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Atmosphere
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Frontiers in Atmospheric Remote Sensing and Modelling
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Frontiers in Atmospheric Remote Sensing and Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (21 July 2023) | Viewed by 8064

Special Issue Editors

Dr. Ying Zhang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: inversion of atmospheric aerosol characteristics; air quality remote sensing; radiative transfer modeling; remote sensing for particle nucleation; air pollution assimilation and forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Sijia Lou

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Interests: interaction between air pollution and weather/climate change; air quality simulation; air quality and health risks
Dr. Qingqing He

E-Mail Website
Guest Editor
School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430062, China
Interests: air pollution modeling; satellite remote sensing; geospatial big data and machine learning; air quality exposure and health assessment

Special Issue Information

Dear Colleagues,

In the context of environmental deterioration and global warming, scientific evidence to aid in a comprehensive understanding of how physical and chemical atmospheric changes and interactions with anthropogenic activities over the past few decades is the basis for improving our living environment, reducing disaster and disease, and supporting environmental management. The advances in remote sensing and modeling not only provide scientific insights to identify changes in meteorological parameters and atmospheric compositions, but also to investigate the underlying driving forces and factors for these dynamics.

Satellite remote sensing has evolved from having a single purpose to having comprehensive detection capabilities over the past few decades. With the support of advanced technologies such as hyperspectrum and polarization, it can detect basic meteorological parameters and identify the characteristics of aerosols, pollution gases, and greenhouse gases (GHGs). As a result, it has been widely used to observe various atmospheric elements.

Modelling is another essential technical branch in the field of atmosphere research. We use models to forecast the weather system and air quality. Moreover, model diagnostics also help people understand air pollution and GHG emissions, transport, chemical production, and deposition. However, considering the lack of knowledge in atmospheric physical/chemical schemes, it is necessary to determine the constraints of models that use satellite/observed data. For example, more accurate surface parameters, air pollution emissions, air pollution components, and cloud parameters could improve model results and support air pollution/GHG control policies.

The improvement of remote sensing data is expected to be helpful in constraining global and regional atmospheric models. Satellite remote sensing data can make up for the insufficiency of ground-based observations at both the spatial and temporal scales. However, although satellite remote sensing technology and models have achieved become better developed in recent years, it is not clear if remote sensing data are being used in all possible tasks. This Special Issue aims to present more detailed information about the current and planned applications for atmosphere remote sensing and modelling to promote the combination of the two fields.

We seek original research papers from frontiers in atmospheric remote sensing and modelling as well as review papers closely following international hotspots based on remote sensing and modelling techniques, mainly including, but not limited to:

  • The development of remote sensing algorithms;
  • The application of satellite products;
  • The assimilation of meteorological/vegetation remote sensing data;
  • The comparison and validation of satellite, ground observations, and simulations;
  • Assimilation technology for remote sensing;
  • The observation and prediction of air quality/GHGs;
  • Climate state analysis based on satellite and simulation;
  • The impact of the COVID-19 pandemic on air pollution/GHGs;
  • The evaluation of global/regional modelling;
  • Atmospheric composition reanalysis data;
  • Air pollution assessment based on satellite and simulation.

Dr. Ying Zhang
Dr. Sijia Lou
Dr. Qingqing He
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • remote sensing
  • modeling
  • assimilation technology
  • air quality
  • weather and climate change
  • geospatial and big data analysis

Published Papers (4 papers)

