This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics...This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics,i.e.granite,limestone,red sandstone,and marble,were tested under uniaxial compression and Brazilian splitting.Nuclear magnetic resonance(NMR)was used to characterize pore structures,while acoustic emission(AE)monitoring captured the temporal evolution of microcracking.The relationships among pore properties,AE b-values,and failure predictability were systematically evaluated.Results show that the overall b-value is primarily controlled by native pore size rather than loading condition.Rocks with larger pores display higher b-value and greater temporal variability,whereas those with smaller pores exhibit lower and more stable b-value.To assess failure predictability,the AE count rate was incorporated into an inverse power law model.The model demonstrates higher predictive accuracy for high-porosity rocks.The average predicted failure time(t_(p))decreases monotonically with porosity:under uniaxial compression,t_(p)for granite,marble,limestone,and sandstone are 2.32,1.82,1.42,and 0.03,respectively;under Brazilian splitting,3.54,3.30,0.10,and 0.03.Among the four rock types,sandstone with the highest porosity exhibits the smallest discrepancy between predicted and actual failure time,whereas granite with the lowest porosity shows the largest.As porosity decreases,prediction accuracy progressively declines for limestone and marble.Overall,the findings indicate that native pore heterogeneity governs both fracture scaling behavior and failure predictability,and that these effects are largely independent of the loading conditions examined in this study.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability ...The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.展开更多
The inherent asymmetry and diversity of the El Niño-Southern Oscillation(ENSO)pose substantial challenges to its prediction.Potential predictability measures the upper limit of predictability for a certain event....The inherent asymmetry and diversity of the El Niño-Southern Oscillation(ENSO)pose substantial challenges to its prediction.Potential predictability measures the upper limit of predictability for a certain event.Assessing the potential predictability of ENSO across varying phases and intensities with sophisticated climate models is crucial for understanding the upper limits of forecasting capabilities and identifying room for future enhancement.Based on the hindcast dataset with a recently developed ensemble forecasting system(the community earth system model,CESM),this study comprehensively investigates potential predictability for ENSO across different phases and intensities.The findings reveal that La Niña events possess higher potential predictability relative to their El Niño counterparts.Strong events exhibit significantly higher potential predictability than weak events within the same phase.The potential predictability of distinct ENSO types is primarily influenced by the seasonal variation inherent to their predictability.Regardless of the event classification,the potential predictability is characterized by a rapid decline from spring onwards,with the apex of this decline occurring in summer.The intensity of the seasonal predictability barrier inversely correlates with the upper limit of potential predictability.Specifically,a weaker(stronger)seasonal barrier is associated with a higher(lower)potential predictability.In addition,there is significant interdecadal variability both in the predictability of warm and cold ENSO events.The potential predictability for La Niña events decreases more slowly with increasing lead months,particularly in recent decades,resulting in an overall higher upper limit of potential predictability for La Niña events than for El Niño events over the past century.Nevertheless,El Niño events have also maintained a high potential predictability.This suggests substantial potential for improvement in future prediction for both.展开更多
This paper examines how a change in health policy uncertainty affects US industry returns using monthly data from January 1985 to September 2020.We employ insample and out-of-sample analyses,and we find evidence that ...This paper examines how a change in health policy uncertainty affects US industry returns using monthly data from January 1985 to September 2020.We employ insample and out-of-sample analyses,and we find evidence that 25 out of 49 considered industries are predictable during the health crisis periods,including severe acute respiratory syndrome and the ongoing coronavirus pandemic.The out-of-sample tests corroborate the evidence for the in-sample predictability.Furthermore,using a mean–variance utility function-based trading strategy,we observe that investors can use this simple tool for their trading strategies and make profits from 2.99 to 11.44%per annum.Our findings are robust after accounting for different business cycles,macroeconomic factor effects,the fluctuation in economic policy uncertainty,and different pandemic phases.These results complement the existing literature on industry return predictability and have potential implications for asset pricing and risk management.展开更多
