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Optical and Laser Remote Sensing of Atmospheric Composition

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 48388

Special Issue Editors

Dr. Ka Lok Chan

E-Mail Website
Guest Editor
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Weßling, Germany
Interests: remote sensing of atmospheric composition; radiative transfer modeling; global chemical transport modeling; long-range transport of atmospheric pollutants; numerical inversion methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Youwen Sun

E-Mail Website
Guest Editor
Hefei institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
Interests: remote sensing of atmospheric gases; fourier transform infrared spectroscopy; global chemical transport modeling; numerical inversion methods; source attribution; environmental instrument development
Prof. Dr. Feng Zhang

E-Mail Website
Guest Editor
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China
Interests: atmospheric radiative transfer; remote sensing of cloud and aerosol; machine learning application in atmospheric remote sensing

Special Issue Information

Dear Colleagues,

It is our great pleasure to organize this Special Issue on “Optical and Laser Remote Sensing of Atmospheric Composition” in the journal of Remote Sensing.

The ability of remote sensing technologies to observe atmospheric composition is particularly useful in capturing spatial and temporal variations, identifying sources, and characterizing physical and chemical properties. This Special Issue focuses on remote sensing of atmospheric composition and seeks contributions on the latest development of new instruments and retrieval algorithms, application of sensor fusion and result of trace gases, and cloud and aerosol detection. We encourage the submission of articles that focus on the detection of atmospheric composition across a variety of instrument types and spatial scales. Results can be derived from existing or planned instruments, including acquired data or modeled results. We invite scientists to contribute research and review articles that explore the following topics:

  • Detection pollution and greenhouse gases;
  • Detection of cloud and aerosols;
  • Development of new instruments and retrieval algorithms;
  • Evaluation and validation of detection or retrieval algorithms;
  • Sensor fusion;
  • Emission estimation using remote sensing data;
  • Data assimilation using remote sensing observations;
  • Sensitivity studies;
  • Uncertainty quantification.

Dr. Ka Lok Chan
Prof. Dr. Youwen Sun
Prof. Dr. Feng Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Trace gas 
  • Cloud 
  • Aerosol 
  • Instrumentation 
  • Algorithm 
  • Sensor fusion 
  • Applications

Published Papers (22 papers)

