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How AI/ML Improve Our Understanding of the Magnetosphere-Ionosphere-Theromosphere-Troposphere?
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How AI/ML Improve Our Understanding of the Magnetosphere-Ionosphere-Theromosphere-Troposphere?

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Upper Atmosphere".

Deadline for manuscript submissions: closed (25 July 2023) | Viewed by 3476

Special Issue Editors

Dr. Andong Hu

E-Mail Website
Guest Editor
Cooperative Institute for Research in Environmental Sciences (CIRES), CU Boulder, Boulder, CO 80309, USA
Interests: machine learning; space weather; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Dr. Sai Gowtam Valluri

E-Mail Website1 Website2
Guest Editor
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Interests: space weather; machine learning; ionosphere; magnetosphere–ionosphere–thermosphere coupling
Dr. Changyong He

E-Mail Website
Guest Editor
National–Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
Interests: thermospheric mass density modeling; TIE-GCM; precise orbit determination

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide recent advancements in the field of upper atmosphere modeling using ML/AI approaches, including magnetosphere, ionosphere, and thermosphere (M–I–T) systems. The availability of long-term datasets and rapidly growing data science techniques has enabled researchers to develop various data-driven modeling approaches that utilize data assimilation and machine learning techniques. M–I–T modeling plays an important role in several applications, such as satellite drag estimation, geomagnetically induced current (GIC) studies and GNSS positioning applications, etc. This topic encompasses innovative modeling methods, model applications, and extreme condition analysis. Other physical and empirical model methods can also be considered.

Topics of interest for the Special Issue include but are not limited to:

  • Forecasting ionospheric parameters using machine learning and data assimilation;
  • Thermosphere density estimation and satellite drag analysis using data-driven modeling techniques;
  • Geomagnetic field modeling using data-driven modeling to forecast GIC risks;
  • M–I–T coupling and response to extreme space weather events;
  • M–I–T modeling with uncertainty-quantification (UQ)-based AI/ML;
  • Forecasting troposphere parameters using machine learning and data assimilation.

Dr. Andong Hu
Dr. Sai Gowtam Valluri
Dr. Changyong 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

  • ionosphere
  • thermosphere
  • AI/ML
  • space weather
  • magnetosphere, ionosphere, and thermosphere (M–I–T) coupling

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Published Papers (2 papers)

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Research

20 pages, 7971 KiB  
Article
Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area
by Yuxuan Su, Junyu Li, Lilong Liu, Xi Guo, Liangke Huang and Mingyun Hu
Atmosphere 2023, 14(9), 1392; https://doi.org/10.3390/atmos14091392 - 3 Sep 2023
Cited by 2 | Viewed by 1416
Abstract
Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine particulate matter that is ≤2.5 μm in diameter (PM2.5), can cause serious harm to human health and life via the induction of respiratory diseases and lung cancer. Therefore, [...] Read more.
Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine particulate matter that is ≤2.5 μm in diameter (PM2.5), can cause serious harm to human health and life via the induction of respiratory diseases and lung cancer. Therefore, accurate prediction of PM2.5 concentrations is important for human health management and governmental environmental management decisions. However, the time-series processing of PM2.5 concentration based only on a single region and a special time period is less explanatory, and thus, the spatial-temporal applicability of the model is more restricted. To address this problem, this paper constructs a PM2.5 concentration prediction optimization model based on Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM). Hourly data of atmospheric pollutants, meteorological parameters, and Precipitable Water Vapor (PWV) of 10 cities in the Beijing-Tianjin-Hebei metropolitan area during the period of 1–30 September 2021/2022 were used as the training set, and the PM2.5 data of 1–7 October 2021/2022 were used for validation. The experimental results show that the CNN-LSTM model optimizes the average root mean square error (RMSE) by 25.52% and 14.30%, the average mean absolute error (MAE) by 26.23% and 15.01%, and the average mean absolute percentage error (MAPE) by 35.64% and 16.98%, as compared to the widely used Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models. In summary, the CNN-LSTM model is superior in terms of applicability and has the highest prediction accuracy in the Beijing-Tianjin-Hebei metropolitan area. The results of this study can provide a reference for the relevant departments in the Beijing-Tianjin-Hebei metropolitan area to predict PM2.5 concentration and its trend in specific time periods. Full article
(This article belongs to the Special Issue How AI/ML Improve Our Understanding of the Magnetosphere-Ionosphere-Theromosphere-Troposphere?)
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17 pages, 3264 KiB  
Article
Determining the Day-to-Day Occurrence of Low-Latitude Scintillation in Equinoxes at Sanya during High Solar Activities (2012–2013)
by Guodong Jia, Weihua Luo, Xiao Yu, Zhengping Zhu and Shanshan Chang
Atmosphere 2023, 14(8), 1242; https://doi.org/10.3390/atmos14081242 - 2 Aug 2023
Cited by 1 | Viewed by 1302
Abstract
Plasma irregularity in the equatorial and low-latitude ionosphere, which leads to ionospheric scintillation, can threaten the operation of radio-based communication and navigation systems. A method for forecasting scintillation activity is still pending. In this study, we examined the performance of ionospheric parameters, including [...] Read more.
Plasma irregularity in the equatorial and low-latitude ionosphere, which leads to ionospheric scintillation, can threaten the operation of radio-based communication and navigation systems. A method for forecasting scintillation activity is still pending. In this study, we examined the performance of ionospheric parameters, including the critical frequency (foF2), peak height of the F2-layer (hmF2), scale height (Hm) and virtual height (h’F), around local sunset from ground-based ionosonde observations, and also the characteristics of Equatorial Ionization Anomaly (EIA) derived from Gravity Recovery and Climate Experiment (GRACE) observations in equinoctial months (March–April and September–October) during high solar activities (2012–2013) at a low-latitude station at Sanya (18.3° N, 109.6° E; dip lat.: 12.8° N), China. Furthermore, the simplified linear growth rate of Rayleigh–Taylor (R–T) instability inferred from ionosonde measurements and EIA strength derived from GRACE observations were used to estimate the day-to-day occurrence of post-sunset scintillation. The results indicate that it is not adequate to determine whether scintillation in a low-latitude region would occur or not based on one ionospheric parameter around sunset. The simplified growth rate of R–T instability can be a good indicator for the day-to-day occurrence of scintillation, especially in combination with variations in EIA strength. An index including the growth rate and EIA variations for the prediction of the post-sunset occurrence of irregularity and scintillation is proposed; the overall prediction accuracy could be about 90%. Our results may provide useful information for the development of a forecasting model of the day-to-day variability of irregularities and scintillation in equatorial and low-latitude regions. Full article
(This article belongs to the Special Issue How AI/ML Improve Our Understanding of the Magnetosphere-Ionosphere-Theromosphere-Troposphere?)
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