MEASURING AEROSOL ABSORPTION FROM OMI NEAR UV OBSERVATIONS

Omar Torres(1), Pawan Bhartia(2), Changwoo Ahn(3)

(1)Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, 1000 Hilltop Rd, Baltimore, MD, 21250, Email: torres@qhearts.gsfc.nasa.gov

(2) NASA Goddard Space Flight Center, Code 613.3 Greenbelt, MD, 20771, Email: Pawan.Bhartia@nasa.gov

(3) Science Systems and Applications Inc., Lanham, MD, 20721, Email: Changwoo.Ahn@ssaihq.com

ABSTRACT An assessment of OMI retrievals of aerosol properties is presented. We compare OMI retrieved values of extinction and absorption optical depth, and aerosol single scattering albedo to AERONET ground-based measurements. An evaluation of the accuracy of the retrievals for a limited number of observations under cloud-free conditions shows a positive offset of about 0.03 in retrieved optical depth. This small offset is an indication of accurate sensor calibration to the 1% level. The retrieved SSA values agree well with RMS differences of 0.02-0.03 compared with the AERONET observations. This result shows that the UV technique is a very valuable tool for measuring aerosol absorption from space. 1. INTRODUCTION The OMI sensor is one of four instruments on the Eos-Aura platform deployed on July 15, 2004. It is a Dutch-Finnish built high resolution spectrograph that measures the upwelling radiance at the top of the atmosphere in the ultraviolet and visible (270-500 nm) regions of the solar spectrum. It has a 2600 km wide swath and provides daily global coverage at a spatial resolution varying from 13x24 km at nadir to 40x135 km at the extremes of the swath. Aura is in a sun-synchronous orbit with ascending node Equator crossing time of 13:45. The Aura spacecraft is part of the A-train, which includes 3 other aerosol sensors Aqua-MODIS, Parasol, and CALIPSO. OMI measurements in the UV are used to derive aerosol properties. There are two major advantages of the near-UV technique for aerosol remote sensing. The reflectance of all terrestrial surfaces (not covered with snow) is small in UV, therefore, the retrieval of aerosol

properties is possible over a larger variety of land surfaces than in the visible, including the arid and semi-arid regions of the world that appear very bright in the visible and near-IR. The second advantage is the strong interaction between aerosol absorption and molecular scattering from below the aerosol layer that allows one to estimate the aerosol absorption capacity of the atmospheric aerosols [1]. 2. THE OMI UV AEROSOL ALGORITHM In adition to the Absorbing Aerosol Index (AAI), the OMI UV aerosol algorithm (OMAERUV) uses measurements in the near-UV to obtain estimates of aerosol extinction (AOD) and absorption optical depth (AAOD) at 388 nm. The AAI is a qualitative parameter that measures the resulting spectral contrast change in the UV when UV-absorbing aerosols (desert dust, smoke, volcanic ash) are present in the atmosphere [2]. The map in Fig. 1 shows the spatial distribution of dust, carbonaceous and pollution aerosols as seen by the OMI AAI over Asia on April 7 2005. The AOD and AAOD quantities are derived using a standard inversion algorithm that uses pre-computed reflectances for a set of assumed aerosol models. Three major aerosol types are considered: desert dust (DST), carbonaceous aerosols associated with biomass burning (BIO), and weakly absorbing sulfate-based aerosols (SLF). Each aerosol type is represented by seven aerosol models of varying single scattering albedo, for a total of twenty-one microphysical models. For a chosen aerosol type, represented by a sub-set of seven aerosol models, the extinction optical depth and single scattering albedo are retrieved by examining the variability of the relationship between the 354-to-388 nm spectral contrast and the 388 nm reflectance [2] as illustrated in Fig.2.

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Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

Figure 1. Desert dust, carbonaceous and pollution aerosols in terms of the OMI AAI on April 7, 2005 Since the retrieval algorithm is sensitive to the aerosol height [2], results are reported for five different assumptions on the location of the aerosol center of mass: at the surface, and at 1.5, 3.0, 6.0 and 10.0 km above the surface. In addition, best estimate values of AOD and AAOD associated with a particular choice of aerosol vertical distribution are made available. The choice of vertical distribution varies with aerosol type and location. For sulfate-based aerosols the assumed vertical distribution is largest at the surface and decreases exponentially with height. Carbonaceous aerosols layers within 30 degrees of the Equator are assumed to have maximum concentration at 3 km above ground level, whereas mid and high-latitude smoke layers are assumed to peak at 6 km. The location of desert dust aerosol layers varies between 1.5 and 10 km, and is given by a multi-year climatological average of Chemical Model Transport (CTM) calculations using the GOCART model. Surface reflectance is prescribed using an existing climatological database derived from the long-term TOMS reflectivity record. A sample OMI retrieval is shown in Fig. 3 that depicts the spatial distribution of the AAI and the resulting retrieved absorption optical depth over

Asia on March 10 2006. Because of cloud contamination the spatial coverage of the AAOD is smaller than the spatial extent of the AAI.

