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WavewatchⅢ模拟和统计方法在最大波高预报方面的评测分析
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作者 王娟娟 侯放 +1 位作者 吴淑萍 王久珂 《海洋预报》 CSCD 北大核心 2024年第1期1-9,共9页
为了研究WavewatchⅢ(WWⅢ)海浪模型对最大波高的模拟能力及其与传统统计关系方法的差异,通过对两次台风浪过程的后报模拟和半年的业务化预报,分析了WWⅢ数值模拟的准确度及其与统计关系方法的精度差异。研究结果表明:WWⅢ数值模拟的最... 为了研究WavewatchⅢ(WWⅢ)海浪模型对最大波高的模拟能力及其与传统统计关系方法的差异,通过对两次台风浪过程的后报模拟和半年的业务化预报,分析了WWⅢ数值模拟的准确度及其与统计关系方法的精度差异。研究结果表明:WWⅢ数值模拟的最大波高(Hmax)的精度略低于有效波高(Hs),但也达到了24 h预报相对误差(H_(max)≥1 m)低于18%、相关系数高于0.94的水平,模拟精度可靠,可以用于业务化预报;与两种统计关系方法(H_(max)和H_(s)分别为1.42和1.52)计算的最大波高相比,数值模拟的精度总体与其相当,但在H_(max)和H_(s)比值大于1.65这种易出现危险的海况下,数值模拟具有更高的准确性,更适合应用于海浪预警报服务。 展开更多
关键词 最大波高 wavewatchⅢ模型 数值模拟 统计关系 预报精度
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Wave forecast in the Atlantic Ocean using a double-stage ConvLSTM network 被引量:1
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作者 Lin Ouyang Fenghua Ling +2 位作者 Yue Li Lei Bai Jing-Jia Luo 《Atmospheric and Oceanic Science Letters》 CSCD 2023年第4期45-50,共6页
海浪预报对海上运输安全至关重要.本研究提出了一种涵盖物理信息的深度学习模型Double-stage ConvLSTM(D-ConvLSTM)以改进大西洋的海浪预报.将D-ConvLSTM模型与海浪持续性预测和原始ConvLSTM模型的预测技巧进行对比.结果表明,预测误差... 海浪预报对海上运输安全至关重要.本研究提出了一种涵盖物理信息的深度学习模型Double-stage ConvLSTM(D-ConvLSTM)以改进大西洋的海浪预报.将D-ConvLSTM模型与海浪持续性预测和原始ConvLSTM模型的预测技巧进行对比.结果表明,预测误差随着预测时长的增加而增加.D-ConvLSTM模型在预测准确度方面优于前二者,且第三天预测的均方根误差低于0.4 m,距平相关系数约在0.8.此外,当使用IFS预测风替代再分析风时,能够产生相似的预测效果.这表明D-ConvLSTM模型的预测能力能够与ECMWF-WAM模式相当,且更节省计算资源和时间. 展开更多
关键词 海浪预测 深度学习 预测模型 大西洋
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Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model
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作者 Lina Wang Yu Cao +2 位作者 Xilin Deng Huitao Liu Changming Dong 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2023年第10期54-66,共13页
As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.Howev... As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.However,challenges in the large demand for computing resources and the improvement of accuracy are currently encountered.To resolve the above mentioned problems,sequence-to-sequence deep learning model(Seq-to-Seq)is applied to intelligently explore the internal law between the continuous wave height data output by the model,so as to realize fast and accurate predictions on wave height data.Simultaneously,ensemble empirical mode decomposition(EEMD)is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition(EMD),and then improves the prediction accuracy.A significant wave height forecast method integrating EEMD with the Seq-to-Seq model(EEMD-Seq-to-Seq)is proposed in this paper,and the prediction models under different time spans are established.Compared with the long short-term memory model,the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors.The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term(3-h,6-h,12-h and 24-h forecast horizon)and long-term(48-h and 72-h forecast horizon)predictions. 展开更多
关键词 significant wave height wave forecasting ensemble empirical mode decomposition(EEMD) Seq-to-Seq long short-term memory
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A hybrid model for numerical wave forecasting and its implementation-Ⅰ.The wind wave model 被引量:14
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作者 Wen Shengchang (S.C. Wen)1, Zhang Dacuo, Chen Bohai and Guo Peifang Institute of Physical Oceanography, Ocean University of Qingdao (Formerly, Shandong College of Oceanography), Qingdao, China 《Acta Oceanologica Sinica》 SCIE CAS CSCD 1989年第1期1-14,共14页
The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This p... The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper. 展开更多
关键词 wave A hybrid model for numerical wave forecasting and its implementation The wind wave model
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A forecasting model for wave heights based on a long short-term memory neural network 被引量:6
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作者 Song Gao Juan Huang +3 位作者 Yaru Li Guiyan Liu Fan Bi Zhipeng Bai 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2021年第1期62-69,共8页
To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with... To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with sea surface wind and wave heights as training samples.The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input,the prediction error produced by the proposed LSTM model at Sta.N01 is 20%,18%and 23%lower than the conventional numerical wave models in terms of the total root mean square error(RMSE),scatter index(SI)and mean absolute error(MAE),respectively.Particularly,for significant wave height in the range of 3–5 m,the prediction accuracy of the LSTM model is improved the most remarkably,with RMSE,SI and MAE all decreasing by 24%.It is also evident that the numbers of hidden neurons,the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy.However,the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used.The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training.Overall,long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting. 展开更多
关键词 long short-term memory marine forecast neural network significant wave height
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Optimization of multi-model ensemble forecasting of typhoon waves 被引量:1
