Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations

Shi, Zheng; Xing, Tingyan; Jin, Guang; Xue, Yong; Che, Yahui

doi:10.3390/rs11080891

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…Large AODs for 3 May can be seen especially over north Russia and Canada. The pollution over Europe and Russia has been observed to transport to Spitsbergen (Treffeisen et al, 2007;Stone et al, 2014;Shi et al, 2019). Although very limited regions are covered by the AATSR AODs provided by the Swansea retrieval, a smooth transition between Swansea retrieved AOD over open water and XBAER above-snow-AOD can be seen, indicating the promising quality of above-snow-AOD derived by XBAER.…”

Section: Results and Evaluationmentioning

confidence: 99%

On the retrieval of aerosol optical depth over cryosphere using passive remote sensing

Mei

Vandenbussche

Розанов

et al. 2020

Remote Sensing of Environment

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The lack of aerosol information over the cryosphere introduces large uncertainties to our understanding of phenomenon, known as the Arctic Amplification (AA) and its feedback mechanisms. The aerosol optical depth (AOD) describes the optical characteristics of aerosol loading. This paper describes a novel algorithm, which retrieves AOD above snow-covered regions from the measurements of the up-welling radiation at the top of atmosphere, observed by the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) instruments. The algorithm optimizes the generic eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) approach for longer wavelengths over the cryosphere. The algorithm utilizes the characteristics of solar bidirectional distribution properties of snow and aerosol at wavelength 3.7 μm to derive above-snow-AOD. Since the impact of fine-mode aerosol on 3.7 μm is ignorable, the retrieved AOD in this manuscript represents mainly coarse-mode dominated part. A novel method to extract the solar reflection part at 3.7 μm is presented and used in the surface parameterization. Two aerosol types (sea saltdominated and dust-dominated) are used and the best-fit type is derived by an iterative procedure, using a Look-Up-Table (LUT) approach. Sensitivity studies of the impact on the retrieved AOD using XBAER algorithm, which investigate the impacts of aerosol type, snow surface emissivity and potential cloud contamination under typical AATSR observation conditions, are presented. The sensitivity studies show that the surface parametrization and aerosol typing are suitable for the retrieval of above-snow-AOD over the Arctic snow-covered region. AOD observations retrieved in this study from AATSR (2002-2012) observation collocated with those from the Aerosol Robotic Network (AERONET) sites over Greenland show good agreement. 72.1% of the match-ups fall into the expected error envelope of (± 0.15AOD ± 0.025). The AATSR derived above-snow-AOD at 0.55 μm research product has also been compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol product, the Mineral Aerosols Profiling from Infrared Radiances (MAPIR) derived Infrared Atmospheric Sounding Interferometer (IASI) AOD research product, and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) AOD simulations over Greenland on April 2011. The comparison reveals that all datasets show similar patterns for the AOD above Greenland. The AOD is smaller in central Greenland and larger over the coastline regions. The XBAER derived above-snow-AOD has improved coverage, as compared to that of the existing AATSR aerosol product. The transition between above-snow-AOD and AOD derived over surrounding ocean surfaces does not indicate any systematic errors. Two aerosol transport events have been well-captured by the XBAER derived above-snow-AOD research product. The new algorithm is also applied to the SLSTR onboard Sentinel-3 demonstrating n...

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Section: Results and Evaluationmentioning

confidence: 99%

On the retrieval of aerosol optical depth over cryosphere using passive remote sensing

Mei

Vandenbussche

Розанов

et al. 2020

Remote Sensing of Environment

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“…Further fundamental improvement will be required, particularly in terms of the accuracy of ship emissions and boundary conditions for the Arctic simulations, although the MODIS data assimilated into the model-calculated AODs provided more accurate atmospheric levels of AOD and PMs in this study. In addition, the algorithm for AOD retrievals over some snow-covered regions (i.e., high surface reflectance) was recently developed by several investigators [93,94]. Moreover, the gap-filling technique based on artificial intelligence (AI) is nowadays a promising method [95,96].…”

Section: Summaries and Conclusionmentioning

confidence: 99%

Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

et al. 2021

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In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

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“…Unfortunately, aerosol readings in the Arctic are sparse due to temporal and spatial limitations [4]. Moreover, the sources of such particles tend to be predominantly remote, further complicating measurements since it is difficult to track their origin.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, 24 existing aerosol retrieval algorithms for passive remote sensing focus mainly on snow and 25 cloud-free regions. Due to this, there is much uncertainty regarding the influence of aero-25 sols and aerosol activity in the Arctic [4]. Therefore, it is especially important to both clar-25 ify existing data in the Arctic and use the prediction ability of DNNs to expand and further 25 extrapolate data.…”

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confidence: 99%

Deep Neural Networks for Aerosol Optical Depth Retrieval

2022

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Aerosol Optical Depth (AOD) is a measure of the extinction of solar radiation by aerosols in the atmosphere. Understanding the variations of global AOD is necessary for precisely determining the role of aerosols. Arctic warming is partially caused by aerosols transported from vast distances, including those released during biomass burning events (BBEs). However, measuring AODs is challenging, typically requiring active LIDAR systems or passive sun photometers. Both are limited to cloud-free conditions; sun photometers provide only point measurements, thus requiring more spatial coverage. A more viable method to obtain accurate AOD may be found through machine learning. This study uses DNNs to estimate Svalbard’s AODs using a minimal set of meteorological parameters (temperature, air mass, water vapor, wind speed, latitude, longitude, and time of year). The mean absolute error (MAE) between predicted and true data was 0.00401 for the entire set and 0.0079 for the validation set. It was then shown that the inclusion of BBE data improves predictions by 42.167%. It was demonstrated that AODs may be accurately estimated without the use of expensive instrumentation, using machine learning and minimal data. Similar models may be developed for other regions, allowing immediate improvement of current meteorological models.

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Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations

Cited by 9 publications

References 28 publications

On the retrieval of aerosol optical depth over cryosphere using passive remote sensing

On the retrieval of aerosol optical depth over cryosphere using passive remote sensing

Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

Deep Neural Networks for Aerosol Optical Depth Retrieval

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