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Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 12514

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Special Issue Editors

Dr. Wenbo Sun

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Guest Editor

NASA Langley Research Center, Hampton, VA 23681-2199, USA
Interests: light scattering; polarized radiative tranfer; satellite remote sensing; computational electrodynamics; aerosol and clouds optics; optical device design

Prof. Dr. Dong Liu

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Guest Editor

State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou 310027, China
Interests: laser detection & testing; lidar; defect detection; interferometry; machine vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Sungsoo Kim

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Guest Editor

Department of Astronomy and Space Science, Kyung Hee University, Yongin-si, Korea
Interests: astronomy and astrophysics; galaxy evolution; extragalactic astronomy; photometry; galaxy formation; star formation; cosmology; galaxy; stars; astrophysics

Dr. Yongxiang Hu

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Guest Editor

NASA Langley Research Center, Hampton, VA 23666, USA
Interests: lidar; aerosols; clouds; radiative transfer; sensors; climatology

Dr. Gorden Videen

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Guest Editor

The Space Science Institute, Boulder, CO, USA
Interests: light scattering
Special Issues, Collections and Topics in MDPI journals

Dr. Qiang Fu

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Guest Editor

University of Washington (Seattle), USA
Interests: light scattering and radiative transfer; atmospheric radiation and clouds; atmospheric aerosols; remote sensing; climate and climate change

Special Issue Information

Dear Colleagues,

A Special Issue of the journal of Remote Sensing on theoretical and observational issues in atmospheric sciences including observation, validation, and theoretical simulation is open for your submission of papers. Active and passive remote-sensing techniques and theories for measuring atmospheric and other environmental variables have advanced rapidly in recent years. Ground-based and in situ measurements of atmospheric components, such as cloud and aerosol particles, are essential to calibrate and validate satellite data. At the same time, fundamental atmospheric physics, such as light scattering and radiative transfer in the atmosphere, is critical for the comprehension of remotely sensed data. Your manuscripts for these research subjects are welcome for this Special Issue. This Special Issue is planned for the 7th International Symposium on Atmospheric Light Scattering and Remote Sensing (ISALSaRS'21) in Suwon, Korea in May, 2021. The ISALSaRS'21 will follow the history of this symposium series and continue to tackle emerging theoretical and observational issues in atmospheric sciences. The symposium welcomes the science community to report their latest results and discuss future directions of atmospheric light scattering and remote sensing. However, this Special Issue of Remote Sensing is open to submissions from all authors, not only the ISALSaRS’21 attendees.

Dr. Wenbo Sun
Dr. Dong Liu
Dr. Sungsoo Kim
Dr. Yongxiang Hu
Dr. Gorden Videen
Dr. Qiang Fu
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

Atmosphere
Light scattering
Radiative transfer
Clouds and aerosols
Active and passive remote sensing
Ground observations

Published Papers (7 papers)

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Research

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20 pages, 7800 KiB

Open AccessArticle

Impacts of 3DEnVar-Based FY-3D MWHS-2 Radiance Assimilation on Numerical Simulations of Landfalling Typhoon Ampil (2018)

by Lixin Song, Feifei Shen, Changliang Shao, Aiqing Shu and Lijian Zhu

Remote Sens. 2022, 14(23), 6037; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236037 - 29 Nov 2022

Cited by 17 | Viewed by 1350

Abstract

The module for assimilating radiance data of the Microwave Humidity Sounder-2 (MWHS-2) onboard the Feng Yun 3D (FY-3D) satellite is built in the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system. The CONV, 3DVar, and EnVar experiments are conducted to investigate the impact of assimilating the new humidity sounder based on Typhoon Ampil (2018). Both the 3DVar and EnVar experiments assimilate FY-3D MWHS-2 radiance data on top of the conventional data, while the CONV experiment only applies conventional data. In the EnVar experiment, notable geopotential height increment is observed around the typhoon, leading the typhoon to move northeast. In addition, the moisture field is improved to some extent. Finally, from the analysis of the dynamic field of the typhoon, it can be found that the EnVar experiment can adjust the dynamic structure of the typhoon. Furthermore, the assimilation of FY-3D MWHS-2 radiance data reduces the forecast error of the typhoon track and intensity. Additionally, the precipitation skill is improved in terms of rainfall pattern and the verification score. This improvement in the precipitation may be closely related to the features of the circulation structure concerning the evolution of the typhoon. The improved prediction of the position and intensity of rainbands in the FY-3D MWHS-2 radiance data assimilation experiment corresponds to a better prediction of typhoon structure. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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30 pages, 42929 KiB

