The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the...The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.展开更多
This study investigates the impact of the salinity barrier layer(BL)on the upper ocean response to Super Typhoon Mangkhut(2018)in the western North Pacific.After the passage of Mangkhut,a noticeable increase(~0.6 psu)...This study investigates the impact of the salinity barrier layer(BL)on the upper ocean response to Super Typhoon Mangkhut(2018)in the western North Pacific.After the passage of Mangkhut,a noticeable increase(~0.6 psu)in sea surface salinity and a weak decrease(<1℃)in sea surface temperature(SST)were observed on the right side of the typhoon track.Mangkhut-induced SST change can be divided into the three stages,corresponding to the variations in BL thickness and SST before,during,and after the passage of Mangkhut.During the pre-typhoon stage,SST slightly warmed due to the entrainment of BL warm water,which suppressed the cooling induced by surface heat fluxes and horizontal advection.During the forced stage,SST cooling was controlled by entrainment,and the preexisting BL reduced the total cooling by 0.89℃ d-1,thus significantly weakening the overall SST cooling induced by Mangkhut.During the relaxation stage,the SST cooling was primarily caused by the entrainment.Our results indicate that a preexisting BL can limit typhoon-induced SST cooling by suppressing the entrainment of cold thermocline water,which contributed to Mangkhut becoming the strongest typhoon in 2018.展开更多
Based on the daily sea surface height and absolute geostrophic velocity data from 1993 to 2015 provided by the AVISO Center of French Space Agency, the surface Kuroshio transport east of Taiwan and its adjacent eddy f...Based on the daily sea surface height and absolute geostrophic velocity data from 1993 to 2015 provided by the AVISO Center of French Space Agency, the surface Kuroshio transport east of Taiwan and its adjacent eddy field(sea surface height anomaly) were analyzed. Four main periods of the surface Kuroshio transport and eddy field east of Taiwan were obtained, which were used to reveal their interactions. The main conclusions are as follows:(1) Based on the wavelet analysis, the surface Kuroshio transport east of Taiwan and its nearby eddy field showed significant seasonal, annual and interannual periods. In addition to the obvious periods of 182 days(0.5 year) and365 days(1 year), there were also more obvious periods of about 860 days(2.35 years) and 2 472 days(6.8 years)for the surface Kuroshio transport. There were also four more obvious periods corresponding to the eddy field of 200 days(0.55 year), 374 days(1 year), 889 days(2.43 years) and 2 374 days(6.5 years), although there were latitudinal variations.(2) Based on both the correlation and causal analysis, the correlation between the surface Kuroshio transport and the nearby eddy field over the above four periods was analyzed, and different Kuroshioeddy interactions, with period and latitudinal variability, were revealed.展开更多
In this study, subsurface eddies near the Vietnam coast of the South China Sea were observed with in situ observations, including Argo, CTD, XBT and some processed and quality controlled data. Based on temperature pro...In this study, subsurface eddies near the Vietnam coast of the South China Sea were observed with in situ observations, including Argo, CTD, XBT and some processed and quality controlled data. Based on temperature profiles from four Argo floats near the coast of Vietnam, a subsurface warm eddy was identified in spring and summer. The multi-year Argo and Global Temperature and Salinity Profile Programme(GTSPP) data were merged on a seasonal basis based on the data interpolating variational analysis(DIVA) method to reconstruct the three-dimensional temperature structure. There is a warm eddy in the central subsurface at 12.5°N, 111°E below300 m depth in spring, which does not exist in autumn and is weak in winter and summer. From CSIRO Atlas of Regional Seas(CARS) and Generalized Digital Environment Model(GDEM) reanalysis data, this subsurface warm eddy is also verified in spring.展开更多
The lack of in situ observations and the uncertainties of the drag coefficient at high wind speeds result in limited understanding of heat flux through the air-sea interface and thus inaccurate estimation of typhoon i...The lack of in situ observations and the uncertainties of the drag coefficient at high wind speeds result in limited understanding of heat flux through the air-sea interface and thus inaccurate estimation of typhoon intensity in numerical models.In this study,buoy observations and numerical simulations from an air-sea coupled model are used to assess the surface heat flux changes and impacts of the drag coefficient parameterization schemes on its simulations during the passage of Typhoon Kalmaegi(2014).Three drag coefficient schemes,which make the drag coefficient increase,level off,and decrease,respectively,are considered.The air-sea coupled model captured both trajectory and intensity changes better than the atmosphere-only model,though with relatively weaker sea surface cooling(SSC)compared to that captured by buoy observations,which led to relatively higher heat flux and thus a stronger typhoon.Different from previous studies,for a moderate typhoon,the coupled simulation with the increasing drag coefficient scheme outputted an intensity most consistent with the observation because of the strongest SSC,reasonable ratio of latent and sensible heat exchange coefficients,and an obvious reduction in the overestimated surface heat flux among all experiments.Results from sensitivity experiments showed that surface heat flux was significantly determined by the drag coefficient-induced SSC rather than the resulting wind speed changes.Only when SSC differs indistinctively(<0.4°C)between the coupled simulations,heat flux showed a weak positive correlation with the drag coefficient-impacted 10-m wind speed.The drag coefficient also played an important role in decreasing heat flux even a long time after the passage of Kalmaegi because of the continuous upwelling from deeper ocean layers driven by the impacted momentum flux through the air-sea interface.展开更多
