MODIS aerosol product analysis for data assimilation: Assessment of over‐ocean level 2 aerosol optical thickness retrievals

Zhang, Jianglong; Reid, Jeffrey S.

doi:10.1029/2005jd006898

Cited by 303 publications

(427 citation statements)

References 31 publications

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“…Such a study is not new, as the relationship between AOD DT−AERONET and ocean surface wind speed has been reported by several studies (e.g., Zhang and Reid, 2006;Shi et al, 2011a;Levy et al, 2013). This study extends the previous analysis to further evaluate the impacts of bubbles relative to the uncertainties in MODIS DT aerosol retrievals under clean marine conditions.…”

Section: Observational Datasupporting

confidence: 58%

“…Indeed, a series of studies suggests that most of the high bias is related to clouds. However, there is a clear lower boundary condition signal as well, with increasing positive AOD bias with wind speed (e.g., Zhang and Reid, 2006;Shi et al, 2011a). Given that sea salt aerosol production, specular reflection (sun glint), and whitecapping all covary with wind speed, AOD retrievals are a potentially confounded system.…”

Section: Introductionmentioning

confidence: 99%

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A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR

Christensen

Zhang

Reid³

et al. 2015

Atmos. Meas. Tech.

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Abstract. Submerged oceanic bubbles, which have a much longer life span than whitecaps or bubble rafts, have been hypothesized to increase the water-leaving radiance and thus affect satellite-based estimates of water-leaving radiance to non-trivial levels. This study explores this effect further to determine whether such bubbles are of sufficient magnitude to impact satellite aerosol optical depth (AOD) retrievals through perturbation of the lower boundary conditions. There has been significant discussion in the community regarding the high positive biases in retrieved AODs in many remote ocean regions. In this study, for the first time, the effects of oceanic bubbles on satellite retrievals of AOD are studied by using a linked Second Simulation of a Satellite Signal in the Solar Spectrum (6S) atmospheric and HydroLight oceanic radiative transfer models. The results suggest an insignificant impact on AOD retrievals in regions with near-surface wind speeds of less than 12 m s −1 . However, the impact of bubbles on aerosol retrievals could be on the order of 0.02-0.04 for higher wind conditions within the scope of our simulations (e.g., winds < 20 m s −1 ). This bias is propagated to global scales using 1 year of Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data to investigate the possible impacts of oceanic bubbles on an enhanced AOD belt observed over the high-latitude southern oceans (also called the enhanced southern oceans anomaly, or ESOA) by some passive satellite sensors. Ultimately, this study is supportive of the null hypothesis: submerged bubbles are not the major contributor to the ESOA feature. This said, as retrievals progress to higher and higher resolutions, such as from airborne platforms, the uniform bubble correction in clean marine conditions should probably be separately accounted for against individual bright whitecaps and bubble rafts.

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Section: Observational Datasupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR

Christensen

Zhang

Reid³

et al. 2015

Atmos. Meas. Tech.

View full text Add to dashboard Cite

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“…Many successful applications of these data to global-and regional-scale questions are already presented in the literature. They range from assessing zonal mean or global aerosol short-wave forcing [37][38][39][40][41][42] and regional long-wave forcing [43], to improving aerosol forecasting through data assimilation [44,45], monitoring dust and pollution plume evolution [46,47] and air quality [48,49], mapping aerosol air mass type evolution [50], and validating aerosol transport model AOD simulations [51,52]. In each case, ways of exploiting the strengths of the MISR and MODIS data have been found, and in many cases, independent validation was performed specific to the application.…”

Section: Application Of Misr and Modis Aerosol Productsmentioning

confidence: 99%

Response to “Toward unified satellite climatology of aerosol properties. 3. MODIS versus MISR versus AERONET”

Kahn

Garay

Nelson

et al. 2011

Journal of Quantitative Spectroscopy and Radiative Transfer

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A recent paper by Mishchenko et al. compares near-coincident MISR, MODIS, and AERONET aerosol optical depth (AOD) products, and reports much poorer agreement than that obtained by the instrument teams and others. We trace the reasons for the discrepancies primarily to differences in (1) the treatment of outliers, (2) the application of absolute vs. relative criteria for testing agreement, and (3) the ways in which seasonally varying spatial distributions of coincident retrievals are taken into account.

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“…A thin layer of absorbing aerosol above clouds can trigger very high AI values especially for regions with optically thick clouds (Meyer et al, 2013;Yu et al, 2012;Alfaro-Contreras et al, 2014;Torres et al, 2012). Also, erroneously high MODIS AOD can be found over 20 cloud edges due to inaccurate cloud screening or cloud 3-D effects (Zhang et al, 2006;Shi et al, 2010). Utilization of the two parameters together provides better detection of the ideal granules for the study.…”

Section: An Aerosol Algorithm For Heavy Smokementioning

confidence: 99%

“…By excluding these optically thick aerosol data, this misclassification can introduce a low bias in aerosol regional climatology and further influence other studies that rely on 15 satellite data. In particular the aerosol modeling and aerosol data assimilation efforts to model and predict the consequences of these events for air quality and visibility forecasts will be misled due to this low bias in the "observed" quantities (Zhang et al, 2006;Benedetti, et al, 2009;Chung et al, 2010). This was indeed the case during the Indonesian smoke event of 2015.…”

mentioning

confidence: 99%

Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

Shi¹,

Levy²,

Eck³

et al. 2018

Preprint

Self Cite

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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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MODIS aerosol product analysis for data assimilation: Assessment of over‐ocean level 2 aerosol optical thickness retrievals

Cited by 303 publications

References 31 publications

A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR

A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR

Response to “Toward unified satellite climatology of aerosol properties. 3. MODIS versus MISR versus AERONET”

Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

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