Comparison of Aerosol Single Scattering Albedo Derived from the Ozone Monitoring Instrument with Aerosol Robotic Network Observations

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1 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 3, Comparison of Aerosol Single Scattering Albedo Derived from the Ozone Monitoring Instrument with Aerosol Robotic Network Observations LIU Qi and HONG Yu-Lan School of Earth and Space Sciences, University of Science and Technology of China, Hefei , China Received 7 February 2012; revised 23 March 2012; accepted 12 April 2012; published 16 May 2012 Abstract The single-scattering albedo (SSA), which quantifies radiative absorption capability, is an important optical property of aerosols. Ground-based methods have been extensively exploited to determine aerosol SSA but there were no satellite-based SSA measurements available until the advent of advanced remote sensing techniques, such as the Ozone Monitoring Instrument (OMI). Although the overall accuracy of OMI SSA is estimated to approach 0.1, its regional availability is unclear. Four-year SSA daily measurements from three Aerosol Robotic Network (AERONET) sites in China (Xianghe, Taihu, and Hong Kong) are chosen to determine the accuracy of OMI SSA in specific locations. The results show that on a global scale, the OMI SSA is systematically higher (with a mean relative bias of 3.5% and a RMS difference of ~0.06) and has poor correlation with the AERONET observations. In the Xianghe, Taihu, and Hong Kong sites, the correlation coefficients are 0.16, 0.47, and 0.44, respectively, suggesting that the distinct qualities of OMI SSA depend on geographic locations and/or dominant aerosol environments. The two types of SSA data yield the best agreement in Taihu and the worst in Hong Kong; the differing behavior is likely caused by varying levels of cloud contamination. The good consistency of the aerosol variation between the two SSA datasets on a seasonal scale is promising. These findings suggest that the current-version OMI SSA product can be applied to qualitatively characterize climatological variations of aerosol properties despite its limited accuracy as an instantaneous measurement. Keywords: aerosol, single scatter albedo, OMI, AERONET Citation: Liu, Q., and Y.-L. Hong, 2012: Comparison of aerosol single scattering albedo derived from the ozone monitoring instrument with aerosol robotic network observations, Atmos. Oceanic Sci. Lett., 5, Introduction The single-scattering albedo (SSA) is defined as the ratio of the scattering coefficient to the extinction coefficient. The SSA is 1.0 for completely scattering aerosols, and it decreases towards 0 with increasing absorption. The SSA of atmospheric aerosols is highly variable; the recently reported SSA at the mid-visible band for continental aerosols ranges from 0.75 to 0.99 (Otto et al., 2007; Zhao and Li, 2007; Osborne et al., 2008). In addition to variables such as surface albedo and cloud presence, the Corresponding author: LIU Qi, lqee@ustc.edu sign of aerosol radiative forcing at the top of the atmosphere is essentially determined by the aerosol SSA (Hansen et al., 1997; Liao and Seinfeld, 1998). Compared with aerosol optical thickness (AOT), it is more difficult to obtain reliable SSA through remote sensing, primarily due to the weakness of signals from aerosols. Remote sensing in the ultraviolet (UV) band provides an opportunity for remote aerosol SSA retrieval and has been utilized by the Ozone Monitoring Instrument (OMI), which benefits from its elevated spectral and spatial resolution (Levelt et al., 2006). Two independent algorithms, the near-uv method, and the multi-wavelength method, are employed to derive aerosol SSA based on OMI observations (Torres et al., 2007). The former method uses radiance measurements at two wavelengths (354 and 388 nm), while the latter one utilizes spectral measurements at up to 14 wavelengths between 343 and 484 nm. The multi-wavelength method is specially designed for the enhanced spectral measurements of the OMI, acting as an extension of the near-uv method by introducing more constraints. Considerable assumptions, however, are required to fulfill the aerosol retrieval, even for the OMI multispectral measurements, because many effects, in addition to aerosol absorption, jointly modify the upwelling radiances at the top of the atmosphere (Veihelmann et al., 2007). Although various satellite AOT retrievals have been evaluated using ground-based measurements throughout the world, satellite SSA retrievals attract less attention, and there has thus far been a lack of relevant studies, especially in China. It is thus unclear to what extent the OMI SSA data can be used to uncover aerosol absorbing characteristics as well as their spatiotemporal variability across China. The primary goal of this study is to clarify the uncertainty of the OMI SSA and to evaluate its availability over China using the SSA from the Aerosol Robotic Network (AERONET) as ground-based validation (Dubovik et al., 2000). 2 Data and methodology 2.1 OMI SSA The OMI onboard the National Aeronautics and Space Administration-Earth Observing System (NASA-EOS) Aura satellite provides a 2600-km-wide swath for measurements, which allows nearly daily global coverage. The feasible retrieval of aerosol SSAs using OMI measurements is possible because of the OMI s sensitivity to