The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provid...The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation)lack physical constraints and can generate significant errors at land-sea boundaries and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The eff ectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4%lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in off shore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.展开更多
Assimilation systems absorb both satellite measurements and Argo observations.This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis,allowing for ...Assimilation systems absorb both satellite measurements and Argo observations.This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis,allowing for better configuration of assimilation parameters.To achieve this,two comparative reconstruction schemes were designed under the optimal interpolation framework.Using a static scheme,an in situ-only field of ocean temperature was derived by correcting climatology with only Argo profiles.Through a dynamic scheme,a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection,and then a combined field was reconstructed by correcting the synthetic field with in situ profiles.For both schemes,a diagnostic iterative method was performed to optimize the background and observation error covariance statics.The root mean square difference(RMSD)of the in situ-only field,synthetic field and combined field were analyzed toward assimilated observations and independent observations,respectively.The rationale behind the distribution of RMSD was discussed using the following diagnostics:(1)The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters,as in the Northwest Pacific Ocean;this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity.(2)The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes;this is determined by the actual noise-to-signal ratio of ocean temperature.(3)The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics;this is determined by both the geographical feature of the synthetic field RMSD(smaller at depth in the extratropics)and that of the covariance correlation scales(smaller in the extratropics).展开更多
Mesoscale eddies play an important role in modulating the ocean circulation.Many previous studies on the threedimensional structure of mesoscale eddies were mainly based on composite analysis,and there are few targete...Mesoscale eddies play an important role in modulating the ocean circulation.Many previous studies on the threedimensional structure of mesoscale eddies were mainly based on composite analysis,and there are few targeted observations for individual eddies.A cyclonic eddy surveyed during an oceanographic cruise in the Northwest Pacific Ocean is investigated in this study.The three-dimensional structure of this cyclonic eddy is revealed by observations and simulated by the four-dimensional variational data assimilation(4 DVAR)system combined with the Regional Ocean Modeling System.The observation and assimilation results together present the characteristics of the cyclonic eddy.The cold eddy has an obvious dual-core structure of temperature anomaly.One core is at 50–150 m and another is at 300–550 m,which both have the average temperature anomaly of approximately-3.5℃.The salinity anomaly core is between 250 m and 500 m,which is approximately-0.3.The horizontal velocity structure is axis-asymmetric and it is enhanced on the eastern side of the cold eddy.In the assimilation experiment,sea level anomaly,sea surface temperature,and in situ measurements are assimilated into the system,and the results of assimilation are close to the observations.Based on the high-resolution assimilation output results,the study also diagnoses the vertical velocity in the mesoscale eddy,which reaches the maximum of approximately 10 m/d.The larger vertical velocity is found to be distributed in the range of 0.5 to 1 time of the normalized radius of the eddy.The validation of the simulation result shows that the 4 DVAR method is effective to reconstruct the three-dimensional structure of mesoscale eddy and the research is an application to study the mesoscale eddy in the Northwest Pacific by combining observation and assimilation methods.展开更多
This special section is the scientific legacy of the 13th National Ocean Data Assimilation and Numerical Simulation Conference of China,which was held in Changsha,China during December 3–4,2020 with more than 160 par...This special section is the scientific legacy of the 13th National Ocean Data Assimilation and Numerical Simulation Conference of China,which was held in Changsha,China during December 3–4,2020 with more than 160 participants from 35 units in China.It continued a series of National Ocean Data Assimilation conferences which began in 2003,which played an important role in the development of ocean data assimilation in China.In this 13th conference,there were 41 oral presentations and 15 posters,which focused on ocean data assimilation methods,ocean reanalysis data,developments and operational application of ocean data assimilation systems,assimilation of new type ocean observations,ocean targeted observation analysis,coupled data assimilation,ocean models and parameter estimation,etc.This conference was hosted by National University of Defense Technology,and cohosted by the Institute of Atmospheric Physics of Chinese Academy of Sciences,the South China Sea Institute of Oceanology of Chinese Academy of Sciences,the Second Institute of Oceanography of the Ministry of Natural Resources,Ocean University of China,Tianjin University and Hohai University.展开更多
