Search Results (30)

During tropical cyclones (TCs), intense exchanges of momentum, heat, and moisture occur across the air–sea interface. The present study was conducted to investigate the role of surface roughness parameterizations under such conditions. To this end, a series of sensitivity experiments was conducted with a focus on Tropical Cyclone Biparjoy, which originated from the Indian Ocean in 2023. The experiments evaluate the impact of different schemes for momentum, thermal, and moisture roughness length on TC track, intensity, significant wave height, and air–sea heat fluxes. The results indicate that the momentum roughness length scheme is critical for accurately forecasting TC track and intensity and for simulating significant wave height; furthermore, Drennan’s parameterization yielded slightly better results in this case, with the smallest track error (72.0 km MAE) among the momentum schemes. Under the premise that Drennan’s parameterization scheme has high accuracy in momentum roughness, sensitivity experiments on thermal and moisture roughness parameterization were conducted. The Drennan–Fairall2014 combination achieved the lowest errors in TC central pressure (4.25 hPa RMSE) and the maximum sustained wind speed (5.31 m/s RMSE). Thermal and moisture roughness mainly affects the efficiency of turbulent heat transfer between the ocean and the atmosphere and thus has a limited impact on the cooling of sea surface temperature, with SST RMSE differences among schemes within 0.3 °C. This effect is mainly confined to the uppermost ocean layer and does not significantly change the thermal structure of the upper layers. Full article

Based on the COAWST (Coupled Ocean–Atmosphere–Wave–Sediment Transport) model, this study developed an atmospheric refractivity forecasting model incorporating ensemble bias correction by combining five bias correction algorithms with the Bayesian Model Averaging (BMA) method. Hindcast tests conducted over the Yellow Sea and Bohai Sea regions demonstrated that the ensemble bias correction enhanced both forecasting accuracy and adaptability. On the one hand, the corrected forecasting outperformed the original COAWST model in terms of mean error (ME), root mean square error (RMSE), and correlation coefficient (CC), with the RMSE reduced by approximately 20% below 3000 m altitude. On the other hand, the corrected forecasting reduced the uncertainty associated with the performance of different algorithms. In particular, during typhoon events, the corrected forecasting maintained stable bias characteristics across different height layers through dynamic weight adjustment. Throughout the hindcast period, the ME of the corrected forecasting was lower than that of any single bias correction algorithm. Moreover, compared with other ensemble methods, the corrected forecasting developed in this study achieved more flexible weight allocation through Bayesian optimization, resulting in lower ME. In addition, the corrected forecasting maintained an improvement of approximately 28% in bias reduction even at a 72 h forecasting lead time, demonstrating their robustness and reliability under complex weather conditions. Full article

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

In this study, a dynamic load forecasting model was developed for offshore wind turbines, based on the COAWST (Coupled Ocean-Atmosphere-Wave-Sediment Transport) model, the GRU (Gated Recurrent Unit) algorithm, and a data assimilation module. The model was able to forecast aerodynamic, wave, and current loads acting on the turbines. Four groups of forecasting tests were conducted to evaluate the model’s performance under different strategies and to assess the impact of atmospheric assimilation on improving dynamic load forecasts. The wind turbines in Cangnan Offshore Wind Farm, located in the west of the East China Sea, were chosen as the study object. The results indicated that the model achieved high forecasting accuracy, with the RMSEs (root mean square errors) of 275.59 kN, 335.85 kN, and 313.51 N, for the aerodynamic, wave, and current loads. The errors were reduced by about 13%, 10.09%, and 6.7% when compared with the original COAWST model, and were also lower than the atmospheric and oceanic reanalysis data. Atmospheric data assimilation was demonstrated to reduce the forecasting RMSE of aerodynamic load by about 12%, and its error improvement was able to be combined with GRU-based error correction. Additionally, atmospheric assimilation mitigated the reduction in temporal variability caused by forecasting error correction, preventing a decrease in the standard deviation of aerodynamic load forecasts. However, atmospheric assimilation had minimal impacts on wave and current load forecasts, with the RMSEs increased by about 2.5% and 0.1%, and had almost the same performance in correlation coefficients and standard deviations. Full article

