Topic Editors

NOAA National Marine Fisheries Service Northwest Regional Office, Pacific Marine Environmental Laboratory, Seattle, WA, USA
Institute of Earth Sciences (ICT), Institute of Research and Advanced Training, University of Évora, 7000-671 Évora, Portugal
Laboratoire d’Optique Atmosphérique, CNRS/Universite Lille, 59655 Villeneuve d’Ascq, France
LATMOS-IPSL, Sorbonne Université (UPMC), UVSQ, CNRS/INSU, 75006 Paris, France

Recent Progress in Aerosol Remote Sensing and Products

Abstract submission deadline
closed (28 August 2022)
Manuscript submission deadline
closed (28 October 2022)
Viewed by
25265

Topic Information

Dear Colleagues,

According to the Intergovernmental Panel on Climate Change, aerosol radiative forcing contributes the largest uncertainty to total radiative forcing estimates. Sources of uncertainty include variability in spatial and temporal distributions of aerosols and interactions between aerosols and clouds. Remote sensing from a multitude of platforms including satellites, aircraft, ships, and ground-based stations has become widespread, greatly increasing the ability to characterize regional and global scale aerosol emissions and distributions. These observations have also been employed to further the understanding of impacts of aerosols on clouds and vice versa.

This Topics project aims to review current uses of remote sensing to improve our present understanding of aerosol emissions, distributions, impacts on clouds and climate, and removal processes. Contributions related to any of these aspects of remote sensing along with developments of novel remote sensing applications and products and their use in modeling aerosol distributions and impacts are welcome.

This multidisciplinary topic will be dedicated to the presentation of scientific papers that can be collected under the common title “Recent Progress in Aerosol Remote Sensing and Products”.

We welcome submissions that include, but are not limited to the following topics:

  • Aerosol emissions, transport, removal, and vertical profiles
  • Remote sensing
  • Measurement methods
  • Radiative forcing
  • Aerosol – cloud interactions
  • Satellite, aircraft, ship, and land-based observations

Dr. Patricia K. Quinn
Prof. Dr. Maria João Costa
Dr. Oleg Dubovik
Prof. Dr. Jean-Christophe Raut
Topic Editors

Keywords

  • aerosols
  • pollutants
  • vertical profiles
  • remote sensing
  • radiative forcing
  • aerosol – cloud interactions

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Atmosphere
atmosphere
2.9 4.1 2010 17.7 Days CHF 2400
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700
Environments
environments
3.7 5.9 2014 23.7 Days CHF 1800
Pollutants
pollutants
- - 2021 21.7 Days CHF 1000

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Published Papers (11 papers)

