Variation of the aerosol optical properties and validation of MODIS AOD products over the eastern edge of the Tibetan Plateau based on ground-based remote sensing in 2017

You, Yingchang; Zhao, Tianliang; Xie, Ying; Zheng, Yu; Zhu, Jun; Xia, Junrong; Cao, Le; Wang, Chenggang; Che, Huizheng; Yao, Ling; Duan, Jingxin; Zhou, Jianxun; Zhou, Xiaoou

doi:10.1016/j.atmosenv.2019.117257

Cited by 14 publications

(10 citation statements)

References 47 publications

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“…Further, due to poor spatial resolution of the MERRA-2 data (0.5 • × 0.625 • ), it is likely unable to capture AOD within a small selected site as observed for other moisture parameters [51]. Similar to such discrepancies, the validation of satellite data over the high-altitude Tibetan Plateau shows low accuracy, as the observed data sometimes fall within the retrieval errors due to very lower AOD [41]. Due to a lack of consistency (or more scatter data) among the selected sites, we did not implement the ground-based offset to MERRA-2 data.…”

Section: Discussionmentioning

confidence: 97%

“…In the recent past, satellite data have become available from the early 2000s. However, such satellite data have several issues over the background mountainous sites when the retrieved parameters are of the order of uncertainties of the satellite data, as reported by several authors [38][39][40][41]. Due to limitations of such satellite data, the use of reanalysis data has several advantages due to availability of sufficiently long-term data (i.e, available from the early 1980s) without any discontinuity.…”

Section: Observational Sitesmentioning

confidence: 99%

“…Such study of long-term continuous data for AOD trend analysis may be the first of this kind in the region. Further, the present study has many important aspects for identifying various climate forcing parameters in the HKH region, where the satellite-based measurements are inaccurate and biased due to extremely low AOD [38][39][40][41]. This paper is organized as follows: Section 2 describes observational sites with data sources; Section 3 describes methodology and data analysis; Section 4 presents results and discussion for yearly AOD trend, seasonal long-term AOD trends and climatological AOD in the HKH sub-regions; and Section 5 summarizes and discusses the implications of the present work.…”

Section: Introductionmentioning

confidence: 99%

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Impacts of Aerosol Loading in the Hindu Kush Himalayan Region Based on MERRA-2 Reanalysis Data

et al. 2021

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The impacts of climate change have severely affected geosphere, biosphere and cryosphere ecosystems in the Hindu Kush Himalayan (HKH) region. The impact has been accelerating further during the last few decades due to rapid increase in anthropogenic activities such as modernization, industrialization and urbanization, along with energy demands. In view of this, the present work attempts to examine aerosol optical depth (AOD) over the HKH region using the long-term homogeneous MERRA-2 reanalysis data from January, 1980 to December, 2020. The AOD trends are examined statistically with student’s t-test (t). Due to a vast landmass, fragile topography and harsh climatic conditions, we categorized the HKH region into three sub-regions, namely, the northwestern and Karakoram (HKH1), the Central (HKH2) and the southeastern Himalaya and Tibetan Plateau (HKH3). Among the sub-regions, the significant enhancement of AOD is observed at several potential sites in the HKH2 region, namely, Pokhara, Nainital, Shimla and Dehradun by 55.75 × 10−4± 3.76 × 10−4, 53.15 × 10−4± 3.94 × 10−4, 51.53 × 10−4± 4.99 × 10−4 and 39.16 × 10−4± 4.08 × 10−4 AOD year−1 (550 nm), respectively, with correlation coefficients (Rs) of 0.86 to 0.93. However, at a sub-regional scale, HKH1, HKH2 and HKH3 exhibit 23.33 × 10−4± 2.28 × 10−4, 32.20 × 10−4± 2.58 × 10−4 and 9.48 × 10−4± 1.21 × 10−4 AOD year−1, respectively. The estimated trends are statistically significant (t> 7.0) with R from 0.81 to 0.91. Seasonally, the present study also shows strong positive AOD trends at several potential sites located in the HKH2 region, such as Pokhara, Nainital, Shimla and Dehradun, with minimum 19.81 × 10−4± 3.38 × 10−4 to maximum 72.95 × 10−4± 4.89 × 10−4 AOD year−1 with statistical significance . In addition, there are also increasing AOD trends at all the high-altitude background sites in all seasons.