Open AccessArticle

Assessments of the Above-Ocean Atmospheric CO₂ Detection Capability of the GAS Instrument Onboard the Next-Generation FengYun-3H Satellite

by Su Chen, Peng Chen, Lei Ding and Delu Pan

Remote Sens. 2022, 14(23), 6032; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236032 - 28 Nov 2022

Cited by 1 | Viewed by 1216

Abstract

The next-generation FengYun-3H satellite carrying a greenhouse gas absorption spectrometer (GAS) is planned for launch by 2024 with a strengthened ability to help researchers understand the global carbon cycle. However, assessments of the atmospheric CO₂-detection capabilities of GAS are still incomplete, mainly in the following aspects: previous studies on the spectral range of GAS instruments often used the weak absorption band of CO₂ molecules (1.61 μm); research on the measurement accuracies of different atmospheric environments above oceans is lacking; and most studies considered land surfaces as the bottom boundaries. Here, we simulated high spectral CO₂ absorption spectra in both the strong and weak bands (2.06 and 1.61 μm) while considering the effects of different instrumental (spectral resolution and sampling rate) and environmental (wind speed, visibility, and rough sea surface) parameters. This is the first atmospheric CO₂ absorption spectrum study to consider rough-sea-surface effects. The preliminary results show that the root mean squared error (RMSE) and mean absolute difference (MAD) values of the atmospheric CO₂ transmittance spectra of GAS are 0.031 and 0.011, respectively, in the 1.61 μm band and 0.05 and 0.033 in the 2.06 μm band, revealing that GAS is competitive among similar CO₂ instruments. This study provides a design reference for next-generation GAS instruments and contributes to spectral data CO₂ processing in the above-sea atmosphere. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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18 pages, 4664 KiB

Open AccessArticle

Impacts of FY-4A AGRI Radiance Data Assimilation on the Forecast of the Super Typhoon “In-Fa” (2021)

by Xuewei Zhang, Dongmei Xu, Ruixia Liu and Feifei Shen

Remote Sens. 2022, 14(19), 4718; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194718 - 21 Sep 2022

Cited by 16 | Viewed by 1668

Abstract

This study assessed the impact of assimilating the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) observations on the Super Typhoon “In-Fa” event based on the Weather Research and Forecasting Data Assimilation (WRFDA) system of the three-dimensional variational data assimilation (3DVAR) method. It was found that the two water vapor channels 9–10 from the full-disk AGRI datasets yield relatively stable results in terms of the track forecast of In-Fa. A new cloud-detection method using a Particle Filter (PF) was firstly employed to remove the cloud-affected observations by identifying the channel’s weighting function. Compared to the other cloud-detection schemes based on the AGRI “Cloud_Binary_Mask” (CLM) products, the PF method is conducive to reducing the track error of typhoon prediction after improving the utilization of observations under clear-sky conditions. Furthermore, the proposed cycling assimilation scheme has a potential positive effect on the intensity forecast of In-Fa. It seems that assimilating the FY-4A AGRI radiance data improves the predictability of Typhoon In-Fa by adjusting the atmospheric environment. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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15 pages, 6450 KiB

Open AccessArticle

Characteristics and Formation Conditions of Thin Phytoplankton Layers in the Northern Gulf of Mexico Revealed by Airborne Lidar

by Yichen Yang, Hangkai Pan, Dekang Zheng, Hongkai Zhao, Yudi Zhou and Dong Liu

Remote Sens. 2022, 14(17), 4179; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174179 - 25 Aug 2022

