Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast res...Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.展开更多
1.Introduction In contrast to the geographical barriers that isolate land masses,the global ocean system is inherently interconnected.This global linkage underscores the necessity of international collaboration.As mar...1.Introduction In contrast to the geographical barriers that isolate land masses,the global ocean system is inherently interconnected.This global linkage underscores the necessity of international collaboration.As marine science and technology enter a period of rapid advancement,there is an urgent need to establish cross-border and cross-regional cooperation to address shared challenges.The ocean’s vital role in global climate change and the sustainable development of human society has become increasingly evident in academic research and has drawn growing public attention.展开更多
The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven.Based on the types and distribution of ocean...The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven.Based on the types and distribution of oceanographic data,this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science.The ocean science has not fully entered the era of big data.There are two ways to expand the amount of oceanographic data to better understanding and man-agement of the ocean.On the data level,fully exploit the potential value of big and small ocean data,and transform the limited,small data into rich,big data,will help to achieve this.On the application level,oceanographic data are of great value if realize the federation of the core data owners and the consumers.The oceanographic data will provide not only a reliable scientific basis for climate,ecological,disaster and other scientific research,but also provide an unprecedented rich source of information that can be used to make predictions of the future.展开更多
Meeting the growing demands for accuracy,resolution and response time of high-pressure microsensors applicated in ocean science and petroleum industry,this paper developed a silicon resonant high pressure microsensor ...Meeting the growing demands for accuracy,resolution and response time of high-pressure microsensors applicated in ocean science and petroleum industry,this paper developed a silicon resonant high pressure microsensor based on volume compressed sensing with dual resonators supported by micro beams.In operation,the frequency of resonators shifts while the volume of microsensor compressed under high pressure.A couple of micro beams were introduced to support resonators and protect resonators from buckling in high pressure.At the meanwhile,the theoretical model of micro beams was established.Based on the expression between geometric parameters of micro beams and pressure sensitivity of resonators,the micro beams of the two resonators were modified that results in different pressure sensitivities of two resonators,which effectively performed temperature self-compensation.An eutectic bonding is adopted for wafer vacuum packaged.Dealing with potentially complex hydraulic measurement,the microsensors were surrounded by silicone oil and sealed with a corrugated diaphragm and a base.The pressure sensitivities of fabricated microsensors were quantified as 0.003 kHz/MPa(~30 ppm/MPa)of Resonator Ⅰ and−0.118 kHz/MPa(~−1311 ppm/MPa)of Resonator Ⅱ under 20℃,which match with theoretical analysis.Finally,the accuracy of this microsensors is better than 0.01%FS with temperature self-compensation under the pressure range of 0.1~70 MPa from−10℃to 50℃,along with a response time better than 10 ms and a resolution of 100 Pa.This paper provided an effective structure of micro beams for resonant high-pressure microsensors combined with volume compressed sensing,derived the quantitative relationship between key structural parameters and sensitivity,and performed a possibility of high accuracy and high resolution measurements of a much wider pressure range.展开更多
Remote sensing time series research and applications are advancing rapidly in land,ocean,and atmosphere science,demonstrating emerging capabilities in space-based monitoring methodologies and diverse application prosp...Remote sensing time series research and applications are advancing rapidly in land,ocean,and atmosphere science,demonstrating emerging capabilities in space-based monitoring methodologies and diverse application prospects.This prompts a comprehensive review of remote sensing time series observations,time series data reconstruction,derived products,and the current progress,challenges,and future directions in their applications.The high-frequency new data,i.e.,a constellation strategy,increasing computing power and advancing deep learning algorithms,are driving a paradigm shift from traditional point-in-time mapping to near-real-time monitoring tasks,and even to modeling integration of parameter inversion and prediction in land,water,and air science.Correspondingly,the 3 main projects,namely,the Global Climate Observing System,the United States Geological Survey/National Aeronautics and Space Administration(USGS/NASA)Landsat Science team,and the China Global Land Surface Satellite(GLASS)team,along with other time series-derived products,have found widespread applications in the research of Earth’s radiation balance and human-land systems.They have also been utilized for tasks such as land use change detection,assessing coastal effects,ocean environment monitoring,and supporting carbon neutrality strategies.Moreover,the 3 critical challenges and future directions were highlighted including multimode time series data fusion,deep learning modeling for task-specific domain adaptation,and fine-scale remote sensing applications by using dense time series.This review distills historical and current developments spanning the last several decades,providing an insightful understanding into the advancements in remote sensing time series data and applications.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42130608 and 42075142)the National Key Research and Development Program of China(Grant No.2020YFA0608000)the CUIT Science and Technology Innovation Capacity Enhancement Program Project(Grant No.KYTD202330)。
文摘Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.