Figure 2. OMI aerosol retrieval scheme to derive aerosol optical depth and single scattering albedo. 3. AERONET OBSERVATIONS AERONET [3] is a worldwide network of CIMEL sun-photometers that measures aerosol optical depth at the surface and sky radiances in a wide angular range at 15 minutes intervals at

several wavelengths. Derived aerosol optical depth and measured sky radiances at 440, 675, 870 and 1020 nm at several values of scattering angle are used as input to a fitting algorithm to retrieve aerosol size distribution and complex refractive index as a function of wavelength [4]. The AERONET retrieved complex refractive

index and particle size distribution parameters are used in the calculation of the single scattering albedo and absorption optical depth at 440, 675, 870 and 1020 nm. Tab.1 shows the location and the predominant aerosol sol type at 28 AERONET stations used in the comparison analysis.

Figure 3. OMI Absorbing Aerosol Index and retrieved Absorption Optical Depth on March 10 2006

Site Location Aerosol Type

Alta_Floresta Agoufou Banizombou IER-Cinzana Ougadougou Mongu Forth Crete Blida Saada Dakar Kanpur Dhadnad Hamim Mukdahan XiangHe GSFC Halifax Bratts_Lake Bondville CARTEL Cove HJAndrews MD Sc Center MVCO SERC Sioux_Falls UCSB Wallops

9S 56W 15N 1W 13N 2E 13N 5W 12N 1W 15S 23E 35N 25E 36N 2E 31N 8W 14N 16W 26N 80E 25N 56E 22N 54E 16N 104E 39N 116E 38N 76W 44N 63W 50N 104W 40N 88W 45N 71W 36N 75W 44N 122W 39N 76W 41N 70W 38N 76W 43N 96W 34N 119W 37N 75W

smoke smoke, dust smoke,dust dust dust, smoke smoke dust, pollution dust dust dust,smoke dust,smoke,pollution dust dust smoke,pollution dust, smoke,pollution weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing weakly absorbing

Table 1. AERONET sites used in the analysis

These sites were selected based on their being representative of urban-industrial pollution, biomass burning carbonaceous aerosols and desert dust (which are the most predominant continental aerosol types), and the availability of data in the two-year period 2005-2006. OMI retrieved AOD values at 388 nm are compared AERONET 380 nm measurements when available. At those AERONET sites where the shortest wavelength available for the AOD measurement is 440 nm, a conversion to 380 nm is done using the Angstrom exponent from the actual AERONET measurements. In addition OMI derived values of single scattering albedo are compared to AERONET retrievals at 441 nm. 4. OPTICAL DEPTH COMPARISON The scatter plot in Figure 1 compares OMI retrieved to AERONET measured AOD at a number of sites representative of weakly absorbing aerosols (GSFC, Halifax, Bratts Lake, Bondville, CARTEL, Cove, HJ Andrews, MD Science Center, MVCO, SERC, Sioux_Falls, UCSB, and Wallops). Sixty percent of the points are within the expected accuracy (larger of 0.05 or 20%). The comparison yields a correlation coefficient of 0.68. Though the rms difference between the two measurements of 0.05 can be

considered reasonable given the uncertainties in the comparisons, one notes that about 10% of the samples are underestimates while the remaining 30% are overestimates in the AERONET range 0.05-0.2. To identify systematic calibration and algorithmic effects from the random retrieval noise, we have binned and averaged the data sets in 0.02intervals. If the scatter in the un-binned data around the one-to-one line were all random noise, the binned-averaged points would fall on the line 45 degree line. As observed in Figure 1, a 0.03 bias in AOD is observed

