基于2002-2020年的Jason系列卫星数据,利用一种高风速计算方法得到431次飓风的风速信息。在此基础上,利用基于再分析的美国飓风中心(The National Hurricane Center, NHC)大西洋和东北太平洋飓风最佳路径数据集进行比对分析,对高风速计...基于2002-2020年的Jason系列卫星数据,利用一种高风速计算方法得到431次飓风的风速信息。在此基础上,利用基于再分析的美国飓风中心(The National Hurricane Center, NHC)大西洋和东北太平洋飓风最佳路径数据集进行比对分析,对高风速计算方法进行了综合评估。文中计算和评估结果显示,8.03~66.93 m/s飓风风速RMSE优于4 m/s;卫星观测风速和NHC飓风最佳路径数据相关系数在0.9以上。这表明文中方法是可靠的,具备热带气旋高风速观测能力。同时,文中结果显示,飓风观测期间几乎都伴随着不同程度的降雨,当风速大于50 m/s时,卫星观测点均处于中到暴雨的环境下。文中研究证明了利用卫星雷达高度计和校正辐射计这对主被动微波遥感器联合获取极端海洋环境下风速信息的可行性,这为提升台风或飓风风速观测能力提供了一种有潜力的技术手段。另外,统计结果显示飓风期间风速和气压也具备很好的线性相关性,利用这种关系可以基于卫星获取的高风速信息来快速计算得到热带气旋中心气压,这将形成卫星对热带气旋风速和中心气压的同步获取能力。展开更多
本文利用中尺度模式WRF-ARW(Weather Research and Forecasting Model-Advanced Research WRF)(Version 4.0)对海南岛不同天气条件下的典型海风锋个例进行了高分辨率数值模拟,通过设计局地城镇化的敏感性试验,重点分析了海南岛沿海城镇...本文利用中尺度模式WRF-ARW(Weather Research and Forecasting Model-Advanced Research WRF)(Version 4.0)对海南岛不同天气条件下的典型海风锋个例进行了高分辨率数值模拟,通过设计局地城镇化的敏感性试验,重点分析了海南岛沿海城镇化对海风锋推进的影响及其可能机制。研究结果表明:海南岛沿海城镇化造成的海风锋结构差异是热力作用和动力作用共同影响的结果;城镇下垫面的摩擦效应与城市热岛的增强阻碍海风向内陆推进,减弱了海风锋途经地区的降温增湿效应,造成海风锋位置相对滞后;而城镇化所引起的高海陆热力差异增强了海风风速及海风辐合,同时导致海风锋前的垂直上升气流和海风环流厚度也明显增强。海风锋发展不同时期,城镇化对海风锋的推进影响有所不同。海风锋发展初期,海陆热力差异引起的推动作用与摩擦效应的阻碍作用相抵消,导致海风锋的推进无明显影响;海风锋发展强盛阶段,城镇化条件下内陆城市与非城市之间的热力差异有所增强,阻碍了海风锋向内陆推进,导致海风锋内陆渗透距离减小。不同天气条件下城市化对海风锋推进的影响有所不同,相比于晴空天气,多云天气下城市与非城市的热力差异稍强,加强了城市热岛效应对海风推进的阻碍作用,导致海风锋滞后距离稍远。此外,当土地利用类型更换为城镇后,净辐射与陆气间交换能量减少,导致其潜热通量显著减小,感热通量值变大,从而升高了下垫面温度,增强了海风的垂直上升运动,进而造成边界层高度的升高。展开更多
Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions ma...Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.展开更多
Tropical cyclones constitute a major risk for coastal communities.To assess their damage potential,accurate predictions of their intensification are needed,which requires a detailed understanding of the evolution of t...Tropical cyclones constitute a major risk for coastal communities.To assess their damage potential,accurate predictions of their intensification are needed,which requires a detailed understanding of the evolution of turbulent heat flux(THF).By combining multiple buoy observations along the south north storm track,we investigated the THF anomalies associated with tropical storm Danas(2019)in the East China Sea(ECS)during its complete life cycle from the intensification stage to the mature stage and finally to its dissipation on land.The storm passage is characterized by strong winds of 10-20 m/s and a sea level pressure below 1000 hPa,resulting in a substantial enhancement of THF.Latent heat(LH)fluxes are most strongly affected by wind speed,with a gradually increasing contribution of humidity along the trajectory.The relative contributions of wind speed and temperature anomalies to sensible heat(SH)depend on the stability of the boundary layer.Under stable conditions,SH variations are driven by wind speed,while under near-neutral conditions,SH variations are driven by temperature.A comparison of the observed THF and associated variables with outputs from the ERA 5 and MERRA 2 reanalysis products reveals that the reanalysis products can reproduce the basic evolution and composition of the observed THF.However,under extreme weather conditions,temperature and humidity variations are poorly captured by ERA 5 and MERRA 2,leading to large LH and SH errors.The differences in the observed and reproduced LH and SH during the passage of Danas amount to 26.1 and 6.6 W/m^(2) for ERA 5,respectively,and to 39.4 and 12.5 W/m^(2) for MERRA 2,respectively.These results demonstrate the need to improve the representation of tropical cyclones in reanalysis products to better predict their intensification process and reduce their damage.展开更多
Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes involved.Here,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a ti...Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes involved.Here,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a time-dependent theory of TC intensification,termed the energetically based dynamical system(EBDS)model,together with the use of a long short-term memory(LSTM)neural network.In time-dependent theory,TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors,expressed as environmental dynamical efficiency.The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using besttrack TC data and global reanalysis data during 1982–2017.The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System(GFS)of the National Centers for Environmental Prediction during 2017–21.The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity.The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data.The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration(CMA)and those by other state-of-art statistical and dynamical forecast systems,except for the 72-h forecast.Particularly at the longer lead times of 96 h and 120 h,the new scheme has smaller forecast errors,with a more than 30%improvement over the official forecasts.展开更多
In contrast to the Pacific and Atlantic Oceans,the Indian Ocean has lacked in-situ observations of wind profiles over open sea areas for decades.In 2021,a shipborne coherent Doppler lidar(CDL)was used to observe in-si...In contrast to the Pacific and Atlantic Oceans,the Indian Ocean has lacked in-situ observations of wind profiles over open sea areas for decades.In 2021,a shipborne coherent Doppler lidar(CDL)was used to observe in-situ wind profiles in the eastern tropical Indian Ocean.This equipment successfully captured low-level jets(LLJs)in the region,and their characteristics were thoroughly analyzed.Results reveal that the observed wind speed of LLJs in the eastern Indian Ocean ranges from 6 m s^(-1) to 10 m s^(-1) during the boreal winter and spring seasons,showing a height range of 0.6 to 1 km and two peak times at 0800 and 2000 UTC.This wind shear is weaker than that in land or offshore areas,ranging from 0 s^(-1) to 0.006 s^(-1).Moreover,the accuracy of the CDL data is compared to that of ERA5 data in the study area.The results indicate that the zonal wind from ERA5 data significantly deviated from the CDL measurement data,and the overall ERA5 data are substantially weaker than the in-situ observations.Notably,ERA5 underestimates northwestward LLJs.展开更多
In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The eff...In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.展开更多
The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer months.Therefore,th...The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer months.Therefore,the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them skillfully.The present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event.A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 index.The integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 index.These crucial signals are then masked in the initial conditions to verify their roles in the prediction.In addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies,we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event,emphasizing the crucial role of the interactions among them and the North Pacific.This approach is also applied to other El Niño events,revealing several new precursor signals.This study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.展开更多
文摘基于2002-2020年的Jason系列卫星数据,利用一种高风速计算方法得到431次飓风的风速信息。在此基础上,利用基于再分析的美国飓风中心(The National Hurricane Center, NHC)大西洋和东北太平洋飓风最佳路径数据集进行比对分析,对高风速计算方法进行了综合评估。文中计算和评估结果显示,8.03~66.93 m/s飓风风速RMSE优于4 m/s;卫星观测风速和NHC飓风最佳路径数据相关系数在0.9以上。这表明文中方法是可靠的,具备热带气旋高风速观测能力。同时,文中结果显示,飓风观测期间几乎都伴随着不同程度的降雨,当风速大于50 m/s时,卫星观测点均处于中到暴雨的环境下。文中研究证明了利用卫星雷达高度计和校正辐射计这对主被动微波遥感器联合获取极端海洋环境下风速信息的可行性,这为提升台风或飓风风速观测能力提供了一种有潜力的技术手段。另外,统计结果显示飓风期间风速和气压也具备很好的线性相关性,利用这种关系可以基于卫星获取的高风速信息来快速计算得到热带气旋中心气压,这将形成卫星对热带气旋风速和中心气压的同步获取能力。
文摘本文利用中尺度模式WRF-ARW(Weather Research and Forecasting Model-Advanced Research WRF)(Version 4.0)对海南岛不同天气条件下的典型海风锋个例进行了高分辨率数值模拟,通过设计局地城镇化的敏感性试验,重点分析了海南岛沿海城镇化对海风锋推进的影响及其可能机制。研究结果表明:海南岛沿海城镇化造成的海风锋结构差异是热力作用和动力作用共同影响的结果;城镇下垫面的摩擦效应与城市热岛的增强阻碍海风向内陆推进,减弱了海风锋途经地区的降温增湿效应,造成海风锋位置相对滞后;而城镇化所引起的高海陆热力差异增强了海风风速及海风辐合,同时导致海风锋前的垂直上升气流和海风环流厚度也明显增强。海风锋发展不同时期,城镇化对海风锋的推进影响有所不同。海风锋发展初期,海陆热力差异引起的推动作用与摩擦效应的阻碍作用相抵消,导致海风锋的推进无明显影响;海风锋发展强盛阶段,城镇化条件下内陆城市与非城市之间的热力差异有所增强,阻碍了海风锋向内陆推进,导致海风锋内陆渗透距离减小。不同天气条件下城市化对海风锋推进的影响有所不同,相比于晴空天气,多云天气下城市与非城市的热力差异稍强,加强了城市热岛效应对海风推进的阻碍作用,导致海风锋滞后距离稍远。此外,当土地利用类型更换为城镇后,净辐射与陆气间交换能量减少,导致其潜热通量显著减小,感热通量值变大,从而升高了下垫面温度,增强了海风的垂直上升运动,进而造成边界层高度的升高。
基金supported by the Shanghai Artificial Intelligence Laboratory and National Natural Science Foundation of China(Grant No.42088101 and 42030605).