2 NO. 3 LIU AND HONG: AEROSOL SINGLE-SCATTERING ALBEDO OF OMI 265 aerosol absorption. Both oceans and land masses (except ice and snow-covered areas) have very low reflectivities in the UV bands and thus act as a dark background in the downward-viewing field (Herman and Bhartia, 1997; Torres et al., 1998). Because aerosol scattering and absorption significantly contribute to the radiances received by airborne sensors, the aerosol can in principle be isolated. Therefore, the optical properties of aerosols can be determined using the appropriate algorithms. Because of the uncertainties in the aerosol size distribution, refractive index, aerosol layer height, surface reflectivity, and cloud-masking techniques, the overall accuracy of the OMI SSA is estimated to approach 0.1 (Torres et al., 2002). However, specific validation is required to assess the availability of the OMI SSA in certain regions. The multi-wavelength level-3 daily gridded ( ) aerosol product (OMAEROe) from 2005 to 2008 is investigated in this study. In the multi-wavelength method, the two primary retrievable parameters are the AOT and the best-fitting aerosol model. The spectral SSA and other properties are actually deduced parameters according to the retrieved best-fitting model (Torres et al., 2007). In this context, the SSA is not an independent retrieval but comes in the form of discrete values representing each of the prescribed aerosol models. The number of aerosol models employed is 24, including 10 weakly absorbing aerosol types, nine biomass burning aerosol types, four desert dust aerosol types, and one volcanic aerosol type (Torres et al., 2007). The SSA of each aerosol model is acquired from laboratory measurements and Mie calculations. The output OMI SSA is generated at five wavelengths: 342.5, 388.0, 442.0, 463.0, and nm. 2.2 AERONET SSA The AERONET, an international collaboration program for the survey of global aerosol characteristics, provides aerosol measurements through ground-based remote sensing measurements obtained by Constructive, collaborative Inquiry-based Multimedia E-learning (CIMEL) scanning solar photometers. The standard operation of the AERONET system consists of direct solar measurements every 15 minutes in seven spectral bands (340, 380, 440, 500, 675, 870, and 1020 nm) and diffuse sky radiances measurements hourly in four spectral bands (440, 675, 870, and 1020 nm) during daytime (Holben et al., 1998). In this study, the version 2.0 level-2 aerosol product (cloud free and assured quality) is used to provide comparisons. The accuracy of the SSA is guaranteed to be ~0.03 for the AOT at 440 nm exceeding 0.4 and for a solar zenith anger larger than 50 (Dubovik et al., 2000). 2.3 Methodology Data collection is performed by matching the day and the location of the observations from the OMI and AERONET. Given the constraint of the same day, the unique OMI grid that is nearest to the AERONET site location is retained. Approximately 6000 coincidences in total are selected from 2005 to 2008 in the worldwide scope that involves over 100 AERONET sites. The coincidences are defined to occur when the two datasets are spatiotemporally matched and both have valid SSA retrievals. In addition, because exact wavelength matching between the OMI and AERONET did not occur, the OMI SSA value at 442 nm is compared to the AERONET SSA at 440 nm. This approximation utilizes the weak dependence of aerosol SSA on wavelength (Dubovik et al., 2000) to avoid interpolating SSA with respect to wavelength. 2.4 The chosen AERONET sites in China Although nearly ten sites situated throughout mainland China are included in the official site list of AERONET, only three sites among them continuously operate and have long enough records released: the Xianghe, Taihu, and Hong Kong sites. The Cimel-318 instrument has been deployed and began routine observation at Xianghe in 2001, while operation at Taihu and Hong Kong began in These three distinct sites are chosen in this study for the evaluation. The Xianghe site (39.75 N, E), near to the southeast of Beijing, is located close to a small town and surrounded primarily by agricultural land and deciduous vegetation (Mi et al., 2007). Xianghe represents a typical rural area in northern China and suffers greatly from dust storms during the spring season. The Taihu site (31.42 N, E) is located on the northern edge of Tai