GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS observations

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2006jd007968, 2007 GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS observations Ana I. Prados, 1 Shobha Kondragunta, 2 Pubu Ciren, 3 and Kenneth R. Knapp 4 Received 25 August 2006; revised 15 January 2007; accepted 3 May 2007; published 2 August [1] The GOES Aerosol/Smoke Product (GASP) is a retrieval of the aerosol optical depth (AOD) using visible imagery. The product currently runs operationally at NOAA/NESDIS in near-real time at 30 min intervals. This high temporal resolution is not possible with polar orbiting instruments which produce one daily image. This work evaluates the GASP AOD from the GOES-12 Imager over North America at various temporal and spatial scales based on comparisons with AOD from the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We find a mean GASP/AERONET correlation of 0.79, rms difference of 0.13 and slope of 0.8, based on a statistical analysis at 10 northeastern U.S. and Canadian sites. The GASP AOD has a slight negative bias when the AOD is greater than 0.35 and a positive bias elsewhere. The absolute bias is less than 30% when the AOD is greater than 0.1. When the AOD is less than 0.15 we find poor correlation and biases greater than 30%. The GASP/ AERONET statistics also indicate that GASP can be used to examine the seasonal and diurnal variability in the AOD over the eastern United States between 1215 and 2115 UTC. GASP/AERONET AOD correlations were generally less than 0.5 elsewhere in the continental United States. Comparisons between the MODIS and GASP AOD over the eastern United States in the summer of 2004 showed agreement within 20% and correlations greater than 0.7 under elevated AOD conditions. Simultaneous comparisons between GASP, MODIS, and AERONET AODs showed good agreement over the northeastern United States and Canada, with higher correlation and lower rms differences in the MODIS/AERONET comparisons than in the GASP/AERONET comparisons. Citation: Prados, A. I., S. Kondragunta, P. Ciren, and K. R. Knapp (2007), GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS observations, J. Geophys. Res., 112,, doi: /2006jd Introduction [2] Despite the recent increase in satellite remote sensing instrumentation for the detection of aerosols and aerosol properties over land surfaces, there remains a need for nearreal time aerosol information at high temporal and spatial resolution. While ground based remote sensing and in situ instrumentation provide accurate aerosol information at high temporal resolution, their sparse geographical coverage limits our ability to monitor and forecast particulate matter concentrations and aerosol properties at many locations worldwide and within the United States. Currently, satellite instrumentation provides the only means to fill these geographical gaps. Examples of satellite remote sensing instrumentation for the detection of aerosols over land include the 1 Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland, USA. 2 Center for Satellite Applications and Research, National Environmental Satellite, Data, and Information Service, NOAA, Camp Springs, Maryland, USA. 3 QSS Inc., Camp Springs, Maryland, USA. 4 National Climatic Data Center, National Environmental Satellite, Data, and Information Service, NOAA, Asheville, North Carolina, USA. Copyright 2007 by the American Geophysical Union /07/2006JD Earth Probe Total Ozone Mapping Spectrometer (TOMS) Aerosol Index [Herman et al., 1997a] and the recent Ozone Measurement Instrument (OMI), both of which detect absorbing aerosols, the Polarization and Directionality of the Earth s Reflectances (POLDER) [Herman et al., 1997b], the Along Track Scanning Radiometer (ATSR-2) [North, 2002], and the Multiangle Imaging Spectroradiometer (MISR) [Liu et al., 2004]. The Moderate Resolution Imaging Spectroradiometer (MODIS) has been used extensively to monitor aerosol properties and Aerosol Optical Depth (AOD) worldwide over land and ocean [Kaufman et al., 1997; Remer et al., 2005]. [3] The GASP algorithm [Knapp et al., 2002, 2005] is a retrieval of Aerosol Optical Depth, currently being used for the monitoring of particulate pollution at high temporal resolution. The satellite s geostationary orbit permits AOD retrievals of up to 15 min frequency. Instruments on polar orbiting satellites provide only one daily image. Availability of aerosol data at high temporal resolution can be important during circumstances of rapidly changing aerosol load affecting human health or the environment (e.g., visibility), such as for example rapidly deteriorating air quality due to dust events, fire smoke, or urban aerosol development, in situ or due to rapidly moving aerosol plumes. 1of15

