A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification

Su, Xin; Wang, Lunche; Zhang, Ming; Qin, Wenmin; Bilal, Muhammad

doi:10.1016/j.rse.2020.112221

Cited by 78 publications

(19 citation statements)

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“…Commonly, the aerosol radiative feedback and properties are studied using observational in-situ and satellite data [28][29][30] as well as atmospheric dispersion models [31,32]. The usage of atmospheric dispersion models allows a separation of local and LTD components of air pollutants but has rarely been conducted on bioaerosols (pollen).…”

Section: Introductionmentioning

confidence: 99%

Air Pollution Affecting Pollen Concentrations through Radiative Feedback in the Atmosphere

et al. 2021

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Episodes with high air pollution and large amounts of aeroallergens expose sensitive individuals to a health damaging cocktail of atmospheric particles. Particulate matter (PM) affects the radiative balance and atmospheric dynamics, hence affecting concentrations of pollutants. The aim of the study is to estimate feedback between meteorology and particles on concentrations of aeroallergens using an extended version of the atmospheric model WRF-Chem. The extension, originally designed for PM and dust, concerns common aeroallergens. We study a birch pollen episode coinciding with an air pollution event containing Saharan dust (late March to early April 2014), using the model results, pollen records from Southern UK and vertical profiles of meteorological observations. During the episode, increased concentrations of birch pollen were calculated over the European continent, causing plumes transported towards the UK. The arrival of these plumes matched well with observations. The lowest parts of the atmospheric boundary layer demonstrate a vertical profile that favours long distance transport, while the pollen record shows pollen types that typically flower at another time. The model calculations show that feedback between meteorology and particles changes pollen concentrations by ±30% and in some cases up to 100%. The atmospheric conditions favoured meteorological feedback mechanisms that changed long distance transport of air pollution and aeroallergens.

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Section: Introductionmentioning

confidence: 99%

Air Pollution Affecting Pollen Concentrations through Radiative Feedback in the Atmosphere

et al. 2021

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“…With the comparison between Himawari-8 IBAA and other AHI algorithms in previous papers, such as different improved DT algorithms (R = 0.86 and RMSE = 0.12 in [21], 2018; R = 0.9 and RMSE = 0.15 in [22]; R > 0.8 in [23]), an improved time-series algorithm (R > 0.8 and RMSE < 0.2 in [59]), the OE method (R = 0.88, RMSE = 0.17 and 69.9% of retrievals falling within EE = ±(0.05 + 0.2 × AOD AERONET ) in [24]), and the monthly spectral base reflectance ratio library method (R = 0.939, RMSE = 0.113 and 82.5% of retrievals falling within EE = ±(0.05 + 0.2 × AOD AERONET in [25]), the IBAA algorithm has certain precision and a simple process. Due to different time range and regions and different ground-based station observation selected for algorithm validation applied in the above papers, there are great uncertainties in such comparisons.…”

Section: Discussionmentioning

confidence: 99%

“…R is the correlation coefficient and RMSE is the root-mean-square error, MD is the mean difference and represents the average value of all AERONET-matched AOD minus AHI AOD. EE is the expected error of ±(0.05 + 0.2 × AOD AERONET ) according to other studies [24,25]. WEE is the percentage of points falling within the EE envelope and UpEE and LowEE are above and below EE envelope.…”

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confidence: 95%

“…She et al (2018) obtained the hourly AOD and surface reflectance by the OE method [24]. Su et al (2020) proposed an algorithm combining DT and DB (Deep Blue) by building a monthly spectral base reflectance ratio library, which is a ratio library of 0.47 µm and 0.51 µm to 2.3 µm [25]. Many other studies have also greatly enriched AHI aerosol inversion [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization

et al. 2021

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The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from AHI data. The algorithm ignores the aerosol effect at 2.3 μm and assumes that the aerosol optical depth does not change within one hour. According to the property that the reflectivity ratio K of two observations at 2.3 μm does not change with wavelength, we constructed the equation for two observations of AHI 0.47 μm band. Then Particle Swarm Optimization (PSO) was used to solve the nonlinear equation. The algorithm was applied to the AHI observations over the Chinese mainland (80°–135°E, 15°–60°N) between April and June 2019 and hourly AOD at 0.47 μm was retrieved. We validated IBAA AOD against the Aerosol Robotic Network (AERONET) sites observation, including surrounding regions as well as the Chinese mainland, and compared it with the AHI L3 V030 hourly AOD product. Validation with AERONET of 2079 matching points shows a correlation coefficient R = 0.82, root-mean-square error RMSE = 0.27, and more than 62% AOD retrieval results within the expected error of ±(0.05 + 0.2 × AODAERONET). Although IBAA does not perform very well in the case of coarse-particle aerosols, the comparison and validation demonstrate it can estimate AHI AOD with good accuracy and wide coverage over land on the whole.

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“…Limited to point locations, ground-based instruments can only support research at the local scale. Satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Himawari Imager (AHI), Ozone Monitoring Instrument (OMI), and Multi-angle Imaging Spectroradiometer (MISR) can provide continuous spatial aerosol property observations, which greatly aid the research of the aerosol effect on the global atmospheric environment and air quality, especially in areas with heavy aerosol loads (e.g., the Indo-Gangetic Plains [IGP] and the North China Plain [NCP]) [13][14][15][16][17][18][19]. However, there are evident differences in the accuracy and applicability of each satellite aerosol product owing to the different retrieval algorithms and spatiotemporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

Huang

Sun

et al. 2021

Remote Sensing

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Long-term (2000–2019) assessment of aerosol loads and dominant aerosol types at spatiotemporal scales using multi-source datasets can provide a strong impetus to the investigation of aerosol loads and to the targeted prevention control of atmospheric pollution in densely populated regions with frequent anthropogenic activities and heavy aerosol emissions. This study uses multi-source aerosol datasets, including Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET), to conduct a long-term variation assessment of aerosol load, high aerosol load frequency, and dominant aerosol types over Asia. The results indicate that regional aerosol type information with adequate spatial resolution can be combined with aerosol optical depth (AOD) values and heavy aerosol load frequency characterization results to explore the key contributors to air pollution. During the study period, the aerosol load over the North China Plain, Central China, Yangtze River Delta, Red River Delta, Sichuan Basin, and Pearl River Delta exhibited an increasing trend from 2000–2009 due to a sharp rise in aerosol emissions with economic development and a declining trend from 2010–2019 under stricter energy conservation controls and emissions reductions. The growth of urban/industrial (UI) type and biomass burning (BB) type aerosol emissions hindered the improvement of the atmospheric environment. Therefore, in future pollution mitigation efforts, focus should be on the control of UI-type and BB-type aerosol emissions. The Indus–Ganges River Plain, Deccan Plateau, and Eastern Ghats show a continuously increasing trend; however, the aerosol load growth rate of the last decade was lower than that of the first decade, which was mainly due to the decrease in the proportion of the mixed type aerosols.

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A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification

Cited by 78 publications

References 78 publications

Air Pollution Affecting Pollen Concentrations through Radiative Feedback in the Atmosphere

Air Pollution Affecting Pollen Concentrations through Radiative Feedback in the Atmosphere

Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

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