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Remote Sensing and Modeling of Greenhouse and Chemically Active Gases...
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Remote Sensing and Modeling of Greenhouse and Chemically Active Gases in the Atmosphere

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 5850

Special Issue Editors

Prof. Dr. Sergei P. Smyshlyaev

E-Mail Website
Guest Editor
Meteoforecast Department, Russian State Hydrometeorological University, Saint Petersburg (ex Leningrad), Voronezhskaya ulitsa, 79, St. Petersburg, Russia
Interests: data assimilation; ozone; nitrous oxide; nitrogen
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu M. Timofeev

E-Mail Website
Guest Editor
Department of the Physics of Atmosphere, Saint Petersburg State University, St. Petersburg, Russia
Interests: carbon dioxide; remote sensing

Special Issue Information

Dear Colleagues,

Many chemically active gases play a significant role in affecting, either directly or indirectly, the climate. Due to the uncontrolled emissions of greenhouse and chemically active gases such as carbon dioxide, methane, nitrogen oxides, sulfur, and others, temperatures are warming on a global scale at unprecedented speed. Climate change has a significant direct and indirect impact on the ecological situation, the biosphere, and the physical/chemical processes responsible for air quality; this has become a real problem in several countries. That is why a deep understanding of the relationship between processes that determine the variability of greenhouse and chemically active gases is urgent. In this Special Issue, we intend to highlight the following issues relating to the study of processes that determine the variability of climatically and chemically active gases:

  • The analysis of spatial and temporal variations, long-term trends, and anomalies in concentrations of greenhouse and chemically active gases based on the results of satellite and ground-based measurements.
  • The validation and comparison of different greenhouse and chemically active gas measurements.
  • The global and regional numerical modeling of processes that determine the variability of greenhouse and chemically active gases.
  • The analysis of chemically active gases and their impact on the global climate.
  • Forecasts of climate impact on the ecology of megacities and industrial regions.
  • Studies on air quality changes under the conditions of climate change and the manmade impact on the environment.

All other relevant contributions are very welcome.

Prof. Dr. Sergei P. Smyshlyaev
Prof. Dr. Yu M. Timofeev
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

  • greenhouse and chemically active gases
  • spatial and temporal analysis
  • validation and comparison of measurements
  • global and regional numerical modeling
  • global climate
  • climate impact
  • ecology of megacities and industrial regions
  • air quality

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Published Papers (3 papers)

