The evaporation duct is a weather phenomenon that often occurs in marine environments and affects the operation of shipborne radar. The most important evaluation parameter is the evaporation duct height (EDH). Forecasting the EDH and adjusting the working parameters and modes of the radar system in advance can greatly improve radar performance. Traditionally, short-term forecast methods have been used to estimate the EDH, which are characterized by low time resolution and poor forecast accuracy. In this study, a novel approach for EDH nowcasting is proposed based on the deep learning network and EDH data measured in the Yellow Sea, China. The factors that affect nowcasting were analyzed. The time resolution and forecast time were 5 min and 0–2 h, respectively. The results show that our proposed method has a higher forecast accuracy than traditional time series forecasting methods and confirm its feasibility and effectiveness.

Ocean current is one of the most important parameters in ocean observation, and ocean current measurement based on electromagnetic induction is becoming more and more important because of its advantages such as simple structure and high measurement accuracy. However, it is difficult to detect weak current signals in a complex marine environment. In this paper, an electromagnetic induction current measurement scheme based on lock-in amplification technology is proposed. Key technologies such as the evaluation of induced current intensity, overall design, circuit design, and orientation design of the current meter were studied. The prototype of the electromagnetic current meter was developed and tested in the laboratory and at sea. The repeatability of current velocity and current direction was higher than 1.5 cm/s and 1.5°, respectively. A comparison test between the electromagnetic current meter prototype and Nortek ADCP (Acoustic Doppler Current Profiler) installed on a buoy at sea was carried out, and the correlation coefficients of the current velocity and current direction datum were 0.90 and 0.96, respectively. Through continuous on-site and fault-free operations at sea, the experimental data show that the electromagnetic current meter has good adaptability at sea, which provides feasible technical and equipment support for ocean current observation.

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

In this study, a diagnostic model for lower atmospheric ducts was developed using the polar weather research and forecasting model. A five-year simulation was then conducted across the entire Arctic region to investigate the temporal and spatial characteristics of lower atmospheric ducts. The model demonstrated excellent performance in simulating modified atmospheric refractivity, with root mean square errors ranging from 0 M to 5 M. The five-year simulation results revealed that duct occurrence rates across the Arctic region were all below 1% and exhibited a negative relationship with latitude. Regarding the difference between surface ducts and elevated ducts, a higher frequency of surface ducts was detected in the Arctic region. The height and thickness of surface ducts were generally lower than those of elevated ducts, but the strength of surface ducts was slightly greater. Regionally, surface ducts mainly occurred in the land areas surrounding the Arctic Ocean, while more elevated ducts were found in the North Atlantic Sea area. Additionally, a negative correlation was observed between the polar vortex indices and the characteristics of ducts, particularly for surface ducts. The ducts in Greenland were notably influenced by polar vortex activity, whereas the ducts in other regions, such as the Norwegian Sea and Kara Sea, were less affected.

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.

In this study, a diagnostic model for evaporation ducts was established based on the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) and the Naval Postgraduate School (NPS) models. Utilizing this model, four sensitivity tests were conducted over the South China Sea from 21 September to 5 October 2008, when four tropical cyclones affected the study domain. These tests were designed with different roughness schemes to investigate the impact mechanisms of wave processes on evaporation duct simulation under extreme weather conditions. The results indicated that wave processes primarily influenced the evaporation duct heights by altering sea surface roughness and dynamical factors. The indirect impacts of waves without dynamical factors were rather weak. Generally, a decrease in local roughness led to increased wind speed, decreased humidity, and a reduced air–sea temperature difference, resulting in the formation of evaporation ducts at higher altitudes. However, this affecting mechanism between roughness and evaporation ducts was also greatly influenced by changes in regional circulation. In the eastern open sea areas of the South China Sea, changes in evaporative ducts were more closely aligned with local impact mechanisms, whereas the changes in the central and western areas demonstrated greater complexity and fewer local impacts due to variations in regional circulation.