The Statistical Characteristics of Atmospheric Ducts Observed Over Stations in Different Regions of American Mainland Based on High-Resolution GPS Radiosonde Soundings

Huang, Lang; Zhao, Xiaofeng; Liu, Yudi

doi:10.3389/fenvs.2022.946226

Cited by 4 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Huang et al. (2022) have observed that the thickness shows similar regularity with the duct height. Base and Top may process similar distributions, and they have height differences simultaneously, as Figure 4 shows.…”

Section: Proposed Methodsmentioning

confidence: 87%

See 1 more Smart Citation

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

Zhu,

Yan,

Jiang

et al. 2024

Radio Science

View full text Add to dashboard Cite

Elevated duct (EleD) is an abnormal atmospheric refraction structure with a suspended trapped layer. The precise and highly resolved elevated duct‐height‐based data (EleDH) is crucial for radio communication systems, especially in electromagnetic wave path loss prediction and EleDH‐producing systems. However, producing high‐resolution EleDH is challenging because of the massive details in the EleDH data. Direct and high‐time refinement procedures mostly lead to unrealistic outcomes. The study provides a Dense‐Linear convolutional neural network (DLCNN)‐based EleDH refinement technique based on the development of statistical downscaling and super‐resolution technologies. Additionally, the stack approach is used, and the refining order is taken into consideration to ensure precision in high‐time refinement and provide reliable outcomes. To demonstrate the strength of DLCNN in capturing complex internal characteristics of EleDH, a new EleD data set is first funded, which only contains the duct height. From this data set, we use the duct height as the core refinement of the EleD's trapped layer and the thickness of the trapped layer to ensure reliable duct height. Seven super‐resolution models are utilized for fair comparisons. The experimental results prove that the DLCNN has the highest refinement performance; also, it obtained excellent generalization capacity, where the minimum and maximum obtained Accuracy(20%), MAE, and RMSE were 85.22% ∓ 88.30%, 36.09 ∓ 45.97 and 8.68 ∓ 10.14, respectively. High‐resolution EleDH improves path loss prediction, where the minimum and maximum obtained bias were 2.37 ∓ 9.51 dB.

show abstract

Section: Proposed Methodsmentioning

confidence: 87%

“…The EleD also creates an electromagnetic blind zone above the atmospheric duct, which harms the identification of air targets (Han et al., 2022). The height is one of EleD's characteristics (Huang et al., 2022). Elevated duct height (EleDH) is related to the local refractive index.…”

Section: Introductionmentioning

confidence: 99%

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

Zhu,

Yan,

Jiang

et al. 2024

Radio Science

View full text Add to dashboard Cite

show abstract

Arctic Atmospheric Ducting Characteristics and Their Connections with Arctic Oscillation and Sea Ice

Qin

Chen

et al. 2022

Atmosphere

View full text Add to dashboard Cite

Atmospheric ducting is an anomalous atmospheric structure that affects electromagnetic wave propagation. In the context of global warming, the navigation capacity of the Arctic is increased, and the atmospheric duct can affect communication and navigation in the Arctic. In this study, based on the European Centre for Medium-Range Weather Forecasts reanalysis data (ERA-interim), the climate characteristics and their variations of atmospheric ducts over the Arctic polar region (north of 60° N) from 1989 to 2018 were analyzed, including the occurrence frequency, spatial distribution, thickness and intensity of the atmospheric ducts. The results show that the overall frequency of atmospheric ducts in the Arctic is low, with the average frequency of all types of ducts being less than 10% throughout the year. The frequency of surface ducts is 2~3 times that of elevated ducts. More than 90% of the atmospheric ducts in the Arctic have a trapped layer with a thickness of less than 100 m, and the average thickness of surface ducts is higher than that of the elevated ducts. The intensity of the Arctic surface ducts is stronger than that of the elevated ducts, with an average intensity of 2.1 M (±2.3 M) to 4.5 M (±4.5 M) for the surface ducts and 1.7 M (±2 M) to 2.5 M (±2.9 M) for the elevated ducts. There is a positive correlation between the ducts’ trapped layer thickness and duct intensity. The variation in atmospheric ducts is responsive to the changes in atmospheric circulation and the sea ice extent. This anomalous circulation changes surface wind in the Arctic, which affects the formation and maintenance of the ducts. The trends of ducts in the Arctic Ocean are consistent with those of the Arctic Sea ice extent, while the Arctic continental and coastal ducts show the opposite trend.

show abstract

Statistical Analysis of Atmospheric Ducts in the Yellow and Bohai Seas of China and Study of Their Electromagnetic Wave Propagation Characteristics

Yang,

Li,

Lin

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Atmospheric ducts play a critical role in the propagation of electromagnetic waves by minimizing signal loss and extending transmission distances, which is essential for radar, communication, and navigation systems. This study leverages meteorological sounding data and reanalysis data to analyze the distribution of atmospheric ducts in the Bohai Sea and Yellow Sea regions of China. The parabolic equation method was employed to simulate the propagation characteristics of electromagnetic waves in evaporation ducts, surface ducts, and mixed duct environments, focusing on the effects of electromagnetic wave frequency and antenna height. In the Bohai Sea region, the height of evaporation ducts peaks at 13 m in spring and autumn, decreasing to 6 m in winter. In the Yellow Sea region, the height reaches 12 m in autumn and drops to 7 m in summer, indicating a heterogeneous distribution. The monthly mean occurrence rate of atmospheric ducts is defined as the number of atmospheric duct events in a given month divided by the total number of samples for that month. Influenced by the summer and winter monsoons, the occurrence rate of surface ducts is higher from May to September and lower from October to April of the following year. In contrast, elevated ducts reach their peak occurrence rate of 60% in October. In an evaporation duct environment, propagation loss gradually increases with distance, and the loss is more pronounced in non-uniform environments. In surface ducts, propagation loss exhibits periodic fluctuations with distance, exceeding 47 dB. The mixed duct environment integrates the characteristics of both evaporation and surface ducts, effectively filling the shadow zone between 10 m and 70 m.

show abstract

The Statistical Characteristics of Atmospheric Ducts Observed Over Stations in Different Regions of American Mainland Based on High-Resolution GPS Radiosonde Soundings

Cited by 4 publications

References 19 publications

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

Arctic Atmospheric Ducting Characteristics and Their Connections with Arctic Oscillation and Sea Ice

Statistical Analysis of Atmospheric Ducts in the Yellow and Bohai Seas of China and Study of Their Electromagnetic Wave Propagation Characteristics

Contact Info

Product

Resources

About