Figure 4. Comparison of OMI retrieved to AERONET measured AOD. It is impossible to identify a single cause for the small offset (0.03) resulting from the analysis illustrated in Figure 4. It could all be due to instrument calibration or the resulting net effect of a combination of factors involving both instrumental and algorithmic factors. Likely algorithmic effects are surface albedo assumptions or sub-pixel cloud contamination. A surface albedo error could go either way whereas sub-pixel cloud contamination would always produce a positive offset. The interpretation of the offset as entirely the result of calibration translates into a 1% accuracy of the measured reflectance. On the other hand, a very small surface albedo error (0.3%) would yield the observed AOD offset. Fig. 5 shows a similar comparison using the OMI data converted to 500 nm and the AERONET 500 nm measurements. Statistically the 500 nm comparison is equivalent to the comparison in the near-UV, indicating that the spectral dependence of the assumed model is realistic. The assumed spectral dependence is based on AERONET long-term statistics for different aerosol types. Fig. 6 shows a comparison at a number of AERONET sites where the aerosol load is typically desert dust. Data from the sites Blida,

Dakar, Hamin, and Saada are plotted in Figure 4. Although a correlation between the two measurements is significant (0.74)), the level of noise in the OMI measurements is high. The increased noise level is probably associated with the uncertainty of the assumed aerosol layer height. The binned data analysis shows the existence of a positive bias as large as 0.3 for AOD values less than 1.0. For values larger than 1.0 the bias becomes negative.

Figure5. As in Fig. 2 at 500 nm.

5. SINGLE SCATTERING ALBEDO AERONET and OMI retrieved values of single scattering albedo were compared at a variety of sites. All AERONET absorption retrievals used in this analysis are for AOD (440 nm) larger than 0.4. Absorption retrievals for smaller AOD values are not considered reliable [4]. The AERONET retrievals at the shortest wavelength reported, i.e., 441 nm are compared to OMI reported single scattering albedo at 388 nm.. This wavelength difference must be taken into account in the interpretation of the results. In the case of carbonaceous aerosols where the main absorbing component is assumed to be black carbon, a weak spectral dependence is expected between 388 and 441 nm. This is because the imaginary component of the refractive index of

Figure 6. As in Fig. 2 for desert dust.

black carbon absorption exhibits a weak spectral dependence from the near-UV to the near-IR [5,6]. Thus the measurements could be considered similar since any spectral dependence in that narrow wavelength range should be relatively small. Fig. 7 shows the result of a comparison of the single scattering albedo at 9 AERONET sites where the aerosol type was identified as either biomass burning particles or pollution was done. The sites (geographical locations are given in Table 1) are: Agoufou, Alta_Floresta, Banizombou, Beijing, Dakar, Kanpur, Mongu, Mukdahan, and XiangHe. Dotted line indicates ±0.03 differences, dashed lines ±0.05.

Figure 7. Single scattering albedo comparison for biomass burning events. The range of variability of the AERONET retrieved SSA values (between 0.77 and 0.98) is slightly larger than the variability of the OMI results (0.79-0.95). The two measurements are representative of different times of the day (OMI early afternoon, AERONET early morning or late afternoon) thus contributing to the scatter. The uncertainty in the satellite retrieval is mainly

associated with the aerosol layer height, cloud contamination, and aerosol type misidentification. Since the OMI still yields SSA values less than 0.9, it is unlikely that clouds are strongly affecting the retrievals. Generally, cloud contamination will drive the retrieved SSA to near unity. The use of standard statistical metrics like the correlation coefficient, are not suitable for evaluating the level of agreement of the two retrieval techniques. Since the overall natural variability of the measured parameter is only 2 or 3 times the expected range of uncertainty (±0.05 or 0.10), low correlation coefficients are likely to result. A more suitable parameter is the root mean square error (rms) that describes the mean closeness of the results for the analyzed sample. The results in Fig. 4 yield an rms of 0.03. About half of the points agree within 0.03, whereas nearly three-fourths are within 0.05. The level of agreement in derived SSA for carbonaceous aerosols reported in Fig. 5 can be considered very good taking into account the difference in the retrieval techniques. 6. SUMMARY The OMI optical depth comparisons are reasonably well correlated with the ground-based observations. Comparison of retrieved single scattering albedo shows a high level of consistency between the two retrieval techniques. In general the retrieved SSA values agree well with rms error of 0.03. This result can be considered remarkable when taking into account the fundamental differences between the inversion approaches. REFERENCES 1. Torres, O., P.K. Bhartia, A. Syniuk, and E.

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