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22 pages, 4886 KiB  
Article
A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition
by Zhiyuan Li, Shun Li, Jiandong Mao, Juan Li, Qiang Wang and Yi Zhang
Remote Sens. 2022, 14(19), 4960; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194960 - 05 Oct 2022
Cited by 9 | Viewed by 1491
Abstract
Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a [...] Read more.
Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 2759 KiB  
Article
Aerosols on the Tropical Island of La Réunion (21°S, 55°E): Assessment of Climatology, Origin of Variability and Trend
by Valentin Duflot, Nelson Bègue, Marie-Léa Pouliquen, Philippe Goloub and Jean-Marc Metzger
Remote Sens. 2022, 14(19), 4945; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194945 - 03 Oct 2022
Cited by 2 | Viewed by 1348
Abstract
Aerosols are essential climate variables that need to be observed at a global scale to monitor the evolution of the atmospheric composition and potential climate impacts. We used the measurements performed over the May 2007–December 2019 period by a ground-based sun photometer installed [...] Read more.
Aerosols are essential climate variables that need to be observed at a global scale to monitor the evolution of the atmospheric composition and potential climate impacts. We used the measurements performed over the May 2007–December 2019 period by a ground-based sun photometer installed at the island of La Réunion (21°S, 55°E), together with a linear regression fitting model, to assess the climatology and types of aerosols reaching this observation site located in a sparsely documented pristine area, and the forcings responsible for the variability of the observed aerosol optical depth (AOD) and related trend. The climatology of the aerosol optical depth (AOD) at 440 nm (AOD440) and Ångström exponent between 500 and 870 nm (α) revealed that sea salts could be considered as the La Réunion AOD440 and α baselines (0.06 ± 0.03 and 0.61 ± 0.40, respectively, from December to August), which were mainly modulated by biomass burning (BB) plumes passing over La Réunion (causing a doubling of AOD440 and α up to 0.13 ± 0.07 and 1.06 ± 0.34, respectively, in October). This was confirmed by the retrieved aerosol volume size distributions showing that the coarse-mode (fine-mode) dominated the total volume concentration for AOD440 lower (higher) than 0.2 with a mean radius equal to 3 μm (0.15 μm). The main contribution to the AOD440 variability over La Réunion was evaluated to be the BB activity (67.4 ± 28.1%), followed by marine aerosols (16.3 ± 4.2%) and large-scale atmospheric structures (5.5 ± 1.7%). The calculated trend for AOD440 equaled 0.02 ± 0.01 per decade (2.6 ± 1.3% per year). These results provide a scientific reference base for upcoming studies dedicated to the quantification of the impact of wildfire emissions on the southwestern Indian Ocean’s atmospheric composition and radiative balance. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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18 pages, 4818 KiB  
Article
Correlation Analysis of CO2 Concentration Based on DMSP-OLS and NPP-VIIRS Integrated Data
by Chen Zuo, Wei Gong, Zhiyu Gao, Deyi Kong, Ruyi Wei and Xin Ma
Remote Sens. 2022, 14(17), 4181; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174181 - 25 Aug 2022
Cited by 7 | Viewed by 1532
Abstract
In view of global warming, caused by the increase in the concentration of greenhouse gases, China has proposed a series of carbon emission reduction policies. It is necessary to obtain the spatiotemporal distribution of carbon emissions accurately. Nighttime light data is recognized as [...] Read more.
In view of global warming, caused by the increase in the concentration of greenhouse gases, China has proposed a series of carbon emission reduction policies. It is necessary to obtain the spatiotemporal distribution of carbon emissions accurately. Nighttime light data is recognized as an important basis for carbon emission estimation. A large number of research results show that there is a positive correlation between nighttime light intensity and carbon emission. However, in the current context of China’s industrial reforms, this positive relationship may not be entirely correct. First, we correct the nighttime light data from different satellites and established a long-term series data set. Then, we verify the positive correlation between nighttime light and carbon emission. However, the time scale of emission data often lags, and the carbon concentration data are released earlier and are more accurate than emission data. Therefore, we propose to investigate the relationship between nighttime light and carbon concentration. It is found that there may be different correlations between nighttime light and the carbon concentration, due to different urban industrial structure and development planning. Therefore, by exploring the relationship between nighttime light and the carbon concentration, the existing carbon emission estimation model can be modified to improve the accuracy of the emission model. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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19 pages, 3781 KiB  
Article
Inversion of Aerosol Particle Size Distribution Using an Improved Stochastic Particle Swarm Optimization Algorithm
by Xin Nie and Qianjun Mao
Remote Sens. 2022, 14(16), 4085; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164085 - 20 Aug 2022
Cited by 3 | Viewed by 1381
Abstract
Aerosol particle size distribution (PSD) is one of the main influencing factors of the radiation effects and climate effects of aerosol. An improved stochastic particle swarm optimization (ISPSO) algorithm is proposed, and the PSD characteristics of aerosols were successfully retrieved from the aerosol [...] Read more.
Aerosol particle size distribution (PSD) is one of the main influencing factors of the radiation effects and climate effects of aerosol. An improved stochastic particle swarm optimization (ISPSO) algorithm is proposed, and the PSD characteristics of aerosols were successfully retrieved from the aerosol optical depth (AOD). The performance analysis shows that the algorithm has good global search ability and convergence performance and will not fall into local optima. Then, the robustness and the ability to resist the noise of the algorithm were verified by adding random errors, using random initial values, and changing the number of samples and inversion parameters, and it was shown that the algorithm has a weak dependence on the initial value. The PSD characteristics of three typical aerosols were inverted, and the results show that the algorithm has good adaptability to the inversion of aerosol PSD. Finally, the PSD characteristics of aerosols from Xianghe and Mezaira under typical weather were inverted based on AERONET data, which shows the effectiveness and advancement of the ISPSO algorithm. This study can provide help for the obtaining of aerosol parameters under poor optical conditions. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 2119 KiB  