In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to overcome its shortage, a...In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to overcome its shortage, an improved interpolation called Support Vector Machine-Kriging interpolation (SVM-Kriging) was proposed in this paper. The SVM-Kriging uses Least Square Support Vector Machine (LS-SVM) to fit the variogram, which needn’t select the basic variogram model and can directly get the optimal variogram of real interpolated field by using SVM to fit the variogram curve automatically. Based on GODAS data, by using the proposed SVM-Kriging and the general Kriging based on other traditional variogram models, the interpolation test was carried out and the interpolated results were analyzed contrastively. The test show that the variogram of SVM-Kriging can avoid the subjectivity of selecting the type of variogram models and the SVM-Kriging is better than the general Kriging based on other variogram model as a whole. Therefore, the SVM-Kriging is a good and adaptive interpolation method.展开更多
INTRODUCTION.Natural disasters,including floods,storms,and tsunamis,pose a great threat to human societies.A recent study highlighted this concern,revealing that billions of people globally were exposed to flood hazar...INTRODUCTION.Natural disasters,including floods,storms,and tsunamis,pose a great threat to human societies.A recent study highlighted this concern,revealing that billions of people globally were exposed to flood hazards.1 In 2023,Super Typhoon Doksuri caused devasting floods in Beijing and Hebei areas,resulting in massive casualties and huge economic losses.Therefore,there is a need for a precise understanding of disaster processes,reliable forecasting of disaster effects,and timely warning of risks to prevent and mitigate major disasters.2 Numerical modeling stands as the predominant approach to meet these demands.However,the predictive accuracy of such numerical models could be degraded because of various factors:oversimplification of real processes,computational errors,fluctuations of complex environments(e.g.,terrains,precipitations,buildings,and plants),and the influence of human activities(e.g.,evacuation and rescue)during disasters.展开更多
基金Supported by the Hunan Provincial Science Fund for Distinguished Young Scholars(No.2023JJ10053)the National Natural Science Foundation of China(No.42276205)。
文摘The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.
基金supported by the National Natural Science Foundation of China(Grant No.42176015)the National Natural Science Foundation of China(Grant No.41605070)+3 种基金the National Key Research and Development Program(Grant No.2021YFC3101500)the Hunan Provincial Natural Science Outstanding Youth Fund(Grant No.2023JJ10053)the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311022001)a project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.SML2021SP207)。
文摘This study investigates the impact of the salinity barrier layer(BL)on the upper ocean response to Super Typhoon Mangkhut(2018)in the western North Pacific.After the passage of Mangkhut,a noticeable increase(~0.6 psu)in sea surface salinity and a weak decrease(<1℃)in sea surface temperature(SST)were observed on the right side of the typhoon track.Mangkhut-induced SST change can be divided into the three stages,corresponding to the variations in BL thickness and SST before,during,and after the passage of Mangkhut.During the pre-typhoon stage,SST slightly warmed due to the entrainment of BL warm water,which suppressed the cooling induced by surface heat fluxes and horizontal advection.During the forced stage,SST cooling was controlled by entrainment,and the preexisting BL reduced the total cooling by 0.89℃ d-1,thus significantly weakening the overall SST cooling induced by Mangkhut.During the relaxation stage,the SST cooling was primarily caused by the entrainment.Our results indicate that a preexisting BL can limit typhoon-induced SST cooling by suppressing the entrainment of cold thermocline water,which contributed to Mangkhut becoming the strongest typhoon in 2018.
基金The National Key R&D Program of China under contract No.2018YFC1406202the National Natural Science Foundation of China under contract Nos 41206002,41706021,41775053 and 91428206the China Postdoctoral Science Foundation under contract No.2014M551711
文摘Based on the daily sea surface height and absolute geostrophic velocity data from 1993 to 2015 provided by the AVISO Center of French Space Agency, the surface Kuroshio transport east of Taiwan and its adjacent eddy field(sea surface height anomaly) were analyzed. Four main periods of the surface Kuroshio transport and eddy field east of Taiwan were obtained, which were used to reveal their interactions. The main conclusions are as follows:(1) Based on the wavelet analysis, the surface Kuroshio transport east of Taiwan and its nearby eddy field showed significant seasonal, annual and interannual periods. In addition to the obvious periods of 182 days(0.5 year) and365 days(1 year), there were also more obvious periods of about 860 days(2.35 years) and 2 472 days(6.8 years)for the surface Kuroshio transport. There were also four more obvious periods corresponding to the eddy field of 200 days(0.55 year), 374 days(1 year), 889 days(2.43 years) and 2 374 days(6.5 years), although there were latitudinal variations.(2) Based on both the correlation and causal analysis, the correlation between the surface Kuroshio transport and the nearby eddy field over the above four periods was analyzed, and different Kuroshioeddy interactions, with period and latitudinal variability, were revealed.
