Aerosol optical depth retrieval over snow using AATSR data

Mei, Linlu; Xue, Yong; Kokhanovsky, Alexander A.; Hoyningen‐Huene, W. von; Istomina, Larysa; Leeuw, G. de; Burrows, J. P.; Jin, Guang; Jing, Yanguo

doi:10.1080/01431161.2013.786197

Cited by 22 publications

(17 citation statements)

References 28 publications

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“…4.2.3. MODIS and AATSR have been used to explore the retrieval of aerosol parameters in Arctic regions (Istomina et al, 2010;Mei et al, 2013). For an overview of aerosol remote sensing in polar regions using ground-based and satellite instruments, see Tomasi et al (2015).…”

Section: Satellitementioning

confidence: 99%

Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes

et al. 2019

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Abstract. The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date, aiming to strengthen research and innovation regarding climate change issues in the Nordic region. CRAICC gathered more than 100 scientists from all Nordic countries in a virtual centre with the objectives of identifying and quantifying the major processes controlling Arctic warming and related feedback mechanisms, outlining strategies to mitigate Arctic warming, and developing Nordic Earth system modelling with a focus on short-lived climate forcers (SLCFs), including natural and anthropogenic aerosols. The outcome of CRAICC is reflected in more than 150 peer-reviewed scientific publications, most of which are in the CRAICC special issue of the journal Atmospheric Chemistry and Physics. This paper presents an overview of the main scientific topics investigated in the centre and provides the reader with a state-of-the-art comprehensive summary of what has been achieved in CRAICC with links to the particular publications for further detail. Faced with a vast amount of scientific discovery, we do not claim to completely summarize the results from CRAICC within this paper, but rather concentrate here on the main results which are related to feedback loops in climate change–cryosphere interactions that affect Arctic amplification.

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Section: Satellitementioning

confidence: 99%

Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes

et al. 2019

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“…Satellite remote sensing, as the most direct and effective way to capture the spatial distributions and temporal variations of aerosols over large regions, has been widely used for researches at the local and global scales compared with ground-based Sun photometers (Garcia et al, 2016;Kahn, 2013;Kokhanovsky et al, 2007;Li et al, 2005;Noh et al, 2009;Qin et al, 2016;Sun et al, 2016;Veefkind et al, 1999;Zheng et al, 2017). During past 20 years, a series of sensors have been launched and used in aerosol related researches over land and ocean, such as National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (Geogdzhayev et al, 2004;Hsu et al, 2017), Multi-angle Imaging SpectroRadiometer (Diner et al, 2005;Limbacher & Kahn, 2017), Advanced Along Track Scanning Radiometer (Grey et al, 2006;Guo et al, 2009;Mei et al, 2013;Xue et al, 2009), Polarization and Directionality of the Earth's Reflectance (Deuze et al, 2001), Sea-Viewing Wide Field-Of-View Sensor (Melin et al, 2007;Sayer et al, 2012), Landsat Operational Land Imager (Tian et al, 2018), MODIS (MODerate-Resolution Imaging Spectroradiometer; Levy et al, 2007), VIIRS (Visible Infrared Imaging Radiometer Suite; Jackson et al, 2013;Su et al, 2015;Zhang et al, 2016), and Chinese FengYun Medium Resolution Spectral Imager (Han et al, 2015;Tong et al, 2011). One of the main challenges for the AOD retrieval using satellites is to isolate aerosol particle scattering contributions from satellite recorded signals, which is the superposition of atmospheric path reflectance including atmospheric molecules and aerosol matters as well as surface reflectance signals .…”

Section: Introductionmentioning

confidence: 99%

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

Yang

Zhao

Sun

et al. 2019

Earth and Space Science

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As a new generation of polar‐orbiting satellites, NPP VIIRS (National Polar‐orbiting Partnership Visible Infrared Imaging Radiometer Suite) provides important supports for the studies of local and global scale climate change, environmental monitoring, and radiant energy balance. This paper proposed an improved deep blue aerosol retrieval algorithm over complex regions based on NPP VIIRS observations. Two improvements were proposed and implemented to the traditional aerosol optical depth (AOD) retrieval algorithm in order to improve the retrieval accuracy, including the construction of the surface reflectance conversion model and aerosol optical parameter acquisition. The retrieval results were extensively evaluated. First, the accuracy of the algorithm was evaluated using AErosol RObotic NETwork ground‐based observations, including conditions over different underlying surfaces, different seasons, and different AOD values. Then, the cross validation was carried out between our AOD retrievals and NPP VIIRS aerosol products. The verification results show that our AOD retrievals agree very well with the AErosol RObotic NETwork AOD with coefficient of determination (R2) ~ 0.85. The improved deep blue algorithm performs well overall under different surface conditions and seasons, while the quality of performance gradually decreases with the increase of AOD. Moreover, the improved algorithm is robust with less bias, root‐mean‐square error, and relative mean bias in the AOD retrievals and can provide more detailed aerosol information compared with the NPP VIIRS aerosol products. These results suggest that the improved deep blue algorithm can obtain high‐precision AOD information, providing better data basis for related researches on air pollution and climate change.

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“…To fully understand the effect of aerosols over the Himalayan region, detailed knowledge regarding the spatio-temporal distributions of aerosols, and their seasonal variability in the atmosphere are required (Bonasoni et al, 2012). Several methods have been used to retrieve AOD over pure snow (Istomina et al, 2009;Mei et al, 2012Mei et al, , 2013, but all these algorithms are restricted to the Arctic region in order to meet the requirement of having a sufficient snow BRDF model (Mei et al, 2013). To date, no algorithm exists to retrieve AOD products over Himalayan cryospheric region (snow and ice surfaces).…”

Section: Introductionmentioning

confidence: 99%

Aerosol Optical Depth Over the Nepalese Cryosphere Derived From an Empirical Model

et al. 2019

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In the Himalayan region, aerosols received much attention because they affect the regional as well as local climate. Aerosol Optical Depth (AOD) observation from satellite are limited in the Himalayan region mainly due to high surface reflectance. To overcome this limitation, we have conducted a multivariate regression analysis to predict the AOD over the cryospheric portion of Nepalese Himalaya. Prediction using three meteorological variables from ERA-Interim: relative humidity, wind velocity components (U10 and V10) were taken into account for model development as independent variables, while the longest time series AOD observation at Pokhara station is used as dependent variable. Model coefficients were found significant at 95 percent level with 0.53 coefficients of determination for daily values. Correlation coefficients between model output and AERONET observations were found to be 0.68, 0.73, 0.75, 0.83, and 0.82 at Lumbini, Kathmandu Bode (KTM-BO), Kathmandu University (KTM-UN), Jomson, and Pyramid laboratory/observatory (EVK2CNR) AERONET stations, respectively. Model overestimate AOD at Jomsom, and EVK2CNR AERONET stations while slightly underestimates AOD in Lumbini, KTM-UN, and KTM-BO AERONET station, respectively. Both model output and MODIS observation showed that the highest AOD over Nepal is observed during winter and pre-monsoon season. While lowest AOD is observed during monsoon, and post-monsoon season. The result of this research supports that the use of linear regression model yields good estimation for daily average AOD in Nepal. The model that we have presented could possibly be used in other mountain regions for climate research.

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Aerosol optical depth retrieval over snow using AATSR data

Cited by 22 publications

References 28 publications

Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes

Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

Aerosol Optical Depth Over the Nepalese Cryosphere Derived From an Empirical Model

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