Wenqi Shi

Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In order to employ a more realistic wind stress drag coefficient that varies with time and space, we corrected the storm surge model using the spatial distribution of the wind stress drag coefficient, which was inverted using the data assimilation method based on the linear expression C_d = (a + b × U₁₀) × 10⁻³. Initially, two grid resolutions of 5′ × 5′ and 10′ × 10′ were applied to the numerical storm surge model and adjoint assimilation model. It was found that the influence of different grid resolutions on the numerical model is almost negligible. But in the adjoint assimilation model, the root mean square (RMS) errors between the simulated and observed storm surge levels under 5′ × 5′ and 10′ × 10′ grid resolutions were 11.6 cm and 15.6 cm, and the average PCC and WSS values for 10 tidal stations changed from 89% and 92% in E3 to 93% and 96% in E4, respectively. The results indicate that the finer grid resolution can yield a closer consistency between the simulation and observations. Subsequently, the effects of assimilation window sizes of 6 h, 3 h, 2 h, and 1 h on simulated storm surge levels were evaluated in an adjoint assimilation model with a 5′ × 5′ grid resolution. The results show that the average RMS errors were 11.6 cm, 10.6 cm, 9.6 cm, and 9.3 cm under four assimilation window sizes. In particular, the RMS errors for the assimilation window sizes of 1 h and 6 h at RuShan station were 3.9 cm and 10.2 cm, a reduction of 61.76%. The PCC and WSS values from RuShan station in E4 and E7 separately showed significant increases, from 85% to 98% and from 92% to 99%. These results demonstrate that when the assimilation window size is smaller, the simulated storm surge level is closer to the observation. Further, the results show that the simulated storm surge levels are closer to the observation when using the wind stress drag coefficient with a finer grid resolution and smaller temporal resolution.

During the winters of 2009/2010 and 2020/2021, observations were carried out at an eastern port of Liaodong Bay to examine the variations in sea ice thickness and atmospheric conditions. The daily ice thickness (DIT) and the cumulative ice thickness (CIT) are the two main observation items related to the thickness of sea ice. For DIT, the sea ice thickness gradually decreases as the temperature increases, and the freezing rate a is 1.48 cm/(°C·d)^1/2. For CIT, when the temperature is −12 °C, the maximum growth rate of ice thickness decreases from 3.5 cm/d to 1.5 cm/d as the ice thickness increases from 0 to 20 cm. The residual method was applied to calculate the oceanic heat flux, which is an important parameter of ice modeling, and both the analytic model (Stefan’s law) and numerical model (high-resolution thermodynamic snow-and-ice model) were utilized in this work. It was found that the accuracy of the simulation results was high when the growth coefficient of the analytic mode was 2.3 cm/(°C·d)^1/2. With an oceanic heat flux of 2 W·m⁻², the maximum error of the numerical model approached 60% in 2010 and 3.7% in 2021. However, using the oceanic heat flux calculated in this work, the maximum error can be significantly reduced to 4.2% in the winter of 2009/2010 and 1.5% in 2020/2021. Additionally, the oceanic heat flux in Liaodong Bay showed a decreasing trend with the increase in ice thickness and air temperature.

Sea ice concentration is an important observation element and a necessary input parameter for the numerical prediction of sea ice. In this study, a new method is developed for the inversion of sea ice concentration (SIC) in Liaodong Bay from MODIS data based on the Normalized Difference Water Index (NDWI). By comparing the inverted results of the NDWI method and the WU’s methods, as well as observations from six shore-based observation points on the east coast of Liaodong Bay in the winter of 2016/2017 and Landsat 8 OLI data, the method based on NDWI is shown to be a useful tool to estimate the SIC. The SIC obtained by the NDWI method is in good agreement with the survey data. The mean absolute error between the SIC obtained by the NDWI method and visual observation is about 10%. In the comparison data, 92% of the absolute error values of SIC are less than 20%. The average value of the error of the SIC obtained by the NDWI method is 1%, and that of the WU’s method is about −10%, which means the accuracy of SIC obtained by the NDWI method is better. The mean deviation of the error of the SIC obtained by the NDWI method and the WU’s method is about 9% and 13%, which shows a certain improvement in precision. The validation results with the Landsat 8 OLI data show the precision of the SIC obtained by the NDWI method is better than the WU’s method, which is consistent with the validation result of the survey data.

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

When petroleum hydrocarbon pollutants enter the ocean, besides the migration under hydrodynamic constraints, their degradation due to environmental conditions also occurs. However, available observations are usually spatiotemporally disperse, which makes it difficult to study the degradation characteristics of pollutants. In this paper, a model of transport and degradation is used to estimate the degradation coefficient of petroleum hydrocarbon pollutants with the adjoint method. Firstly, the results of a comprehensive physical–chemical–biological test of the degradation of petroleum hydrocarbon pollutants in Laizhou Bay provide a reference for setting the degradation coefficient on the time scale. In ideal twin experiments, the mean absolute errors between observations and simulation results obtain an obvious reduction, and the given distributions can be inverted effectively, demonstrating the feasibility of the model. In a practical experiment, the actual distribution of petroleum hydrocarbon pollutants in Laizhou Bay is simulated, and the simulation results are in good agreement with the observed ones. Meanwhile, the spatial distribution of the degradation coefficient is inverted, making the simulation results closer to the actual observations.