Significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation. Haiyang-2C (HY-2C), the second operational satellite in China’s ocean dynamics exploration series, can provide all-weather, all-day, global observations of wave height, wind, and temperature. An altimeter can only measure the nadir wave height and other information, and a scatterometer can obtain the wind field with a wide swath. In this paper, a deep learning approach is applied to produce wide swath SWH data through the wind field using a scatterometer and the nadir wave height taken from an altimeter. Two test sets, 1-month data at 6 min intervals and 1-day data with an interval of 10 s, are fed into the trained model. Experiments indicate that the extending nadir SWH yields using a real-time wide swath grid product along a track, which can support oceanographic study, is superior for taking the swell characteristics of ERA5 into account as the input of the wide swath SWH model. In conclusion, the results demonstrate the effectiveness and feasibility of the wide swath SWH model.

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

The bivariate probability distribution of significant wave heights and mean wave periods has an indispensable guiding role in the implementation of offshore engineering, which has attracted great attention. This work gives a new bivariate method to describe the bivariate distribution of significant wave height and mean wave period at the NanJi, BeiShuang, and XiaoMaiDao stations from 2018 to 2020. A mixed lognormal distribution is used for univariate probability analysis of wave data, and the method of connecting two mixed lognormal distributions with copula functions is applied to construct bivariate distribution. The results show that compared with Weibull and lognormal distributions, the mixed lognormal distribution shows good performance in fitting marginal distributions. In the bivariate probability analysis, the conditional model overestimates the probability of lower wave heights, and the bivariate function model has a poor fitting effect in the region with larger periods. In contrast, the copula model based on mixed lognormal distribution is more suited to describe the joint distribution of significant wave height and mean wave period.

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