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25 pages, 5181 KiB  
Article
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S.
by Xinxin Ye, Mina Deshler, Alexi Lyapustin, Yujie Wang, Shobha Kondragunta and Pablo Saide
Remote Sens. 2022, 14(23), 6113; https://doi.org/10.3390/rs14236113 - 02 Dec 2022
Cited by 3 | Viewed by 1580
Abstract
Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are [...] Read more.
Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less strict cloud masking. These results can be used as a guide for applications of satellite AOD retrievals during wildfire events and provide insights on future improvement of retrieval algorithms under heavy smoke conditions. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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12 pages, 2921 KiB  
Communication
Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing
by Yue Yang, Jan Cermak, Kangzhuo Yang, Eva Pauli and Yunping Chen
Remote Sens. 2022, 14(18), 4677; https://doi.org/10.3390/rs14184677 - 19 Sep 2022
Cited by 3 | Viewed by 1898
Abstract
Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived [...] Read more.
Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived from Sentinel-2) and land cover products (60 m and 100 m, respectively) for Beijing and its surroundings from 2017 to 2019. Contribution analysis is performed to quantitatively evaluate the influences of land cover on regional AOD over the study area. Patterns of aerosol distribution remarkably vary in time and space. Statistics of seasonal average AOD peak in spring and then progressively decline from summer through autumn to winter. High AOD values coincide with a low normalized difference vegetation index (NDVI) and a high normalized difference built-up index (NDBI). Urban and built-up land is a major contributor to regional AOD in the study area, especially in spring; forest and grassland always reduce AOD. Anthropogenic activities have a non-negligible influence on AOD and can even reverse the contribution of a land cover type to aerosols. Insights of the study promote the comprehension of the impacts of land cover on aerosols and air pollution and contribute to the planning of land use within a city. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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18 pages, 5169 KiB  
Article
Long-Term Investigation of Aerosols in the Urmia Lake Region in the Middle East by Ground-Based and Satellite Data in 2000–2021
by Abbas Ranjbar Saadat Abadi, Nasim Hossein Hamzeh, Karim Shukurov, Christian Opp and Umesh Chandra Dumka
Remote Sens. 2022, 14(15), 3827; https://doi.org/10.3390/rs14153827 - 08 Aug 2022
Cited by 10 | Viewed by 2473
Abstract
Dried lake beds are some of the largest sources of dust in the world and have caused environmental problems in the surrounding areas in recent decades. In the present work, we studied the monthly and annual occurrence of dust storms at selected weather [...] Read more.
Dried lake beds are some of the largest sources of dust in the world and have caused environmental problems in the surrounding areas in recent decades. In the present work, we studied the monthly and annual occurrence of dust storms at selected weather stations around Urmia Lake in northwestern (NW) Iran. Furthermore, we investigated the variations in the daily aerosol optical depth (AOD at 550 nm) and the Ångström exponent (at 412/470 nm), as well as the vertical profile of the total aerosol extinction coefficient and AOD at 532 nm, using space-borne MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and CALIPSO Satellite LiDAR data over the Urmia Lake region (36–39°N, 44–47°E). The monthly variations of AOD550 and AOD532 for the regions 37–39°N and 46–59°E were compared, and it was found that the CALIPSO AOD532 and MODIS AOD532 (reconstructed using the Ångström exponent) were in good agreement. In general, the dust storms during 2000–2021 increased the AOD550 above average around the Urmia Lake. The vertical profile of aerosols showed that the largest contribution to total aerosol loading over the Urmia Lake was from 1.5–3 km, 1.5–4 km, 1.5–5 km, and 1.5–3 km during winter, spring, summer, and autumn seasons, respectively. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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17 pages, 3323 KiB  
Article
Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
by Yamei Shu, Shuguang Liu, Zhao Wang, Jingfeng Xiao, Yi Shi, Xi Peng, Haiqiang Gao, Yingping Wang, Wenping Yuan, Wende Yan, Ying Ning and Qinyuan Li
Remote Sens. 2022, 14(12), 2759; https://doi.org/10.3390/rs14122759 - 08 Jun 2022
Cited by 5 | Viewed by 2168
Abstract
Aerosols affect the gross primary productivity (GPP) of plants by absorbing and scattering solar radiation. However, it is still an open question whether and to what extent the effects of aerosol on the diffuse fraction (Df) can enhance GPP globally. We [...] Read more.