2 [4] GASP imagery has been used for qualitative analysis of optically thick aerosols plumes due to fires over South America [Zhang and Christopher, 2001; Knapp et al., 2002] and the continental United States [Prados et al., 2004], and during the recent ICARTT/INTEX-A field campaign. Knapp et al. [2005] presented a validation of the GASP algorithm for the GOES-8 Imager and a method to correct surface effects. In this work, we present the first validation of the GASP algorithm over North America for the GOES-12 Imager. The validation is based on comparisons with AOD observations at 32 stations from the Aerosol Robotic Network (AERONET) for the calendar years 2004 and 2005 at various temporal and spatial scales. We also show comparisons to MODIS AOD observations from 2004 and Aerosol Optical Depth Retrievals From GOES Imagery [5] Current GASP retrievals are performed with GOES- 12 (East) imager data, but the algorithm has also been run for the GOES-8 imager [Knapp et al., 2002, 2005]. The imager measures the top-of-the-atmosphere radiance in one visible channel and four infrared channels. The AOD retrievals use only the visible ( nm) channel. The cloud masking algorithm, based on Clouds from AVHRR (CLAVR), uses IR channels 2 (3.9 um) and 4 (10.7 um) [Stowe et al., 1999; Heidinger et al., 2001]. The spatial resolution of the GOES imager visible channel is 1 1 km, but the data are first averaged to 4 4 km resolution, in order to match the resolution of the IR channels. In addition, for all GASP data presented in this work the retrieved AOD at each pixel has been smoothed by taking the average of the AOD for every 3 3 pixels, resulting in a product of approximately km resolution. [6] The first step in calculating the GASP AOD is estimating the surface reflectivity. Here, we first create a composite clear-sky background image for each time of day. This image is created by using the visible image from the past 28 days including the current image, and then selecting the second darkest pixel at each location over that time period. The second darkest pixel is chosen to reduce cloud shadow contamination [Knapp et al., 2005]. The optimum number of days used in retrieving the surface reflectivity is discussed in detail by Knapp et al. [2005], who concluded that it varies seasonally. A longer time period increases the chances of finding an image representative of background aerosol conditions. However, because of the temporal variability in the surface reflectivity, the image selected for calculating the surface reflectivity might not be representative of the entire time period. We retrieve the surface reflectivity for the composite image using a Look Up Table (LUT) generated with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model [Vermote et al., 1997]. A GOES-12 spectral response function was used in generating the LUT. The GASP AOD is reported at 550 nm, one of the 6S model wavelengths, and because aerosol scattering increases toward the short wavelength end of the GOES-12 ( nm) visible band. For the surface retrieval, we assume a background AOD of 0.02 [Knapp et al., 2005]. [7] The retrieval accounts for aerosol extinction, Rayleigh scattering, ozone absorption, and water vapor absorption. Rayleigh scattering is calculated using surface topography data from the National Geophysical Data Center Terrain- Base data set and assuming a U.S. standard atmosphere [McClatchey et al., 1972]. Gaseous absorption is calculated using a standard U.S. Atmosphere water vapor profile and ozone column. The calculated surface reflectivity and the LUT from the 6S model are used to retrieve the actual AOD for each time period. The algorithm assumes the continental aerosol model used in 6S for the entire domain, for which the single scattering albedo is The model description can be found at [Vermote et al., 1997]. Knapp [2002] compared the observed and modeled GOES-8 signal for a continental, maritime, and urban model, and reported best agreement for the continental model. Currently, the GASP AOD is retrieved half hourly in near-real time (within half an hour) during sunlit portions of the day, i.e., between 1045 UTC to 2345 UTC depending on location and time of the year. Additionally, the bad data are screened out using the following criteria: When the retrieved AOD is greater than 2.0, which helps reduce cloud contamination, the retrieved surface reflectivity is less than 0.5% or greater than 15%, the standard deviation of the AOD over a 3 3 box surrounding each pixel is greater than 0.2, and when the cloud screening algorithm identified a cloud for that pixel. Bad data are also screened out when the signal is less than 0.1. The signal is defined as the ratio of the change in the satellite detected reflectance over the change in the AOD and is calculated with the LUT for an AOD of 0.02 and 0.16 using the retrieved surface reflectivity and current angular information. We also use a cloud uniformity test that screens out the data at a particular pixel if the 3 3 box surrounding each pixel is not cloud free and when there are less than 15 cloud free pixels in each 5 5 box. Negative AOD retrievals, about 15% of the total, are screened out as well. Negative retrievals occur primarily because of high biases in the retrieved surface reflectivity. [8] GASP visible radiances ( goes/goes-calibration/goes-vis-ch-calibration.htm) are calibrated according to the following expression: ref ¼ Cal* ðc CoÞ*a* expðbnþ where ref is the reflectivity, C and Co are the satellite measured counts and dark count (29) respectively, Cal is the calibration factor, and a and b are constants. To account for signal degradation, n in the exponent represents the number of days since 30 April 2003, the satellite launch date. [9] One of the main differences between the GOES-8 and GOES-12 imager is the lack of a 12 um channel in the GOES-12 imager, which is used in the CLAVR cloud masking algorithm for the screening of cirrus clouds. However, we examined GOES-8 AODs from the year 2001 at several AERONET sites with and without the use of this channel and found no significant differences in the comparisons with AERONET AODs. Furthermore, the 12 um channel screen test only eliminated a very small fraction of additional GOES pixels. 2of15