Article
Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei Xinqiao International Airport
by Jun Duan, Min Qin, Wu Fang, Zhitang Liao, Huaqiao Gui, Zheng Shi, Haining Yang, Fanhao Meng, Dou Shao, Jiaqi Hu, Baobin Han, Pinhua Xie and Wenqing Liu
Remote Sens. 2022, 14(16), 3927; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163927 - 12 Aug 2022
Viewed by 1381
Abstract
Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the [...] Read more.
Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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20 pages, 6609 KiB  
Article
Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period
by Xiu Wu, Jinlong Yuan, Tianwen Wei, Yunpeng Zhang, Kenan Wu and Haiyun Xia
Remote Sens. 2022, 14(12), 2908; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122908 - 17 Jun 2022
Cited by 3 | Viewed by 1467
Abstract
In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and [...] Read more.
In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and low uncertainty. Research sites are mostly concentrated in cities, while the long-term analysis of aerosol optical depth (AOD) for the pollution transmission channel in rural Beijing is still lacking. Here, we obtained an AOD monitoring dataset from August 2017 to March 2019 using the ground-based CE-318 sun photometer at the Gucheng meteorological observation site in southwest Beijing. These sun photometer AOD data were used for the ground-based validation of MODIS (Moderate Resolution Imaging Spectroradiometer) and AHI (Advanced Himawari Imager) AOD data. It was found that MODIS and AHI can reflect AOD variation trends by sun photometer on daily, monthly, and seasonal scales. The original AOD measurements of the sun photometer show good correlations with satellite observations by MODIS (R = 0.97), and AHI (R = 0.89), respectively, corresponding to their different optimal spatial and temporal windows for matching with collocated satellite ground pixels. However, MODIS is less stable for aerosols of different concentrations and particle sizes. Most of the linear regression intercepts between the satellite and the photometer are less than 0.1, indicating that the errors due to surface reflectance in the inversion are small, and the slope is least biased (AHI: slope = 0.91, MODIS: slope = 0.18) in the noon period (11 a.m.–2 p.m.) and most biased in summer (AHI: slope = 0.77, MODIS: slope = 1.31), probably due to errors in the aerosol model. The daily and seasonal variation trends between CE-318 AOD measurements in the Gucheng site and fine particulate observations from the national air quality site nearby were also compared and investigated. In addition, a typical haze–dust complex pollution event in North China was analyzed and the changes in AOD during the pollution event were quantified. In processing, we use sun photometer and satellite AOD data in combination with meteorological and PM data. Overall, this paper has implications for the study of AOD evolution patterns at different time scales, the association between PM2.5 concentrations and AOD changes, and pollution monitoring. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 12407 KiB  
Article
High Spatiotemporal Resolution PM2.5 Concentration Estimation with Machine Learning Algorithm: A Case Study for Wildfire in California
by Qian Cui, Feng Zhang, Shaoyun Fu, Xiaoli Wei, Yue Ma and Kun Wu
Remote Sens. 2022, 14(7), 1635; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071635 - 29 Mar 2022
Cited by 4 | Viewed by 2423
Abstract
As an aggregate of suspended particulate matter in the air, atmospheric aerosols can affect the regional climate. With the help of satellite remote sensing technology to retrieve AOD (aerosol optical depth) on a global or regional scale, accurate estimation of PM2.5 concentration has [...] Read more.
As an aggregate of suspended particulate matter in the air, atmospheric aerosols can affect the regional climate. With the help of satellite remote sensing technology to retrieve AOD (aerosol optical depth) on a global or regional scale, accurate estimation of PM2.5 concentration has become an important task to quantify the spatiotemporal distribution of AOD and PM2.5. However, due to the limitations of satellite platforms, sensors, and inversion algorithms, the spatiotemporal resolution of current major AOD products is still relatively low. Meanwhile, for the impact of cloud, the AOD products often have a serious data gap problem, which also objectively limits the spatiotemporal coverage of predicted PM2.5 concentration. Therefore, how to effectively improve the spatiotemporal resolution and coverage of PM2.5 concentration under the requisite accuracy is still a grand challenge. In this study, the fused high spatial-temporal resolution AOD data in our previous study were used to estimate the ground PM2.5 concentration through machine learning algorithms, the deep belief network (DBN). The PM2.5 data had spatiotemporal autocorrelation in geostatistics and followed the Gaussian kernel distribution. Hence, the autocorrelation model modified by Gaussian kernel function integrated with DBN algorithm, named Geoi-DBN, was used to estimate PM2.5 concentration. The cross-validation results showed that the Geoi-DBN (R2 = 0.86, RMSE = 6.84 µg m−3) performed better than the original DBN (R2 = 0.67, RMSE = 10.46 µg m−3). The final high quality PM2.5 concentration data can be applied for urban air quality monitoring and related PM2.5 exposure risk assessment such as wildfire. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 3618 KiB  
Article
Impact of Vertical Profiles of Aerosols on the Photolysis Rates in the Lower Troposphere from the Synergy of Photometer and Ceilometer Measurements in Raciborz, Poland, for the Period 2015–2020
by Aleksander Pietruczuk, Alnilam Fernandes, Artur Szkop and Janusz Krzyścin
Remote Sens. 2022, 14(5), 1057; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051057 - 22 Feb 2022
Cited by 1 | Viewed by 1290
Abstract
The effect of the aerosol vertical distribution on photolysis frequencies of O3 and NO2 is studied. Aerosol measurements in Raciborz (50.08° N, 18.19° E), Poland, made using the CIMEL Sun photometer and collocated CHM-15k “Nimbus” ceilometer are analyzed for the period [...] Read more.