Lake, which is the third largest freshwater lake in China. Surrounded by several large cities in the Yangtze delta, the Taihu site is dominated by the presence of industrial aerosols (Mi et al., 2007). The Hong Kong site (22.30 N, E) is at the library of the Hong Kong Polytechnic University, which is a central location in Hong Kong. The perpetual anthropogenic pollution present in Hong Kong along with the sea salts carried in by the sea breeze lead to heterogeneous aerosol conditions at that site. 3 Comparing the OMI SSA with the AERONET retrievals 3.1 On a global scale The frequency distributions of the OMI SSA and the AERONET SSA, derived by considering all of the coincidences throughout the globe, are shown in Fig. 1. The mode of AERONET SSA is situated at 0.90 and exhibits less frequent large and small SSAs, suggesting that the data accurately represent the physical conditions at the site. The mode value of the OMI SSA data, however, is located at the maximum of Large OMI SSAs seem to appear more frequently, with SSAs of approximately 0.95 constituting greater than 40% of the data. The OMI SSA has more unexpectedly large values compared with the AERONET retrievals, indicating a systematic overestimation. These notable positive biases are likely partially caused by cloud contamination. Due to the relatively coarse resolution of the OMI (greater than 10 km), sub-pixel cloud contamination frequently occurs and leads to the overestimation of AOT and the underestimation of the single scattering co-albedo (Torres et al., 1998). This

3 266 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 5 apparent discrepancy is resolved using more rigorous cloud-free criteria to refine the coincidences. Regarding the mean values of the two datasets, for the OMI SSA and for the AERONET SSA, there is a positive relative bias of 3.5% for the OMI SSA and a rootmean-square (RMS) difference of The overall correlation between the two datasets has a very low value of less than 0.2. Furthermore, the SSA values that are less than 0.84 are completely missing from the OMI retrieval. 3.2 At the three sites in China Daily data from 2005 to 2008 are available for Xianghe and Hong Kong, but only the two-year data in 2005 and 2006 are available for Taihu. There are 225 coincidences in total for Xianghe, and the sample volume is just 68 and 48 for Taihu and Hong Kong, respectively. The much smaller number of coincidences in Taihu and Hong Kong is likely a result of a lack of cloud-free situations. Generally, the satellite SSA retrievals modestly agree with the AERONET SSA, with RMS differences of 0.06, 0.05, and 0.1 in Xianghe, Taihu, and Hong Kong, respectively. Figure 2 shows the scatter plot for the three sites in China. In general, a poor quality of the OMI SSA retrievals is observed. The worst consistency between the OMI SSA and the AERONET SSA occurs at Xianghe, with a correlation coefficient of approximately In Taihu and Hong Kong, although there are fewer samples available, the correlations improve, with values of 0.47 and 0.44 in Taihu and Hong Kong, respectively. Nevertheless, the dynamic range of the SSA characterized by the AERONET measurements is completely obscured in the OMI SSA data. For example, in Taihu, the notable variability of SSA between 0.80 and 0.98 is represented by four SSA levels. It is apparent that the current multiwavelength OMI retrieval is too insensitive to reflect the significant variation of the aerosol SSA. Insufficient information embedded in the multi-wavelength measurements is likely the underlying reason for the insensitivity (Veihelmann et al., 2007), for which the inversion cannot be reasonably constrained. Thus, the instantaneous OMI SSA retrievals have significant uncertainties, and they cannot be used to quantify aerosol properties, even on daily scales. To acquire more knowledge about the characteristics of the OMI SSA, the temporal sequences of aerosol SSAs for each site in China are shown in Fig. 3. Here, the time sequence is not continuous, which is just an artificial simulation of the particular days that are classified as cloud-free and have valid aerosol retrievals. In Xianghe, the OMI SSA approximately reflects the temporal variation but is limited to only large SSA values. Most aerosols with SSA values less than are mistakenly assigned and overestimated to various extents. Despite the overall positive bias of the OMI SSA, there is short-term variation in the SSA. For instance, the abrupt increases of the SSA near days 20, 100, and 180 are all correctly represented. In particular, when the cases with an OMI SSA of less than are excluded, the correla- Figure 1 The SSA frequency distribution for (a) AERONET SSA at 440 nm and (b) OMI SSA at 442 nm. Figure 2 The scatter plot for the spatiotemporally matched OMI SSA and the AERONET SSA at the three sites: (a) Xianghe, (b) Taihu, and (c) Hong Kong.