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Research

18 pages, 10479 KiB  
Article
Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China
by Jiaxin Wang, Minqiang Zhou, Bavo Langerock, Weidong Nan, Ting Wang and Pucai Wang
Remote Sens. 2024, 16(5), 900; https://doi.org/10.3390/rs16050900 - 3 Mar 2024
Viewed by 1043
Abstract
Carbon dioxide (CO2) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In this study, we investigate the CO2 retrieval strategy using the Network for the [...] Read more.
Carbon dioxide (CO2) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In this study, we investigate the CO2 retrieval strategy using the Network for the Detection of Atmospheric Composition Change–Infrared Working Group (NDACC–IRWG) type spectra between August 2018 and April 2022 (~4 years) at Xianghe, China, aiming to find the optimal observed spectra, retrieval window, and spectroscopy. Two spectral regions, near 2600 and 4800 cm−1, are analyzed. The differences in column-averaged dry-air mole fraction of CO2 (XCO2) derived from spectroscopies (ATM18, ATM20, HITRAN2016, and HITRAN2020) can be up to 1.65 ± 0.95 ppm and 7.96 ± 2.02 ppm for NDACC-type 2600 cm−1 and 4800 cm−1 retrievals, respectively, which is mainly due to the CO2 differences in air-broadened Lorentzian HWHM coefficient (γair) and line intensity (S). HITRAN2020 provides the best fitting, and the retrieved CO2 columns and profiles from both 2600 and 4800 cm−1 are compared to the co-located Total Column Carbon Observing Network (TCCON) measurements and the greenhouse gas reanalysis dataset from the Copernicus Atmosphere Monitoring Service (CAMS). The amplitude of XCO2 seasonal variation derived from the NDACC-type (4800 cm−1) is closer to the TCCON measurements than that from the NDACC-type (2600 cm−1). Moreover, the NDACC-type (2600 cm−1) retrievals are strongly affected by the a priori profile. For tropospheric XCO2, the correlation coefficient between NDACC-type (4800 cm−1) and CAMS model is 0.73, which is higher than that between NDACC-type (2600 cm−1) and CAMS model (R = 0.56). Full article
(This article belongs to the Special Issue Remote Sensing and Modeling of Greenhouse and Chemically Active Gases in the Atmosphere)
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16 pages, 10318 KiB  
Article
Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites
by Qichen Ni, Minqiang Zhou, Jiaxin Wang, Ting Wang, Gengchen Wang and Pucai Wang
Remote Sens. 2023, 15(18), 4499; https://doi.org/10.3390/rs15184499 - 13 Sep 2023
Cited by 3 | Viewed by 1447
Abstract
Methane (CH4) is an important greenhouse as well as a chemically active gas. Accurate monitoring and understanding of its spatiotemporal distribution are crucial for effective mitigation strategies. Nowadays, satellite measurements are widely used for CH4 studies. Here, we use the [...] Read more.
Methane (CH4) is an important greenhouse as well as a chemically active gas. Accurate monitoring and understanding of its spatiotemporal distribution are crucial for effective mitigation strategies. Nowadays, satellite measurements are widely used for CH4 studies. Here, we use the CH4 products from four commonly used satellites (GOSAT, TROPOMI, ARIS, and IASI) during the period from 2018 to 2020 to investigate the spatiotemporal variations of CH4 in China. In spite of the same target (CH4) for the four satellites, differences among them exist in terms of the instrument, spectrum, and retrieval algorithm. The GOSAT and TROPOMI CH4 retrievals use shortwave infrared spectra, with a better sensitivity near the surface, while the IASI and AIRS CH4 retrievals use thermal infrared spectra, showing a good sensitivity in the mid–upper troposphere but a weak sensitivity in the lower troposphere. The GOSAT and TROPOMI observe high CH4 concentrations in the east and south and low concentrations in the west and north, which is highly related to the CH4 emissions. The IASI and AIRS show a more uniform CH4 distribution over China, which reflects the variation of CH4 at a high altitude. However, a large discrepancy is observed between the IASI and AIRS despite using a similar retrieval band, e.g., significant differences in the seasonal variations of CH4 are observed between the IASI and AIRS across several regions in China. This study highlights the CH4 differences observed by the four satellites in China, and caution must be taken when using these satellite products. Full article
(This article belongs to the Special Issue Remote Sensing and Modeling of Greenhouse and Chemically Active Gases in the Atmosphere)
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32 pages, 26513 KiB  
Article
Six Years of IKFS-2 Global Ozone Total Column Measurements
by Alexander Polyakov, Yana Virolainen, Georgy Nerobelov, Dmitry Kozlov and Yury Timofeyev
Remote Sens. 2023, 15(9), 2481; https://doi.org/10.3390/rs15092481 - 8 May 2023
Cited by 2 | Viewed by 2767
Abstract
Atmospheric ozone plays an important role in the biosphere’s absorbing of dangerous solar UV radiation and its contributions to the Earth’s climate. Nowadays, ozone variations are widely monitored by different local and remote sensing methods. Satellite methods can provide data on the global [...] Read more.
Atmospheric ozone plays an important role in the biosphere’s absorbing of dangerous solar UV radiation and its contributions to the Earth’s climate. Nowadays, ozone variations are widely monitored by different local and remote sensing methods. Satellite methods can provide data on the global distribution of ozone and its anomalies. In contrast to measurement techniques based on solar radiation measurements, Fourier-transform infrared (FTIR) satellite measurements of thermal radiation provide information, regardless of solar illumination. The global distribution of total ozone columns (TOCs) measured by the IKFS-2 spectrometer aboard the “Meteor M N2” satellite for the period of 2015 to 2020 is presented. The retrieval algorithm uses the artificial neural network (ANN) based on measurements of TOCs by the Aura OMI instrument and the method of principal components for representing IKFS-2 spectral measurements. Latitudinal and seasonal dependencies on the ANN training errors are analyzed and considered as a first approximation of the TOC measurement errors. The TOCs derived by the IKFS-2 instrument are compared to independent ground-based and satellite data. The average differences between the IKFS-2 data and the independent TOC measurements are up to 2% (IKFS-2 usually slightly underestimates the other data), and the standard deviations of differences (SDDs) vary from 2 to 4%. At the same time, both the analysis of the ANN approximation errors of the OMI data and the comparison of the IKFS-2 results with independent data demonstrate an increase in discrepancies towards the poles. In the spring–winter period, SDDs reach 8% in the Southern and 6% in the Northern Hemisphere. The technique presented can be used to process the IKFS-2 spectral data, and as a result, it can provide global information on the TOCs in the period of 2015–2020, regardless of the solar illumination and the presence of clouds. Full article
(This article belongs to the Special Issue Remote Sensing and Modeling of Greenhouse and Chemically Active Gases in the Atmosphere)
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