Ocean Modelling最新文献

Landslide tsunamis, responsible for thousands of deaths and significant damage in recent years, necessitate the allocation of sufficient time and resources for studying these extreme natural hazards. This study offers a step change in the field by conducting a large number of three-dimensional numerical experiments, validated by physical tests, to develop a predictive equation for the maximum initial amplitude of tsunamis generated by subaerial landslides. We first conducted a few 3D physical experiments in a wave basin which were then applied for the validation of a 3D numerical model based on the Flow3D-HYDRO package. Consequently, we delivered 100 simulations using the validated model by varying parameters such as landslide volume, water depth, slope angle and travel distance. This large database was subsequently employed to develop a predictive equation for the maximum initial tsunami amplitude. For the first time, we considered travel distance as an independent parameter for developing the predictive equation, which can significantly improve the predication accuracy. The predictive equation was tested for the case of the 2018 Anak Krakatau subaerial landslide tsunami and produced satisfactory results.

Coastal flooding affects low-lying communities worldwide and is expected to increase with climate change, especially along reef-lined coasts, where wave-driven flooding is particularly prevalent. However, current regional modeling approaches are either insufficient or too computationally expensive to accurately assess risks in these complex environments. This study introduces and validates an improved computationally efficient and physics-based approach to compute dynamic wave-driven regional flooding on reef-lined coasts. We coupled a simplified-physics flood model (SFINCS) with a one-dimensional wave transformation model (XBeach-1D). To assess the performance of the proposed approach, we compared its results with results from a fully resolving two-dimensional wave transformation model (XBeach-2D). We applied this approach for a range of storms and sea-level rise scenarios for two contrasting reef-lined coastal geomorphologies: one low relief area and one high relief area. Our findings reveal that SFINCS coupled with XBeach-1D generates flood extents comparable to those produced by XBeach-2D, with a hit rate of 92%. However, this method tends to underpredict the flood extent of weaker, high-frequency storms and overpredict stronger, low-frequency storms. Across scenarios, our approach overpredicted the mean flood water depth, with a positive bias of 7 cm and root mean square difference of 15 cm. Offering approximately 100 times greater computational efficiency than its two-dimensional XBeach counterpart, this flood modeling technique is recommended for wave-driven flood modeling in scenarios with high computational demands, such as modeling numerous scenarios or undertaking detailed regional-scale modeling.

On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research & Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.

Variation and seasonal reduction in the Upstream Kuroshio Transport (UKT) have important impacts on surrounding climate and oceanic circulation systems. Therefore, reliable UKT prediction is crucial. In this paper, we propose an intelligent UKT prediction model, KuroshioNet, which is firstly pre-trained with simulation data generated by the Regional Ocean Modeling System (ROMS) and then fine-tuned with reanalysis data of the Simple Ocean Data Assimilation (SODA). Operating at a five-day time resolution and a 0.5°spatial resolution, KuroshioNet has the capability to predict multivariate fields associated with upstream Kuroshio, including 3D variables like velocity, temperature, as well as salinity and 2D variables like sea surface height. Subsequently, the UKT is computed from the predicted fields. We evaluate and analyze the experimental results, which show that KuroshioNet has a lead time of 55 days for UKT prediction. In order to enhance the physical interpretability of KuroshioNet, we conduct an ablation experiment to evaluate the impact of each predictor on prediction skill. It demonstrates that selecting zonal velocity, meridional velocity, temperature, salinity, and SSH contributes to UKT prediction by KuroshioNet. Besides, by analyzing model performance and visualizing what the convolutional kernels learn, we find that KuroshioNet, which has learned from ROMS data, is capable of obtaining better initial performance and acquiring more active kernels to better learn the features in SODA data. Furthermore, we identify the targeted observation sensitive area of UKT seasonal reduction by KuroshioNet with the saliency map method, which is situated to the east of upstream kuroshio. The sensitive area is consistent with the result identified by numerical models and yields 38.1% improvement on prediction demonstrated by observing system simulation experiments.

