Search

Typhoon waves possess significant destructive potential, and their numerical simulation relies on accurate sea surface wind fields. An evaluation of different combinations of the radial air pressure distribution coefficient B and the radius of maximum wind speed (R max) in the Holland wind field (HWF) model was conducted to determine the optimal configuration. The HWF and the ERA5 wind field (EWF) were used as input wind fields to drive the typhoon wave model for China's coastal waters. Validation results indicated that neither wind field accurately reflected real conditions; therefore, a hybrid wind field (HBWF) was created by combining HWF and EWF using weighting coefficients that vary with the radius of wind speed to enhance accuracy. Simulation results showed that the HBWF improved the accuracy of significant wave heights (SWHs), with a mean relative error of 25.29%, compared to 32.48% for HWF and 27.94% for EWF. Additionally, HBWF also demonstrated the best performance in terms of root mean square error (RMSE) and consistency index. Overall, the HBWF enhances the simulation accuracy of typhoon waves in China's coastal waters. [ABSTRACT FROM AUTHOR]

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively. [ABSTRACT FROM AUTHOR]

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China's offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China's offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions. [ABSTRACT FROM AUTHOR]

The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China's offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China's ocean satellite data in ocean wave data assimilation and prediction. [ABSTRACT FROM AUTHOR]

The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China's offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites' SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors. [ABSTRACT FROM AUTHOR]

In recent years, most of the research on typhoon in Fujian Province of China has stayed in the typhoon wave simulation under the influence of wind fields. In order to study the influence of land terrain on typhoon wave fields, the terrain height parameters are introduced into the wind field simulation through the third-generation typhoon wave numerical model. The numerical simulation results of the model are verified and analyzed by referring to the data of significant wave height and wind speed of typhoon Fung-wong (200808) and typhoon Jangmi (200815) when it is across Taiwan Island. The results show that the relative error of the maximum wind speed and the significant wave height is reduced after the wind field optimization, which is more consistent with the observed data. The model accuracy is improved after optimization; Typhoon Fung-Wong, which crossed Taiwan, showed the most significant reduction in wind speed. Significant wave height is positively correlated with wind speed. The wave height distribution shows a gradually decreasing trend from the cyclone center to the coast, and the significant wave heights of the typhoon moving direction show pronounced asymmetry. Affected by the land topography, the distribution of signifi- cant wave height generally shows a downward trend, and the area of height water level area decreases. [ABSTRACT FROM AUTHOR]

Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bidirectional long short-term memory and the attention mechanism (CNNBiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input into the BiLSTM network. Meanwhile, the BiLSTM model is applied to fully extract the features of the associated information of time series data. Besides, the attention mechanism is used to assign probability weight to the output information of the BiLSTM layer units, and finally, a trainingmodel is constructed. Up to 24-h prediction experiments are conducted under normal and extreme conditions, respectively. Under the normal wave condition, for 3-, 6-, 12- and 24-h forecasting, the mean values of the correlation coefficients on the test set are 0.996, 0.991, 0.980, and 0.945, respectively. The corresponding mean values of the root mean square errors are measured at 0.063 m, 0.105 m, 0.172 m, and 0.281 m, respectively. Under the typhoon-forced extreme condition, the model based on CNN-BiLSTM-Attention is trained by typhooninduced SWH extracted from the WW3 reanalysis data. For 3-, 6-, 12- and 24-h forecasting, the mean values of correlation coefficients on the test set are respectively 0.993, 0.983, 0.958, and 0.921, and the averaged root mean square errors are 0.159 m, 0.257 m, 0.437 m, and 0.555 m, respectively. The model performs better than that trained by all the WW3 reanalysis data. The result suggests that the proposed algorithm can be applied to the 2D wave forecast with higher accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The extremal spatial dependence of significant wave height (H S) in the South China Sea (SCS) is discussed by combining altimeter observations of ocean satellites and ERA5 reanalysis dataset from the period 2011–2023. A spatial conditional extremes (SCE) model allowing different classes of extremal dependence is established, which uses a reliable Markov chain Monte Carlo (MCMC) scheme to realize the parameter inference. In this work, four transects are selected in the northern South China Sea (NSCS) and the central South China Sea (CSCS) respectively, which are approximately southwest-northeast (SW-NE) and northwest-southeast (NW-SE), and the H S data is transformed to the standard Laplace scale. The model fitting results show that the extremal dependence characteristics of different regions and directions in the SCS are different. The mean dependence of H S in NSCS decays faster than in CSCS, and the mean dependence on NW-SE transects decays faster than SW-NE transects. In general, the SW-NE transect in CSCS is observed a longer-range extremal dependence, which is potentially related to the storm direction in the SCS. • Simultaneous events of extreme oceans at multiple locations often have greater risks. • Important to analyze the spatial dependence of extreme wave height. • Satellite altimeter (HY-2) and ERA5 reanalysis dataset incorporated. • Spatial conditional extremes model used to quantify extremal spatial dependence of the effective wave heights for four transects in the South China Sea. • Extreme wave height shows greater extremal dependence from southwest to northeast than from northwest to southeast in the central South China Sea. [ABSTRACT FROM AUTHOR]