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Research

13 pages, 2327 KiB  
Article
Machine Learning Model-Based Estimation of XCO2 with High Spatiotemporal Resolution in China
by Sicong He, Yanbin Yuan, Zihui Wang, Lan Luo, Zili Zhang, Heng Dong and Chengfang Zhang
Atmosphere 2023, 14(3), 436; https://doi.org/10.3390/atmos14030436 - 22 Feb 2023
Cited by 4 | Viewed by 2036
Abstract
As the most abundant greenhouse gas in the atmosphere, CO2 has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO2 is of great significance in climate research. However, existing CO2 monitoring methods [...] Read more.
As the most abundant greenhouse gas in the atmosphere, CO2 has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO2 is of great significance in climate research. However, existing CO2 monitoring methods have great limitations, and it is difficult to obtain large-scale monitoring data with high spatial resolution, thus limiting the effective monitoring of carbon sources and sinks. To obtain complete Chinese daily-scale CO2 information, we used OCO-2 XCO2 data, Carbon Tracker XCO2 data, and multivariate geographic data to build a model training data set, which was then combined with various machine learning models including Random Forest, Extreme Random Forest, XGBoost, LightGBM, and CatBoost. The results indicated that the Random Forest model presented the best performance, with a cross-validation R2 of 0.878 and RMSE of 1.123 ppm. According to the final estimation results, in terms of spatial distribution, the highest multi-year average RF XCO2 value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In terms of time, from 2016 to 2018, the annual XCO2 in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal effects, the multi-year average XCO2 was highest in spring (407.76 ± 1.72 ppm) and lowest in summer (403.15 ± 3.36ppm). Compared with the Carbon-Tracker data, the XCO2 data set constructed in this study showed more detailed spatial changes, thus, can be effectively used to identify potentially important carbon sources and sinks. Full article
(This article belongs to the Special Issue Frontiers in Atmospheric Remote Sensing and Modelling)
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17 pages, 5108 KiB  
Article
Uncertainty Analysis of Remote Sensing Underlying Surface in Land–Atmosphere Interaction Simulated Using Land Surface Models
by Xiaolu Ling, Hao Gao, Jian Gao, Wenhao Liu and Zeyu Tang
Atmosphere 2023, 14(2), 370; https://doi.org/10.3390/atmos14020370 - 13 Feb 2023
Viewed by 1067
Abstract
This paper reports a comparative experiment using remote sensing underlying surface data (ESACCI) and Community Land Model underlying surface data (CLM_LS) to analyze the uncertainty of land surface types in land–atmosphere interaction. The results showed that the global distribution of ESACCI cropland is [...] Read more.
This paper reports a comparative experiment using remote sensing underlying surface data (ESACCI) and Community Land Model underlying surface data (CLM_LS) to analyze the uncertainty of land surface types in land–atmosphere interaction. The results showed that the global distribution of ESACCI cropland is larger than that of CLM_LS, and there is a great degree of difference in some regions, which can reach more than 50% regionally. Furthermore, the changes of the underlying surface conditions can be transmitted to the model results through the data itself, resulting in the uncertainty of the surface energy balance, surface micro-meteorological elements, and surface water balance simulated by the model, which further affects the climate simulation effect. Full article
(This article belongs to the Special Issue Frontiers in Atmospheric Remote Sensing and Modelling)
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17 pages, 4574 KiB  
Article
A Numerically Sensitive Study of Two Continuous Heavy-Pollution Episodes in the Southern Sichuan Basin of China
by Li Chen, Chunhong Zhou, Lei Zhang and Shigong Wang
Atmosphere 2022, 13(11), 1771; https://doi.org/10.3390/atmos13111771 - 27 Oct 2022
Viewed by 1799
Abstract
To explore the causes of pollution formation and changes in the complex topography of the Sichuan Basin, China, and improve the comprehensive simulation capability of pollution models, we use two online coupling models, WRF/Chem and WRF/CUACE, to simulate two heavy pollution episode that [...] Read more.