基金The National Natural Science Foundation of China under contract Nos 91428206,41206002,41706021 and41775053the China Postdoctoral Science Foundation under contract No.2014M551711the Strategic Priority Research Program of the Chinese Academy of Sciences under contract No.XDA11010103
文摘In this study, subsurface eddies near the Vietnam coast of the South China Sea were observed with in situ observations, including Argo, CTD, XBT and some processed and quality controlled data. Based on temperature profiles from four Argo floats near the coast of Vietnam, a subsurface warm eddy was identified in spring and summer. The multi-year Argo and Global Temperature and Salinity Profile Programme(GTSPP) data were merged on a seasonal basis based on the data interpolating variational analysis(DIVA) method to reconstruct the three-dimensional temperature structure. There is a warm eddy in the central subsurface at 12.5°N, 111°E below300 m depth in spring, which does not exist in autumn and is weak in winter and summer. From CSIRO Atlas of Regional Seas(CARS) and Generalized Digital Environment Model(GDEM) reanalysis data, this subsurface warm eddy is also verified in spring.
基金supported by the National Natural Science Foundation of China under Grant Nos. 41775053, 41976003, and 42192552the National Key Research and Development Program of China under Grant Nos. 2019YFC1510001 and 2019YFC1510102support has been provided by the National Program on Global Change and Air-Sea Interaction (GASI-IPOVAI-04)
文摘The lack of in situ observations and the uncertainties of the drag coefficient at high wind speeds result in limited understanding of heat flux through the air-sea interface and thus inaccurate estimation of typhoon intensity in numerical models.In this study,buoy observations and numerical simulations from an air-sea coupled model are used to assess the surface heat flux changes and impacts of the drag coefficient parameterization schemes on its simulations during the passage of Typhoon Kalmaegi(2014).Three drag coefficient schemes,which make the drag coefficient increase,level off,and decrease,respectively,are considered.The air-sea coupled model captured both trajectory and intensity changes better than the atmosphere-only model,though with relatively weaker sea surface cooling(SSC)compared to that captured by buoy observations,which led to relatively higher heat flux and thus a stronger typhoon.Different from previous studies,for a moderate typhoon,the coupled simulation with the increasing drag coefficient scheme outputted an intensity most consistent with the observation because of the strongest SSC,reasonable ratio of latent and sensible heat exchange coefficients,and an obvious reduction in the overestimated surface heat flux among all experiments.Results from sensitivity experiments showed that surface heat flux was significantly determined by the drag coefficient-induced SSC rather than the resulting wind speed changes.Only when SSC differs indistinctively(<0.4°C)between the coupled simulations,heat flux showed a weak positive correlation with the drag coefficient-impacted 10-m wind speed.The drag coefficient also played an important role in decreasing heat flux even a long time after the passage of Kalmaegi because of the continuous upwelling from deeper ocean layers driven by the impacted momentum flux through the air-sea interface.
基金Supported by the National Natural Science Foundation of China(Nos.41675097,41375113)。
文摘The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation)lack physical constraints and can generate significant errors at land-sea boundaries and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The eff ectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4%lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in off shore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.
基金The National Natural Science Foundation of China under contract Nos 41706021 and 41976188。
文摘Assimilation systems absorb both satellite measurements and Argo observations.This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis,allowing for better configuration of assimilation parameters.To achieve this,two comparative reconstruction schemes were designed under the optimal interpolation framework.Using a static scheme,an in situ-only field of ocean temperature was derived by correcting climatology with only Argo profiles.Through a dynamic scheme,a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection,and then a combined field was reconstructed by correcting the synthetic field with in situ profiles.For both schemes,a diagnostic iterative method was performed to optimize the background and observation error covariance statics.The root mean square difference(RMSD)of the in situ-only field,synthetic field and combined field were analyzed toward assimilated observations and independent observations,respectively.The rationale behind the distribution of RMSD was discussed using the following diagnostics:(1)The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters,as in the Northwest Pacific Ocean;this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity.(2)The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes;this is determined by the actual noise-to-signal ratio of ocean temperature.(3)The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics;this is determined by both the geographical feature of the synthetic field RMSD(smaller at depth in the extratropics)and that of the covariance correlation scales(smaller in the extratropics).
