A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

Yang, Cheng; Wang, Jian; Shi, Yafei

doi:10.3390/rs14215484

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…a = 1/273. To express and explain the curvature of the Earth more conveniently, N is usually replaced by the modified refractivity M. The EDH can be obtained through the modified refractivity profile (M profile) [30,31]. The modified refractivity can be expressed by Formula (3).…”

Section: Direct Detection Methodsmentioning

confidence: 99%

Research on a Multimodel Fusion Diagnosis Method for Evaporation Ducts in the East China Sea

Zhang,

Qiu,

Fan

et al. 2023

Sensors

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Evaporation ducts are abnormal states of the atmosphere in the air–sea boundary layer that directly affect the propagation trajectory of electromagnetic (EM) waves. Therefore, an accurate diagnosis of the evaporation duct height (EDH) is important for studying the propagation trajectory of EM waves in evaporation ducts. Most evaporation duct models (EDMs) based on the Monin–Obukhov similarity theory are empirical methods. Different EDMs have different levels of environmental adaptability. Evaporation duct diagnosis methods based on machine learning methods only consider the mathematical relationship between data and do not explore the physical mechanism of evaporation ducts. To solve the above problems, this study observed the meteorological and hydrological parameters of the five layers of the low-altitude atmosphere in the East China Sea on board the research vessel Xiangyanghong 18 in April 2021 and obtained the atmospheric refractivity profile. An evaporation duct multimodel fusion diagnosis method (MMF) based on a library for support vector machines (LIBSVM) is proposed. First, based on the observed meteorological and hydrological data, the differences between the EDH diagnosis results of different EDMs and MMF were analyzed. When ASTD ≥ 0, the average errors of the diagnostic results of BYC, NPS, NWA, NRL, LKB, and MMF are 2.57 m, 2.92 m, 2.67 m, 3.27 m, 2.57 m, and 0.24 m, respectively. When ASTD < 0, the average errors are 2.95 m, 2.94 m, 2.98 m, 2.99 m, 2.97 m, and 0.41 m, respectively. Then, the EM wave path loss accuracy analysis was performed on the EDH diagnosis results of the NPS model and the MMF. When ASTD ≥ 0, the average path loss errors of the NPS model and MMF are 5.44 dB and 2.74 dB, respectively. When ASTD < 0, the average errors are 5.21 dB and 3.46 dB, respectively. The results show that the MMF is suitable for EDH diagnosis, and the diagnosis accuracy is higher than other models.

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Section: Direct Detection Methodsmentioning

confidence: 99%

Research on a Multimodel Fusion Diagnosis Method for Evaporation Ducts in the East China Sea

Zhang,

Qiu,

Fan

et al. 2023

Sensors

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“…In the contribution by Yang et al, entitled "A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data", an EDH prediction network using MLP is proposed [8]. A multidimensional EDH prediction model is constructed using spatial and temporal "additional data" derived from meteorological measurements.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

Jeon

2023

Remote Sensing

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“…Evaporation ducts are the most commonly occurring duct type and are often considered a near-permanent global phenomenon. Hence, extensive focus has been on studying the influence of evaporation ducts on radar and radio communications [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Utö Observatory for Analysing Atmospheric Ducting Events over Baltic Coastal and Marine Waters

Rautiainen,

Tyynelä,

Lensu

et al. 2023

Remote Sensing

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Maritime safety relies on navigation, detection, and radio communication technologies that function through electromagnetic radiation. Propagation of electromagnetic radiation can be impacted by a disruptive phenomenon known as ducting. Our four-week study using a X-band coastal radar and various meteorological and marine observations, including vertical profiles of temperature, humidity and wind, in early spring 2022 concluded that the combination of measurements at the Utö observatory provides a reliable means of detecting ducting in the Archipelago Sea. The modified refractivity calculated from the vertical profiles for the 22–59 m and 32–59 m altitude layers and coastal radar over-the-horizon observations agree 77% and 85% of the time, respectively. As such, the modified refractivity gradient can be considered a good indicator for over-the-horizon detection with the Utö coastal radar over the open sea. The horizontal wind profiles also revealed a low-level jet at the radar height that often coincided with the ducting observations. To quantify the results, we created an empirical ducting index which showed that ducting is spatially variable, showing the capabilities of the Utö observatory for research oriented towards monitoring and improving maritime safety and security.

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A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

Cited by 3 publications

References 42 publications

Research on a Multimodel Fusion Diagnosis Method for Evaporation Ducts in the East China Sea

Research on a Multimodel Fusion Diagnosis Method for Evaporation Ducts in the East China Sea

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

Utö Observatory for Analysing Atmospheric Ducting Events over Baltic Coastal and Marine Waters

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