3 Figure 1. GASP AOD Image. The color scale indicates the AOD range, and the grayscale is the visible imagery, used when the AOD data have been screened out, due primarily to the presence of clouds. [10] GASP imagery are available at gov/ps/fire/gasp/gasp.html. A sample image is shown in Figure 1. The color scale is used to indicate the AOD (0 to 1.3). When the data have been screened out, we plot the corresponding value for the current visible image, shown in the grayscale. [11] The main sources of uncertainty the GASP AOD retrieval have been examined in detail by Knapp et al. [2002] and include assumptions about the aerosol layer height in the radiative transfer model, the Lambertian surface assumption in the surface reflectance distribution, cloud shadows, and the clear sky composite image selected for calculating the surface reflectivity (discussed above). Assumptions in the value of the background AOD in the clear sky composite image can also be a significant source of uncertainty since the current algorithm does not account for geographical or temporal variability in the background AOD, and instead assumes a temporally constant value of 0.02 throughout the entire domain. In addition, instrumental noise and calibration errors also propagate into the retrieved AOD. Knapp et al. [2002] concluded that GASP AOD retrieval errors range between 18 and 34%. [12] Other potential sources of uncertainty in the AOD retrieval are errors in the cloud-mask algorithm, which would lead to a high bias in the retrieved AOD, and errors in the aerosol model assumption in the 6S radiative transfer simulation. A recent study (S. Kondragunta et al., Air quality forecast verification using satellite data, submitted to Journal of Applied Meteorology and Climatology, 2006) examined the effect of the aerosol models on retrieved GASP AODs in the summer of 2004 and concluded that while a biomass burning model led to lower GASP/AERO- NET rms differences, the linear regression fit between the two data sets was moderately better when using the continental model in the current GASP algorithm. Thus, depending on how representative the current model is of the actual aerosol characteristics, we will find various degrees of additional error in the retrieved AOD. This source of error is further discussed later in the text. 3. Comparison of GASP and AERONET AOD at Selected Sites [13] For this work, we use AERONET data Level 1.5 ( AERONET is a global network of ground based sun/sky radiometers and AERONET Figure 2. Map showing the location of AERONET stations used in this work. Also shown (colored dots) is the GASP/AERONET correlation. Scale is from 0.2 to of15

4 Figure 3. GASP/AERONET correlation plots for the full 2004 calendar year at CCNY, New York; CARTEL, Canada; HJ Andrews, Oregon; Konza, Kansas; SERC, Maryland, UCLA, California; Walker Branch, Tennessee; and Wallops Island, Virginia. Also included in each plot are the number of matches (n), correlation factor (r) and root mean square (rms) difference. Also indicated is the linear regression fit using all the individual matches and the 1-to-1 line. The regression equation is shown at the top. AODs have an estimated accuracy of ±0.02 [Holben et al., 1998]. Because there is no AERONET AOD at 550 nm, we calculate it using the spectral dependence of the AOD at the two nearest wavelengths, generally 675 and 500 nm, then solving for the Angstrom exponent by logarithmic interpolation. The error introduced in the interpolation is between 0% and 10% [Eck et al., 1999]. [14] We compare the GASP and AERONET AOD at 32 U.S. and Canadian sites. Their locations are shown in Figure 2. Location names were obtained from gsfc.nasa.gov. Our analysis focuses on the east coast, since that is where the majority of the AERONET sites are located. [15] Shown in Figure 3 is the AERONET versus GASP AOD at eight AERONET sites for the calendar year 2004 between 1215 UTC and 2115 UTC. The correlation (r), rms difference and mean GASP and AERONET AOD are also shown at these and other AERONET sites in Tables 1 and 2, separated by geographical region and year for 2004 and AERONET data were not available at all sites both 4of15