The effect of the aerosol vertical distribution on photolysis frequencies of O3 and NO2 is studied. Aerosol measurements in Raciborz (50.08° N, 18.19° E), Poland, made using the CIMEL Sun photometer and collocated CHM-15k “Nimbus” ceilometer are analyzed for the period 2015–2020. Vertical profiles of the aerosol extinction are derived from the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm combining the ceilometer measurements of the aerosol backscattering coefficient with the collocated CIMEL measurements of the columnar characteristics of aerosols. The photolysis frequencies are calculated at the three levels in the lower troposphere (the surface and 0.5 and 2 km above the surface) using a radiative transfer model, Tropospheric Ultraviolet and Visible (TUV), for various settings of aerosol optical properties in the model input. The importance of the aerosol vertical distribution on photolysis frequencies is inferred by analyzing statistics of the differences between the output of the model, including the extinction profile from the GRASP algorithm, and the default TUV model (based on columnar aerosol characteristics by the CIMEL Sun photometer and Elterman’s extinction profile). For model levels above the surface, standard deviation, 2.5th percentile, 97.5th percentile, and the extremes, calculated from relative differences between these input settings, are comparable with the pertaining statistical values for the input pair providing changes of photolysis frequencies only due to the variability of the columnar aerosol characteristics. This indicates that the vertical properties of aerosols affect the distribution of the photolysis frequencies in the lower troposphere on a similar scale to that due to variations in columnar aerosol characteristics. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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16 pages, 7300 KiB  
Article
Polarization Lidar Measurements of Dust Optical Properties at the Junction of the Taklimakan Desert–Tibetan Plateau
by Qingqing Dong, Zhongwei Huang, Wuren Li, Ze Li, Xiaodong Song, Wentao Liu, Tianhe Wang, Jianrong Bi and Jinsen Shi
Remote Sens. 2022, 14(3), 558; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030558 - 25 Jan 2022
Cited by 21 | Viewed by 2952
Abstract
Previous studies have shown that dust aerosols may accelerate the melting of snow and glaciers over the Tibetan Plateau. To investigate the vertical structure of dust aerosols, we conducted a ground-based observation by using multi-wavelength polarization lidar which is designed for continuous network [...] Read more.
Previous studies have shown that dust aerosols may accelerate the melting of snow and glaciers over the Tibetan Plateau. To investigate the vertical structure of dust aerosols, we conducted a ground-based observation by using multi-wavelength polarization lidar which is designed for continuous network measurements. In this study, we used the lidar observation from September to October 2020 at the Ruoqiang site (39.0°N, 88.2°E; 894 m ASL), located at the junction of the Taklimakan Desert–Tibetan Plateau. Our results showed that dust aerosols can be lifted up to 5 km from the ground, which is comparable with the elevation of the Tibetan Plateau in autumn with a mass concentration of 400–900 μg m−3. Moreover, the particle depolarization ratio (PDR) of the lifted dust aerosols at 532 nm and 355 nm are 0.34 ± 0.03 and 0.25 ± 0.04, respectively, indicating the high degree of non-sphericity in shape. In addition, extinction-related Ångström exponents are very small (0.11 ± 0.24), implying the large values in size. Based on ground-based lidar observation, this study proved that coarse non-spherical Taklimakan dust with high concentration can be transported to the Tibetan Plateau, suggesting its possible impacts on the regional climate and ecosystem. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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19 pages, 12533 KiB  
Article
Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements
by Chunhui Zhao, Chengxin Zhang, Jinan Lin, Shuntian Wang, Hanyang Liu, Hongyu Wu and Cheng Liu
Remote Sens. 2022, 14(2), 419; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020419 - 17 Jan 2022
Cited by 7 | Viewed by 2210
Abstract
Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 [...] Read more.
Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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18 pages, 3193 KiB  
Article
Aerosols Direct Radiative Effects Combined Ground-Based Lidar and Sun-Photometer Observations: Cases Comparison between Haze and Dust Events in Beijing
by Yuanxin Liang, Huizheng Che, Hong Wang, Wenjie Zhang, Lei Li, Yu Zheng, Ke Gui, Peng Zhang and Xiaoye Zhang
Remote Sens. 2022, 14(2), 266; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020266 - 07 Jan 2022
Cited by 8 | Viewed by 1999
Abstract
Aerosols can affect vertical thermal structure during heavily polluted episodes (HPEs). Here, we selected four typical HPEs in 2018, which were further subdivided into dust and haze events. The vertical distribution of aerosols extinction coefficient (EC) and variations in columnar optical properties were [...] Read more.
Aerosols can affect vertical thermal structure during heavily polluted episodes (HPEs). Here, we selected four typical HPEs in 2018, which were further subdivided into dust and haze events. The vertical distribution of aerosols extinction coefficient (EC) and variations in columnar optical properties were investigated based on sun-photometer and Lidar observation at an urban site in Beijing. The vertical characteristics in shortwave radiative heating rate (HR) of aerosols were studied using NASA/Goddard radiative transfer model along with observational data. In the haze episode, EC layer is less than 1.5 km and shows strong scattering, with single-scattering albedo (SSA440nm) of ~0.97. The heating effects are observed at the middle and upper atmosphere, and slight heating effects are found at the lower layer. The mean HR within 1.5 km can be up to 16.3 K day−1 with EC of 1.27 km−1, whereas the HR within 0.5 km is only 1.3 K day−1. In the dust episode, dust aerosols present the absorption with SSA440nm of ~0.88, which would heat the lower atmosphere to promote vertical turbulence, and the height of EC layer can be up to 2.0–3.5 km. In addition, the strong heating effects of dust layer produced cooling effects near the surface. Therefore, the accurate measurement of aerosols optical properties in HPEs is of great significance for modeling aerosols direct radiative effects. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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21 pages, 6510 KiB  
Article
Estimation of Maximum Hail Diameters from FY-4A Satellite Data with a Machine Learning Method
by Qiong Wu, Yi-Xuan Shou, Lei-Ming Ma, Qifeng Lu and Rui Wang
Remote Sens. 2022, 14(1), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010073 - 24 Dec 2021
Cited by 2 | Viewed by 2748
Abstract