4 NO. 3 LIU AND HONG: AEROSOL SINGLE-SCATTERING ALBEDO OF OMI 267 Figure 3 Comparison of the daily series of OMI SSAs (open circles) versus AERONET SSAs (solid circles) during the observationally valid days at the three sites: (a) Xianghe, (b) Taihu, and (c) Hong Kong. tion coefficient for the sub-sample improves to These improved fitting cases are observed to primarily occur during summer and autumn. Obviously, an underlying seasonality for aerosols in northern China is scattering-dominant in summer and autumn and absorbing-dominant in winter and spring because it is nearly consistent in both SSA datasets. In Taihu, the OMI SSA successfully captures the general SSA trends, but detailed discrepancies are present in the data. When considering the seasons in these 68-day records, the OMI SSA is observed to be much lower during winter and spring and higher in summer and autumn, which fits well with the AERONET SSA. Hence, based on the OMI SSA measurements, it is reasonable to conclude that the seasonality of aerosols in Taihu is similar to that in Xianghe, i.e., more absorbing during winter and spring and more scattering during summer and autumn (Liu et al., 2011). In Hong Kong, the OMI SSA also reasonably outlines the fundamental SSA trends on a seasonal scale, in agreement with the analysis in Taihu. Note that the OMI retrievals are deficient in characterizing the daily variation in aerosol SSA but provide a reliable seasonal pattern, especially when the systematic positive biases are ignored. The applicability of the OMI SSA on a larger temporal scale will be further explored in the following section. 3.3 Seasonal comparison of the SSAs from the OMI and AERONET Apparently, the quality of the daily OMI SSA data is rather limited. Possibly due to cloud contamination during OMI data retrieval, an invariably positive bias exists for the calculation of the SSA at the individual sites and for the global statistics. The discrete value of the SSA retrieval in the OMI algorithm enhances the above uncertainty and reduces the correlation with the AERONET measurements, making the OMI SSA less suitable to quantitatively characterize aerosol optical properties. Nevertheless, if the data are averaged to represent a larger temporal scale, the reasonable consistency in the aerosol trend between the two datasets is promising. Because satellite remote sensing is advantageous for climatological applications, rather than being highly representative on a local scale, it is meaningful to use the OMI SSA for long-term climatological records. The seasonal variation in the investigated OMI SSA is shown in Fig. 4. For this potential climatological application, the correlation coefficient increases significantly and has a value of 0.89, 0.98, and 0.89 at Xianghe, Taihu, and Hong Kong, respectively. In spite of the considerable

5 268 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 5 Figure 4 Comparison of the monthly OMI SSA with the AERONET SSA at the three sites in mainland China: (a) Xianghe, (b) Taihu, and (c) Hong Kong. The summer mean in Hong Kong is vacant because no valid aerosol retrievals were obtained. positive biases that remain evident in the seasonal mean, the OMI SSA is shown to be representative of the seasonal oscillation at each site. In this respect, the OMI SSA is almost equivalent to the AERONET SSA. The application of the OMI SSA should be more advantageous because its scope extends beyond the local scope of the ground measurements. It is clear that, even on a seasonal scale, the OMI SSA at Hong Kong is the most problematic; the bias of the seasonal mean is consistently higher than Presumably, this is attributed to a larger number of cloud contamination occurrences. We believe an additional sample filter using a rigorous screening for the presence of clouds could achieve more seasonal consistency between the OMI and AERONET datasets. Implementation of this sample filter will be conducted in the next study by comparing the instantaneous AERONET data, rather than the daily mean, with the OMI level-2 aerosol products. 4 Summary and conclusion Operating as a member of the A-Train project for over seven years, the OMI/Aura has accumulated numerous aerosol measurements. The most prominent product, which has been a challenge for previous satellite techniques, is the aerosol SSA, which is evaluated in this study by using the AERONET SSA as the ground-based validation. According to an analysis using approximately 6000 daily coincidences across the globe, the OMI SSA is notably overestimated and correlates poorly with that of AERONET, indicating the OMI SSA in its current version is far from satisfactory. In addition to the uncertainty resulting from the retrieval algorithm, cloud contamination may significantly contribute to the positive biases because of the OMI s ~10-km field of view. This cloud contamination could likely be mitigated