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Influenceof native pores on the size distribution and predictability of rock failure 认领 引用
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作者 Lichang Wang Qi Hao +4 位作者 Yuchen Zhong Xiling Liu Qin Xie Xiaoran Tian Feifei Qin 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1447-1459,共13页
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(tp)decreases monotonically with porosity:under uniaxial compression,tpfor 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
关键词 Rock deformation test Acoustic emission(AE) b-value Inverse power law model Predictability of catastrophic failure
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Unveiling the regulatory mechanisms of rock fracture behavior and catastrophic failure predictability:The key role of porosity 认领 引用
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作者 Qi Hao Yuchen Zhong +3 位作者 Xiling Liu Qinjie Zhang Tubing Yin Zhiguo Li 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第5期1061-1077,共17页
This study investigates the influence of porosity on the ModeⅠfracture behavior of granite and the predictability of catastrophic failure.Pores are defined in a broad sense to include intrinsic pores and microcrack-t... This study investigates the influence of porosity on the ModeⅠfracture behavior of granite and the predictability of catastrophic failure.Pores are defined in a broad sense to include intrinsic pores and microcrack-type defect structures that collectively contribute to rock heterogeneity.Granite specimens were heat-treated at ambient temperature,450℃,and 900℃to induce porosity variations,which were quantified using Nuclear Magnetic Resonance(NMR).Three-point bending(TPB)tests were conducted with real-time monitoring using Digital Image Correlation(DIC)and Acoustic Emission(AE).The results show that increasing porosity significantly reduces rock strength,fracture toughness,and fracture energy.As porosity increases from 0.68%to 1.33%,the crack initiation and unstable fracture toughness decrease by 94.6%and 87.0%,while crack mouth opening displacement(CMOD),fracture process zone(FPZ)size,and fracture surface roughness increase.AE results indicate that low-porosity specimens exhibit few high-energy events typical of abrupt brittle fracture,whereas high-porosity specimens generate numerous low-energy events associated with distributed microcrack coalescence.Time-Reversed Omori Law(TROL)analysis shows that higher porosity leads to predicted failure times closer to actual collapse,indicating improved predictability.These results demonstrate that pore-related heterogeneity plays a key role in regulating fracture behavior and catastrophic failure predictability. 展开更多
关键词 Porosity Three-point-bending(TPB) Fracture behavior Acoustic emission(AE) Catastrophic failure predictability
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Impact of high-frequency atmospheric noise on ENSO predictability 认领 引用
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作者 Guangyu Chi Ang Li +1 位作者 Yishuai Jin Xiaopei Lin 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第4期49-59,共11页
This study investigates the mechanism by which high-frequency atmospheric noise affects the predictability of El NiñoSouthern Oscillation(ENSO).Based on the community climate system model version 4(CCSM4),two set... This study investigates the mechanism by which high-frequency atmospheric noise affects the predictability of El NiñoSouthern Oscillation(ENSO).Based on the community climate system model version 4(CCSM4),two sets of comparative experiments were conducted:control(CTRL)and interactive ensemble(IE)simulations with reduced atmospheric noise.The analysis combining the linear inverse model(LIM)and the recharge oscillator model(ROM)shows that the IE method significantly improves the predictability of ENSO by effectively suppressing high-frequency atmospheric noise.Specifically,the LIM correlation coefficient of IE data is significantly improved compared to CTRL data within a 12-month forecast time frame.Mechanistic analysis revealed that under the IE mode,the system exhibits stronger thermocline feedback,with both the regulatory effect of thermocline depth anomalies on sea surface temperature(SST)and their response to SST significantly enhanced.This indicates that the long-term stability signals represented by subsurface heat content are more easily extracted under reduced noise conditions,thereby providing additional predictive information for ENSO forecasting. 展开更多
关键词 atmospheric noise predictability of ENSO linear inverse model recharge oscillator model subsurface heat content
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Predictability Study of Weather and Climate Events Related to Artificial Intelligence Models 认领 引用 被引量：9
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作者 Mu MU Bo QIN Guokun DAI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第1期1-8,共8页
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
关键词 predictability artificial intelligence models simulation and forecasting nonlinear optimization cognition–observation–model paradigm
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Industry return predictability using health policy uncertainty 认领 引用
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作者 Thach Pham Deepa Bannigidadmath Robert Powell 《Financial Innovation》 2025年第1期2336-2377,共42页
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