The Hong Kong Observatory has been using a parametric storm surge model to forecast the rise of sea level due to the passage of tropical cyclones. This model includes an offset parameter to account for the rise in sea level due to other meteorological factors. By adding the sea level rise forecast to the astronomical tide prediction using the harmonic analysis method, coastal sea level prediction can be produced for the sites with tidal observations, which supports the high water level forecast operation and alert service for risk assessment of sea flooding in Hong Kong. The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modelling System, which comprises the Weather Research and Forecasting (WRF) Model and Regional Ocean Modelling System (ROMS), which in itself is coupled with wave model WaveWatch III and nearshore wave model SWAN, was tested with tropical cyclone cases where there was significant water level rise in Hong Kong. This case study includes two super typhoons, namely Hato in 2017 and Mangkhut in 2018, three cases of the combined effect of tropical cyclone and northeast monsoon, including Typhoon Kompasu in 2021, Typhoon Nesat and Severe Tropical Storm Nalgae in 2022, as well as two cases of monsoon-induced sea level anomalies in February 2022 and February 2023. This study aims to evaluate the ability of the WRF-ROMS-SWAN model to downscale the meteorological fields and the performance of the coupled models in capturing the maximum sea levels under the influence of significant weather events. The results suggested that both configurations could reproduce the sea level variations with a high coefficient of determination (R²) of around 0.9. However, the WRF-ROMS-SWAN model gave better results with a reduced RMSE in the surface wind and sea level anomaly predictions. Except for some cases where the atmospheric model has introduced errors during the downscaling of the ERA5 dataset, bias in the peak sea levels could be reduced by the WRF-ROMS-SWAN coupled model. The study result serves as one of the bases for the implementation of the three-way coupled atmosphere–ocean–wave modelling system for producing an integrated forecast of storm surge or sea level anomalies due to meteorological factors, as well as meteorological and oceanographic parameters as an upgrade to the two-way coupled Operational Marine Forecasting System in the Hong Kong Observatory. Full article

Wave-induced mixing can enhance vertical mixing in the upper ocean, facilitating the exchange of heat and momentum between the surface and deeper layers, thereby influencing ocean circulation and climate patterns. Building on previous research, this study proposes a wave-induced mixing parameterization scheme (referred to as EXP3) specifically designed for typhoon periods. This scheme was integrated into the fully coupled ocean–wave–atmosphere model COAWST and applied to analyze Typhoon In-Fa (2021) as a case study. The simulation results were validated against publicly available data, demonstrating a good overall match with observed phenomena. Subsequently, a comparative analysis was conducted between the EXP3 scheme, the previous scheme (EXP2) and the original model scheme (EXP1). Validation against Argo and Drifter buoy data revealed that both EXP2 and EXP3, which include wave-induced mixing effects, resulted in a decrease in the simulated mixed layer depth (MLD) and mixed layer temperature (MLT), with EXP3 showing closer alignment with the observed data. Compared to the other two experiments, EXP3 enhanced vertical motion in the ocean due to intensified wave-induced mixing, leading to increased upper-layer water divergence and upwelling, a decrease in sea surface temperature and accelerated rightward deflection of surface currents. This phenomenon not only altered the temperature structure of the ocean surface layer but also significantly impacted the regional ocean dynamics. Full article

In this study, a diagnostic model for evaporation ducts was established based on the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) and the Naval Postgraduate School (NPS) models. Utilizing this model, four sensitivity tests were conducted over the South China Sea from 21 September to 5 October 2008, when four tropical cyclones affected the study domain. These tests were designed with different roughness schemes to investigate the impact mechanisms of wave processes on evaporation duct simulation under extreme weather conditions. The results indicated that wave processes primarily influenced the evaporation duct heights by altering sea surface roughness and dynamical factors. The indirect impacts of waves without dynamical factors were rather weak. Generally, a decrease in local roughness led to increased wind speed, decreased humidity, and a reduced air–sea temperature difference, resulting in the formation of evaporation ducts at higher altitudes. However, this affecting mechanism between roughness and evaporation ducts was also greatly influenced by changes in regional circulation. In the eastern open sea areas of the South China Sea, changes in evaporative ducts were more closely aligned with local impact mechanisms, whereas the changes in the central and western areas demonstrated greater complexity and fewer local impacts due to variations in regional circulation. Full article