5 Table 1. GASP/AERONET Comparisons 2004 Station n04 r04 rms04 GASP04 AER04 Northeastern and Mid-Atlantic United States and Canada CARTEL, Can Howland, Can Egbert, Can MVCO, ME CCNY, NY Brookhaven, NY Science Ctr, MD SERC, MD GSFC, MD Wallops, VA COVE, VA Southeastern United States Columbia, SC Dry Tortugas, Fl Walker Branch, TN Central United States and Canada Bondville, Il Kellog, MI Konza, KA Bratts Lake, Can California, Southwestern and Northwestern United States Boulder, CO Fresno, CA La Jolla, CA Maricopa, AZ Monterey, CA S. Nicholas, CA UCLA, CA UCSB, CA HJ Andrews, OR Missoula, MT Railroad Valley, NV Rimrock, WA Sevilleta, NM Tomstone, AZ years. The linear regression fit is also shown for the AERONET sites in Figure 3. In addition, the GASP/AERO- NET correlation for 2004 is also shown in Figure 2 (large colored dots). The color scale ranges from 0.2 to 0.8. [16] Coincident pairs were identified by first finding the GASP pixel that contains each AERONET site and using a temporal coincidence criterion of 30 min or less. Statistics were also performed with a temporal coincidence criterion of 15 min or less, and no significant differences were found in the comparisons. [17] For the eight sites in the northeastern and mid- Atlantic United States, the correlation is between 0.68 and 0.88 with the exception of Wallops Island in 2005 (r = 0.46), although we do find the lowest rms differences at Wallops in The GASP/AERONET scatter plots are shown in Figure 3 for CCNY, New York; SERC, Maryland; and Wallops Island, Virginia for Overall, we find similar statistics among east coast sites. In eastern Canada, the correlation at CARTEL, Egbert and Howland is between 0.62 and In the southeast, where comparisons are also further inland, we find less agreement between the two data sets, as shown in Figure 3 at Walker Branch, Tennessee and also in Columbia, South Carolina. The GASP AOD is biased high relative to the AERONET AOD at most eastern sites (see Tables 1 and 2). [18] Correlation plots are also shown in Figure 3 at one central U.S. site, Konza, Kansas and also in the west at UCLA, California and HJ Andrews, Oregon. We find a lower correlation between the GASP and AERONET AOD in the central and western half of the continental United States, with correlations less than 0.5 at most locations. With the exception of Bondville, Illinois, this lower correlation is partly due to the lower AOD range at these less polluted locations (see Tables 1 and 2). We also expect lower correlations in the central and western United States due to the higher surface reflectivity. Knapp [2002] calculated the signal to noise ratio (SNR) in the GASP AOD at several stations in the United States for the GOES-8 imager and found lowest SNR at western sites with bright surfaces and highest SNR at eastern sites, and in particular oceanic and coastal sites. The GASPAOD bias is location-dependent over the central United States. In the western United States GASP is biased high at most locations. [19] There is a relatively large amount of scatter in the comparisons throughout the domain, as indicated by the large rms differences. Causes for this scatter include random 5of15

6 Table 2. GASP/AERONET Comparisons 2005 Station n05 r05 rms05 GASP05 AER05 Northeastern and Mid-Atlantic United States CARTEL, Can Howland, Can n n n n n Egbert, Can MVCO, ME n n n n n CCNY, NY Brookhaven, NY n n n n n Science Ctr, MD SERC, MD GSFC, MD Wallops, VA COVE, VA Southeastern United States Columbia, SC n n n n n Dry Tortugas, Fl n n n n n Walker Branch, TN Central United States and Canada Bondville, Il Kellog, MI Konza, KA Bratts Lake, Can California, Southwestern and Northwestern United States Boulder, CO Fresno, CA La Jolla, CA n n n n n Maricopa, AZ Monterey, CA n n n n n S. Nicholas, CA n n n n n UCLA, CA n n n n n UCSB, CA HJ Andrews, OR n n n n n Missoula, MT Railroad Valley, NV Rimrock, WA Sevilleta, NM Tombstone, AZ n n n n n errors in the GASP retrieval, discussed in the previous section, as well as geophysical inhomogeneity within the space coincidence criteria. [20] In Figures 4 and 5 we present a more detailed statistical analysis of the GASP/AERONET comparisons over the mid-atlantic, northeastern United States and Canada, hereafter referred to as eastern sites. There were not enough AERONET sites over the central, southeastern, and western United States to perform this analysis. Additionally, differences in the GASP/AERONET comparisons from site to site in other parts of the United States, such as California, makes it difficult to validate the GASP algorithm regionally elsewhere. These site to site differences are due in part to greater variation in the surface reflectivity. The eastern analysis is based on 2004 AOD GASP/AERONET coincident measurements at the AERONET sites in the first part of Table 1 with the exception of Egbert. The most southernly site is COVE, Virginia, and the most northernly site is CARTEL, Canada. [21] Figure 4 shows the results of GASP/AERONET statistics separately by time of day over the eastern United States between 1045 UTC and 2245 UTC. The goal of this analysis is to assess separately the quality of the GASP algorithm throughout the day and to determine whether the GASP AOD can be used to examine the diurnal variability in the aerosol column over the northeastern United States and Canada. Shown in Figure 4 are the correlation, rms and mean differences (top), and the slope and intercept (bottom). Also shown in the bottom plot are the number of points used for the statistics at each time. [22] The GASP/AERONET correlation varies between 0.65 and 0.85 and does not exhibit a diurnal pattern. The rms differences are relatively constant from 1215 UTC to 2115 UTC and vary between 0.11 and 0.15 but increase considerably before and after that time period. There is a high bias in the GASP AOD for all time periods, particularly between 1600 and 1845 UTC, and before 1215 UTC and after 2115 UTC. We find a similar behavior in the slope. In addition, the slope is about 1 or less between 1215 UTC and 2215 UTC, but greater than 1 before and after that time period. The intercept ranges from close to zero at the beginning of the day to about 0.11 near 1600 UTC, with no distinct diurnal pattern. However, the intercept is relatively constant between 1345 and 2245 UTC. Overall, the GASP/AERONET AOD statistics are best from 1215 UTC to 2115 UTC. Before and after that time period, absolute 6of15

