Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data

Sayer, A. M.; Hsu, N. Christina; Bettenhausen, C.; Jeong, Manhee

doi:10.1002/jgrd.50600

Cited by 534 publications

(516 citation statements)

References 46 publications

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“…A revised version of the Collection 6 MODIS Deep Blue algorithm Sayer et al, 2013] is applied to measurements made by VIIRS to create the aerosol products (AOD and AE) over land surfaces. Over ocean we use an algorithm that has been used for various applications: retrieving aerosol properties from Geostationary Ocean Color Imager [Lee et al, 2010b], testing effects of nonspherical dust models on AOD retrieval accuracy [Lee et al, 2012], correcting for thin cirrus clouds in AOD retrievals [Lee et al, 2013], and deriving aerosol direct radiative effects [Lee et al, 2014].…”

Section: Viirs Deep Blue Aerosol Productsmentioning

confidence: 99%

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

et al. 2015

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This study extends the application of the previously developed Aerosol Single-scattering albedo and layer Height Estimation (ASHE) algorithm, which was originally applied to smoke aerosols only, to both smoke and dust aerosols by including nonspherical dust properties in the retrieval process. The main purpose of the algorithm is to derive aerosol height information over wide areas using aerosol products from multiple satellite sensors simultaneously: aerosol optical depth (AOD) and Ångström exponent from the Visible Infrared Imaging Radiometer Suite (VIIRS), UV aerosol index from the Ozone Mapping and Profiler Suite (OMPS), and total backscatter coefficient profile from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The case studies suggest that the ASHE algorithm performs well for both smoke and dust aerosols, showing root-mean-square error of the retrieved aerosol height as compared to CALIOP observations from 0.58 to 1.31 km and mean bias from À0.70 to 1.13 km. In addition, the algorithm shows the ability to retrieve single-scattering albedo to within 0.03 of Aerosol Robotic Network inversion data for moderate to thick aerosol loadings (AOD of~1.0). For typical single-layered aerosol cases, the estimated uncertainty in the retrieved height ranges from 1.20 to 1.80 km over land and from 1.15 to 1.58 km over ocean when favorable conditions are met. Larger errors are observed for multilayered aerosol events, due to the limited sensitivities of the passive sensors to such cases.

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Section: Viirs Deep Blue Aerosol Productsmentioning

confidence: 99%

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

et al. 2015

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“…The AErosol RObotic NETwork (AERONET) is a global aerosol-monitoring network of sun/sky observing radiometers that is commonly used as a benchmark for validating satellite-retrieved AOD (Holben et al, 1998;Levy et al, 2010Levy et al, , 2013Remer et al, 2005;Sayer et al, 2013;Reid, 2006, Shi et al, 2011). The instruments measure attenuated solar energy through two modes: direct sun and scanning sky (Holben et al, 1998).…”

Section: Aeronet Sun/sky Aerosol Productsmentioning

confidence: 99%

Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

Shi¹,

Levy²,

Eck³

et al. 2018

Preprint

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Abstract. The Indonesian fire and smoke event of 2015 was an extreme episode that affected public health and caused severe economic and environmental damage. The MODIS Dark Target (DT) aerosol algorithm, developed for global applications, significantly underestimated regional aerosol optical depth (AOD) during this episode. The larger-than-globalaveraged uncertainties in DT product over this event were due to both an overly zealous set of masks that mistook heavy smoke plumes for clouds and/or inland water, and also an aerosol model developed for generic global aerosol conditions. 15Using Aerosol Robotic Network (AERONET) Version 3 sky inversions of local AERONET stations, we created a specific aerosol model for the extreme event. Thus, using this new less-absorbing aerosol model, cloud masking based on results of the MODIS cloud optical properties algorithm, and relaxed thresholds on both inland water tests and upper limits of the AOD retrieval we created a research algorithm, and applied it to 80 appropriate MODIS granules during the event.Collocating and comparing with AERONET AOD shows that the research algorithm doubles the number of MODIS 20 retrievals greater than 1.0, while also significantly improving agreement with AERONET. The final results show that the operational DT algorithm had missed approximately 0.22 of the regional mean AOD, but as much as AOD = 3.0 for individual 0.5° grid boxes. This amount of missing AOD can skew the perception of the severity of the event, affect estimates of regional aerosol forcing, and alter aerosol modeling and forecasting that assimilate MODIS aerosol data products. These results will influence the future development of the global DT aerosol algorithm. 25

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“…Although the statistical numbers in the comparison depend on the area under study, comparisons between areas are always possible. A recent validation of MODIS Collection 6 AOD a (Aqua), derived using the DB algorithm with AOD SP from six AERONET stations in Central and South America (CSA) and seven in eastern North America (ENA), was reported by Sayer et al (2013). The number of pairs of collocated MODIS and AERONET daily averaged observa- tions for CSA (ENA) was 3032 (4155).…”

Section: Comparison Of Aod Retrievals From Sun Photometer and Modis Smentioning

confidence: 99%

“…Most are around AE = 1.5, followed by a second maximum at AE= 1.8. The first, 1.5, is a regional default value for AE t and AE a Sayer et al, 2013) assumed by the DB algorithm in the case of low AOD values (AOD t or AOD a < 0.2). The second is associated with the fact that the AE t and AE a values allowed by the aerosol optical models in Collection 6 are constrained between 0 and 1.8 to avoid unrealistic values .…”

Section: Comparison Of åNgström Exponent By Sunmentioning

confidence: 99%

“…This comparative analysis does not aim to be a validation study of the MODIS sensor since many works during the long history of the MODIS sensor on the Terra and Aqua platforms have sought to improve its features (these include Kaufman et al, 1997a, b;Tanré et al, 1997;Remer et al, 2002Remer et al, , 2005Remer et al, , 2006Hsu, et al, 2004Hsu, et al, , 2006Hsu, et al, , 2013Levy et al, 2007Levy et al, , 2009Levy et al, , 2010Levy et al, , 2013Levy et al, , 2015Sayer et al, 2013Sayer et al, , 2014 https://darktarget.gsfc.nasa.gov/atbd/overview, last access: 9 April 2018). However, compared to other areas of the world, no studies have been reported in the Caribbean region and in Cuba in particular (Papadimas et al, 2009;Mishchenko et al, 2010;Kahn et al, 2011;Bennouna et al, 2011Bennouna et al, , 2013Witte et al, 2011;Gkikas et al, 2013Gkikas et al, , 2016Levy et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

Marrero¹,

Revilla

Parrado³

et al. 2018

Atmos. Meas. Tech.

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Abstract. In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AOD t ) and Aqua (AOD a ) satellites and those measured using a sun photometer (AOD SP ) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AOD ta ) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AOD t , AOD a , AOD ta compared to AOD SP . For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AOD ta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AOD t , AOD a , AOD ta vs. AOD SP , with a slight overestimation of satellite data compared to AOD SP , (f ). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AOD ta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AOD SP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS-sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

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Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data

Cited by 534 publications

References 46 publications

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

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