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Atmosphere
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Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition)
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Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition)

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1119

Special Issue Editors

Dr. Cheng Wang

E-Mail Website
Guest Editor
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China
Interests: ionopshere; synthetic aperture radar; radio wave propagation
Special Issues, Collections and Topics in MDPI journals
Dr. Yifei Ji

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha 410073, China
Interests: ionospheric propagation; synthetic aperture radar (SAR); over-the-horizon radar
Dr. Yong Wang

E-Mail Website
Guest Editor
Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong University, Weihai 264209, China
Interests: ionosphere–magnetosphere coupling; ionospheric scintillation; space weather

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume in a series of publications dedicated to the “Radar Sensing Atmosphere: Modelling, Imaging and Prediction” (https://www.mdpi.com/journal/atmosphere/special_issues/radar_sensing_atmosphere).

Radar is a powerful tool that can be used to monitor an atmospheric state, which can measure and sense the boundary layer, troposphere, and ionosphere to forecast future weather, even in space. Moreover, the obtained atmospheric data can also be used to correct atmospheric errors in remote sensing observations, communication, and navigation systems. Therefore, it is very important to measure and monitor the atmospheric state. At present, many radar sensing technologies have been widely used for atmospheric state monitoring, including direct measurements from radar instruments such as weather radars, cloud radars, and wind profile radars, as well as indirect calculations of tropospheric liquid water content (LWC), ice water content (IWC), and ionospheric total electronic content (TEC) using ground radar data. Radar sensing platforms can be implemented on the ground, in the air, in near space, or even on a satellite. In addition, the utilized frequency is also extended from traditional microwave frequency bands to millimeter wave and terahertz, as well as P-band, high frequency (HF), and other long-wave frequency bands. In short, the development of the technology and equipment in atmospheric radar detection has exciting prospects. This Special Issue focuses on the latest developments in atmospheric modeling, equipment, and detection methods using radar sensing.

Dr. Cheng Wang
Dr. Yifei Ji
Dr. Yong Wang
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

  • ionospheric sounding
  • ionospheric effect and compensation for radar signals
  • tropospheric liquid/ice water content retrieval
  • radar measurement for severe weather
  • radar characteristic simulation of severe weather
  • artificial intelligence in weather monitoring, prediction and forecast

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Published Papers (3 papers)

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Research

17 pages, 3785 KB  
Article
Feasibility Study of Microwave Radiometer Neural Network Modeling Method Based on Reanalysis Data
by Xuan Liu, Qinglin Zhu, Xiang Dong, Houcai Chen, Tingting Shu, Wenxin Wang and Bin Xu
Atmosphere 2025, 16(10), 1194; https://doi.org/10.3390/atmos16101194 - 16 Oct 2025
Abstract
To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed [...] Read more.
To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed by the Back Propagation (BP) neural network, based on FNL reanalysis data from Qingdao, China. The model’s accuracy was evaluated by comparing retrieval results with synchronous radiosonde data, with an analysis of seasonal variations. Results indicate that the Root Mean Square Error (RMSE) of temperature profiles are 1.15 °C in the near-surface layer (0–2 km) and 2.05 °C in the mid-to-upper layers (>2 km). The comprehensive RMSE for relative humidity, water vapor density, and Integrated Water Vaper (IWV) are 17.27%, 0.96 g/m3, and 1.37 mm, respectively. Overall, the errors are relatively small, and the retrieval results exhibit strong spatiotemporal consistency with radiosonde data. The error increases most rapidly within the lower atmosphere (<2 km), with distinct seasonal differences observed. Temperature and relative humidity retrieval accuracies peak in summer, whereas water vapor density and IWV retrievals perform best in winter and worst in summer. This study confirms that reanalysis data–based modeling effectively addresses the issue of limited radiosonde coverage. This method is applicable to atmospheric remote sensing in regions lacking radiosonde data, such as oceans and plateaus. It provides a feasible solution to the regional limitations of microwave radiometer applications and expands the potential uses of reanalysis data. Full article
(This article belongs to the Special Issue Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition))
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22 pages, 7360 KB  
Article
Evaporation Duct Height Short-Term Prediction Based on Bayesian Hyperparameter Optimization
by Ye-Wen Wu, Yu Zhang, Zhi-Qiang Fan, Han-Yi Chen, Sheng-Lin Zhang and Yu-Qiang Zhang
Atmosphere 2025, 16(10), 1126; https://doi.org/10.3390/atmos16101126 - 25 Sep 2025
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Abstract
Accurately predicting evaporation duct height (EDH) is a crucial technology for enabling over-the-horizon communication and radar detection at sea. To address the issues of overfitting in neural network training and the low efficiency of manual hyperparameter tuning in conventional evaporation duct height (EDH) [...] Read more.
Accurately predicting evaporation duct height (EDH) is a crucial technology for enabling over-the-horizon communication and radar detection at sea. To address the issues of overfitting in neural network training and the low efficiency of manual hyperparameter tuning in conventional evaporation duct height (EDH) prediction, this study proposes the application of Bayesian optimization (BO)-based deep learning techniques to EDH forecasting. Specifically, we developed a novel BO–LSTM hybrid model to enhance the predictive accuracy of EDH. First, based on the CFSv2 reanalysis data from 2011 to 2020, we employed the NPS model to calculate the hourly evaporation duct height (EDH) over the Yongshu Reef region in the South China Sea. Then, the Mann–Kendall (M–K) method and the Augmented Dickey–Fuller (ADF) test were employed to analyze the overall trend and stationarity of the EDH time series in the Yongshu Reef area. The results indicate a significant declining trend in EDH in recent years, and the time series is stationary. This suggests that the data can enhance the convergence speed and prediction stability of neural network models. Finally, the BO–LSTM model was utilized for 24 h short-term forecasting of the EDH time series. The results demonstrate that BO–LSTM can effectively predict EDH values for the next 24 h, with the prediction accuracy gradually decreasing as the forecast horizon extends. Specifically, the 1 h forecast achieves a root mean square error (RMSE) of 0.592 m, a mean absolute error (MAE) of 0.407 m, and a model goodness-of-fit (R2) of 0.961. In contrast, the 24 h forecast shows an RMSE of 2.393 m, MAE of 1.808 m, and R2 of only 0.362. A comparative analysis between BO–LSTM and LSTM reveals that BO–LSTM exhibits marginally superior accuracy over LSTM for 1–15 h forecasts, with its performance advantage becoming increasingly pronounced for longer forecast horizons. This confirms that the Bayesian optimization-based hyperparameter tuning method significantly enhances model prediction accuracy. Full article
(This article belongs to the Special Issue Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition))
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15 pages, 4149 KB  
Article
A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification
by Junzhi Li, Xin Ning and Yong Wang
Atmosphere 2025, 16(10), 1120; https://doi.org/10.3390/atmos16101120 - 24 Sep 2025
Viewed by 356
Abstract
Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite [...] Read more.
Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article
(This article belongs to the Special Issue Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition))
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