The magnitude of damage caused by hail depends on its size; however, direct observation or indirect estimation of hail size remains a significant challenge. One primary reason for estimations by proxy, such as through remote sensing methods, is that empirical relationships or statistical [...] Read more.
The magnitude of damage caused by hail depends on its size; however, direct observation or indirect estimation of hail size remains a significant challenge. One primary reason for estimations by proxy, such as through remote sensing methods, is that empirical relationships or statistical models established in one region may not apply to other areas. This study employs a machine learning method to build a hail size estimation model without assuming relations in advance. It uses FY-4A AGRI data to provide cloud-top information and ERA5 data to add vertical environment information. Before training the model, we conducted a principal component analysis (PCA) to analyze the highly influential factors on hail sizes. A total of 18 features, composed of four groups, namely brightness temperature (BT), the difference in BT (BTD), thermodynamics, and dynamics groups, were chosen from 29 original features. Dynamic and BTD features show superior performance in identifying large hail. Although the selected features are more closely correlated to hail sizes than unselected ones, the relationships are complicated and nonlinear. As a result, a two-layer regression back propagation neural network (BPNN) model with powerful fitting ability is trained with selected features to predict maximum hail diameter (MHD). The linear fitting R2 between predicted and observed MHDs is 0.52 on the test set, which signifies that our model performs well compared with other hail size estimation models. We also examine the model concerning all three hail cases in Shanghai, China, between 2019 and 2021. The model attained more satisfactory results than the radar-based maximum estimated hail size (MEHS) method, which overestimates the MHDs, thus further supporting the operational applications of our model. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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28 pages, 13702 KiB  
Article
Retrieval of O3, NO2, BrO and OClO Columns from Ground-Based Zenith Scattered Light DOAS Measurements in Summer and Autumn over the Northern Tibetan Plateau
by Siyang Cheng, Jianzhong Ma, Xiangdong Zheng, Myojeong Gu, Sebastian Donner, Steffen Dörner, Wenqian Zhang, Jun Du, Xing Li, Zhiyong Liang, Jinguang Lv and Thomas Wagner
Remote Sens. 2021, 13(21), 4242; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214242 - 22 Oct 2021
Cited by 4 | Viewed by 1583
Abstract
Ground-based zenith scattered light differential optical absorption spectroscopy (DOAS) measurements were performed in summer and autumn (27 May–30 November) 2020 at Golmud (94°54′ E, 36°25′ N; 2807.6 m altitude) to investigate the abundances and temporal variations of ozone (O3) and its [...] Read more.
Ground-based zenith scattered light differential optical absorption spectroscopy (DOAS) measurements were performed in summer and autumn (27 May–30 November) 2020 at Golmud (94°54′ E, 36°25′ N; 2807.6 m altitude) to investigate the abundances and temporal variations of ozone (O3) and its depleting substances over the northern Tibetan Plateau (TP). The differential slant column densities (dSCDs) of O3, nitrogen dioxide (NO2), bromine monoxide (BrO), and chlorine dioxide (OClO) were simultaneously retrieved from scattered solar spectra in the zenith direction during the twilight period. The O3 vertical column densities (VCDs) were derived by applying the Langley plot method, for which we investigated the sensitivities to the chosen wavelength, the a-priori O3 profile and the aerosol extinction profile used in O3 air mass factor (AMF) simulation as well as the selected solar zenith angle (SZA) range. The mean O3 VCDs from June to November 2020 are 7.21 × 1018 molec·cm−2 and 7.18 × 1018 molec·cm−2 at sunrise and sunset, respectively. The derived monthly variations of the O3 VCDs, ranging from a minimum of 6.9 × 1018 molec·cm−2 in October to 7.5 × 1018 molec·cm−2 in November, well matched the OMI satellite product, with a correlation coefficient R = 0.98. The NO2 VCDs at SZA = 90°, calculated by a modified Langley plot method, were systematically larger at sunset than at sunrise as expected with a pm/am ratio of ~1.56. The maximum of the monthly NO2 VCDs, averaged between sunrise and sunset, was 3.40 × 1015 molec·cm−2 in July. The overall trends of the NO2 VCDs were gradually decreasing with the time and similarly observed by the ground-based zenith DOAS and OMI. The average level of the BrO dSCD90°–80° (i.e., dSCD between 90° and 80° SZA) was 2.06 × 1014 molec·cm−2 during the period of June–November 2020. The monthly BrO dSCD90°–80° presented peaks in August and July for sunrise and sunset, respectively, and slowly increased after October. During the whole campaign period, the OClO abundance was lower than the detection limit of the instrument. This was to be expected because during that season the stratospheric temperatures were above the formation temperature of polar stratospheric clouds. Nevertheless, this finding is still of importance, because it indicates that the OClO analysis works well and is ready to be used during periods when enhanced OClO abundances can be expected. As a whole, ground-based zenith DOAS observations can serve as an effective way to measure the columns of O3 and its depleting substances over the TP. The aforementioned results are helpful in investigating stratospheric O3 chemistry over the third pole of the world. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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24 pages, 10087 KiB  
Article
Vertical Structure of Air Pollutant Transport Flux as Determined by Ground-Based Remote Sensing Observations in Fen-Wei Plain, China
by Xiangguang Ji, Qihou Hu, Bo Hu, Shuntian Wang, Hanyang Liu, Chengzhi Xing, Hua Lin and Jinan Lin
Remote Sens. 2021, 13(18), 3664; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183664 - 14 Sep 2021
Cited by 6 | Viewed by 2327
Abstract
Air pollutant transport plays an important role in local air quality, but field observations of transport fluxes, especially their vertical distributions, are very limited. We characterized the vertical structures of transport fluxes in central Luoyang, Fen-Wei Plain, China, in winter based on observations [...] Read more.