To explore the causes of pollution formation and changes in the complex topography of the Sichuan Basin, China, and improve the comprehensive simulation capability of pollution models, we use two online coupling models, WRF/Chem and WRF/CUACE, to simulate two heavy pollution episode that successively occurred in the southern part of Sichuan Province from 15 December 2016 to 11 January 2017 in this paper. Additionally, two sets of meteorological physics parameterization schemes MET1 and MET2 are designed, and four groups of experiments are carried out. The results suggest that the two models are good at simulating the static weather parameters such as temperature, low speed of winds and boundary layer height. The four groups of tests can accurately simulate the beginning, maintenance and turning point of the two pollution episodes’ life cycles. CUACE shows better performance in terms of higher correlation coefficients and lower errors in most of the particles and particulate components evaluations. It also performs better in the competitive mechanism of sulfate and nitrate against ammonium in the thermodynamic equilibrium mechanism. In addition, the evaluation of PM2.5 and the component simulation show that CUACE is more capable of simulating the mechanisms of heavy pollutions in southern Sichuan. Meanwhile, MET2 scheme is more appropriate for the simulation than MET1 dose. Based on the simulated concentrations of components and their precursors, the models overestimate the conversion of NO2 to nitrate but underestimate the conversion of SO2 to sulfate, which is the essential cause of the general overestimation of nitrate. Therefore, reducing the overestimation of nitrate is one major target for future model improvement. Full article
(This article belongs to the Special Issue Frontiers in Atmospheric Remote Sensing and Modelling)
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24 pages, 5999 KiB  
Article
The Effects of Local Pollution and Transport Dust on Aerosol Properties in Typical Arid Regions of Central Asia during DAO-K Measurement
by Yuanyuan Wei, Zhengqiang Li, Ying Zhang, Kaitao Li, Jie Chen, Zongren Peng, Qiaoyun Hu, Philippe Goloub and Yang Ou
Atmosphere 2022, 13(5), 729; https://doi.org/10.3390/atmos13050729 - 2 May 2022
Cited by 3 | Viewed by 1754
Abstract
Dust aerosol has an impact on both the regional radiation balance and the global radiative forcing estimation. The Taklimakan Desert is the focus of the present research on the optical and micro-physical characteristics of the dust aerosol characteristics in Central Asia. However, our [...] Read more.
Dust aerosol has an impact on both the regional radiation balance and the global radiative forcing estimation. The Taklimakan Desert is the focus of the present research on the optical and micro-physical characteristics of the dust aerosol characteristics in Central Asia. However, our knowledge is still limited regarding this typical arid region. The DAO-K (Dust Aerosol Observation-Kashgar) campaign in April 2019 presented a great opportunity to understand further the effects of local pollution and transported dust on the optical and physical characteristics of the background aerosol in Kashgar. In the present study, the consistency of the simultaneous observations is tested, based on the optical closure method. Three periods dominated by the regional background dust (RBD), local polluted dust (LPD), and Taklimakan transported dust (TTD), are identified through the backward trajectories, combined with the dust scores from AIRS (Atmospheric Infrared Sounder). The variations of the optical and micro-physical properties of dust aerosols are then studied, while a direct comparison of the total column and near surface is conducted. Generally, the mineral dust is supposed to be primarily composed of silicate minerals, which are mostly very weakly absorbing in the visible spectrum. Although there is very clean air (with PM2.5 of 21 μg/m3), a strong absorption (with an SSA of 0.77, AAE of 1.62) is still observed during the period dominated by the regional background dust aerosol. The near-surface observations show that there is PM2.5 pollution of ~98 μg/m3, with strong absorption in the Kashgar site during the whole observation. Local pollution can obviously enhance the absorption (with an SSA of 0.72, AAE of 1.58) of dust aerosol at the visible spectrum. This is caused by the increase in submicron fine particles (such as soot) with effective radii of 0.14 μm, 0.17 μm, and 0.34 μm. The transported Taklimakan dust aerosol has a relatively stable composition and strong scattering characteristics (with an SSA of 0.86, AAE of ~2.0). In comparison to the total column aerosol, the near-surface aerosol has the smaller size and the stronger absorption. Moreover, there is a very strong scattering of the total column aerosol. Even the local emission with the strong absorption has a fairly minor effect on the total column SSA. The comparison also shows that the peak radii of the total column PVSD is nearly twice as high as that of the near-surface PVSD. This work contributes to building a relationship between the remote sensing (total column) observations and the near-surface aerosol properties, and has the potential to improve the accuracy of the radiative forcing estimation in Kashgar. Full article
(This article belongs to the Special Issue Frontiers in Atmospheric Remote Sensing and Modelling)
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