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作者 Shun-qi Pan Yang-ming Fan +1 位作者 Jia-ming Chen Chia-chuen Kao 《Water Science and Engineering》 EI CAS CSCD 2016年第1期52-57,共6页
Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communit... Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles). The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the opti- mization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to imnlement and practieal for real-time wave forecasting. 展开更多
关键词 wave modeling OPTIMIZATION forecasting Typhoon waves waveWATCH III Locally weighted learning algorithm
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Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide–Wind–Wave Conditions 被引量:2
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作者 ZHANG Wei-na HUANG Hui-ming +2 位作者 WANG Yi-gang CHEN Da-ke ZHANG lin 《China Ocean Engineering》 SCIE EI CSCD 2018年第1期99-109,共11页
Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by esta... Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4. 展开更多
关键词 in situ drifting experiment mechanistic drifting forecast model tide–wind–wave coupled conditions small semi-submersible drifter daily displacement
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CNN-BiLSTM-Attention Model in Forecasting Wave Height over South-East China Seas 被引量:2
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作者 Lina Wang Xilin Deng +4 位作者 Peng Ge Changming Dong Brandon J.Bethel Leqing Yang Jinyue Xia 《Computers, Materials & Continua》 SCIE EI 2022年第10期2151-2168,共18页
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)sign... 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 training model 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. 展开更多
关键词 Conv2D CNN-BiLSTM-Attention wave forecasting significant wave height TYPHOON
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Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas 被引量:5
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作者 WANG Guansuo ZHAO Chang +2 位作者 XU Jiangling QIAO Fangli XIA Changshui 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2016年第2期19-28,共10页
An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas(OCFS-C) is developed based on parallelized circulation and wave models. It has been in operation sin... An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas(OCFS-C) is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China's seas has been updated to(1/24)° from the global model with(1/2)°resolution. Besides, daily remote sensing sea surface temperature(SST) data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth(MLD) and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores(SS) is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value(more than 0.90). Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean. 展开更多
关键词 operational forecast sea surface temperature mixed layer depth lead time subsurface temperature ocean circulation-surface wave coupled forecast system China's seas
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A hybrid model for numerical wave forecasting and its implementation-Ⅱ .The discrete part and implementation of the model 被引量:3
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作者 Zhang Dacuo, Wu Zengmao, Jiang Decai, Wang Wei, Chen Bohai, Tai Weitao, Wen Shengchang,Xu Qichun and Guo Peifang 《Acta Oceanologica Sinica》 SCIE CAS CSCD 1992年第2期157-178,共22页
In the first part of the present paper we have explained why we manage to formulate another wave prediction model when so many of them, including the so-called third generation model, have already been in use. The win... In the first part of the present paper we have explained why we manage to formulate another wave prediction model when so many of them, including the so-called third generation model, have already been in use. The wind-wave part of the proposed model has also been given. Now we proceed to discuss the swell part,the implementation of the model as a prediction method,mumerical experiments done with ideal wind fields and hindcasts made in the Bohai Sea,in the neighboring seas adjacent to China and in the Northwest Pacific. 展开更多
关键词 wave The discrete part and implementation of the model A hybrid model for numerical wave forecasting and its implementation
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Case Study of a Short-Term Wave Energy Forecasting Scheme:North Indian Ocean 被引量:1
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作者 ZHENG Chongwei SONG Hui 《Journal of Ocean University of China》 SCIE CAS CSCD 2021年第3期463-477,共15页