Aerosols affect the gross primary productivity (GPP) of plants by absorbing and scattering solar radiation. However, it is still an open question whether and to what extent the effects of aerosol on the diffuse fraction (Df) can enhance GPP globally. We quantified the aerosol diffuse fertilization effect (DFE) and incorporated it into a light use efficiency (LUE) model, EC-LUE. The new model is driven by aerosol optical depth (AOD) data and is referred to as AOD-LUE. The eddy correlation variance (EC) of the FLUXNET2015 dataset was used to calibrate and validate the model. The results showed that the newly developed AOD-LUE model improved the performance in simulating GPP across all ecosystem types (R2 from 0.6 to 0.68), with the highest performance for mixed forest (average R2 from 0.71 to 0.77) and evergreen broadleaf forest (average R2 from 0.34 to 0.45). The maximum LUE of diffuse photosynthetic active radiation (PAR) (3.61 g C m−2 MJ−1) was larger than that of direct PAR (1.68 g C m−2 MJ−1) through parameter optimization, indicating that the aerosol DFE seriously affects the estimation of GPP, and the separation of diffuse PAR and direct PAR in the GPP model is necessary. In addition, we used AOD-LUE to quantify the impact of aerosol on GPP. Specifically, aerosols impaired GPP in closed shrub (CSH) by 6.45% but enhanced the GPP of grassland (GRA) and deciduous broadleaf forest (DBF) by 3.19% and 2.63%, respectively. Our study stresses the importance of understanding aerosol-radiation interactions and incorporating aerosol effects into regional and global GPP models. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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17 pages, 3900 KiB  
Article
Combining Sun-Photometer, PM Monitor and SMPS to Inverse the Missing Columnar AVSD and Analyze Its Characteristics in Central China
by Ao Miao, Shikuan Jin, Yingying Ma, Boming Liu, Nan Jiang, Wenzhuo He, Xiaokun Qian and Yifan Zheng
Atmosphere 2022, 13(6), 915; https://doi.org/10.3390/atmos13060915 - 05 Jun 2022
Viewed by 1775
Abstract
Columnar aerosol volume size distribution (AVSD) is an important atmospheric parameter that shows aerosol microphysical properties and can be used to analyze the impact of aerosols on the radiation budget balance, as well as regional climate effects. Usually, columnar AVSD can be obtained [...] Read more.
Columnar aerosol volume size distribution (AVSD) is an important atmospheric parameter that shows aerosol microphysical properties and can be used to analyze the impact of aerosols on the radiation budget balance, as well as regional climate effects. Usually, columnar AVSD can be obtained by using a sun photometer, but its observation conditions are relatively strict, and the columnar AVSD will be missing in cloudy or hazy weather due to cloud cover and other factors. This study introduces a novel algorithm for inversion of missing columnar AVSD under haze periods by using a machine learning approach and other ground-based observations. The principle is as follows. We are based on joint observational experiments. Since the scanning mobility particle sizer (SMPS) and particulate matter (PM) monitor sample the surface data, they can be stitched together to obtain the surface AVSD according to their observation range. Additionally, the sun-photometer scans the whole sky, so it can obtain columnar AVSD and aerosol optical depth (AOD). Then we use the back propagation neural network (BPNN) model to establish the relationship between the surface AVSD and the columnar AVSD and add AOD as a constraint. Next, the model is trained with the observation data of the same period. After the model training is completed, the surface AVSD and AOD can be used to invert the missing columnar AVSD during the haze period. In experiments on the 2015 dataset, the results show that the correlation coefficient and root mean square error between our model inversion results and the original sun photometer observations were 0.967 and 0.008 in winter, 0.968 and 0.010 in spring, 0.969 and 0.013 in summer, 0.972 and 0.007 in autumn, respectively. It shows a generally good performance that can be applied to the four seasons. Furthermore, the method was applied to fill the missing columnar AVSD of Wuhan, a city in central China, under adverse weather conditions. The final results were shown to be consistent with the climatic characteristics of Wuhan. Therefore, it can indeed solve the problem that sun photometer observations are heavily dependent on weather conditions, contributing to a more comprehensive study of the effects of aerosols on climate and radiation balance. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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16 pages, 5173 KiB  