Modeling a double-peaked spectrum naturally derives the significant wave heights H s 1 for the swell system and H s 2 for the wind-sea system. However, few studies have examined whether H s 1 and H s 2 are consistent with the observed significant wave heights of swell H s s and wind-sea H s w . The purpose of this research is to develop a high-precision double-peaked spectral model with the spectrum-related significant wave heights H s 1 and H s 2 that closely match the observed H s s and H s w calculated by the wind-sea and swell separation method, respectively. A novel double-peaked spectral model based on the combination of two dimensionless JONSWAP spectra is found to be advantageous over the classical GH98 model (Guedes Soares and Henriques, 1998) and the APA19 model (Akbari et al., 2019) in the applications to three field datasets of coastal China. Specifically, the present model is comparable to the APA19 model in terms of fitting accuracy but with less computation, and both are substantially superior to the GH98 model. The present model outperforms the APA19 and GH98 models regarding the correlation between spectrum-related and observed significant wave heights. Furthermore, the ordinates of the peaks of the present model are precisely consistent with those of the observed spectrum, whereas they are frequently underestimated or overestimated by the GH98 model and the APA19 model. • A novel double-peaked spectral model is proposed and confirmed effective by field data. • A high-precision fit with exact match to the observed spectral peaks. • Strong consistency between the observed and modeled significant wave heights. [ABSTRACT FROM AUTHOR]

• Provided a framework to estimate simultaneous tropical cyclone (TC) wind and wave hazard. • Assessed the probability distribution of TC wind speed and significant wave height. • Compared block maximum and event-based assessment results for TC wind and wave. • Derived companion load factor considering simultaneous TC wind and wave actions. • Presented deaggregation results. A simulation-based framework to estimate tropical cyclone (TC) induced wind and wave hazards is proposed by considering the TC tracks, wind field model, and significant wave height model. The proposed approach is applied to several offshore sites located near the coastline of mainland China. Both the block maximum analysis and event-based analysis are carried out. It was concluded that the marginal distribution of the annual maximum wind speed and of annual maximum significant wave height could be modelled using the generalized extreme value distribution. Plots of paired samples in terms of probability of the annual maximum wind speed (significant wave height) and of its companion significant wave height (wind speed) suggested that they could be characterized using Gumbel copula. The ranked correlation coefficient (i.e., Kendall's tau) varies from about 0.4 to 0.7, depending on the considered paired random variable and site. The implication of the results in assigning the load combination factor for combined TC wind and wave load effect is discussed. The deaggregation results indicate that the identified most likely event causing p -quantile of the annual maximum wind speed differs from that identified from p -quantile of the annual maximum significant wave height. Therefore, for the reliability analysis, risk modelling, and emergency preparedness, it is recommended that two sets of hazard characterizations are be considered: one based on the maximum of wind speed and its companion wave height, and the other based on maximum wave height and its companion wind speed. [ABSTRACT FROM AUTHOR]