关键词 Industry return predictability Health policy uncertainty Out-of-sample predictability
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A Comparison of the Practical Predictability of Hail with Initial Perturbations of Climatological and Flow-Dependent Uncertainty in Ensembles 认领 引用
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作者 Xiaofei LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第7期1349-1364,共16页
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
关键词 hail predictability uncertainty climatological perturbation flow-dependent perturbation
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Predictability of the Summer 2022 Yangtze River Valley Heatwave in Multiple Seasonal Forecast Systems 认领 引用 被引量：4
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作者 Jinqing ZUO Jianshuang CAO +5 位作者 Lijuan CHEN Yu NIE Daquan ZHANG Adam A.SCAIFE Nick J.DUNSTONE Steven C.HARDIMAN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第6期1156-1166,共11页
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
关键词 the summer 2022 YRV heatwaves real-time prediction skill operational seasonal forecast systems Eurasian mid-to-high latitude teleconnection
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Study of the potential predictability of ENSO with different phases and intensities in the CESM 认领 引用
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作者 Le Zhang Ting Liu Dake Chen 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2025年第8期1-9,共9页
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
关键词 El Niño La Niña potential predictability forecast barriers interdecadal
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Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model 认领 引用 被引量：3
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作者 YU Liang MU Mu Yanshan YU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2014年第3期647-656,共10页
ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribut... ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model. 展开更多
关键词 ENSO predictability spring predictability barrier initial errors parameter errors error growth
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Robust tests of stock return predictability under heavy-tailed innovations 认领 引用
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作者 WONG Hsin-Chieh CHUNG Meng-Hua +1 位作者 FUH Cheng-Der PANG Tian-xiao 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2025年第1期149-168,共20页
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
关键词 domain of attraction of the normal law heavy-tailed least squares estimator predictive regres-sion unit root robust test
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The Nature and Predictability of the East Asian Extreme Cold Events of 2020/21 认领 引用 被引量：25
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作者 Guokun DAI Chunxiang LI +2 位作者 Zhe HAN Dehai LUO Yao YAO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2022年第4期566-575,共10页
Three extreme cold events invaded China during the early winter period between December 2020 to mid-January 2021 and caused drastic temperature drops,setting new low-temperature records at many stations during 6−8 Jan... Three extreme cold events invaded China during the early winter period between December 2020 to mid-January 2021 and caused drastic temperature drops,setting new low-temperature records at many stations during 6−8 January 2021.These cold events occurred under background conditions of low Arctic sea ice extent and a La Niña event.This is somewhat expected since the coupled effect of large Arctic sea ice loss in autumn and sea surface temperature cooling in the tropical Pacific usually favors cold event occurrences in Eurasia.Further diagnosis reveals that the first cold event is related to the southward movement of the polar vortex and the second one is related to a continent-wide ridge,while both the southward polar vortex and the Asian blocking are crucial for the third event.Here,we evaluate the forecast skill for these three events utilizing the operational forecasts from the ECMWF model.We find that the third event had the highest predictability since it achieves the best skill in forecasting the East Asian cooling among the three events.Therefore,the predictability of these cold events,as well as their relationships with the atmospheric initial conditions,Arctic sea ice,and La Niña deserve further investigation. 展开更多
关键词 extreme cold event predictability Arctic atmospheric initial conditions Arctic sea ice La Niña
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Insights into Convective-scale Predictability in East China: Error Growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events 认领 引用 被引量：17
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作者 Xiaoran ZHUANG Jinzhong MIN +3 位作者 Liu ZHANG Shizhang WANG Naigeng WU Haonan ZHU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2020年第8期893-911,共19页
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB)of East China.T... This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB)of East China.The scale-dependent error growth(ensemble variability)and associated impact on precipitation forecasts(precipitation uncertainties)were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing.The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing.This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales.The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale,suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties,especially for the strong-forcing regime.Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors.Specifically,small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing.Meanwhile,larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale.Consequently,these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB. 展开更多