7 Figure 4. GASP/AERONET statistics over the eastern United States separately by time of day from 1015 UTC to 2245 UTC: (a) correlation (r), root mean square difference (rms) and mean difference and (b) slope and intercept. The number of points used in the statistics at each time is shown at the top. and rms differences increase considerably and the slope changes from less to greater than one. One reason why errors in the aerosol retrieval are more likely at the beginning and end of the sunlit portion of the day is that cloud shadows are more prevalent at high solar zenith angles. If the background image chosen to calculate the surface reflectivity contains cloud shadows, this will lead to a low bias in the calculated surface reflectivity, and consequently a high bias the retrieved AOD. Greater surface angular effects can also contribute to greater biases at that time of day. All further use of the GASP AOD in this work is limited to between 1215 and 2115 UTC (with the exception of Figures 6 and 7). [23] To further quantify the biases in the GASP AOD in the eastern United States we compare the GASP and AERONET AOD for discrete AOD bins. First, we identified GASP/AERONET coincident pairs for the full calendar year 2004 at the 10 eastern AERONET sites used in the analysis shown in Figure 4, and using the same coincidence criteria as described above. No significant differences in the statistics where found when using more strict temporal criteria. Then, we calculate the average AERONET AOD in 0.1 AOD bins, and plot it against the corresponding average GASP AOD. The results are shown in Figure 5 for 6291 coincident pairs between 1215 and 2115 UTC (red crosses). The black solid lines indicate the linear regression fit, and the 1-to-1 line. The dashed line is the random error (34%) as estimated by Knapp et al. [2002]. Error bars indicate the standard deviation of the mean. Also shown (blue line) is the percentage difference between the GASP and AERO- NET AOD for each bin. [24] There is a high bias when the AOD is less than about 0.35 and a low bias when the AOD is greater than There is a large percentage difference between the GASP and AERONET AOD when the AOD is less than 0.1. However, because the algorithm assumes a background AOD of 0.02 we do not expect to be able to retrieve when the AOD is below When the AOD is between 0.1 and 0.2 there is a high bias in the GASP AOD of about 30%, and when the AOD is greater than 0.2, the bias drops to less than 12%. Similarly, Remer et al. [2005] also find a high bias in the MODIS AOD for AOD values less than about 0.25, and a low bias elsewhere, based on 5906 MODIS/AERONET matches globally over land at 550 nm. We find an average correlation of about 0.79 and rms difference of A similar analysis prior to 1215 UTC and after 2115 UTC yields a persistent high bias for all AOD bins. [25] The linear regression, using all individual data points, yields a slope and intercept of 0.8 and respectively. The purpose of this analysis is to find a time-independent slope and intercept that best represents the relationship between the GASP and AERONET AOD over the northeastern United States and Canada. Although the analysis by Remer et al. [2005] uses AERONET sites globally over land, we note that this study found a very similar slope, intercept, and correlation of 0.78, 0.068, and 0.80 respectively. 4. Diurnal and Seasonal Variability in the GASP AOD [26] To compare the diurnal variability in the GASP and AERONET AOD we first find all coincident pairs for the calendar year 2004, using the same coincidence criteria as above. Then, for each AERONET station, we calculate the mean GASP AOD at each retrieval time and compare it to the corresponding average AERONET AOD. The results are shown in Figure 6 at CARTEL, CCNY, HJ Andrews, Konza, SERC, UCLA, Walker Branch and Wallops Island. The standard deviation of the mean is indicated by the errors bars. [27] The diurnal variation in the GASP AOD shows moderate to good agreement with the AERONET AOD at eastern sites, as shown for CCNY, SERC, and Wallops, as Figure 5. AERONET versus GASP AOD for 0.1 AOD bins using data from 10 eastern U.S. and Canadian sites from January to December 2004 and between 1215 UTC and 2115 UTC (indicated by the crosses). The black lines indicate the linear regression using all individual data points and the 1-to-1 line. The dashed line indicates the retrieval error as estimated by Knapp et al. [2002]. Also shown (blue line) is the percentage difference (GASP-AERONET) for each bin. 7of15

8 Figure 6. Comparison of the GASP and AERONET diurnal variability at the same locations as shown in Figure 3. Error bars indicate the standard deviation of the mean. expected based in the analysis shown in Figure 4. The comparisons also indicate a high bias in the GASP AOD at the beginning and end of the day for most locations, discussed in the previous section. However, we note that for many eastern sites, such as SERC, there is a minimum in the AERONET AOD between 1500 and 2000 UTC, and then an increase toward the end and beginning of the day, albeit less dramatically than in the case of the GASP AOD. Thus, although this feature is exaggerated in the GASP AOD, it appears to be real, and it is being captured in the satellite observations. [28] For central U.S. sites there is a characteristic increase around 16 UTC that is not observed in the AERONET data, as indicated here at Konza. A similar diurnal cycle, not observed in the AERONET data, is also present in the GASP AOD at Columbia, SC and Boulder, CO, and for the California stations at La Jolla, San Nicholas, and Fresno. At UCLA, and UCSB, CA there is gradual increase in both the GASP and AERONET AOD until about 19 UTC, with little variability after that time. Comparisons at UCLA are also shown in Figure 6. In the northwest, we find a gradual increase in the GASP AOD throughout the day, but good quantitative agreement with the AERONET AOD. Thus the GASP AOD is suitable for studying the diurnal variability in aerosols in the eastern United States between 1215 UTC 8of15

