Effect of Wind Speed on Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth over the North Pacific

Merkulova, Lena; Freud, Eyal; Mårtensson, E. M.; Nilsson, E. Douglas; Glantz, Paul

doi:10.3390/atmos9020060

Cited by 8 publications

(4 citation statements)

References 76 publications

(125 reference statements)

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“…This bias was not related to any covariance with cloud fraction. Further, the range of wind speed-dependent bias is within the range of values examined in other studies [72] for overall wind speed AOD 550 covariability. Given the fine mode error and similar findings for cloud contamination, it is possible that the corrections need updating for the aerosol optical model or lower boundary condition.…”

Section: Wind Speedsupporting

confidence: 73%

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

et al. 2022

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Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research.

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Section: Wind Speedsupporting

confidence: 73%

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

et al. 2022

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“…Therefore, the aerosol signal accounts for a smaller proportion of satellite-observed reflectance, leading to greater AOD and AE bias. AOD and AE both have a relatively strong relationship with wind speed, which may be another possible reason why the retrieval bias is dependent on wind speed [46].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Validation and Comparison of Three VIIRS Aerosol Products over the Ocean on a Global Scale

Feng

et al. 2022

Remote Sensing

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Three parallel Visible/Infrared Imager Radiometer Suite (VIIRS) aerosol products (SOAR, NOAA, and AERDT) provided data since 2012. It is necessary to study the performances and advantages of different products. This study aims to analyze the accuracy and error of these products over the ocean and compare them with each other. The results show that the three VIIRS ocean aerosol retrievals (including total aerosol optical depth (AOD), fine mode fraction, Ångström exponent (AE), and fine AOD (AODF)) correlate well with AErosol RObotic NETwork (AERONET) retrievals (e.g., correlation >0.895 for AOD and >0.825 for AE), which are comparable to the newest moderate-resolution imaging spectro-radiometer (MODIS) retrievals. Overall, the SOAR retrievals with quality filtering have the best validation accuracy of all parameters. Therefore, it is more recommended to use. The differences in the annual AOD spatial patterns of different products are small (bias < 0.016), but their AE spatial patterns are evidently different (bias > 0.315), indicating the large uncertainty of VIIRS AE. Error analysis shows that the scattering angle and wind speed affect aerosol retrieval. Application of the non-spherical dust model may reduce the dependence of retrieval bias on the scattering angle. Overall, this study provides validation support for VIIRS products usage and possible algorithm improvements.

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“…Several studies within different fields have been conducted by employing information captured in the Mauna Loa Observatory (MLO). For instance, Mims III et al [19] performed continuous observations of solar UV radiation under different sky conditions, Barnes et al [20] presented a study of stratospheric aerosol measurements over MLO, Merkulova et al [21] investigated the surface-wind speed impacts on aerosol optical depth (AOD), and Sussmann and Rettinger [22] aimed to quantify the influence of the COVID-19 pandemic on atmospheric CO2 accumulation, to mention some of them. However, the distribution of the CO2 data obtained from MLO measurements may be slightly influenced in the short term by local emissions and processes, and by the meteorological conditions of Mauna Loa; these short-term local variations cannot be considered as part of the global carbon dioxide trend [11,16] .…”

Section: Introductionmentioning

confidence: 99%

Temporal Variography for the Evaluation of Atmospheric Carbon Dioxide Monitoring

Torres¹,

Toro²,

Gálvez³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Since 1958, the Mauna Loa Observatory (MLO) has continuously monitored carbon dioxide variations using nondispersive infrared (NDIR) sensors, with the Keeling curve as an early indicator of the anthropogenic contribution to global atmospheric carbon dioxide. The increasing CO2 levels are alarming and have led to international agreements that promote cleaner industrial activities. However, any change in global behavior would not immediately cause detectable changes in the MLO data; the extent to which global and long-term term trends are conflated with local and short-term variations remains unclear. Hence the current paper verifies the performance of the sampling and measurement systems of MLO, using existing data published within the months of October and November 2020, which comply with the temporal continuity requirements of chronostatistics. It has been determined that the components of the MLO air including carbon dioxide are well mixed due to their particular location. Beyond this, the variographic analysis distinguishes between small (<5%) and large (>10%) variability contributions due to sampling, including graphical depictions of MLO data. Coupled with the precision of the method being better than 0.2 ppm, it has been determined that the sampling and measurement protocols are highly suitable to meet the objective of representing CO2 fluctuations over time. The variographic application also manages to quantify short-term variabilities resulting from the local processes of the region where the observatory is located. The results support the furthering of multiscaled temporal analysis of atmospheric CO2, and potentially the incorporation of CO2 variographic parameters into empirical and semi-empirical climate models.

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Effect of Wind Speed on Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth over the North Pacific

Cited by 8 publications

References 76 publications

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Comprehensive Validation and Comparison of Three VIIRS Aerosol Products over the Ocean on a Global Scale

Temporal Variography for the Evaluation of Atmospheric Carbon Dioxide Monitoring

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