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.

Ocean waves are created by energy passing through water, causing it to move in a circular motion and have a crucial impact on the safety of ship navigation, offshore engineering construction, and marine disaster early warning. Therefore, developing high-precision, real-time wave observation technology to accurately obtain wave parameters is very important. This study employs a One-Vertical-Two-Inclined Millimeter-Wave Radar Array (1V2I-MMWRA) to observe wave parameters in the South China Sea. Based on the measured displacement time series, significant wave height, mean wave height, significant wave period, and mean wave period were estimated using both the zero-crossing method and spectral estimation. The system performance was validated against an air–sea interface flux buoy. Experimental results demonstrate that the zero-crossing method exhibits superior precision. The Root-Mean-Square Errors (RMSEs) for the aforementioned parameters were 0.13 m, 0.11 m, 0.81 s, and 0.46 s, respectively. In contrast, spectral estimation yielded higher RMSEs of 0.20 m, 0.16 m, 1.07 s, and 0.74 s, primarily attributed to increased deviations during typhoon passage. Furthermore, directional spectrum analysis reveals that peak frequency and Power Spectral Density (PSD) intensify with the strengthening of the typhoon, while estimated wave directions align closely with in situ measurements. These findings confirm the high reliability of the 1V2I-MMWRA under extreme conditions, highlighting its distinct advantages of lower power consumption and ease of deployment.