Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performan...Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.展开更多
Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the...Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the period of 1979-2022.The results show that the TPA-DM,the dominant pattern of interannual variability in the tropical Pacific and Atlantic regions,exhibits a significant negative correlation with NCSP.The positive phase of TPA-DM induces subsidence over the Maritime Continent through a zonal circulation pattern,which initiates a Pacific-Japan-like wave train along the East Asian coast.The circulation anomalies lead to moisture deficits and convergence subsidence over North China,leading to below-normal rainfall.Further analysis reveals that cooler SST in the Southern Tropical Atlantic facilitates the persistence of the TPA-DM by stimulating the anomalous Walker circulation associated with wind-evaporation-SST-convection feedback.展开更多
The dominant annual cycle of sea surface temperature(SST)in the tropical Pacific exhibits an antisymmetric mode,which explains 83.4%total variance,and serves as a background of El Niño-Southern Oscillation(ENSO)....The dominant annual cycle of sea surface temperature(SST)in the tropical Pacific exhibits an antisymmetric mode,which explains 83.4%total variance,and serves as a background of El Niño-Southern Oscillation(ENSO).However,there is no consensus yet on its anomalous impacts on the phase and amplitude of ENSO.Based on data during 1982-2022,results show that anomalies of the antisymmetric mode can affect the evolution of ENSO on the interannual scale via Bjerknes feedback,in which the positive(negative)phase of the antisymmetric mode can strengthen El Niño(La Niña)in boreal winter via an earlier(delayed)seasonal cycle transition and larger(smaller)annual mean.The magnitude of the SST anomalies in the equatorial eastern Pacific can reach more than±0.3◦C,regulated by the changes in the antisymmetric mode based on random sensitivity analysis.Results reveal the spatial pattern of the annual cycle associated with the seasonal phase-locking of ENSO evolution and provide new insight into the impact of the annual cycle of background SST on ENSO,which possibly carries important implications for forecasting ENSO.展开更多
利用欧洲中期天气预报中心提供的全球大气再分析资料、美国国家海洋和大气管理局提供的高分辨率海表温度逐日数据集和最佳路径数据集(the international best track archive for climate stewardship,IBTrACS)等资料,采用WRF(weather re...利用欧洲中期天气预报中心提供的全球大气再分析资料、美国国家海洋和大气管理局提供的高分辨率海表温度逐日数据集和最佳路径数据集(the international best track archive for climate stewardship,IBTrACS)等资料,采用WRF(weather research and forecasting)模式,对2022年第11号台风“轩岚诺”在东海黑潮快速增强的原因进行了诊断分析和数值模拟研究。结果表明,台风快速增强期间其中心处于东海黑潮主轴上,此时台风中心远离高空槽和高空急流,环境风垂直切变在增强时段维持在较高水平,超过8 m·s^(-1),均不利于台风增强,而东海黑潮区海表温度异常达到1~3℃,为台风发展提供大量热通量,同时26℃等温线深度达85~110 m,0~250 m深度范围内海水温度异常在台风快速增强前达2.5℃左右,表明上层海洋热含量丰富,有助于抑制台风经过引起的冷却作用。因此,东海黑潮区显著的海表温度暖异常和海洋上层热含量正异常对台风的快速增强起了关键作用。展开更多
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008200)the Independent Research Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2022SP505)。
文摘Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.
基金jointly supported by the Second Tibetan Plateau Scientific Expedition and Research Program[grant number-ber 2019QZKK0103]the National Natural Science Foundation of China[grant number 42293294]the China Meteorological Admin-istration Climate Change Special Program[grant number QBZ202303]。
文摘Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the period of 1979-2022.The results show that the TPA-DM,the dominant pattern of interannual variability in the tropical Pacific and Atlantic regions,exhibits a significant negative correlation with NCSP.The positive phase of TPA-DM induces subsidence over the Maritime Continent through a zonal circulation pattern,which initiates a Pacific-Japan-like wave train along the East Asian coast.The circulation anomalies lead to moisture deficits and convergence subsidence over North China,leading to below-normal rainfall.Further analysis reveals that cooler SST in the Southern Tropical Atlantic facilitates the persistence of the TPA-DM by stimulating the anomalous Walker circulation associated with wind-evaporation-SST-convection feedback.
基金jointly supported by the National Natural Science Foundation of China [grant numbers U2242205 and 41830969]the S&T Development Fund of CAMS [grant number 2023KJ036]the Basic Scientific Research and Operation Foundation of CAMS [grant number 2023Z018]。
文摘The dominant annual cycle of sea surface temperature(SST)in the tropical Pacific exhibits an antisymmetric mode,which explains 83.4%total variance,and serves as a background of El Niño-Southern Oscillation(ENSO).However,there is no consensus yet on its anomalous impacts on the phase and amplitude of ENSO.Based on data during 1982-2022,results show that anomalies of the antisymmetric mode can affect the evolution of ENSO on the interannual scale via Bjerknes feedback,in which the positive(negative)phase of the antisymmetric mode can strengthen El Niño(La Niña)in boreal winter via an earlier(delayed)seasonal cycle transition and larger(smaller)annual mean.The magnitude of the SST anomalies in the equatorial eastern Pacific can reach more than±0.3◦C,regulated by the changes in the antisymmetric mode based on random sensitivity analysis.Results reveal the spatial pattern of the annual cycle associated with the seasonal phase-locking of ENSO evolution and provide new insight into the impact of the annual cycle of background SST on ENSO,which possibly carries important implications for forecasting ENSO.
文摘利用欧洲中期天气预报中心提供的全球大气再分析资料、美国国家海洋和大气管理局提供的高分辨率海表温度逐日数据集和最佳路径数据集(the international best track archive for climate stewardship,IBTrACS)等资料,采用WRF(weather research and forecasting)模式,对2022年第11号台风“轩岚诺”在东海黑潮快速增强的原因进行了诊断分析和数值模拟研究。结果表明,台风快速增强期间其中心处于东海黑潮主轴上,此时台风中心远离高空槽和高空急流,环境风垂直切变在增强时段维持在较高水平,超过8 m·s^(-1),均不利于台风增强,而东海黑潮区海表温度异常达到1~3℃,为台风发展提供大量热通量,同时26℃等温线深度达85~110 m,0~250 m深度范围内海水温度异常在台风快速增强前达2.5℃左右,表明上层海洋热含量丰富,有助于抑制台风经过引起的冷却作用。因此,东海黑潮区显著的海表温度暖异常和海洋上层热含量正异常对台风的快速增强起了关键作用。