Air pollutant transport plays an important role in local air quality, but field observations of transport fluxes, especially their vertical distributions, are very limited. We characterized the vertical structures of transport fluxes in central Luoyang, Fen-Wei Plain, China, in winter based on observations of vertical air pollutant and wind profiles using multi-axis differential optical absorption spectroscopy (MAX-DOAS) and Doppler wind lidar, respectively. The northwest and the northeast are the two privileged wind directions. The wind direction and total transport scenarios were dominantly the northwest during clear days, turning to the northeast during the polluted days. Increased transport flux intensities of aerosol were found at altitudes below 400 m on heavily polluted days from the northeast to the southwest over the city. Considering pollution dependence on wind directions and speeds, surface-dominated northeast transport may contribute to local haze events. Northwest winds transporting clean air masses were dominant during clean periods and flux profiles characterized by high altitudes between 200 and 600 m in Luoyang. During the COVID-19 lockdown period in late January and February, clear reductions in transport flux were found for NO2 from the northeast and for HCHO from the northwest, while the corresponding main transport altitude remained unchanged. Our findings provide better understandings of regional transport characteristics, especially at different altitudes. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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16 pages, 7971 KiB  
Article
A Comparison of the Different Stages of Dust Events over Beijing in March 2021: The Effects of the Vertical Structure on Near-Surface Particle Concentration
by Futing Wang, Ting Yang, Zifa Wang, Jie Cao, Benli Liu, Jianbao Liu, Shengqian Chen, Shulin Liu and Binghao Jia
Remote Sens. 2021, 13(18), 3580; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183580 - 08 Sep 2021
Cited by 13 | Viewed by 2089
Abstract
Mineral dust is of great importance to climate change, air quality, and human health. In this study, multisource data, including the reanalysis data and remote sensing data, were used to compare the three dust events that occurred in the March of 2021 over [...] Read more.
Mineral dust is of great importance to climate change, air quality, and human health. In this study, multisource data, including the reanalysis data and remote sensing data, were used to compare the three dust events that occurred in the March of 2021 over Beijing and reveal the effects of atmospheric vertical structure on near-surface dust concentration. The combined effect of the Mongolian cyclone and a wide persistent cold-front induced two events (E1: from March 15 to 16 and E3: from March 28 to 29). E1 was more intense, more extensive, and longer-lasting than E3 due to the combination of the stronger Mongolian cyclone, slower high/cold surface pressure, and the low-level jet. However, under the appropriate configurations of temperature and pressure fields between high and low altitudes, weak updrafts were still induced and could elevate dust up to 850 hPa, as occurred during E2 on March 22 and 23. The dust emission was inferior to E1 and E3, which contributes to the low dust concentration near the surface in E2. On the other hand, the downdraft strength directly affected both the vertical distribution of dust and the concentration of surface particles. There was a strong temporal consistency between the occurrence of the downdraft and the dust touchdown. In E1, the continuous strong downdraft caused the maximum dust concentration to be above 4000 μg/m3 at around 200 m. In contrast, the maximum height of the dust mass concentration in E3 occurred at about 800 m due to the transient downdraft, which weakened its effect on surface visibility. Besides, the weak vertical motion in E2 caused most of the dust to become suspended in the air. Overall, the large dust emission resulted from active updrafts in the source region, and the lengthy strong downdrafts led to the ultrahigh particle concentration near the surface. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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23 pages, 5721 KiB  
Article
Observations by Ground-Based MAX-DOAS of the Vertical Characters of Winter Pollution and the Influencing Factors of HONO Generation in Shanghai, China
by Shiqi Xu, Shanshan Wang, Men Xia, Hua Lin, Chengzhi Xing, Xiangguang Ji, Wenjing Su, Wei Tan, Cheng Liu and Qihou Hu
Remote Sens. 2021, 13(17), 3518; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173518 - 04 Sep 2021
Cited by 8 | Viewed by 2516
Abstract
Analyzing vertical distribution characters of air pollutants is conducive to study the mechanisms under polluted atmospheric conditions. Nitrous acid (HONO) is a kind of crucial species in photochemical cycles. Exploring the influence and sources of HONO in air pollution at different altitudes offers [...] Read more.
Analyzing vertical distribution characters of air pollutants is conducive to study the mechanisms under polluted atmospheric conditions. Nitrous acid (HONO) is a kind of crucial species in photochemical cycles. Exploring the influence and sources of HONO in air pollution at different altitudes offers some insights into the research of tropospheric oxidation chemistry processes. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements were conducted in Shanghai, China, from December 2017 to March 2018 to investigate vertical distributions and diurnal variations of trace gases (NO2, HONO, HCHO, SO2, and water vapor) and aerosol extinction coefficient in the boundary layer. Aerosol and NO2 showed decreasing profile exponentially, SO2 and HCHO concentrations were observed relatively high values in the middle layer. SO2 was caused by industrial emissions, while HCHO was from secondary sources. As for HONO, below 0.82 km, the heterogeneous reactions of NO2 impacted on forming HONO, while in the upper layers, vertical diffusion might be the dominant source. The contribution of OH production from HONO photolysis at different altitudes was mainly controlled by the concentration of HONO. MAX-DOAS measurements characterize the vertical structure of air pollutants in Shanghai and provide further understanding for HONO formation, which can help deploy advanced measurement platforms of regional air pollution over eastern China. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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29 pages, 13083 KiB  
Article
Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning
by Yushan Zhang, Kun Wu, Jinglin Zhang, Feng Zhang, Haixia Xiao, Fuchang Wang, Jianyin Zhou, Yi Song and Liang Peng
Remote Sens. 2021, 13(16), 3332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163332 - 23 Aug 2021
Cited by 11 | Viewed by 3532
Abstract
The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational [...] Read more.
The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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19 pages, 89711 KiB  
Article
Profiling of Dust and Urban Haze Mass Concentrations during the 2019 National Day Parade in Beijing by Polarization Raman Lidar