Accurate and timely early warning systems are a vital component in mitigating the risks faced by coastal communities worldwide. Unlike aggregated wave parameters, information extracted from the complete directional wave spectra is often indispensable in the development of such systems in multi-modal environments, such as remote islands, where concurrent waves from various directions are common. Dynamically simulating the wave propagation, although accurate, can be computationally demanding and time-consuming, particularly for resource-constrained communities. In this study, we introduce as an alternative, a novel additive hybrid model known as BinWaves. This model relies on the propagation of a reduced number of monochromatic wave systems and linear wave theory, facilitating the efficient reconstruction of the full directional wave spectra in nearshore areas. To showcase the capabilities of BinWaves, we have implemented the system in the Pacific Islands of Samoa and American Samoa and validated it against full spectral numerical simulations and available buoy data. Given its similar accuracy and higher computational efficiency when compared with dynamic wave models, BinWaves has proven to be a great alternative for reconstructing historical time series, or, more importantly analysing climate change scenarios, tasks that go beyond the capacities of small islands developing states.

Tropical cyclones cause increases in sea surface chlorophyll-a concentration, which is important for studying variations in the regional marine environment. Precisely forecasting the variations of sea surface chlorophyll-a concentration induced by tropical cyclones remains a challenge. In this research, a bidirectional long short-term memory (BiLSTM) neural network deep learning model was applied to predict the variations of sea surface chlorophyll-a concentration induced by typhoons in the Western North Pacific (WNP). Typhoons occurring between 2011 and 2020 were used as training cases and those from 2021 to 2022 as forecasting and testing cases. The input variables of the deep learning model include the sea surface wind at 10 meters (U₁₀ and V₁₀), sea surface temperature anomaly (SSTA), and sea surface chlorophyll-a concentration. The output variable was the chlorophyll-a concentration one day after the passage of the typhoon. Data from the previous 7 days were used to predict the chlorophyll-a concentration one day after the typhoon's passage, and the rolling forecast method was employed to predict chlorophyll-a concentration in the following 7 days. To assess the impact of input variables on the model's forecasting performance, ablation experiments were conducted. The results showed that when using U₁₀, V₁₀, and chlorophyll-a from the previous seven days as inputs, the model exhibited the best overall forecasting performance. Taking Typhoons Chanthu, In-fa, and Malou as examples, the root mean square error (RMSE) for the forecast results are 0.0143 mg · m⁻³, 0.0087 mg · m⁻³, and 0.0030 mg · m⁻³, the mean absolute errors (MAE) are 0.0072 mg · m⁻³, 0.0074 mg · m⁻³, and 0.0025 mg · m⁻³, and the spatial anomaly correlation coefficients (ACC) are 0.9968, 0.9775, and 0.9721, respectively. The results reveal that the most accurate forecasting performance was observed during the mid-phase of the moderate-intensity Typhoon Muifa, with RMSE, MAE, and ACC values of 0.0040 mg · m⁻³, 0.0032 mg · m⁻³, and 0.9894, respectively. The BiLSTM neural network model had the best forecasting performance for typhoons of moderate intensity and during the mid-term phase. This is because moderate-intensity typhoons or the mature phase of any typhoon tend to have relatively stable and more predictable paths, resulting in better predictions of chlorophyll-a concentrations. In future work, we intend to extend our training and forecasting to typhoons of various intensities, aiming to further refine and enhance predictive performance.

The current effects on waves (CEW) are of interest owing to their importance for our understanding of wave dynamics. However, there is a lack of research on the effects of multiscale currents on waves in the northern South China Sea. In this study, we conducted a series of process-oriented numerical experiments to quantitatively investigate the characteristics of multiscale currents and their effects on surface waves. The results indicate that the high-resolution simulated currents with tides show more submesoscale processes, where the spatial variability of significant wave height (Hs) at the 10–100 km scale exceeds that in low-resolution simulated currents by a factor of 24 and that in tideless simulated currents by a factor of 39. The divergent component of the surface current dominates the CEW in the northern South China Sea. High-resolution currents induce more refraction of wind waves with shorter wave periods. Furthermore, we investigated the impact of tropical cyclones on the CEW and found that they briefly increase the divergence and relative vorticity of surface currents while temporarily weakening the modulation of submesoscale CEW. This research highlights the importance of submesoscale currents and tidal currents in wave simulations, thus contributing to the improvement of observational and numerical simulation methods.