Article
A New Coupling Method for PM2.5 Concentration Estimation by the Satellite-Based Semiempirical Model and Numerical Model
by Shuyun Yuan, Ying Li, Jinhui Gao and Fangwen Bao
Remote Sens. 2022, 14(10), 2360; https://doi.org/10.3390/rs14102360 - 13 May 2022
Cited by 3 | Viewed by 1693
Abstract
Aerosol optical and chemical properties play a major role in the retrieval of PM2.5 concentrations based on aerosol optical depth (AOD) data from satellites in the conventional semiempirical model (SEM). However, limited observation information hinders the high-resolution estimation of PM2.5. [...] Read more.
Aerosol optical and chemical properties play a major role in the retrieval of PM2.5 concentrations based on aerosol optical depth (AOD) data from satellites in the conventional semiempirical model (SEM). However, limited observation information hinders the high-resolution estimation of PM2.5. Therefore, a new method for evaluating near-surface PM2.5 at high spatial resolution is developed by coupling the SEM and the chemical transport model (CTM)-based numerical (CSEN) model. The numerical model can provide large-scale information for aerosol properties with high spatial resolution at a large scale based on emissions and meteorology, though it can still be biased in simulating absolute PM2.5 concentrations. Therefore, the two crucial aerosol characteristic parameters, including the coefficient integrated humidity effect (γ′) and the comprehensive reference value of aerosol properties (K) in SEM, have been redefined using the WRF-Chem numerical model. Improved model performance was observed for these results compared with the original SEM results. The monthly averaged correlation coefficients (R) by CSEN were 0.92, 0.82, 0.84, and 0.83 in January, April, July, and October, respectively, whereas those of the SEM were 0.80, 0.77, 0.72, and 0.72, respectively. All the statistical metrics of the model validation showed significant improvements in all seasons. The reduced biases of estimated PM2.5 by CSEN indicated the effect of hygroscopic growth and aerosol properties affected by the meteorology on the relationship between AOD and estimated PM2.5 concentrations, especially in winter and summer. The better performance of the CSEN model provides insight for air quality monitoring at different scales, which supplies important information for air pollution control policies and health impact analysis. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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13 pages, 11102 KiB  
Article
Retrieval of High-Resolution Aerosol Optical Depth for Urban Air Pollution Monitoring
by Rui Bai, Yong Xue, Xingxing Jiang, Chunlin Jin and Yuxin Sun
Atmosphere 2022, 13(5), 756; https://doi.org/10.3390/atmos13050756 - 07 May 2022
Cited by 4 | Viewed by 2291
Abstract
Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the [...] Read more.
Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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12 pages, 3139 KiB  
Article
Effect of Polygonal Agglomerated Ice Crystals on Laser Scattering
by Shenhe Ren, Ming Gao, Ze Nan, Mingjun Wang and Yan Li
Atmosphere 2022, 13(3), 369; https://doi.org/10.3390/atmos13030369 - 22 Feb 2022
Viewed by 1318
Abstract
Cirrus clouds contain a large number of irregular small ice crystals. These solid ice crystals cause energy loss and reduce the signal-to-noise ratio at the receiver, causing errors in reception. Considering the random motion and structural diversity of ice particles in cirrus clouds, [...] Read more.
Cirrus clouds contain a large number of irregular small ice crystals. These solid ice crystals cause energy loss and reduce the signal-to-noise ratio at the receiver, causing errors in reception. Considering the random motion and structural diversity of ice particles in cirrus clouds, the discrete dipole approximation method was used to establish sphere-sphere, sphere–ellipsoid, sphere-hexagonal prism, and sphere-hexagonal plate ice particle models. The effects of different agglomerated ice particles on the laser extinction, absorption, and scattering efficiency, as well as the laser intensity and Mueller matrix elements, were analyzed, and the scattering characteristics of agglomerated ice particles in different spatial orientations were preliminarily explored. The results show that the spatial orientation of the clustered particles has great influence on the scattering characteristics. The maximum relative error of the scattering efficiency was 200%, and the maximum relative error value of the elements of the Mueller matrix reaches 800-fold. The results of this study provide theoretical support for further analysis of the scattering characteristics of ice crystal particles with complex agglomeration structures and for further study of the scattering characteristics of randomly moving agglomeration particles in cirrus clouds. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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24 pages, 7747 KiB  