This study developed a coupled atmospheric–marine model using the COAWST model system for the Harima Nada area between spring 2010 and winter 2011 to evaluate the seasonal influence of the Kako River’s discharge in the sea. The Kako River is one of the largest rivers in southwest Japan, contributing almost half of the freshwater discharged in the Harima Nada region in the Seto Inland Sea. Validation was conducted for the entire period, showing a good performance for the atmospheric and marine variables selected. Multiple experiments injecting an inert tracer in the Kako River estuary were performed to simulate the seasonal river water distribution from the estuary into the sea and to analyze the seasonal differences in concentration patterns and mean residence times in Harima Nada. Because the study area is shallow, the results were evaluated at the surface and 10 m depth layers and showed significant seasonal differences in tracer distribution, circulation patterns, and mean residence times for the region. On the other hand, differences seemed to not be significant during the same season at different depths. The obtained results also agreed with the area’s natural water circulation, showing that the Kako River waters tend to distribute towards the west coast of Harima Nada in the warmer seasons but shift towards the east in winter. The influence of the Kako River in the center of the study area is seasonal and strongly dependent on the direction of the horizontal velocities more than their magnitude. The mean residence times varied seasonally from approximately 30 days in spring to 12 days in fall. The magnitude of the horizontal velocity was found to be maximum during summer when circulation patterns at the surface and 10 m depth in the central part of Harima Nada also seem to promote the strongest horizontal and vertical mixes. Full article

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases. Full article

The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height. Full article

In this study, a forecasting model was developed based on the COAWST and atmospheric 3D EnVar module to investigate the effects of assimilation of the sounding and COSMIC–2 data on the forecasting of the revised atmospheric refraction. Three groups of 72 h forecasting tests, with assimilation of different data obtained for a period of one month, were constructed over the Yellow Sea. The results revealed that the bias of the revised atmospheric refraction was the lowest if both the sounding and COSMIC–2 data were assimilated. As a result of the assimilation of the hybrid data, the mean bias reduced by 6.09–6.28% within an altitude of 10 km, and the greatest reduction occurred below the altitude of 3000 m. In contrast, the test that assimilated only the sounding data led to an increase in bias at several levels. This increased bias was corrected after the introduction of the COSMIC–2 data, with the mean correction of 1.6 M within the middle and lower troposphere. During the typhoon period, the improvements in the assimilation were more significant than usual. The improved forecasts of the revised atmospheric refraction were mainly due to the moisture changes within the middle and lower troposphere, while the changes in the upper troposphere were influenced by multiple factors. Full article

Despite numerous studies, the impact of global warming on the tropical cyclone (TC) track and intensity by reasons of data inhomogeneity in remote sensing and large natural variability over a relatively short period of observation is still controversial. Three carbon-emission sensitivity experiments are conducted to investigate how TC track and intensity respond to changes in the oceanic and atmospheric environment under global warming. The results show a high sensitivity of the simulated TC track and intensity to global warming. On one hand, with increase in carbon emissions, the western Pacific subtropical high expands notably, increasing the poleward steering flow and eventually leading to a poleward shift of TC. On the other hand, the underlying sea-surface temperature and surface-entropy flux increase and, thus, favor the convections near the eyewall. Moreover, the TC structure becomes more upright, which is closely related to the larger pressure gradient near the eyewall. As a result, TC intensity increases with carbon emissions. However, this increase is notably smaller than the maximum potential intensity theory as the TC intensity can reach a threshold if carbon emission still increases in the future. The involved mechanisms on the changes of TC track and intensity are also revealed. Full article

