Statistical Analysis of the Spatiotemporal Distribution of Lower Atmospheric Ducts over the Seas Adjacent to China, Based on the ECMWF Reanalysis Dataset

Zhou, Yaqin; Liu, Yi; Qiao, Jiandong; Li, Jinze; Zhou, Chen

doi:10.3390/rs14194864

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…This high correlation indicated a strong relationship between the thermal conditions of the underlying surface and the duct occurrence. In regions with higher SST, the ducts occurred more often [36]. On the other hand, variables such as air temperature, humidity, and atmospheric pressure played important roles in the formation of ducts but exhibited spatial correlation coefficients mostly below 0.6, lacking the strong correlations observed in SST.…”

Section: Spatial Distributions Of the Forecasted Duct Characteristics...mentioning

confidence: 89%

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

Liu,

Zhao,

Zou

et al. 2024

JMSE

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The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height.

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Section: Spatial Distributions Of the Forecasted Duct Characteristics...mentioning

confidence: 89%

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

Liu,

Zhao,

Zou

et al. 2024

JMSE

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“…To get better resolution and accurate EleDH, more advanced hardware is required for direct measurement, remote sensing inversion, and numerical model prediction. Fortunately, the geographical correlation of the EleDH data enables the economic refinement of EleDH via the application of deep learning models (Zhou et al., 2022). However, there is limited research on EleDH refinement.…”

Section: Introductionmentioning

confidence: 99%

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

Zhu,

Yan,

Jiang

et al. 2024

Radio Science

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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.

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“…For lower atmospheric ducts at sea, the ERA5 reanalysis (The Fifth Generation ECMWF Atmospheric Reanalysis) data are often used for analysis. Yinhe Cheng [10], Yong Zhou [27], I. Sirkoval [28], and Axel von Engeln [29] have studied the characteristics of atmospheric ducts in different regions.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Ducts and Their Electromagnetic Propagation Characteristics in the Northwestern South China Sea

Yang

Wang

2023

Remote Sensing

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The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic weather stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ECMWF in studying atmospheric ducts using measured data, we analyzed the spatial–temporal distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. The study found that the NCEP CFSv2 data accurately capture the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height calculated from ERA5 and the measured data have good consistency. The occurrence rate and height of the evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns; conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layers, mainly concentrated from June to August. Monsoons play a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. In the end, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in various duct environments within a typical atmospheric duct structure.

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Statistical Analysis of the Spatiotemporal Distribution of Lower Atmospheric Ducts over the Seas Adjacent to China, Based on the ECMWF Reanalysis Dataset

Cited by 6 publications

References 24 publications

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

Atmospheric Ducts and Their Electromagnetic Propagation Characteristics in the Northwestern South China Sea

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