基金the Earth Sciences Divisions of the Chinese Academy of Sciences(CAS)for their guidance and strong support。
文摘1.Introduction In contrast to the geographical barriers that isolate land masses,the global ocean system is inherently interconnected.This global linkage underscores the necessity of international collaboration.As marine science and technology enter a period of rapid advancement,there is an urgent need to establish cross-border and cross-regional cooperation to address shared challenges.The ocean’s vital role in global climate change and the sustainable development of human society has become increasingly evident in academic research and has drawn growing public attention.
基金the National Natural Science Foundation of China[Nos.41906182,L1824025/XK2018DXC002 and 42030406]Shandong Province's Marine S&T Fund for Pilot National Laboratory for Marine Science and Technology(Qingdao)[No.2018SDKJ0102-8]+1 种基金the Marine Science&Technology Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)[Grant No.2018SDKJ102]the National Key Research and Development Program of China[Nos.2019YFD0901001,2018YFC1407003 and 2017YFC1405300].
文摘The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven.Based on the types and distribution of oceanographic data,this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science.The ocean science has not fully entered the era of big data.There are two ways to expand the amount of oceanographic data to better understanding and man-agement of the ocean.On the data level,fully exploit the potential value of big and small ocean data,and transform the limited,small data into rich,big data,will help to achieve this.On the application level,oceanographic data are of great value if realize the federation of the core data owners and the consumers.The oceanographic data will provide not only a reliable scientific basis for climate,ecological,disaster and other scientific research,but also provide an unprecedented rich source of information that can be used to make predictions of the future.
基金funded in part by the National Key R&D Program of China under Grant 2023YFC2410600in part by the National Natural Science Foundation of China under Grant 62301536 and Grant 62121003+2 种基金in part by the Youth Innovation Promotion Association CAS Grant 2023134 and Grant 2022121in part by the Shandong Province Science and Technology Small and Medium-sized Enterprises Innovation Ability Improvement Project under Grant 2023TSGC0211in part by the Instrument Research and Development of CAS under Grant PTYQ2024BJ0009.
文摘Meeting the growing demands for accuracy,resolution and response time of high-pressure microsensors applicated in ocean science and petroleum industry,this paper developed a silicon resonant high pressure microsensor based on volume compressed sensing with dual resonators supported by micro beams.In operation,the frequency of resonators shifts while the volume of microsensor compressed under high pressure.A couple of micro beams were introduced to support resonators and protect resonators from buckling in high pressure.At the meanwhile,the theoretical model of micro beams was established.Based on the expression between geometric parameters of micro beams and pressure sensitivity of resonators,the micro beams of the two resonators were modified that results in different pressure sensitivities of two resonators,which effectively performed temperature self-compensation.An eutectic bonding is adopted for wafer vacuum packaged.Dealing with potentially complex hydraulic measurement,the microsensors were surrounded by silicone oil and sealed with a corrugated diaphragm and a base.The pressure sensitivities of fabricated microsensors were quantified as 0.003 kHz/MPa(~30 ppm/MPa)of Resonator Ⅰ and−0.118 kHz/MPa(~−1311 ppm/MPa)of Resonator Ⅱ under 20℃,which match with theoretical analysis.Finally,the accuracy of this microsensors is better than 0.01%FS with temperature self-compensation under the pressure range of 0.1~70 MPa from−10℃to 50℃,along with a response time better than 10 ms and a resolution of 100 Pa.This paper provided an effective structure of micro beams for resonant high-pressure microsensors combined with volume compressed sensing,derived the quantitative relationship between key structural parameters and sensitivity,and performed a possibility of high accuracy and high resolution measurements of a much wider pressure range.
基金supported by the National Nature Science Foundation of China(grant numbers 42425001 and 42071399).
文摘Remote sensing time series research and applications are advancing rapidly in land,ocean,and atmosphere science,demonstrating emerging capabilities in space-based monitoring methodologies and diverse application prospects.This prompts a comprehensive review of remote sensing time series observations,time series data reconstruction,derived products,and the current progress,challenges,and future directions in their applications.The high-frequency new data,i.e.,a constellation strategy,increasing computing power and advancing deep learning algorithms,are driving a paradigm shift from traditional point-in-time mapping to near-real-time monitoring tasks,and even to modeling integration of parameter inversion and prediction in land,water,and air science.Correspondingly,the 3 main projects,namely,the Global Climate Observing System,the United States Geological Survey/National Aeronautics and Space Administration(USGS/NASA)Landsat Science team,and the China Global Land Surface Satellite(GLASS)team,along with other time series-derived products,have found widespread applications in the research of Earth’s radiation balance and human-land systems.They have also been utilized for tasks such as land use change detection,assessing coastal effects,ocean environment monitoring,and supporting carbon neutrality strategies.Moreover,the 3 critical challenges and future directions were highlighted including multimode time series data fusion,deep learning modeling for task-specific domain adaptation,and fine-scale remote sensing applications by using dense time series.This review distills historical and current developments spanning the last several decades,providing an insightful understanding into the advancements in remote sensing time series data and applications.