This paper provides a robust test of predictability under the predictive regression model with possible heavy-tailed innovations assumption,in which the predictive variable is persistent and its innovations are highly...This paper provides a robust test of predictability under the predictive regression model with possible heavy-tailed innovations assumption,in which the predictive variable is persistent and its innovations are highly correlated with returns.To this end,we propose a robust test which can capture empirical phenomena such as heavy tails,stationary,and local to unity.Moreover,we develop related asymptotic results without the second-moment assumption between the predictive variable and returns.To make the proposed test reasonable,we propose a generalized correlation and provide theoretical support.To illustrate the applicability of the test,we perform a simulation study for the impact of heavy-tailed innovations on predictability,as well as direct and/or indirect implementation of heavy-tailed innovations to predictability via the unit root phenomenon.Finally,we provide an empirical study for further illustration,to which the proposed test is applied to a U.S.equity data set.展开更多
The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predicta...The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predictability remains elusive. This study assessed the real-time one-month-lead prediction skill of the summer 2022 YRV heatwaves using 12operational seasonal forecast systems. Results indicate that most individual forecast systems and their multi-model ensemble(MME) mean exhibited limited skill in predicting the 2022 YRV heatwaves. Notably, after the removal of the linear trend, the predicted 2-m air temperature anomalies were generally negative in the YRV, except for the Met Office Glo Sea6 system, which captured a moderate warm anomaly. While the models successfully simulated the influence of La Ni?a on the East Asian–western North Pacific atmospheric circulation and associated YRV temperature anomalies, only Glo Sea6 reasonably captured the observed relationship between the YRV heatwaves and an atmospheric teleconnection extending from the North Atlantic to the Eurasian mid-to-high latitudes. Such an atmospheric teleconnection plays a crucial role in intensifying the YRV heatwaves. In contrast, other seasonal forecast systems and the MME predicted a distinctly different atmospheric circulation pattern, particularly over the Eurasian mid-to-high latitudes, and failed to reproduce the observed relationship between the YRV heatwaves and Eurasian mid-to-high latitude atmospheric circulation anomalies.These findings underscore the importance of accurately representing the Eurasian mid-to-high latitude atmospheric teleconnection for successful YRV heatwave prediction.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42172316)the Major National Science and Technology Project for Deep Earth(Grant No.2024ZD100380X)the Natural Science Foundation of Hunan Province of China(2025JJ20030).
文摘This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics,i.e.granite,limestone,red sandstone,and marble,were tested under uniaxial compression and Brazilian splitting.Nuclear magnetic resonance(NMR)was used to characterize pore structures,while acoustic emission(AE)monitoring captured the temporal evolution of microcracking.The relationships among pore properties,AE b-values,and failure predictability were systematically evaluated.Results show that the overall b-value is primarily controlled by native pore size rather than loading condition.Rocks with larger pores display higher b-value and greater temporal variability,whereas those with smaller pores exhibit lower and more stable b-value.To assess failure predictability,the AE count rate was incorporated into an inverse power law model.The model demonstrates higher predictive accuracy for high-porosity rocks.The average predicted failure time(t_(p))decreases monotonically with porosity:under uniaxial compression,t_(p)for granite,marble,limestone,and sandstone are 2.32,1.82,1.42,and 0.03,respectively;under Brazilian splitting,3.54,3.30,0.10,and 0.03.Among the four rock types,sandstone with the highest porosity exhibits the smallest discrepancy between predicted and actual failure time,whereas granite with the lowest porosity shows the largest.As porosity decreases,prediction accuracy progressively declines for limestone and marble.Overall,the findings indicate that native pore heterogeneity governs both fracture scaling behavior and failure predictability,and that these effects are largely independent of the loading conditions examined in this study.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金supported by the National Natural Science Foundation of China(Grant Nos.42005005 and 42030607)the Science and Technology Department of Shaanxi Province(Grant No.2024JC-YBQN-0248)+2 种基金the Education Department of Shaanxi Province(Grant No.23JK0686)a Xi'an Science and Technology Project(Grant No.22GXFW0131)the Young Talent fund of the University Association for Science and Technology in Shaanxi(Grant No.20210706)。
文摘The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.
基金The fund from Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.SML2021SP310the National Natural Science Foundation of China under contract Nos 42227901 and 42475061the Key R&D Program of Zhejiang Province under contract No.2024C03257.