关键词 convective-scale predictability error growth strong forcing weak forcing scale interaction
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Recent Advances in China on the Predictability of Weather and Climate 认领 引用 被引量：18
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作者 Wansuo DUAN Lichao YANG +4 位作者 Mu MU Bin WANG Xueshun SHEN Zhiyong MENG Ruiqiang DING 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2023年第8期1521-1547,共27页
This article summarizes the progress made in predictability studies of weather and climate in recent years in China,with a main focus on advances in methods to study error growth dynamics and reduce uncertainties in t... This article summarizes the progress made in predictability studies of weather and climate in recent years in China,with a main focus on advances in methods to study error growth dynamics and reduce uncertainties in the forecasting of weather and climate.Specifically,it covers(a)advances in methods to study weather and climate predictability dynamics,especially those in nonlinear optimal perturbation methods associated with initial errors and model errors and their applications to ensemble forecasting and target observations,(b)new data assimilation algorithms for initialization of predictions and novel assimilation approaches to neutralize the combined effects of initial and model errors for weather and climate,(c)applications of new statistical approaches to climate predictions,and(d)studies on meso-to small-scale weather system predictability dynamics.Some of the major frontiers and challenges remaining in predictability studies are addressed in this context. 展开更多
关键词 predictability target observation data assimilation ensemble forecasting
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The Predictability Problems in Numerical Weather and Climate Prediction 认领 引用 被引量：52
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作者 穆穆 段晚锁 王家城 《Advances in Atmospheric Sciences》 SCIE CAS 2002年第2期191-204,共14页
The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which... The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems. 展开更多
关键词 Predictability Weather Climate Numerical model Optimization
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Recent Advances in Predictability Studies in China (1999-2002) 认领 引用 被引量：26
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作者 穆穆 段晚锁 丑纪范 《Advances in Atmospheric Sciences》 SCIE EI CAS 2004年第3期437-443,共7页
Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability... Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed, which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealed by NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the modei predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate, which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance Ievel of 0.10. In addition, in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coemcient are calculated to explore the distribution characteristics of the mean-square errors. Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-terrn climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internai dynamical process. 展开更多
关键词 predictability prediction perturbation computational uncertainty weather climate
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The“Spring Predictability Barrier” Phenomenon of ENSO Predictions Generated with the FGOALS-g Model 认领 引用 被引量：2
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作者 WEI Chao DUAN Wan-Suo 《Atmospheric and Oceanic Science Letters》 2010年第2期87-92,共6页
Using the sea surface temperature （SST） predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil （FGOALS-g）, an analysis of the prediction errors was performed for... Using the sea surface temperature （SST） predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil （FGOALS-g）, an analysis of the prediction errors was performed for the seasonally dependent predictability of SST anomalies both for neutral years and for the growth/decay phase of El Nino/La Nina events. The study results indicated that for the SST predictions relating to the growth phase and the decay phase of El Nino events, the prediction errors have a seasonally dependent evolution. The largest increase in errors occurred in the spring season, which indicates that a prominent spring predictability barrier （SPB） occurs during an El Nino-Southern Oscillation （ENSO） warming episode. Furthermore, the SPB associated with the growth-phase prediction is more prominent than that associated with the decay-phase prediction. However, for the neutral years and for the growth and decay phases of La Nifia events, the SPB phenomenon was less prominent. These results indicate that the SPB phenomenon depends extensively on the ENSO events themselves. In particular, the SPB depends on the phases of the ENSO events. These results may provide useful knowledge for improving ENSO forecasting. 展开更多
关键词 ENSO event spring predictability barrier prediction error predictability
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The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China:2020/21 La Niña and 2020 Arctic Sea Ice Loss 认领 引用 被引量：11