by Zhuang Wang, Cheng Liu, Yunsheng Dong, Qihou Hu, Ting Liu, Yizhi Zhu and Chengzhi Xing
Remote Sens. 2021, 13(16), 3326; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163326 - 23 Aug 2021
Cited by 12 | Viewed by 2111
Abstract
The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio [...] Read more.
The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio at 355 nm are 0.13 ± 0.05 and 52 ± 9 sr, respectively. It is the typical value of a mixture of dust and urban haze. Here we quantify the contributions of cross-regional transported natural dust and urban haze mass concentrations to Beijing’s air quality. There is a significant correlation between urban haze mass concentrations and surface PM2.5 (R = 0.74, p < 0.01). The contributions of local emissions to air pollution during the 2019 National Day parade were insignificant, mainly affected by regional transport, including urban haze in North China plain and Guanzhong Plain (Hebei, Tianjin, Shandong, and Shanxi), and dust aerosol in Mongolia regions and Xinjiang. Moreover, the trans-regional transmission of natural dust dominated the air pollution during the 2019 National Day parade, with a relative contribution to particulate matter mass concentrations exceeding 74% below 4 km. Our results highlight that controlling anthropogenic emissions over regional scales and focusing on the effects of natural dust is crucial and effective to improve Beijing’s air quality. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 3668 KiB  
Article
Investigation on the Relationship between Satellite Air Quality Measurements and Industrial Production by Generalized Additive Modeling
by Chao Tong, Chengxin Zhang and Cheng Liu
Remote Sens. 2021, 13(16), 3137; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163137 - 08 Aug 2021
Cited by 6 | Viewed by 2037
Abstract
The development of the green economy is universally recognized as a solution to natural resource shortages and environmental pollution. When exploring and developing a green economy, it is important to study the relationships between the environment and economic development. As opposed to descriptive [...] Read more.
The development of the green economy is universally recognized as a solution to natural resource shortages and environmental pollution. When exploring and developing a green economy, it is important to study the relationships between the environment and economic development. As opposed to descriptive and qualitative research without modeling or based on environmental Kuznets curves, quantitative relationships between environmental protection and economic development must be identified for exploration and practice. In this paper, we used the generalized additive model (GAM) regression method to identify relationships between atmospheric pollutants (e.g., NO2, SO2 and CO) from remote sensing and in situ measurements and their driving effectors, including meteorology and economic indicators. Three representative cities in the Anhui province, such as Hefei (technology-based industry), Tongling (resource-based industry) and Huangshan (tourism-based industry), were studied from 2016 to 2020. After eliminating the influence of meteorological factors, the relationship between air quality indexes and industrial production in the target cities was clearly observed. Taking Hefei, for example, when the normalized output of chemical products increases by one unit, the effect on atmospheric NO2 content increases by about 20%. When the normalized output of chemical product increases by one unit, the effect on atmospheric SO2 content increases by about 10%. When chemical and steel product outputs increase by one unit, the effect on atmospheric CO content increases by 25% and 20%, respectively. These results can help different cities predict local economic development trends varying by the changes in air quality and adjust local industrial structure. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 8865 KiB  
Article
Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations
by Fenglin Sun, Bo Li, Min Min and Danyu Qin
Remote Sens. 2021, 13(11), 2229; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112229 - 07 Jun 2021
Cited by 13 | Viewed by 2997
Abstract
Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly [...] Read more.
Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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17 pages, 5061 KiB  
Article
Remote Sensing of Atmospheric Hydrogen Fluoride (HF) over Hefei, China with Ground-Based High-Resolution Fourier Transform Infrared (FTIR) Spectrometry
by Hao Yin, Youwen Sun, Cheng Liu, Wei Wang, Changgong Shan and Lingling Zha
Remote Sens. 2021, 13(4), 791; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040791 - 21 Feb 2021
Cited by 15 | Viewed by 2601
Abstract
Remote sensing of atmospheric hydrogen fluoride (HF) is challenging because it has weak absorption signatures in the atmosphere and is surrounded by strong absorption lines from interfering gases. In this study, we first present a multi-year time series of HF total columns over [...] Read more.
Remote sensing of atmospheric hydrogen fluoride (HF) is challenging because it has weak absorption signatures in the atmosphere and is surrounded by strong absorption lines from interfering gases. In this study, we first present a multi-year time series of HF total columns over Hefei, China by using high-resolution ground-based Fourier transform infrared (FTIR) spectrometry. Both near-infrared (NIR) and mid-infrared (MIR) solar spectra suites, which are recorded following the requirements of Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC), respectively, are used to retrieve total column of HF (THF) and column-averaged dry-air mole fractions of HF (XHF). The NIR and MIR observations are generally in good agreement with a correlation coefficient (R) of 0.87, but the NIR observations are found to be (6.90 ± 1.07 (1σ)) pptv, which is lower than the MIR observations. By correcting this bias, the combination of NIR and MIR observations discloses that the XHF over Hefei showed a maximum monthly mean value of (64.05 ± 3.93) pptv in March and a minimum monthly mean value of (45.15 ± 2.93) pptv in September. The observed XHF time series from 2015 to 2020 showed a negative trend of (−0.38 ± 0.22) % per year. The variability of XHF is inversely correlated with the tropopause height, indicating that the variability of tropopause height is a key factor that drives the seasonal cycle of HF in the stratosphere. This study can enhance the understanding of ground-based high-resolution remote sensing techniques for atmospheric HF and its evolution in the stratosphere and contribute to forming new reliable remote sensing data for research on climate change. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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Review

