This population decreases rapidly with d and the rate of decrease is a function of both the cloud fraction (CF) and solar zenith angle (SZA). For ex- ample, for a cloud field with CF ∼0.5 and SZA ∼30 degrees, the clear pixel population is about 5% and 1% for d > 2 km and d > 3 km, respectively (Wen et al., 2007). Therefore, the existing one-dimensional (1-D) operational satellite re- trievals of AOD can be successfully applied for a quite lim- ited number of remote (e.g., d > 2 km) clear pixels. The mea- sured “remote” aerosol properties may differ substantially from their “near-cloud” counterparts (Su et al., 2008; Tackett and Di Girolamo, 2009; Twohy et al., 2009).
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Accuracy, precision, and temperature dependence of Pandora total ozone measurements estimated from a comparison with the Brewer triad in Toronto

Accuracy, precision, and temperature dependence of Pandora total ozone measurements estimated from a comparison with the Brewer triad in Toronto

shows the estimated residual ozone variability using residual type 1 data, while Fig. 3b shows the variability using residual type 2. Figure 3a and b demonstrate that residual type 1 data have larger variability than type 2 data, indicating that us- ing the daily mean value as the low-frequency signal did not fully remove the natural ozone variability. Ideally, the ran- dom uncertainty estimate should only contain random noise caused by the instrument and no natural ozone variation. Scatter plots of Brewer vs. Pandora residual ozone (Fig. 4) illustrate the same results. Figure 4 shows that the correla- tion coefficients for residual type 1 (R = 0.813 for Brewer no. 8 vs. Pandora no. 103, see Fig. 4a; 0.909 for Brewer no. 8 vs. Pandora no. 104, see Fig. 4b) are higher than the ones for residual type 2 (0.333 for Brewer no. 8 vs. Pandora no. 103, see Fig. 4c; 0.688 for Brewer no. 8 vs. Pandora no. 104, see Fig. 4d). The low correlation coefficients for ozone residual type 2 data indicate that the ozone variability has been largely removed from Pandora and Brewer data. Thus when we use residual ozone type 2, even with relatively small sample size, the estimated uncertainties for Pandoras are still consistent with those obtained from comparisons with other Brewers having larger sample sizes (see Fig. 2c and d, Brewer no. 187 column).
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Technical Note: Determination of aerosol optical properties by a calibrated sky imager

Technical Note: Determination of aerosol optical properties by a calibrated sky imager

While a full discussion of the radiometric calibration pro- cedures has not been published in refereed literature, and is beyond the scope of this paper, an overview is given in Shields et al. (1998). A somewhat briefer overview is given here. The system is thermally stabilized, and set up with a “live zero”, i.e. a positive response to no light, called a dark image. The dark images acquired at the same expo- sure as the field images are subtracted from the field images. Two types of linearity are measured and adjusted for. One is the non-linearity effect that results from the finite shut- ter timing impacts. The other is the non-linearity effect that results from the CCD response. The absolute radiance for each filter is measured at several lamp positions and/or ex- posure settings, so that we may have the redundancy needed to assess the relative uncertainty in the absolute calibration procedure. This response is corrected to adjust the response for individual systems to the standard filter response for the WSI. Finally, a uniformity calibration corrects for the non- uniformities within the image which are caused by the optics and the camera characteristics. Also, even though the Point Spread Function is quite tight (about 0.5 pixels), the wings of this function can cause a small stray light error. Mea- surements to assess this offset are taken and corrected for. A lamp traceable to NIST is used, and all electronics in the calibration room are kept in calibration.
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Three-year ground based measurements of Aerosol Optical Depth over the Eastern Mediterranean: the urban environment of Athens

Three-year ground based measurements of Aerosol Optical Depth over the Eastern Mediterranean: the urban environment of Athens

AOD at 500 nm. This main pattern is more or less reproduced during the different seasons (Fig. 4b). Three points are worth noting: (i) the steep morning increase in autumn which then decreases in the afternoon, with diurnal departures in the range from −19% to 24%, (ii) the clear midday maximum in winter, and (iii) the noisy pattern in spring due to the presence of dust storm episodes. Smirnov et al. (2002) report

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Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective

Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective

The largest systematic discrepancy is found between Dataset 1 and 5, due simply to the use of different calibra- tions. This is not surprising, as the AOD signal is extracted from a reflectance signal that is so faint that any change in the calibration may substantially alter the retrieval of AOD. From this finding, one may thus make a conjecture that the differences in the calibration as given in Table 1 might ac- count for a great deal to the AOD differences shown here. As such, to make the AOD compatible, we must assure ra- diances measured by different sensors agree first. The SNO calibration method applied to the PATMOS-X is therefore a sound and valid approach to bring the historical AVHRR data into agreement with the MODIS. For trend detection, however, absolute calibration is less important than other fac- tors. The same input data, however, do not necessarily lead to the same AOD retrieval. The AOD from dataset 2 dif- fers considerably from the MODIS AOD, even though their calibrated radiances are similar. This is because of large dif- ferences in the aerosol model and treatment of surface re- flectance. In narrowing the discrepancy, Zhao et al. (2004) adopted a relatively more realistic aerosol model based on the validation with the AERONET observation. After re- tuning based on AERONET observations, the revised algo- rithm (dataset 4) leads to the AOD in close proximity with the MODIS products. This is supported by the finding of Jeong et al. (2005) showing very large differences brought by applying two distinct models of aerosol size distribution, the power law and bi-lognormal functions to the same radi- ance data. Ironically, not only does the dataset 4 AOD agree broadly with the MODIS AOD, it also agrees with the GACP AOD, while the latter employed the power-law size distri- bution, as is shown more clearly in Fig. 4. Such a level of consistency in AOD is unprecedented, even though regional discrepancies may still be much larger. Yet there is still dis- agreement in the tendency over the short overlapping period (see Fig. 1) from weak positive (MODIS) to weak negative (GACP, PATMOX-x, SeaWiFS, and MISR), which we think is due to the differences in the cloud screening schemes and warrants a more detailed investigation.
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