Short-term forecasts of wave energy play a key role in the daily operation,maintenance planning,and electrical grid operation of power farms.In this study,we propose a short-term wave energy forecast scheme and use th... Short-term forecasts of wave energy play a key role in the daily operation,maintenance planning,and electrical grid operation of power farms.In this study,we propose a short-term wave energy forecast scheme and use the North Indian Ocean(NIO)as a case study.Compared with the traditional forecast scheme,our proposed scheme considers more forecast elements.In addition to the traditional short-term forecast factors related to wave energy(wave power,significant wave height(SWH),wave period),our scheme emphasizes the forecast of a series of key factors that are closely related to the effectiveness of the energy output,capture efficiency,and conversion efficiency.These factors include the available rate,total storage,effective storage,co-occurrence of wave power-wave direction,co-occurrence of the SWH-wave period,and the wave energy at key points.In the regional nesting of nu-merical simulations of wave energy in the NIO,the selection of the southern boundary is found to have a significant impact on the simulation precision,especially during periods of strong swell processes of the South Indian Ocean(SIO)westerly.During tropical cyclone‘VARDAH’in the NIO,as compared with the simulation precision obtained with no expansion of the southern boundary(scheme-1),when the southern boundary is extended to the tropical SIO(scheme-2),the improvement in simulation precision is significant,with an obvious increase in the correlation coefficient and decrease in error.In addition,the improvement is much more significant when the southern boundary extends to the SIO westerly(scheme-3).In the case of strong swell processes generated by the SIO westerly,the improvement obtained by scheme-3 is even more significant. 展开更多
关键词 wave energy short-term forecast regional nesting boundary condition
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Analysis of a Cold Wave Process in Jiujiang and Its Numerical Model Forecast 被引量:1
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作者 Jingjing ZHANG Yuting FEI Rong LI 《Meteorological and Environmental Research》 CAS 2021年第3期11-14,共4页
The cold wave weather process in Jiujiang in the early spring of February 2020 was analyzed.The results show that the establishment of blocking high near Lake Baikal and the rapid southward of cold air after accumulat... The cold wave weather process in Jiujiang in the early spring of February 2020 was analyzed.The results show that the establishment of blocking high near Lake Baikal and the rapid southward of cold air after accumulation resulted in the cold wave weather accompanied by strong cooling,hale and rain(snow)weather in Jiujiang.Before the cold wave broke out,the ground warmed up significantly,which was also one of thermal conditions for this cold wave weather.Water vapor conditions were abundant at middle and low levels;at 850 hPa,temperature dropped by 12-14℃during February 14-15,and-4℃isotherm appeared in the southern part of central Jiangxi,which is a favorable condition for rain(snow)in most areas of Jiujiang. 展开更多
关键词 Cold wave Weather process Jiujiang Numerical model forecast
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Moment magnitudes of two large Turkish earthquakes on February 6,2023 from long-period coda 被引量:7
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作者 Xinyu Jiang Xiaodong Song +1 位作者 Tian Li Kaixin Wu 《Earthquake Science》 2023年第2期169-174,共6页
Two large earthquakes(an earthquake doublet)occurred in south-central Turkey on February 6,2023,causing massive damages and casualties.The magnitudes and the relative sizes of the two mainshocks are essential informat... Two large earthquakes(an earthquake doublet)occurred in south-central Turkey on February 6,2023,causing massive damages and casualties.The magnitudes and the relative sizes of the two mainshocks are essential information for scientific research and public awareness.There are obvious discrepancies among the results that have been reported so far,which may be revised and updated later.Here we applied a novel and reliable long-period coda moment magnitude method to the two large earthquakes.The moment magnitudes(with one standard error)are 7.95±0.013 and 7.86±0.012,respectively,which are larger than all the previous reports.The first mainshock,which matches the largest recorded earthquakes in the Turkish history,is slightly larger than the second one by 0.11±0.035 in magnitude or by 0.04 to 0.18 at 95%confidence level. 展开更多
关键词 2023 Turkish earthquakes coda wave moment magnitude long-period
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Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas 被引量:3
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作者 Junchuan Sun Zexun Wei +9 位作者 Tengfei Xu Meng Sun Kun Liu Yongzeng Yang Li Chen Hong Zhao Xunqiang Yin Weizhong Feng Zhiyuan Zhang Yonggang Wang 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2019年第4期154-166,共13页
A 72-h fine-resolution atmosphere-wave-ocean coupled forecasting system was developed for the South China Sea and its adjacent seas. The forecasting model domain covers from from 15°S to 45°N in latitude and... A 72-h fine-resolution atmosphere-wave-ocean coupled forecasting system was developed for the South China Sea and its adjacent seas. The forecasting model domain covers from from 15°S to 45°N in latitude and 99°E to135°E in longitude including the Bohai Sea, the Yellow Sea, the East China Sea, the South China Sea and the Indonesian seas. To get precise initial conditions for the coupled forecasting model, the forecasting system conducts a 24-h hindcast simulation with data assimilation before forecasting. The Ensemble Adjustment Kalman Filter(EAKF) data assimilation method was adopted for the wave model MASNUM with assimilating Jason-2 significant wave height(SWH) data. The EAKF data assimilation method was also introduced to the ROMS