Evaluating the impact of aerosol particles above cloud on cloud optical depth retrievals from MODIS

Alfaro-Contreras, R.; Zhang, Jianglong; Campbell, James; Holz, Robert E.; Reid, Jeffrey S.

doi:10.1002/2013jd021270

Cited by 28 publications

(33 citation statements)

References 53 publications

(89 reference statements)

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“…For identification of ACA, 5 km CALIOP 532 nm cloud and aerosol layer products are used (Winker et al, , 2010 for resolving aerosol extinction above apparent cloudtop heights in each respective product file (e.g., Yu et al, 2012;Alfaro-Contreras et al, 2014). The 532 nm abovecloud aerosol optical depth (ACAOD) is then solved by integrating the extinction coefficient over those corresponding bins (Liu et al, 2015;Kacenelenbogen et al, 2014).…”

Section: Data Sets and Methodologymentioning

confidence: 99%

“…retrievals are uniquely biased (Wilcox et al, 2009;Meyer et al, 2013;Alfaro-Contreras et al, 2014;Li et al, 2014). ACA further perturbs regional radiation budgets by absorbing and reflecting radiation from the cloud layers underneath the unidentified aerosol particle layer (e.g., Haywood et al, 2004), which again must be accounted for when estimating global cloud and aerosol forcing budgets and regional semidirect impact on static stability and cloud feedback.…”

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confidence: 99%

“…Ozone Monitoring Instrument (OMI) measurements have also been used for studying ACA events (e.g., Wilcox et al, 2009;Yu et al, 2012;Alfaro-Contreras et al, 2014). In particular, the OMI aerosol index (AI), computed using the difference between observed and calculated ultraviolet (UV) radiances (Torres et al, 2007), has been used to locate UV-absorbing aerosols suspended over bright cloud decks (e.g., Yu et al, 2012;Torres et al, 2012).…”

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confidence: 99%

“…In particular, the OMI aerosol index (AI), computed using the difference between observed and calculated ultraviolet (UV) radiances (Torres et al, 2007), has been used to locate UV-absorbing aerosols suspended over bright cloud decks (e.g., Yu et al, 2012;Torres et al, 2012). This technique, originally used on the Total Ozone Mapping Spectrometer (TOMS), can only be used to detect UV-absorbing aerosols, such as biomass burning smoke and desert dust aerosols, and is sensitive to underneath cloud properties (e.g., Yu et al, 2012;Alfaro-Contreras et al, 2014). Further, and compared with CALIOP, OMI measurements represent a relatively large surface footprint of 13 × 24 km at nadir, which limits cloud-clearing efficacies since footprints of this size are prone to sub-pixel cloud contamination (Torres et al, 2007).…”

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confidence: 99%

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Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

Alfaro-Contreras

Zhang

Campbell³

et al. 2016

Atmos. Chem. Phys.

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Abstract. Seven and a half years (June 2006 to November 2013) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol and cloud layer products are compared with collocated Ozone Monitoring Instrument (OMI) aerosol index (AI) data and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products in order to investigate variability in estimates of biannual and monthly above-cloud aerosol (ACA) events globally. The active-(CALIOP) and passive-based (OMI-MODIS) techniques have their advantages and caveats for ACA detection, and thus both are used to derive a thorough and robust comparison of daytime cloudy-sky ACA distribution and climatology. For the first time, baseline above-cloud aerosol optical depth (ACAOD) and AI thresholds are derived and examined (AI = 1.0, ACAOD = 0.015) for each sensor. Both OMI-MODIS and CALIOP-based daytime spatial distributions of ACA events show similar patterns during both study periods (December-May) and (June-November). Divergence exists in some regions, however, such as Southeast Asia during June through November, where daytime cloudy-sky ACA frequencies of up to 10 % are found from CALIOP yet are non-existent from the OMI-based method. Conversely, annual cloudy-sky ACA frequencies of 20-30 % are reported over northern Africa from the OMI-based method yet are largely undetected by the CALIOP-based method. Using a collocated OMI-MODIS-CALIOP data set, our study suggests that the cloudy-sky ACA frequency differences between the OMI-MODIS-and CALIOP-based methods are mostly due to differences in cloud detection capability between MODIS and CALIOP as well as QA flags used. An increasing interannual variability of ∼ 0.3-0.4 % per year (since 2009) in global monthly cloudy-sky ACA daytime frequency of occurrence is found using the OMI-MODIS-based method. Yet, CALIOP-based global daytime ACA frequencies exhibit a near-zero interannual variability. Further analysis suggests that the OMI-derived interannual variability in cloudy-sky ACA frequency may be affected by OMI row anomalies in later years. A few regions are found to have increasing slopes in interannual variability in cloudysky ACA frequency, including the Middle East and India. Regions with slightly negative slopes of the interannual variability in cloudy-sky ACA frequencies are found over South America and China, while remaining regions in the study show nearly zero change in ACA frequencies over time. The interannual variability in ACA frequency is not, however, statistically significant on both global and regional scales, given the relatively limited sample sizes. A longer data record of ACA events is needed in order to establish significant trends of ACA frequency regionally and globally.

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Section: Data Sets and Methodologymentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

Alfaro-Contreras

Zhang

Campbell³

et al. 2016

Atmos. Chem. Phys.

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“…Still, those parameters need to be used with caution. 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).…”

Section: An Aerosol Algorithm For Heavy Smokementioning

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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Simultaneously inferring above‐cloud absorbing aerosol optical thickness and underlying liquid phase cloud optical and microphysical properties using MODIS

2015

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The regional haze over the southeast (SE) Atlantic Ocean induced by biomass burning in southern Africa can be problematic for passive imager-based retrievals of the underlying quasi-permanent marine boundary layer (MBL) clouds and for estimates of top-of-atmosphere (TOA) aerosol direct radiative effect (DRE). Here an algorithm is introduced to simultaneously retrieve above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT), and cloud effective particle radius (CER) of the underlying MBL clouds while also providing pixel-level estimates of retrieval uncertainty. This approach utilizes reflectance measurements at six Moderate Resolution Imaging Spectroradiometer (MODIS) channels from the visible to the shortwave infrared. Retrievals are run under two aerosol model assumptions on 8 years (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) of June-October Aqua MODIS data over the SE Atlantic, from which a regional cloud and above-cloud aerosol climatology is produced. The cloud retrieval methodology is shown to yield COT and CER consistent with those from the MODIS operational cloud product (MOD06) when forcing AOT to zero, while the full COT-CER-AOT retrievals that account for the above-cloud aerosol attenuation increase regional monthly mean COT and CER by up to 9% and 2%, respectively. Retrieved AOT is roughly 3 to 5 times larger than the collocated 532 nm Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals, though closer agreement is observed with the CALIOP 1064 nm retrievals, a result consistent with previous case study analyses. Regional cloudy-sky above-cloud aerosol DRE calculations are also performed that illustrate the importance of the aerosol model assumption and underlying cloud retrievals.

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Evaluating the impact of aerosol particles above cloud on cloud optical depth retrievals from MODIS

Cited by 28 publications

References 53 publications

Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

Simultaneously inferring above‐cloud absorbing aerosol optical thickness and underlying liquid phase cloud optical and microphysical properties using MODIS

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