Based on the integrated results of multiple data types including MBES (Multi-Beam Echo Sounding) and historical topography maps,the LSR (Linear Sand Ridges) on the ECS (East China Sea) shelf are identified,divided int...Based on the integrated results of multiple data types including MBES (Multi-Beam Echo Sounding) and historical topography maps,the LSR (Linear Sand Ridges) on the ECS (East China Sea) shelf are identified,divided into subareas,and classified.The distribution of sand ridge crests is also established.The strikes of the LSR on the ECS shelf fall in a normal distribution with the center point being 155° azimuth with additional peak points at 125°,130°,140°,and 180° azimuth.The distribution of the ECS shelf sand ridges is congested in the central area,sparse in the south and north ends,divergent and bifurcated in the eastern area,and densely convergent in the western area.The LSR are divided into seven subzones according to the strikes and distribution of the sand ridges;estuary mouth ridges and open shelf sand ridges are identified and marked out.The high amplitude change of sea level resulting from the glacial-interglacial cycle is the main cause of the vast development of sand ridges on the ECS shelf.Abundant sediments on the shelf carried by the PYR (Paleo-Yangtze River) are the material source for the LSR formation,and the negative seafloor topography influences the strikes of LSR.Based on the effects of LSR distribution,change of sea level,and the simulation of ancient tidal currents,the evolution of the LSR on the ECS shelf is divided into four main stages:Stage Ⅰ before 14.5 ka BP,Stage Ⅱ between 12 and 14 ka BP,Stage Ⅲ from 1.5 to 9.5 ka BP,and Stage Ⅳ after 9 ka BP.展开更多
Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research ...Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos.40506017,40876051)Oceanic Research Project (Nos.908-ZC-Ⅰ-07,908-ZC-Ⅱ-05)
文摘Based on the integrated results of multiple data types including MBES (Multi-Beam Echo Sounding) and historical topography maps,the LSR (Linear Sand Ridges) on the ECS (East China Sea) shelf are identified,divided into subareas,and classified.The distribution of sand ridge crests is also established.The strikes of the LSR on the ECS shelf fall in a normal distribution with the center point being 155° azimuth with additional peak points at 125°,130°,140°,and 180° azimuth.The distribution of the ECS shelf sand ridges is congested in the central area,sparse in the south and north ends,divergent and bifurcated in the eastern area,and densely convergent in the western area.The LSR are divided into seven subzones according to the strikes and distribution of the sand ridges;estuary mouth ridges and open shelf sand ridges are identified and marked out.The high amplitude change of sea level resulting from the glacial-interglacial cycle is the main cause of the vast development of sand ridges on the ECS shelf.Abundant sediments on the shelf carried by the PYR (Paleo-Yangtze River) are the material source for the LSR formation,and the negative seafloor topography influences the strikes of LSR.Based on the effects of LSR distribution,change of sea level,and the simulation of ancient tidal currents,the evolution of the LSR on the ECS shelf is divided into four main stages:Stage Ⅰ before 14.5 ka BP,Stage Ⅱ between 12 and 14 ka BP,Stage Ⅲ from 1.5 to 9.5 ka BP,and Stage Ⅳ after 9 ka BP.
基金funding from the Key Laboratory Foundation of National Defence Technology under Grant 61424010208National Natural Science Foundation of China(Nos.62002276,41911530242 and 41975142)+3 种基金5150 Spring Specialists(05492018012 and 05762018039)Major Program of the National Social Science Fund of China(Grant No.17ZDA092)333 High-LevelTalent Cultivation Project of Jiangsu Province(BRA2018332)Royal Society of Edinburgh,UK andChina Natural Science Foundation Council(RSE Reference:62967)_Liu)_2018)_2)under their Joint International Projects Funding Scheme and Basic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20191398 and BK20180794).
文摘Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.