Typhoon Noru (2022) was a historic storm that caused significant damage to the central region of Vietnam. Typhoon Noru has caused strong winds and torrential rainfall in Da Nang, Quang Nam, and Quang Ngai. Quang Nam Province saw many trees and power lines fall, and many areas were flooded. The Da Nang government has reported the typhoon toppled many trees, blew the rooftops of three houses, damaged the walls of several schools, and caused a power outage at some 3200 substations. It resulted in widespread flooding in coastal areas and downstream from the Vu Gia-Thu Bon River river basin. This study evaluates the impact of Typhoon Noru. The results show that: (1) The numerical simulation was applied to re-analyze the offshore meteorological field with the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model as an input for 2D wave propagation and hydraulic models; (2) The study couples the 1D and 2D models in MIKE FLOOD to simulate the flood and inundation caused by Typhoon Noru in the study area. The calibration and validation results of the 1D hydraulic model, the 2D wave propagation model, and the 2D hydrodynamic model were reasonably good, with a Nash coefficient ranging from 0.84 to 0.96 and a percent bias (BIAS) of −0.9% to 7.5%. The results of the simulation showed that the flood and inundation caused by Typhoon Noru resulted in significant damage in two districts: Thang Binh in Quang Nam province and Hoa Vang in Da Nang province. The practical significance of these results is that they provide valuable support for warning systems and troubleshooting efforts related to the impact of typhoons. Full article

This study provides, to the best of our knowledge, the first detailed analysis of how surface oil modifies air–sea interactions in a two-way coupled model, i.e., the coupled–ocean–atmosphere–wave–sediment–transport (COAWST) model, modified to account for oil-related changes in air–sea fluxes. This study investigates the effects of oil on surface roughness, surface wind, surface and near-surface temperature differences, and boundary-layer stability and how those conditions ultimately affect surface stress. We first conducted twin-coupled modeling simulations with and without the influence of oil over the Deepwater Horizon (DWH) oil spill period (20 April to 5 May 2010) in the Gulf of Mexico. Then, we compared the results by using a modularized flux model with parameterizations selected to match those selected in the coupled model adapted to either ignore or account for different atmospheric/oceanic processes in calculating surface stress. When non-oil inputs to the bulk formula were treated as being unchanged by oil, the surface stress changes were always negative because of oil-related dampening of the surface roughness alone. However, the oil-related changes to 10 m wind speeds and boundary-layer stability were found to play a dominant role in surface stress changes relative to those due to the oil-related surface roughness changes, highlighting that most of the changes in surface stress were due to oil-related changes in wind speed and boundary-layer stability. Finally, the oil-related changes in surface stress due to the combined oil-related changes in surface roughness, surface wind, and boundary-layer stability were not large enough to have a major impact on the surface current and surface oil transport, indicating that the feedback from the surface oil to the surface oil movement itself is insignificant in forecasting surface oil transport unless the fractional oil coverage is much larger than the value found in this study. Full article

Typhoons are serious natural disasters in coastal areas. During the summer of 2011, successive typhoons Nesat and Nalgae appeared in the South China Sea, providing a unique opportunity for us to study the response of the upper ocean to successive typhoons. We comprehensively use satellite data and COAWST model data to explore the effects of successive typhoons on the temperature structure of the South China Sea. Nesat caused the sea surface temperature to decrease by up to 4.4 °C on the right side of the typhoon path, and the ensuing Nalgae caused the temperature to decrease by up to 2.2 °C. Because Nesat had already cooled the ocean, the response to Nalgae was more to the left of the track than one would normally expect. The upwelling dominates the change in subsurface temperature. Based on the increase caused by Nesat, the isotherm was further raised by Nalgae. The isotherm rising amplitude is larger in the upper and deeper layer and is smaller in the middle layer in the depth range of 0–200 m. Heat budget analysis indicates that in the area close to the typhoon path, vertical diffusion is the main reason for the decrease in ocean surface temperature, while total advection suppresses the decrease in temperature. In the area with a larger distance from the typhoon path, vertical diffusion and total advection lead to the decrease in ocean surface temperature, and total advection will gradually contribute more to temperature change and become the dominant factor. On the right side of the typhoon track, the reduction of the contribution rate of vertical diffusion with distance from typhoon track is slower than that on the left side of the typhoon track. Whether Nesat or Nalgae, the intensity and depth of effects of vertical diffusion on the right side of typhoon path are greater than those on the left side of typhoon path, and the near-inertial periodic oscillation of local temperature change rate is more obvious. When the vertical diffusion is weak, the influence of vertical advection and horizontal advection is deeper. Moreover, the near-inertial periodic oscillation of the local temperature change occurs in lower depth after Nalgae passed through than that after Nesat. The typhoon intensity of the two typhoons shows the opposite change: the first typhoon increases, and the second typhoon weakens. Therefore, the special case of successive typhoons should be fully considered in typhoon prediction to improve accuracy. Full article