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作者 Fei ZHENG Ji-Ping LIU +6 位作者 Xiang-Hui FANG Mi-Rong SONG Chao-Yuan YANG Yuan YUAN Ke-Xin LI Ji WANG Jiang ZHU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2022年第4期658-675,共18页
Several consecutive extreme cold events impacted China during the first half of winter 2020/21,breaking the low-temperature records in many cities.How to make accurate climate predictions of extreme cold events is sti... Several consecutive extreme cold events impacted China during the first half of winter 2020/21,breaking the low-temperature records in many cities.How to make accurate climate predictions of extreme cold events is still an urgent issue.The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes,further influencing the cold conditions in China.However,climate models failed to predict these two ocean environments at expected lead times.Most seasonal climate forecasts only predicted the 2020/21 La Niña after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1-2 month advancement.In this work,the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored.For the 2020/21 La Niña prediction,through sensitivity experiments involving different atmospheric-oceanic initial conditions,the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event.A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Niña development from the early spring of 2020.For predicting the Arctic sea ice loss in autumn 2020,an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model,which swept abnormally hot air over Siberia into the Arctic Ocean,is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent. 展开更多
关键词 extreme cold event predictability La Niña Arctic sea ice loss
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Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander 认领 引用 被引量：30
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作者 WANG Qiang MU Mu Henk A.DIJKSTRA 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2012年第1期118-134,共17页
A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simu... A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander （KLM） state using the conditional nonlinear optimal parameter perturbation （CNOP-P） method. Because of their relatively large uncertainties, three model parameters were considered： the interracial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 （SPG2） algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation （CNOP-I） method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates. 展开更多
关键词 conditional nonlinear optimal perturbation Kuroshio large meander predictability model parameters
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An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization 认领 引用 被引量：8
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作者 Mingkui LI Shaoqing ZHANG +17 位作者 Lixin WU Xiaopei LIN Ping CHANG Gohkan DANABASOGLU Zhiqiang WEI Xiaolin YU Huiqin HU Xiaohui MA Weiwei MA Haoran ZHAO Dongning JIA Xin LIU Kai MAO Youwei MA Yingjing JIANG Xue WANG Guangliang LIU Yuhu CHEN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2020年第9期939-950,共12页
Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses... Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions. 展开更多
关键词 high-resolution coupled model tropical cyclone formation predictability TC genesis coupled data assimilation
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The Predictability of a Squall Line in South China on 23 April 2007 认领 引用 被引量：15
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作者 吴多常 孟智勇 严大春 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2013年第2期485-502,共18页
This study investigated the predictability of a squall line associated with a quasi-stationary front on 23 April 2007 in South China through deterministic and probabilistic forecasts. Our results show that the squalll... This study investigated the predictability of a squall line associated with a quasi-stationary front on 23 April 2007 in South China through deterministic and probabilistic forecasts. Our results show that the squallline simulation was very sensitive to model error from horizontal resolution and uncertainties in physical parameterization schemes. At least a 10-km grid size was necessary to decently capture this squall line. The simulated squall line with a grid size of 4.5 km was most sensitive to long-wave radiation parameterization schemes relative to other physical schemes such as microphysics and planetary boundary layer. For a grid size from 20 to 5 km, a cumulus parameterization scheme degraded the squall-line simulation （relative to turning it off）, with a more severe degradation to grid size -10 km than 〉10 km. The sensitivity of the squall-line simulation to initial error was investigated through ensemble forecast. The performance of the ensemble simulation of the squall line was very sensitive to the initial error. Approximately 15% of the ensemble members decently captured the evolution of the squall line, 25% failed, and 60% dislocated the squall line. Using different combinations of physical parameterization schemes for different members can improve the probabilistic forecast. The lead time of this case was only a few hours. Error growth was clearly associated with moist convection development. A linear improvement in the performance of the squall line simulation was observed when the initial error was decreased gradually, with the largest contribution from initial moisture field. 展开更多
关键词 squall line predictability South China ensemble moisture
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