model with assimilating sea surface temperature(SST), mean absolute dynamic topography(MADT) and Argo profiles data. To improve simulation of the structure of temperature and salinity, the vertical mixing scheme of the ocean model was improved by considering the surface wave induced vertical mixing and internal wave induced vertical mixing. The wave and current models were integrated from January 2014 to October 2015 driven by the ECMWF reanalysis 6 hourly mean dataset with data assimilation. Then the coupled atmosphere-wave-ocean forecasting system was carried out 14 months operational running since November 2015. The forecasting outputs include atmospheric forecast products, wave forecast products and ocean forecast products. A series of observation data are used to evaluate the coupled forecasting results, including the wind, SHW, ocean temperature and velocity.The forecasting results are in good agreement with observation data. The prediction practice for more than one year indicates that the coupled forecasting system performs stably and predict relatively accurate, which can support the shipping safety, the fisheries and the oil exploitation. 展开更多
关键词 South China Sea COAWST MODEL MASNUM MODEL atmosphere-wave-ocean forecasting system data ASSIMILATION
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Operational Wave Now- and Forecast in the German Bight as a Basis for the Assessment of Wave-Induced Hydrodynamic Loads on Coastal Dikes
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作者 DREIER Norman FROHLE Peter 《Journal of Ocean University of China》 SCIE CAS CSCD 2017年第6期991-997,共7页
The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based earl... The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now-and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5–6 December, 2013(German name ‘Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the Eur Otop(2016) approach. 展开更多
关键词 German Bight North Sea wave forecast Cosmo-Model SWAN hydrodynamic loads wave RUN-UP EurOtop
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A Spatiotemporal Interactive Processing Bias Correction Method for Operational Ocean Wave Forecasts
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作者 AI Bo YU Mengchao +5 位作者 GUO Jingtian ZHANG Wei JIANG Tao LIU Aichao WEN Lianjie LI Wenbo 《Journal of Ocean University of China》 SCIE CAS CSCD 2022年第2期277-290,共14页
Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictio... Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictions of numerical models and the actual sea state has been observed,predictions can only be released after correction by forecasters.This paper proposes a spati-otemporal interactive processing bias correction method to correct numerical prediction fields applied to the production and release of operational ocean wave forecasting products.The proposed method combines the advantages of numerical models and Forecast Discussion;specifically,it integrates subjective and objective information to achieve interactive spatiotemporal correc-tions for numerical prediction.The method corrects the single-time numerical prediction field in space by spatial interpolation and sub-zone numerical analyses using numerical model grid data in combination with real-time observations and the artificial judg-ment of forecasters to achieve numerical prediction accuracy.The difference between the original numerical prediction field and the spatial correction field is interpolated to an adjacent time series by successive correction analysis,thereby achieving highly efficient correction for multi-time forecasting fields.In this paper,the significant wave height forecasts from the European Centre for Medium-Range Weather Forecasts are used as background field for forecasting correction and analysis.Results indicate that the proposed method has good application potential for the bias correction of numerical predictions under different sea states.The method takes into account spatial correlations for the numerical prediction field and the time series development of the numerical model to correct numerical predictions efficiently. 展开更多
关键词 numerical models ocean wave forecasts spatial interpolation time series interpolation successive correction
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CHGS method for numerical forecasting of typhoon waves-Ⅰ. Spectrum of waves in growing phase
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作者 Sui Shifeng South China Sea Institute of Oceanology, Academia Sinica, Guangzhou, China 《Acta Oceanologica Sinica》 SCIE CAS CSCD 1990年第3期343-352,共10页
Owing to the fact that the wind speed and direction of typhoon vary rapidly with time and space in typhoon fetch; the nearer to the typhoon eye the greater the wind velocity, and the shorter the wind fetch the smaller... Owing to the fact that the wind speed and direction of typhoon vary rapidly with time and space in typhoon fetch; the nearer to the typhoon eye the greater the wind velocity, and the shorter the wind fetch the smaller the wind time,as a result,the more difficult for the wind wave to fully grow. Hence.in typhoon wave numerical calculation it is impossible to use the model for a fully grown wave spectrum. Lately, the author et at. presented a CHGS method for numerical forecasting of typhoon waves, where a model for the growing wave spectrum was set up (see Eq. (2) in the text). The model involves a parameter indicating the growing degree of wind wave, i. e. ,the mean wave age β. When βvalue is small, the wave energy is chiefly concentrated near the peak frequency, so that the spectral peak gets high and steep; with the increase of β the spectral shape gradually gets lower and gentler; when β=Ⅰ, the wave fully grows, the growing spectrum becomes a fully grown P-M spectrum. The model also shows a spectral “overshooting” phenomenon within the “balance zone”. 展开更多
