This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm.

In this study, water levels observed at tide stations in the Bohai Sea, Yellow Sea, and East China Sea during Typhoons 7203 and 8509 were assimilated into a numerical assimilation storm surge model combined with regularization technique to study the wind-stress drag coefficient. The Tikhonov regularization technique with different regularization parameters was tested during the assimilation. Using the regularization technique, the storm surge elevations were successfully simulated in the whole sea areas during Typhoons 7203 and 8509. The storm surge elevations calculated with the regularization technique and the elevations calculated with independent point method were separately compared with the observed data. Comparison results demonstrated that the former was closer to the observed data. The regularization technique had the best performance when the regularization parameter was 100. The spatial distribution of the inverted drag coefficient, storm surge elevations, and the wind fields during both typhoons were presented. Simulated results indicated that the change of drag coefficient is more significant in the coastal regions of the Bohai Sea and north of the Yellow Sea. Further analysis showed that the rising water elevation in the Bohai Sea is mostly attributed to the influence of onshore winds, and the negative storm surge in the South Yellow Sea is mainly caused by offshore winds.

In the storm surge model, the wind drag coefficient C_d is a critical parameter that has a great influence on the forecast of the storm surge level. In the present study, the effect of various wind drag coefficient parameterizations on the storm surge level is investigated in the Bohai Sea, Yellow Sea and East China Sea for Typhoons 7203 and 7303. Firstly, the impacts of initial values of a and b in the linear expression C_d = (a + b × U₁₀) × 10⁻³ on the pure storm surge model are evaluated based on the data assimilation method. Results indicate that when a and b (i.e., the wind drag coefficients given by Smith, Wu, Geernaert et al. and Mel et al.) are non-zeros, the performance of the model has little difference, and the result from Wu is slightly better. However, they are superior to the performance of the model adopting zero initial values. Then, we discuss the influences of diverse ways of calculating wind drag coefficients, which are inverted by data assimilation method (including both linear and constant C_d) and given in the form of linear formulas, on simulating pure storm surge level. They show that the data assimilation-based coefficients greatly exceed those of the ordinary coefficient formulas. Moreover, the wind drag coefficient in the linear form is a little better than that in constant form when the data assimilation method is used. Finally, the assessment of the impact of astronomical tides on the storm surge level is conducted, and the simulation demonstrates that the storm surge model, which has the combination of four constituents (M₂, S₂, K₁, O₁) and wind drag coefficient inverted by the data assimilation method with the linear C_d, exhibits the best performance.

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.

The impacts of different wave spectrum formulations with high frequency tail on the Stokes drift are studied in this paper. Two wave spectrum formulations are described. One is from the Donelan and Pierson spectrum extended to the high frequency range (denoted by EDP spectrum) by Paskyabi and Jenkins, and the other is a combined spectrum (termed the Combi spectrum) provided by Tsagareli. Using the EDP spectrum and Combi spectrum, the Stokes drift velocities and Stokes transports are calculated for the different wind speeds. The results show that the high frequency spectral tail of the surface gravity waves obviously changes the Stokes drift velocity on the sea surface, but has little impact on the Stokes transport.