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Section: Discussionmentioning

confidence: 97%

Section: Observational Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impacts of Aerosol Loading in the Hindu Kush Himalayan Region Based on MERRA-2 Reanalysis Data

et al. 2021

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“…This is because AGRI retrievals are performed if there are less than 9 cloudy IR pixels within an FOR. Such higher temporal and spatial resolution data are important in the climate sensitive regions such as the high-altitude mountain terrain region of Tibetan Plateau in particular where there is limited number of ground-based observations [44]. It should be noted that both spatial and temporal resolutions will be improved for moisture soundings from the future Fengyun-4 GIIRS measurements.…”

Section: Introductionmentioning

confidence: 99%

FENGYUN-4A Advanced Geosynchronous Radiation Imager Layered Precipitable Water Vapor Products’ Comprehensive Evaluation Based on Quality Control System

Yong

et al. 2022

Atmosphere

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A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products.

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“…Aerosols are produced from a variety of anthropogenic and natural sources with a typical size ranging from 0.001 to 10 μm. Natural aerosol particle includes sulphate aerosols from volcanic eruption, sea salt particle from ocean surface, mineral dust aerosols and wind borne dust (Shen et al 2019;You et al 2020). Aerosol loading or the amount of aerosols in the atmosphere is usually quantified by mass concentration or by an optical measure, i.e.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal assessment of ambient air quality, their health effects and improvement during COVID-19 lockdown in one of the most polluted cities of India

Markandeya

Verma²,

Mishra

et al. 2020

Environ Sci Pollut Res

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The present work aims to investigate seasonal variations in air pollution levels in Lucknow and assess the ambient air quality of the city together with highlighting the health impacts of major pollutants like PM 10 , PM 2.5 , SO 2 , NO 2 , Pb, Ni and aerosols from 2010 to 2019. The maximum and minimum values of PM 10 , PM 2.5 , SO 2 , NO 2 , Pb and Ni were found to be 270.75 and 122.45 μg/m 3 , 124.95 and 95.52 μg/m 3 , 25.60 and 8.05 μg/m 3 , 75.65 and 23.85 μg/m 3 , 0.66 and 0.03 μg/m 3 and 0.07 and 0.01 ng/m 3 , respectively. Health impact of particulate matter has also been assessed with AirQ+, and it was estimated that long-term exposure of PM 10 was attributed to between 37 and 48% for post-neonatal (age 1–12 months) mortality rate due to all causes, whereas long-term attributable proportions in mortality due to exposure of PM 2.5 were to about 19 to 28% from all causes. Further, an attempt has also been made to evaluate the impact of lockdown amid COVID-19 on the ambient air quality of Lucknow. During the lockdown, PM 2.5 levels reduced by 65% (at Gomti Nagar), 23% (at central school), 79% (at Lalbagh) and 35% (at Talkatora), due to which, air quality index of Gomti Nagar came down to 43, well below 50 which falls in the healthy range. NO 2 levels also came down. However, levels of SO 2 did not show significant reduction. Correlating the data between aerosol optical depth and Angstrom exponent by Pearson correlation analysis revealed a significant positive correlation ( r = 0.65, P < 0.001).

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Variation of the aerosol optical properties and validation of MODIS AOD products over the eastern edge of the Tibetan Plateau based on ground-based remote sensing in 2017

Cited by 14 publications

References 47 publications

Impacts of Aerosol Loading in the Hindu Kush Himalayan Region Based on MERRA-2 Reanalysis Data

Impacts of Aerosol Loading in the Hindu Kush Himalayan Region Based on MERRA-2 Reanalysis Data

FENGYUN-4A Advanced Geosynchronous Radiation Imager Layered Precipitable Water Vapor Products’ Comprehensive Evaluation Based on Quality Control System

Spatio-temporal assessment of ambient air quality, their health effects and improvement during COVID-19 lockdown in one of the most polluted cities of India

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