文摘The inherent asymmetry and diversity of the El Niño-Southern Oscillation(ENSO)pose substantial challenges to its prediction.Potential predictability measures the upper limit of predictability for a certain event.Assessing the potential predictability of ENSO across varying phases and intensities with sophisticated climate models is crucial for understanding the upper limits of forecasting capabilities and identifying room for future enhancement.Based on the hindcast dataset with a recently developed ensemble forecasting system(the community earth system model,CESM),this study comprehensively investigates potential predictability for ENSO across different phases and intensities.The findings reveal that La Niña events possess higher potential predictability relative to their El Niño counterparts.Strong events exhibit significantly higher potential predictability than weak events within the same phase.The potential predictability of distinct ENSO types is primarily influenced by the seasonal variation inherent to their predictability.Regardless of the event classification,the potential predictability is characterized by a rapid decline from spring onwards,with the apex of this decline occurring in summer.The intensity of the seasonal predictability barrier inversely correlates with the upper limit of potential predictability.Specifically,a weaker(stronger)seasonal barrier is associated with a higher(lower)potential predictability.In addition,there is significant interdecadal variability both in the predictability of warm and cold ENSO events.The potential predictability for La Niña events decreases more slowly with increasing lead months,particularly in recent decades,resulting in an overall higher upper limit of potential predictability for La Niña events than for El Niño events over the past century.Nevertheless,El Niño events have also maintained a high potential predictability.This suggests substantial potential for improvement in future prediction for both.
文摘This paper examines how a change in health policy uncertainty affects US industry returns using monthly data from January 1985 to September 2020.We employ insample and out-of-sample analyses,and we find evidence that 25 out of 49 considered industries are predictable during the health crisis periods,including severe acute respiratory syndrome and the ongoing coronavirus pandemic.The out-of-sample tests corroborate the evidence for the in-sample predictability.Furthermore,using a mean–variance utility function-based trading strategy,we observe that investors can use this simple tool for their trading strategies and make profits from 2.99 to 11.44%per annum.Our findings are robust after accounting for different business cycles,macroeconomic factor effects,the fluctuation in economic policy uncertainty,and different pandemic phases.These results complement the existing literature on industry return predictability and have potential implications for asset pricing and risk management.
基金The research of WONG Hsin-Chieh is partially supported by the NSTC(111-2118-M-305-004-MY2)the research of PANG Tian-xiao is partially supported by the National Social Science Foundation of China(21BTJ067)。
文摘This paper provides a robust test of predictability under the predictive regression model with possible heavy-tailed innovations assumption,in which the predictive variable is persistent and its innovations are highly correlated with returns.To this end,we propose a robust test which can capture empirical phenomena such as heavy tails,stationary,and local to unity.Moreover,we develop related asymptotic results without the second-moment assumption between the predictive variable and returns.To make the proposed test reasonable,we propose a generalized correlation and provide theoretical support.To illustrate the applicability of the test,we perform a simulation study for the impact of heavy-tailed innovations on predictability,as well as direct and/or indirect implementation of heavy-tailed innovations to predictability via the unit root phenomenon.Finally,we provide an empirical study for further illustration,to which the proposed test is applied to a U.S.equity data set.
基金jointly supported by the National Key Research and Development Program of China (2023YFC3007503)the Joint Research Project for Meteorological Capacity Improvement (22NLTSZ002)+4 种基金the National Natural Science Foundations of China (Grant Nos.42375064, 41975102, 41730964, 42175047)the China Meteorological Administration Key Innovation Team for Climate Prediction (CMA2023ZD03)the Met Office Climate Science for Service Partnership (CSSP) China project under the International Science Partnerships Fund (ISPF)the Special Project of Innovation and Development of China Meteorological Administration (CXFZ2024J004)the China Yangtze Power Co.,Ltd.Research Project (Grant No.2423020054)。
文摘The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predictability remains elusive. This study assessed the real-time one-month-lead prediction skill of the summer 2022 YRV heatwaves using 12operational seasonal forecast systems. Results indicate that most individual forecast systems and their multi-model ensemble(MME) mean exhibited limited skill in predicting the 2022 YRV heatwaves. Notably, after the removal of the linear trend, the predicted 2-m air temperature anomalies were generally negative in the YRV, except for the Met Office Glo Sea6 system, which captured a moderate warm anomaly. While the models successfully simulated the influence of La Ni?a on the East Asian–western North Pacific atmospheric circulation and associated YRV temperature anomalies, only Glo Sea6 reasonably captured the observed relationship between the YRV heatwaves and an atmospheric teleconnection extending from the North Atlantic to the Eurasian mid-to-high latitudes. Such an atmospheric teleconnection plays a crucial role in intensifying the YRV heatwaves. In contrast, other seasonal forecast systems and the MME predicted a distinctly different atmospheric circulation pattern, particularly over the Eurasian mid-to-high latitudes, and failed to reproduce the observed relationship between the YRV heatwaves and Eurasian mid-to-high latitude atmospheric circulation anomalies.These findings underscore the importance of accurately representing the Eurasian mid-to-high latitude atmospheric teleconnection for successful YRV heatwave prediction.