Jump to: Research

14 pages, 3157 KiB  
Review
Distributed Feedback Interband Cascade Laser Based Laser Heterodyne Radiometer for Column Density of HDO and CH4 Measurements at Dunhuang, Northwest of China
by Xingji Lu, Yinbo Huang, Pengfei Wu, Dandan Liu, Hongliang Ma, Guishi Wang and Zhensong Cao
Remote Sens. 2022, 14(6), 1489; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061489 - 19 Mar 2022
Cited by 7 | Viewed by 1752
Abstract
Remote sensing of HDO and CH4 could provide valuable information on environmental and climatological studies. In a recent contribution, we reported a 3.53 μm distributed feedback (DFB) inter-band cascade laser (ICL)-based heterodyne radiometer. In the present work, we present the details of [...] Read more.
Remote sensing of HDO and CH4 could provide valuable information on environmental and climatological studies. In a recent contribution, we reported a 3.53 μm distributed feedback (DFB) inter-band cascade laser (ICL)-based heterodyne radiometer. In the present work, we present the details of measurements and inversions of HDO and CH4 at Dunhuang, Northwest of China. The instrument line shape (ILS) of laser heterodyne radiometer (LHR) is discussed firstly, and the spectral resolution is about 0.004 cm−1 theoretically according to the ILS. Furthermore, the retrieval algorithm, optimal estimation method (OEM), combined with LBLRTM (Line-by-line Radiative Transfer Model) for retrieving the densities of atmospheric HDO and CH4 are investigated. The HDO densities were retrieved to be less than 1.0 ppmv, while the CH4 densities were around 1.79 ppmv from 20 to 24 July 2018. The correlation coefficient of water vapor densities retrieved by LHR and EM27/SUN is around 0.6, the potential reasons for the differences were discussed. Finally, in order to better understand the retrieval procedure, the Jacobian value and the Averaging Kernels are also discussed. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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