One important tool at our disposal to evaluate the robustness of Global Circulation Models (GCMs) is to understand the horizontal discretization of the dynamical core under a shallow water approximation. Here, we evaluate the accuracy and stability of different methods used in, or adequate for, unstructured ocean models considering shallow water models. Our results show that the schemes have different accuracy capabilities, with the A- (NICAM) and B-grid (FeSOM 2.0) schemes providing at least 1st order accuracy in most operators and time integrated variables, while the two C-grid (ICON and MPAS) schemes display more difficulty in adequately approximating the horizontal dynamics. Moreover, the theory of the inertia-gravity wave representation on regular grids can be extended for our unstructured based schemes, where from least to most accurate we have: A-, B, and C-grid, respectively. Considering only C-grid schemes, the MPAS scheme has shown a more accurate representation of inertia-gravity waves than ICON. In terms of stability, we see that both A- and C-grid MPAS scheme display the best stability properties, but the A-grid scheme relies on artificial diffusion, while the C-grid scheme does not. Alongside, the B-grid and C-grid ICON schemes are within the least stable. Finally, in an effort to understand the effects of potential instabilities in ICON, we note that the full 3D model without a filtering term does not destabilize as it is integrated in time. However, spurious oscillations are responsible for decreasing the kinetic energy of the oceanic currents. Furthermore, an additional decrease of the currents’ turbulent kinetic energy is also observed, creating a spurious mixing, which also plays a role in the strength decrease of these oceanic currents.

Waves produced by tropical cyclones (TCs) can be estimated using non-stationary wave models forced with time-varying wind fields. However, dynamical simulations are time and computationally demanding at regional-scale domains since high temporal and spatial resolutions are required to correctly simulate TC-induced wave propagation processes. Applications such as early warning systems, coastal risk assessments and future climate projections benefit from fast and accurate estimates of wave fields induced by close-to-real storm tracks geometry. The proposed SHyTCWaves methodology constitutes a novel tool capable of estimating the spatio-temporal variability of directional wave spectra produced by TCs in deep waters, using a hybrid approach and statistical techniques to reduce CPU time effort. This work demonstrates that TC-induced waves can be reconstructed using a stop-motion approach based on the addition of successive 6 h periods of time-varying storm conditions. The developed hybrid model reduces a TC track to a number of segments that are parameterized in terms of 10 representative TC features, and generates a library of cases dynamically pre-computed which allow to ensemble consecutive 6 h analog segments representing the original TC track. The metamodel has been compared and corrected with available satellite data, and its applicability is exemplified for TC Ofa in the South Pacific.

The iceberg draft prediction is vital to mitigate the collision risk of deep keel icebergs with the seafloor-founded infrastructures, including the subsea pipelines, wellheads, hydrocarbon loading equipment, and communication cables crossing the Arctic and subarctic areas since the drifting icebergs may gouge the ocean floor and the physical and operational integrity of the submarine structures would be threatened. In this study, the iceberg drafts were simulated using the generalized structure of the group method of data handling (GS-GMDH) algorithm for the first time. The parameters affecting the iceberg drafts were determined, and five GS-GMDH models comprising GS-GMDH 1 to GS-GMDH 5 were developed utilizing those parameters governing. A dataset comprising 161 distinct case studies measured in the most significant field investigations of iceberg characteristics was generated, and the GS-GMDH models were trained through 60 % of the data, the rest of the data (i.e., 40 %) were considered for the GS-GMDH models’ validation. By defining different scenarios, the most accurate GS-GMDH model and the most important input parameters were identified. The sensitivity analysis demonstrated that the iceberg width ratio (W/H) and the iceberg shape factor (S_f) were identified as the most influencing input parameters. The comparison between the performance of the premium GS-GMDH model and the group method of data handling (GMDH), artificial neural network (ANN) algorithms, and the empirical models proved that the GS-GMDH model simulated the iceberg drafts with the highest level of precision and correlation along with the lowest degree of complexity. Based on the partial derivative sensitivity analysis (PDSA), the magnitude of iceberg drafts grew by increasing the value of the iceberg width and iceberg length. Ultimately, a GS-GMDH-based equation was presented to estimate the iceberg drafts for practical applications, particularly in the early stages of iceberg management projects and engineering designs.