In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their ...In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.展开更多
A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-sc...A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-scale(SGS)turbulent fluxes associated with convective clouds in idealized tropical cyclone(TC)simulations.To evaluate the capability of the modified scheme in simulating real TCs,two sets of simulations of TC Soudelor(2015),one with the modified scheme and the other with the original scheme,are conducted.Comparisons with observations and coarse-grained results from large eddy simulation benchmarks demonstrate that the modified scheme improves the forecasting of the intensity and structure,as well as the SGS turbulent fluxes of Soudelor.Using the modified turbulence scheme,a TC with stronger intensity,smaller size,a shallower but stronger inflow layer,and a more intense but less inclined convective updraft is simulated.The rapid intensification process and secondary eyewall features can also be captured better by the modified scheme.By analyzing the mechanism by which turbulent transport impacts the intensity and structure of TCs,it is shown that accurately representing the turbulent transport associated with convective clouds above the planetary boundary layer helps to initiate the TC spin-up process.展开更多
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
Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving mod...Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed wit...The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.展开更多
Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in we...Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.展开更多
In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In t...In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations...Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.展开更多
Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article ex...Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.展开更多
The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kineti...The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.展开更多
The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship bet...The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship between K-aberration and word length pattern is developed.展开更多
基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project...基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project)数值模拟试验,探讨了植被水力方案的引入对中国夏季降水模拟的影响。结果表明,植被水力方案的引入能够显著降低CAS-ESM2.0模式对中国夏季降水气候态的模拟偏差,特别是显著改进了中国东部、青藏高原降水的低估,青藏高原以东的川西地区降水高估的偏差,同时也改善了夏季降水年际变率和极端大雨日数的模拟性能。进一步分析显示,植被水力方案的改进显著减小了土壤湿度在长江流域偏干、青藏高原偏湿的模式模拟偏差,降低了我国中东部以及青藏高原地表感热通量和潜热通量的模拟偏差,改善了模式对陆气相互作用过程的模拟能力。陆气相互作用的改进显著提升了模式对东亚季风环流的模拟,改进后的模式模拟的西北太平洋海平面气压的负偏差显著降低,有利于西南季风以及西北太平洋向我国东部的水汽输送,同时在对流层低层出现反气旋异常响应,有效改善了中国东部南风偏弱及水汽辐合偏弱的模拟偏差,使得我国东部降水负偏差显著减小。以上结果表明,包括植被水力过程的陆气相互作用的合理表述是改善东亚夏季降水模拟的重要途径之一。展开更多
基金supported by the National Natural Science Foundation of China(No.52374264)the National Key Technologies Research and Development Program of China(No.2024YFC2909600)the Major Science and Technology Projects in Yunnan Province(No.202402AB080010).
文摘In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFC3000803)the National Natural Science Foundation of China(Grant Nos.42375149,41975133 and 42205070)the Shanghai Pujiang Program(Grant No.22PJ1415900)。
文摘A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-scale(SGS)turbulent fluxes associated with convective clouds in idealized tropical cyclone(TC)simulations.To evaluate the capability of the modified scheme in simulating real TCs,two sets of simulations of TC Soudelor(2015),one with the modified scheme and the other with the original scheme,are conducted.Comparisons with observations and coarse-grained results from large eddy simulation benchmarks demonstrate that the modified scheme improves the forecasting of the intensity and structure,as well as the SGS turbulent fluxes of Soudelor.Using the modified turbulence scheme,a TC with stronger intensity,smaller size,a shallower but stronger inflow layer,and a more intense but less inclined convective updraft is simulated.The rapid intensification process and secondary eyewall features can also be captured better by the modified scheme.By analyzing the mechanism by which turbulent transport impacts the intensity and structure of TCs,it is shown that accurately representing the turbulent transport associated with convective clouds above the planetary boundary layer helps to initiate the TC spin-up process.
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 National Key Research and Development Program of China under contract No.2021YFC3101602the National Natural Science Foundation of China under contract Nos 42176017 and 41976019.
文摘Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金Supported by the National Natural Science Foundation of China(No.42276019)the Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Waters(No.GSTOEW)。
文摘The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.
基金supported by the National Natural Science Foundations of China(Grant Nos.42305163 and U22A20577)the Construction Project of Weather Modification Ability in Central China(Grant No.ZQC-H22256)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760300)the Projects of the Earth System Numerical Simulation Facility(Grant Nos.2024-EL-PT-000707,2023-ELPT-000482,2023-EL-ZD-00026,and 2022-EL-PT-00083)the STS Program of the Inner Mongolia Meteorological Service,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,and Institute of Atmospheric Physics,Chinese Academy of Sciences(Grant No.2021CG0047)。
文摘Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.
基金support of the National Natural Science Foundation of China(No.12372221)is acknowledged.
文摘In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
基金This research was funded by the National Natural Science Foundation of China(Grant Nos.31870426).
文摘Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.
文摘Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.
基金The National Key Research and Development Program of China under contract No.2022YFC3105002the National Natural Science Foundation of China under contract No.42176020the project from the Key Laboratory of Marine Environmental Information Technology,Ministry of Natural Resources,under contract No.2023GFW-1047.
文摘The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.
文摘The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship between K-aberration and word length pattern is developed.
文摘基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project)数值模拟试验,探讨了植被水力方案的引入对中国夏季降水模拟的影响。结果表明,植被水力方案的引入能够显著降低CAS-ESM2.0模式对中国夏季降水气候态的模拟偏差,特别是显著改进了中国东部、青藏高原降水的低估,青藏高原以东的川西地区降水高估的偏差,同时也改善了夏季降水年际变率和极端大雨日数的模拟性能。进一步分析显示,植被水力方案的改进显著减小了土壤湿度在长江流域偏干、青藏高原偏湿的模式模拟偏差,降低了我国中东部以及青藏高原地表感热通量和潜热通量的模拟偏差,改善了模式对陆气相互作用过程的模拟能力。陆气相互作用的改进显著提升了模式对东亚季风环流的模拟,改进后的模式模拟的西北太平洋海平面气压的负偏差显著降低,有利于西南季风以及西北太平洋向我国东部的水汽输送,同时在对流层低层出现反气旋异常响应,有效改善了中国东部南风偏弱及水汽辐合偏弱的模拟偏差,使得我国东部降水负偏差显著减小。以上结果表明,包括植被水力过程的陆气相互作用的合理表述是改善东亚夏季降水模拟的重要途径之一。