9 Figure 7. GASP AOD bias as a function of solar zenith angle for the geographical regions defined in Table 1 (except southeastern United States). The lines indicate different aerosol model assumptions in the radiative transfer calculation. Black line is the current (continental) aerosol model, green line is a neutral aerosol model, and red line is an urban (highest single scattering albedo) model for the eastern, central, and western United States. The corresponding value of the single scattering albedo is shown at the top of each plot. and about 2115 UTC but not at Midwest or most western sites. [29] There are several reasons to expect differences in the diurnal pattern of the GASP AOD bias for eastern versus western sites. Figure 7 shows the mean bias between the GASP and AERONET AOD for eastern, central, and western sites (as grouped in Table 1) as a function of solar zenith angle for the calendar year Negative numbers indicate solar zenith angles prior to local noon. This analysis excludes locations in the southeastern United States. The GOES-12 imager is situated near 75 west. Thus, for sites along the east coast the angle between the directions of the sun and the satellite, as drawn from a point on the surface, are more symmetric relative to local noon and we would expect greater symmetry and/or a different diurnal pattern in the AOD biases than in more westerly locations. Likewise, errors due to the assumed phase function in the aerosol model vary with the scattering angle, and hence will occur at a different time of day depending on geographical location. [30] The noise in the satellite observations used in calculating the 28-day composite images inherently contributes to a high bias in the retrieved AOD. Because we pick the lowest or second darkest pixel, the noise results in a choice of second darkest pixel that is too low, contributing to a high bias in the retrieved AOD. In addition, because the noise in the aerosol signal is larger over brighter or more heterogeneous surfaces [Knapp, 2002], we expect a higher bias in the GASP AOD over the central and western United States. [31] Another potential source of bias in the GASP AOD comes from assumptions in the aerosol model used in the AOD retrieval. Figure 7 also shows the 2004 GASP AOD bias using three different model assumptions in the GASP retrieval, continental (0.89), used in the current algorithm, neutral (0.92), and urban (0.95), where the number in parenthesis indicates the value of the single scattering albedo (SSA). The neutral and urban models were obtained from the Collection 05 MODIS Algorithm Theoretical Basis Document (ATBD) [Remer et al., 2006] which describes in detail the optical properties and size distribution for each model. For all locations, an increase in the SSA leads to an average annual decrease in the corresponding retrieved AOD. In the eastern United States, the bias becomes predominantly negative and does not improve in absolute value. In the central United States, the bias improves in the afternoon but, in the morning, where the GASP AOD is lower than the AERONET AOD, there is an increase in the absolute bias. In the western United States, the two new model assumptions lead to a slight decrease in the GASP AOD bias during most of the day. Future studies will examine the effect of the aerosol model assumption on the retrieved AOD at smaller temporal and spatial scales. We did not find a significant improvement in the GASP AOD correlation or rms differences (data not shown here). [32] One potential source of time-dependent bias in the retrieved AOD is the assumption of a time-independent background AOD of 0.02 when calculating the surface reflectivity. To test the accuracy of these assumptions, in Figure 8 we plot the mean difference between all coincident GASP and AERONET pairs and the minimum AERONET AOD as a function of time of day, for July and August Because the GASP surface reflectivity is calculated by selecting the second darkest image from the past 28 days, we first find the minimum AERONET AOD for each successive 28 day period over the two month period, and then compute the average minimum AOD, separately for each time of day. The minimum AERONET AOD presumably represents the true background AOD over that time period. There is considerable variability throughout the day, and from site to site. Ciren et al. [2006] also reported significant daily and seasonal variability in the minimum AOD, based on a 13 year climatology from 1992 to 2005 at several AERONET sites. At some locations over the eastern United States they report an increase at early and late hours in the minimum AOD, such as shown at Wallops in Figure 8. However, this increase, which is not accounted for in the current algorithm, would lead to a high bias in the calculated surface reflectivity, and thus a low bias in the retrieved GASP AOD and high solar zenith angles, contrary to the 9of15