Cited by 3 | Viewed by 1684

Abstract

The thin layers in the ocean are temporally-coherent aggregations of phytoplankton with high concentrations at small vertical scales, presenting important hotspots of ecological activity. Lidar could identify thin phytoplankton layers at a large spatial scale due to its capabilities of profile detection with a high efficiency. However, studies that linked thin layers to environmental factors are few, which limits our understanding of the layer formation mechanism. This paper investigates the characteristics and formation conditions of thin phytoplankton layers in the northern Gulf of Mexico using airborne lidar. The results depict that the chlorophyll concentration determines the formation probability of the phytoplankton layer. The layer is mainly formed at concentrations less than 6 mg m⁻³ and mostly distributed at 2 mg m⁻³. In addition, layer thicknesses were within 5 m and layer depths were mainly in the range of 10–15 m. Layer depths in the nearshore region were shallower than those in the offshore region. We conclude that the characteristics and formation conditions of the thin phytoplankton layers depend on the nutrients and light that are related to the seabed topography, turbidity, eddies and upwelling. The findings of this paper will enhance the understanding of layer formation mechanisms. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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16 pages, 2696 KiB

Open AccessArticle

Lidar- and UAV-Based Vertical Observation of Spring Ozone and Particulate Matter in Nanjing, China

by Yawei Qu, Ming Zhao, Tijian Wang, Shu Li, Mengmeng Li, Min Xie and Bingliang Zhuang

Remote Sens. 2022, 14(13), 3051; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133051 - 25 Jun 2022

Cited by 6 | Viewed by 1489

Abstract

The rapid urbanization in China is accompanied by increasingly serious air pollution. Particulate matter and ozone are the main air pollutants, and the study of their vertical distribution and correlation plays an important role in the synergistic air pollution control. In this study, we performed Lidar- and UAV-based observations in spring in Nanjing, China. The average concentrations of surface ozone and PM_2.5 during the observation period are 87.78 µg m⁻³ and 43.48 µg m⁻³, respectively. Vertically, ozone reaches a maximum in the upper boundary layer, while the aerosol extinction coefficient decreases with height. Generally, ozone and aerosol are negatively correlated below 650 m. The correlation coefficient increases with altitude and reaches a maximum of 0.379 at 1875 m. Within the boundary layer, ozone and aerosols are negatively correlated on days with particulate pollution (PM_2.5 > 35 μg m⁻³), while on clean days they are positively correlated. Above the boundary layer, the correlation coefficient is usually positive, regardless of the presence of particulate pollution. The UAV study compensates for Lidar detections below 500 m. We found that ozone concentration is higher in the upper layers than in the near-surface layers, and that ozone depletion is faster in the near-surface layers after sunset. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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17 pages, 6471 KiB

Open AccessArticle

Experimental Analysis of Atmospheric Ducts and Navigation Radar Over-the-Horizon Detection

by Li-Feng Huang, Cheng-Guo Liu, Hong-Guang Wang, Qing-Lin Zhu, Li-Jun Zhang, Jie Han, Yu-Sheng Zhang and Qian-Nan Wang

Remote Sens. 2022, 14(11), 2588; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112588 - 27 May 2022

Cited by 16 | Viewed by 2446

Abstract

Since the height of sea detection radar antenna and ship targets is relatively low, it is generally believed that its over-the-horizon detection is mainly caused by the evaporation duct at sea. To fully understand the influence of atmospheric ducts on radar over-the-horizon detection, a shore-based navigation radar was used to carry out over-the-horizon detection experiments; radiosondes were used to measure the atmospheric profile and evaporation duct monitoring equipment was used to measure the evaporation duct. Based on experimental data and model simulation, a comparative analysis of a navigation radar’s over-the-horizon detection, the evaporation duct, and the lower atmospheric duct is presented in this study. The results show that the atmospheric duct can affect the signal propagation of the navigation radar, thus resulting in over-the-horizon detection. The long-range over-the-horizon detection of the navigation radar is caused by the strong lower atmospheric duct, while the evaporation duct can generally only form weak over-the-horizon detection, which is different from the general cognition. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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Other

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16 pages, 1691 KiB

Open AccessTechnical Note

Rayleigh Lidar Signal Denoising Method Combined with WT, EEMD and LOWESS to Improve Retrieval Accuracy

by Yijian Zhang, Tong Wu, Xianzhong Zhang, Yue Sun, Yu Wang, Shijie Li, Xinqi Li, Kai Zhong, Zhaoai Yan, Degang Xu and Jianquan Yao

Remote Sens. 2022, 14(14), 3270; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143270 - 07 Jul 2022

Cited by 9 | Viewed by 1597

Abstract

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively. Full article

(This article belongs to the Special Issue Remote Sensing on Theoretical and Observational Issues in Atmospheric Sciences)

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