基金The National Key R&D Program of China under contract No.2018YFC1406202the National Natural Science Foundation of China under contract Nos 41811530301,41830964 and 41976188。
文摘Mesoscale eddies play an important role in modulating the ocean circulation.Many previous studies on the threedimensional structure of mesoscale eddies were mainly based on composite analysis,and there are few targeted observations for individual eddies.A cyclonic eddy surveyed during an oceanographic cruise in the Northwest Pacific Ocean is investigated in this study.The three-dimensional structure of this cyclonic eddy is revealed by observations and simulated by the four-dimensional variational data assimilation(4 DVAR)system combined with the Regional Ocean Modeling System.The observation and assimilation results together present the characteristics of the cyclonic eddy.The cold eddy has an obvious dual-core structure of temperature anomaly.One core is at 50–150 m and another is at 300–550 m,which both have the average temperature anomaly of approximately-3.5℃.The salinity anomaly core is between 250 m and 500 m,which is approximately-0.3.The horizontal velocity structure is axis-asymmetric and it is enhanced on the eastern side of the cold eddy.In the assimilation experiment,sea level anomaly,sea surface temperature,and in situ measurements are assimilated into the system,and the results of assimilation are close to the observations.Based on the high-resolution assimilation output results,the study also diagnoses the vertical velocity in the mesoscale eddy,which reaches the maximum of approximately 10 m/d.The larger vertical velocity is found to be distributed in the range of 0.5 to 1 time of the normalized radius of the eddy.The validation of the simulation result shows that the 4 DVAR method is effective to reconstruct the three-dimensional structure of mesoscale eddy and the research is an application to study the mesoscale eddy in the Northwest Pacific by combining observation and assimilation methods.
基金The National Key Research and Development Program of China under contract No.2018YFC1406202the National Natural Science Foundation of China under contract No.41830964.
文摘This special section is the scientific legacy of the 13th National Ocean Data Assimilation and Numerical Simulation Conference of China,which was held in Changsha,China during December 3–4,2020 with more than 160 participants from 35 units in China.It continued a series of National Ocean Data Assimilation conferences which began in 2003,which played an important role in the development of ocean data assimilation in China.In this 13th conference,there were 41 oral presentations and 15 posters,which focused on ocean data assimilation methods,ocean reanalysis data,developments and operational application of ocean data assimilation systems,assimilation of new type ocean observations,ocean targeted observation analysis,coupled data assimilation,ocean models and parameter estimation,etc.This conference was hosted by National University of Defense Technology,and cohosted by the Institute of Atmospheric Physics of Chinese Academy of Sciences,the South China Sea Institute of Oceanology of Chinese Academy of Sciences,the Second Institute of Oceanography of the Ministry of Natural Resources,Ocean University of China,Tianjin University and Hohai University.
文摘In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to overcome its shortage, an improved interpolation called Support Vector Machine-Kriging interpolation (SVM-Kriging) was proposed in this paper. The SVM-Kriging uses Least Square Support Vector Machine (LS-SVM) to fit the variogram, which needn’t select the basic variogram model and can directly get the optimal variogram of real interpolated field by using SVM to fit the variogram curve automatically. Based on GODAS data, by using the proposed SVM-Kriging and the general Kriging based on other traditional variogram models, the interpolation test was carried out and the interpolated results were analyzed contrastively. The test show that the variogram of SVM-Kriging can avoid the subjectivity of selecting the type of variogram models and the SVM-Kriging is better than the general Kriging based on other variogram model as a whole. Therefore, the SVM-Kriging is a good and adaptive interpolation method.
基金supported by the National Natural Science Foundation of China(nos.42306217,62202477,72225011,and 42276205)Hunan Provincial Natural Science Foundation of China(2023JJ10053)the National Key Research and Development Program of China(2021YFC3101500).
文摘INTRODUCTION.Natural disasters,including floods,storms,and tsunamis,pose a great threat to human societies.A recent study highlighted this concern,revealing that billions of people globally were exposed to flood hazards.1 In 2023,Super Typhoon Doksuri caused devasting floods in Beijing and Hebei areas,resulting in massive casualties and huge economic losses.Therefore,there is a need for a precise understanding of disaster processes,reliable forecasting of disaster effects,and timely warning of risks to prevent and mitigate major disasters.2 Numerical modeling stands as the predominant approach to meet these demands.However,the predictive accuracy of such numerical models could be degraded because of various factors:oversimplification of real processes,computational errors,fluctuations of complex environments(e.g.,terrains,precipitations,buildings,and plants),and the influence of human activities(e.g.,evacuation and rescue)during disasters.