10 Figure 8. Mean difference in the GASP and AERONET AOD (black) and minimum AERONET AOD (red) for each time of day at the same stations as in Figures 3 and 6. findings from the GASP/AERONET comparisons. Figure 8 indicates that generally, errors in the minimum AOD assumption in the GASP algorithm alone cannot explain the observed biases in the GASP AOD. At CARTEL and Konza, however, both quantities are negatively correlated, as expected. [33] The seasonal variability in the GASP and AERO- NET AOD is shown in Figure 9. Again, coincident pairs are identified prior to calculating the monthly averages. We show data at COVE, Virginia, an Island site about 14 km offshore, rather than at Wallops, as AERONET data at Wallops were not available for several months in The seasonal variability is very similar in both data sets at CARTEL, COVE, SERC, and CCNY. Because COVE is an ocean site, the dark uniform surface reflectivity most likely accounts for the good agreement between the GASP and AERONET AOD compared to most other eastern sites. The high bias in the GASP AOD is most pronounced in January through April at all eastern sites. [34] The monthly variability in the AOD at Walker Branch and UCLA is captured as well, but there is less quantitative agreement than at other sites, consistent with the previous analysis. Finally, the GASP algorithm captures the low aerosol conditions at HJ Andrews, Oregon with very little variability from March through November. This analysis demonstrates that GASP is suitable for studying the 10 of 15

11 Figure 9. Monthly mean of the GASP and AERONET AOD at eight AERONET sites in Error bars are the standard deviation of the mean. seasonal cycle in the AOD at many locations throughout the United States. 5. Comparison of GASP, MODIS, and AERONET AOD [35] To further evaluate the GASP AOD over the United States, in this section we present GASP/MODIS and simultaneous GASP/MODIS/AERONET AOD comparisons. The MODIS AOD was obtained from MOD04_L2 data files, version 4.0.1, provided by the University of Wisconsin, Madison. For detailed description of the MODIS AOD algorithm, see Chu et al. [2003] and Remer et al. [2005]. [36] In Figure 10 we show GASP/AERONET (black crosses) and MODIS/AERONET (red crosses) coincidences at six AERONET sites for March 2004 to December The coincidence criteria used for the MODIS/AERONET comparisons are the same as for the GASP/AERONET comparisons, i.e., we pick the pixel that contains the AERONET site and use a temporal coincidence criterion of 30 min or less. However, here all correlations have been limited to when coincidences were found among all three data sets. It is important to keep in mind that the GASP/ 11 of 15

12 Figure 10. AERONET/GASP/MODIS coincidences for March 2004 to December GASP/ AERONET comparisons are in black; MODIS/AERONET comparisons are in red. Also shown in each plot are the number of coincidences and the GASP/AERONET and MODIS/AERONET correlation and rms difference. Both the GASP and MODIS coincidences correspond to the pixel containing each AERONET site. The temporal criteria was 30 min or less. AERONET correlations shown here are limited to the MODIS overpass time, generally between 1400 and 1600 UTC over the eastern United States, depending on time of year, and after 1700 UTC in the western half of the United States. The MODIS spatial resolution is km, which is comparable to the km resolution for the GASP AOD. Also shown in each plot are the correlation and rms differences for GASP and MODIS, and the number of matches. Except for UCSB, MODIS/AERONET correlations are higher and rms differences are lower than GASP/ AERONET comparisons. [37] In Figure 11 we plot, from west to east, the GASP/ AERONET and MODIS/AERONET correlation (top plot) and rms difference (bottom plot) at 25 AERONET stations. Also shown in the bottom plot are the number of points used for the statistics at each station. The stations have been numbered in order of increasing longitude from west to east. Station numbers are shown in Table of 15

13 Figure 11. Summary of GASP/AERONET and MODIS/ AERONET statistics separately by station. See Table 3 for stations numbers. GASP statistics are in black, and MODIS statistics are in red. (top) Correlation and (bottom) root mean square difference. The number of points used in the statistics for each station is indicated at the top. [38] Over the eastern United States, Canada, and the central and southeastern United States (stations 11 25), the MODIS/AERONET correlation is higher than the GASP/AERONET correlation, and the GASP/AERONET rms difference is generally greater than the MODIS/AERO- NET rms. We expect lower rms differences between MODIS and AERONET primarily due to the availability of additional IR channels for estimating the surface reflectivity, versus the 1 channel retrieval used in the GASP AOD retrievals. In addition the MODIS retrievals also benefit Table 3. Station Numbers Used in Figure 11 Station Station Number HJAndrews 1 Monterey 2 Fresno 3 UCSB 4 UCLA 5 La Jolla 6 Missoula 7 Rimrock 8 Boulder 9 Bratts Lake 10 Konza 11 BONDVILLE 12 Kellogg LTER 13 Walker Branch 14 Egbert 15 GSFC 16 MD Science Center 17 SERC 18 COVE 19 Wallops 20 CCNY 21 Brookhaven 22 CARTEL 23 MVCO 24 Howland 25 Figure 12. GASP and MODIS Comparisons showing the correlation factor for (a) July 2004 and (b) August from a cloud screening algorithm performed at higher pixel resolution than the 4 4 km GOES screening, plus on board calibration, both of which can contribute to greater accuracy. [39] It is also interesting to note the similar variation in the correlation and rms difference from station to station for both satellite instruments in the central and eastern United States and Canada. In the western United States (stations 1 10) it is not clear whether the GASP or the MODIS algorithm is performing better, as the satellite/aeronet comparisons vary significantly from site to site. Generally, we find poor to moderate correlation and highly variable rms differences from site to site. At some stations, particularly in the western half of the United States, the GASP/ AERONET correlations are higher and rms differences are lower than those listed in Table 1, despite the lesser number of matches. This suggests that the GASP/AERONET comparisons in this analysis may be benefiting from additional cloud screening in the MODIS cloud masking algorithm. However, more robust statistics and detailed analysis are needed to ascertain this. [40] In Figures 12 and 13 we compare the GASP and MODIS AOD in July and August 2004 throughout the 13 of 15