关键词 Spectrum of waves in growing phase CHGS method for numerical forecasting of typhoon waves
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Using Long-Period Body Wave to Inverse Moment Tensors of the M_S6.8 Jiashi,Xinjiang Earthquake in 2003 and the Moderate and Small Earthquakes before and after It
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作者 Tu Hongwei Wan Xiuhong +2 位作者 Zhao Cuiping Wang Haitao Luo Guofu 《Earthquake Research in China》 2008年第4期460-469,共10页
An M_S6.8 strong earthquake took place in Jiashi,Xinjiang on February 24 of 2003.The digital wave form data recorded in Kashi and Wushi stations are selected to inverse the moment tensor solutions for the strong earth... An M_S6.8 strong earthquake took place in Jiashi,Xinjiang on February 24 of 2003.The digital wave form data recorded in Kashi and Wushi stations are selected to inverse the moment tensor solutions for the strong earthquake and the moderate and small earthquakes before and after it(108 earthquakes in 2001~2004).67 focal mechanism solutions have been calculated,and the results agree with those from Harvard University and USGS.The analysis reveals that before the strong earthquake,the moderate and small earthquake distribution was dispersed,and after the event the distribution was mainly concentrated around the strong earthquake.Before the strong earthquake,the seismic faults of the mid and small events had the character of strike-slip and normal faulting,and after the event,they exhibit strike-slip and thrust faulting.The region is dominated by near-NS horizontal compression from the southern block after the strong earthquake. 展开更多
关键词 long-period body wave Inversion of seismic moment tensor Reliability analysis Jiashi of Xinjiang
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Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and Forecasts to 2150
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作者 Mazurkin Peter Matveevich 《Journal of Environmental & Earth Sciences》 2023年第1期17-32,共16页
The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations ... The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming. 展开更多
关键词 INDIA 4 weather stations Average monthly temperature waves of behavior Sum of wavelets Verification forecasts
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Wavelet Analysis of Average
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作者 Peter Mazurkin 《Journal of Atmospheric Science Research》 2023年第2期1-20,共20页
The identification method in the CurveExpert-1.40 software environment revealed asymmetric wavelets of changes in the average monthly temperature of New Delhi from 1931 to 2021.The maximum increment for 80 years of th... The identification method in the CurveExpert-1.40 software environment revealed asymmetric wavelets of changes in the average monthly temperature of New Delhi from 1931 to 2021.The maximum increment for 80 years of the average monthly temperature of 5.1℃was in March 2010.An analysis of the wave patterns of the dynamics of the average monthly temperature up to 2110 was carried out.For forecasting,formulas were adopted containing four components,among which the second component is the critical heat wave of India.The first component is the Mandelbrot law(in physics).It shows the natural trend of decreasing temperature.The second component increases according to the critical law.The third component with a correlation coefficient of 0.9522 has an annual fluctuation cycle.The fourth component with a semi-annual cycle shows the influence of vegetation cover.The warming level of 2010 will repeat again in 2035-2040.From 2040 the temperature will rise steadily.June is the hottest month.At the same time,the maximum temperature of 35.1℃in 2010 in June will again reach by 2076.But according to the second component of the heat wave,the temperature will rise from 0.54℃to 16.29°C.The annual and semi-annual cycles had an insignificant effect on the June temperature dynamics.Thus,the identification method on the example of meteorological observations in New Delhi made it possible to obtain summary models containing a different number of components.The temperature at a height of 2 m is insufficient.On the surface,according to space measurements,the temperature reaches 55°C.As a result,in order to identify more accurate asymmetric wavelets for forecasting,the results of satellite measurements of the surface temperature of India at various geographical locations of meteorological stations are additionally required. 展开更多
关键词 New Delhi Average monthly temperature waves of behavior 1931-2021 Sum of wavelets VERIFICATION forecasts up to 2110
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