文摘Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.
基金Supported by the National Natural Science Foundation of China(Nos.42122040,42076016)。
文摘Tropical cyclones constitute a major risk for coastal communities.To assess their damage potential,accurate predictions of their intensification are needed,which requires a detailed understanding of the evolution of turbulent heat flux(THF).By combining multiple buoy observations along the south north storm track,we investigated the THF anomalies associated with tropical storm Danas(2019)in the East China Sea(ECS)during its complete life cycle from the intensification stage to the mature stage and finally to its dissipation on land.The storm passage is characterized by strong winds of 10-20 m/s and a sea level pressure below 1000 hPa,resulting in a substantial enhancement of THF.Latent heat(LH)fluxes are most strongly affected by wind speed,with a gradually increasing contribution of humidity along the trajectory.The relative contributions of wind speed and temperature anomalies to sensible heat(SH)depend on the stability of the boundary layer.Under stable conditions,SH variations are driven by wind speed,while under near-neutral conditions,SH variations are driven by temperature.A comparison of the observed THF and associated variables with outputs from the ERA 5 and MERRA 2 reanalysis products reveals that the reanalysis products can reproduce the basic evolution and composition of the observed THF.However,under extreme weather conditions,temperature and humidity variations are poorly captured by ERA 5 and MERRA 2,leading to large LH and SH errors.The differences in the observed and reproduced LH and SH during the passage of Danas amount to 26.1 and 6.6 W/m^(2) for ERA 5,respectively,and to 39.4 and 12.5 W/m^(2) for MERRA 2,respectively.These results demonstrate the need to improve the representation of tropical cyclones in reanalysis products to better predict their intensification process and reduce their damage.
基金supported by the National Key R&D Program of China(Grant No.2017YFC1501604)the National Natural Science Foundation of China(Grant Nos.41875114 and 41875057).
文摘Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes involved.Here,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a time-dependent theory of TC intensification,termed the energetically based dynamical system(EBDS)model,together with the use of a long short-term memory(LSTM)neural network.In time-dependent theory,TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors,expressed as environmental dynamical efficiency.The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using besttrack TC data and global reanalysis data during 1982–2017.The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System(GFS)of the National Centers for Environmental Prediction during 2017–21.The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity.The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data.The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration(CMA)and those by other state-of-art statistical and dynamical forecast systems,except for the 72-h forecast.Particularly at the longer lead times of 96 h and 120 h,the new scheme has smaller forecast errors,with a more than 30%improvement over the official forecasts.
基金supported by the Taishan Scholars Programs of Shandong Province(No.tsqn201909165)the Global Change and Air-Sea Interaction Program(Nos.GASI-04-QYQH-03,GASI-01-WIND-STwin)the National Natural Science Foundation of China(Nos.41876028,42349910).
文摘In contrast to the Pacific and Atlantic Oceans,the Indian Ocean has lacked in-situ observations of wind profiles over open sea areas for decades.In 2021,a shipborne coherent Doppler lidar(CDL)was used to observe in-situ wind profiles in the eastern tropical Indian Ocean.This equipment successfully captured low-level jets(LLJs)in the region,and their characteristics were thoroughly analyzed.Results reveal that the observed wind speed of LLJs in the eastern Indian Ocean ranges from 6 m s^(-1) to 10 m s^(-1) during the boreal winter and spring seasons,showing a height range of 0.6 to 1 km and two peak times at 0800 and 2000 UTC.This wind shear is weaker than that in land or offshore areas,ranging from 0 s^(-1) to 0.006 s^(-1).Moreover,the accuracy of the CDL data is compared to that of ERA5 data in the study area.The results indicate that the zonal wind from ERA5 data significantly deviated from the CDL measurement data,and the overall ERA5 data are substantially weaker than the in-situ observations.Notably,ERA5 underestimates northwestward LLJs.
基金supported by the National Natural Science Foundation of China(Grant Nos.42225501 and 42105059)the National Key Scientific and Tech-nological Infrastructure project“Earth System Numerical Simula-tion Facility”(EarthLab).
文摘In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.
基金supported by the National Key R&D Program of China(2019YFA0606703)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202025).
文摘The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer months.Therefore,the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them skillfully.The present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event.A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 index.The integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 index.These crucial signals are then masked in the initial conditions to verify their roles in the prediction.In addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies,we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event,emphasizing the crucial role of the interactions among them and the North Pacific.This approach is also applied to other El Niño events,revealing several new precursor signals.This study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.