14 and southwestern United States, there is a distinct high bias in the GASP AOD. While the source of this bias warrants future investigation, we expect the least agreement in this region because of the difficulty of the AOD retrieval for both GASP and MODIS over these highly reflective surfaces during summer. Figure 13. GASP and MODIS Comparisons showing the difference (%) for (a) July 2004 and (b) August United States and southern Canada. The AOD from both satellite instruments was compared by finding the MODIS pixel with latitude and longitude closest to each GASP pixel and within 30 min. However, the statistics shown here are for 1 1 boxes, using all the GASP/MODIS coincident pairs for each month contained within each box. [41] The correlation for each month separately is shown in Figures 12a and 12b, and the bias (%) is shown in Figures 13a and 13b. The correlation indicates a few geographical features, primarily highest correlations over the eastern half of the continental United States, Atlantic Ocean, and over Canada in July. The high correlation over Canada and the central United States is partly due to several Canadian and Alaskan fire plumes transported into southern Canada and the United States that month. In August (Figure 12b), there is no clear spatial pattern in the correlations over the eastern half of the United States, and as in July we find good correlation over the ocean and in parts of Canada where transported smoke lead to very large increases in the AOD. We find less than 20% difference over the northeastern, southeastern, and much of the mid- Atlantic region during both months. We also find reasonable agreement in California and parts of Oregon. In the central 6. Discussion and Conclusions [42] GASP AOD imagery are currently used at NOAA/ NESDIS for near real time monitoring of anthropogenic and natural aerosols over ocean and land, to help identify the sources of high aerosols over the continental United States and Canada, and track the long transport of polluted plumes. The GASP AOD is a unique product because it is available at high temporal resolution, currently every 30 min, unlike polar orbiting instruments which provide one daily image. GASP is also currently used to aid in the planning of aircraft and ship deployments for air quality field campaigns. [43] In this work the GASP AOD, based on GOES-12 visible radiances, was evaluated over North America on the basis of comparisons with AERONET and MODIS AOD. On the basis of an analysis at 10 eastern U.S. and Canadian sites, the GASP/AERONET correlation, rms difference, and slope for 2004 are 0.79, 0.13, and 0.8 respectively. One of the purposes of this analysis is to find a time-independent slope and intercept that best represents the relationship between the GASP and AERONET AOD over the northeastern United States and Canada. These correlations and rms differences are in agreement with GOES-8 analysis for 2001 by Knapp et al. [2005]. [44] Good agreement was found in the diurnal and monthly variability of the AOD between GASP and AERO- NET at these 10 sites and a detailed statistical analysis by time of day indicates that the GASP AOD is suitable for monitoring the daily variability in aerosols between 12:15 and 21:15 UTC over the northeastern United States and Canada. GASP has a high bias prior and after this time period, and this discrepancy could not be explained by the assumption of a constant background AOD throughout the day. [45] Over the central and western United States, correlations between GASP and AERONET were 0.5 or less. We expect lower correlations in the western United States due primarily to a much lower signal to noise ratio compared to the eastern United States. In the central United States, GASP is biased low prior to local noon, and high after local noon, making the product less useful than in the eastern United States for studying the diurnal variability in aerosols. In the western United States, GASP is biased high for most of the day although good quantitative agreement was found at some coastal sites. Using either a neutral or urban aerosol model in the radiative transfer calculation versus the existing continental aerosol model decreased the GASP AOD bias about local noon and after local noon in the central and western United States. [46] GASP and MODIS differences are within about 20% in the eastern United States under high AOD conditions. MODIS/AERONET correlations were only slightly better than GASP/AERONET correlations and MODIS/AERO- NET rms differences were moderately smaller than GASP/ 14 of 15

15 AERONET rms differences when all three data sets were compared simultaneously over the eastern United States. [47] These results demonstrate the usefulness of the GASP AOD retrieval algorithm for monitoring particulate pollution, as well as for future studies examining the diurnal and monthly variability in aerosols over the continental United States and Canada. Future work will further explore the cause of the high bias and rms differences in the GASP AOD before 1215 UTC and after 2115 UTC, and examine ways to reduce the noise and high bias in the GASP AOD during low aerosol conditions. [48] Acknowledgments. We wish to thank Istvan Laszlo for invaluable discussions and help in the preparation of this document, Raymond Hoff for useful insights and feedback on the document, and Tony Wimmers for providing the MODIS data. This project was funded by the GIMPAP and G-PSDI programs at NOAA/NESDIS. 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