Article
Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing
by Nivedita Sanwlani and Reshmi Das
Remote Sens. 2022, 14(3), 761; https://doi.org/10.3390/rs14030761 - 07 Feb 2022
Cited by 2 | Viewed by 2213
Abstract
Mineral dust aerosols are composed of a complex mixture of silicates, carbonates, oxides, and sulfates. The minerals’ chemical composition and size distribution are vital parameters to evaluate dust environmental impacts. However, the quantification of minerals remains a challenge due to the sparse in [...] Read more.
Mineral dust aerosols are composed of a complex mixture of silicates, carbonates, oxides, and sulfates. The minerals’ chemical composition and size distribution are vital parameters to evaluate dust environmental impacts. However, the quantification of minerals remains a challenge due to the sparse in situ measurements of dust samples. Here we derive the size-resolved mineralogical composition of airborne dust aerosols from MODIS (Terra and Aqua) satellite-acquired optical measurements and compare it with chemically analyzed elemental (Al, Fe, Ca, Mg) concentrations of aerosols for PM2.5 and PM10 from Chonburi, Chiang Rai, and Bangkok in Thailand, and from Singapore. MODIS-derived mineral retrievals exhibited high correlations with elemental concentrations with R2 ≥ 0.84 for PM2.5 and ≥0.96 for PM10. High mineral dust activity was detected in the vicinity of biomass-burning areas with gypsum and calcite exhibiting tracer characteristics of combustion. The spatiotemporal pattern of the MODIS-derived minerals matched with Ozone Monitoring Instrument (OMI)-derived dust, sulfates, and carbonaceous aerosols, indicating the model’s consistency. Variation in aerosol loading by ±90% led to deviation in the mineral concentration by <10%. An uncertainty of 6.4% between AERONET-measured and MODIS-derived AOD corresponds to a < ± 2% uncertainty in MODIS-derived mineral concentration, demonstrating the robustness of the model. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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22 pages, 5664 KiB  
Article
Lidar Ratio Regional Transfer Method for Extinction Coefficient Accuracy Improvement in Lidar Networks
by Yicheng Tong, Sijie Chen, Da Xiao, Kai Zhang, Jing Fang, Chong Liu, Yibing Shen and Dong Liu
Remote Sens. 2022, 14(3), 626; https://doi.org/10.3390/rs14030626 - 28 Jan 2022
Cited by 1 | Viewed by 2620
Abstract
Lidar networks are essential to study the three-dimensional distribution of aerosols on a regional scale. At present, both Mie-scattering lidar (ML) and advanced lidars are being used in lidar networks. The latter can retrieve extinction coefficients without strict assumptions of the lidar ratio, [...] Read more.
Lidar networks are essential to study the three-dimensional distribution of aerosols on a regional scale. At present, both Mie-scattering lidar (ML) and advanced lidars are being used in lidar networks. The latter can retrieve extinction coefficients without strict assumptions of the lidar ratio, such as Raman lidar (RL) or high-spectral-resolution lidar (HSRL). In order to balance the data quality and instrument costs for the lidar network, the lidar ratio regional transfer method in a lidar network is proposed in this paper. We developed a Lidar Ratio and Aerosol Fraction Non-linear Regression (LR-AFNR) model between the lidar ratio and corresponding absorbing aerosol fraction (this paper studied two types of absorbing aerosols: dust and carbonaceous). The aerosol fraction of the sun photometer retrieval was used as a medium to transfer the lidar ratio of HSRL retrieval to a certain range of MLs. This lidar ratio can be the input parameter for ML retrieval and enables the improvement of the extinction coefficient accuracy. The results show that the LR-APNR model is applicable to atmospheric conditions with high mineral dust or carbonaceous aerosol loading, and the maximum relative error of the ML extinction coefficient can be reduced from 46% (dust) and 64% (carbonaceous aerosol) to 20%. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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18 pages, 2765 KiB  
Article
Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning
by Zhenxing Liu, Jianhua Chang, Hongxu Li, Sicheng Chen and Tengfei Dai
Remote Sens. 2022, 14(2), 418; https://doi.org/10.3390/rs14020418 - 17 Jan 2022
Cited by 6 | Viewed by 2171
Abstract
Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on [...] Read more.
Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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