with a strict screen for cloud presence using independent measurements with a higher resolution. The inherent discrete values of the OMI SSA retrievals are the primary cause of the low correlation coefficient between the OMI and AERONET datasets. The very limited information content (two to four degrees of information content freedom, Veihelmann et al., 2007), even in the hyper-spectral OMI observations, does not meet the requirements for the prescribed aerosol models. To improve the OMI retrieval, the aerosol models may require further investigation to represent a variety of aerosol environments. The systematic positive bias of the OMI SSA is also clear at the Xianghe, Taihu, and Hong Kong sites, among which the discrepancy in SSA varies greatly. The OMI

6 NO. 3 LIU AND HONG: AEROSOL SINGLE-SCATTERING ALBEDO OF OMI 269 SSA in Taihu exhibits a relatively high correlation of ~0.5 and a low bias, with more than 40% of the retrievals falling into the AERONET uncertainty range (~0.03). The low-value SSA in Xianghe, which is dominated by winter-to-spring strongly absorbing aerosols, is almost obscured in the OMI retrievals. In Hong Kong, the significant positive bias is probably caused by the frequent occurrence of cloudy conditions. Despite the modest quality of the OMI daily SSA, the seasonal mean consistently agrees well with that of the AERONET at Xianghe, Taihu, and Hong Kong, with correlation coefficients of approximately 0.9 at these sites. This result demonstrates that the OMI successfully captures the seasonal pattern of aerosol SSA in a specific region, although the absolute magnitude of SSA needs to be improved. This finding is promising because a large-scale climatological application, rather than a local interpretation, is preferred for satellite observations. It is feasible to explore aerosol climatology at least on a semi-quantitative level, relying on complementary OMI data along with the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Acknowledgements. The authors are grateful to the anonymous reviewers for their helpful comments. This research is supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-Q11-03) and the National Natural Science Foundation of China (Grant Nos and ). The OMI aerosol products (OMAEROe) were derived from the MIRADOR data release system managed by NASA Goddard Space Flight Center (GSFC), and the AERONET data were obtained from the website References Dubovik, O., A. Smirnov, B. N. Holben, et al., 2000: Accuracy assessment of aerosol optical properties retrieval from AERONET sun and sky radiance measurements, J. Geophys. Res., 105, Hansen, J., M. Sato, and R. Ruedy, 1997: Radiative forcing and climate response, J. Geophys. Res., 102, Herman, J. R., and P. K. Bhartia, 1997: Global distribution of UV- absorbing aerosols form Nimbus 7/TOMS data, J. Geophys. Res., 102, D14, Holben, B. N., T. F. Eck, I. Slutsker, et al., 1998: AERONET A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, Levelt, P. F., E. Hilsenrath, G. W. Leppelmeier, et al., 2006: Science objectives of the Ozone Monitoring Instrument, IEEE Trans. Geosci. Remote Sens., 44(5), Liao, H., and J. H. Seinfeld, 1998: Radiative forcing by mineral dust aerosols: Sensitivity to key variables, J. Geophys. Res., 103, Liu, Q., W. Ding, and Y. Fu, 2011: The seasonal variations of aerosols over East Asia as jointly inferred from MODIS and OMI, Atmos. Oceanic Sci. Lett, 4(6), Mi, W., Z. Li, X. Xia, et al., 2007: Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two Aerosol Robotic Network stations in China, J. Geophys. Res., 112, D22S08, doi: /2007jd Osborne, S. R., B. T. Johnson, J. M. Haywood, et al., 2008: Physical and optical properties of mineral dust aerosol during the Dust and Biomass-burning Experiment, J. Geophys. Res., 113, D00C03, doi: /2007jd Otto, S., M. de Reus, T. Trautmann, et al., 2007: Atmospheric radiative effects of an in situ measured Saharan dust plume and the role of large particles, Atmos. Chem. Phys., 7, Torres, O., P. K. Bhartia, J. R. Herman, et al., 1998: Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation, J. Geophys. Res., 103, Torres, O., R. Decae, P. Veefkind, et al., 2002: OMI aerosol retrieval algorithm, in: OMI Algorithm Theoretical Basis Document. Vol. III. Clouds, Aerosols and Surface UV Irradiance, ATBD-OMI-03, P. Stammes and R. Noordhoek (Eds.), De Bilt, Netherlands, Torres, O., A. Tanskanen, B. Veihelmann, et al., 2007: Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview, J. Geophys. Res., 112, D24S47, doi: /2007JD Veihelmann, B., P. F. Levelt, P. Stammes, et al., 2007: Simulation study of the aerosol information content in OMI spectral reflectance measurements, Atmos. Chem. Phys., 7, Zhao, F., and Z. Li, 2007: Estimation of aerosol single scattering albedo from solar direct spectral radiance and total broadband irradiances measured in China, J. Geophys. Res., 112, D22S03, doi: /2006jd