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
The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using ...The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.展开更多
In south China, warm-sector rainstorms are significantly different from the traditional frontal rainstorms due to complex mechanism, which brings great challenges to their forecast. In this study, based on ensemble fo...In south China, warm-sector rainstorms are significantly different from the traditional frontal rainstorms due to complex mechanism, which brings great challenges to their forecast. In this study, based on ensemble forecasting, the high-resolution mesoscale numerical forecast model WRF was used to investigate the effect of initial errors on a warmsector rainstorm and a frontal rainstorm under the same circulation in south China, respectively. We analyzed the sensitivity of forecast errors to the initial errors and their evolution characteristics for the warm-sector and the frontal rainstorm. Additionally, the difference of the predictability was compared via adjusting the initial values of the GOOD member and the BAD member. Compared with the frontal rainstorm, the warm-sector rainstorm was more sensitive to initial error, which increased faster in the warm-sector. Furthermore, the magnitude of error in the warm-sector rainstorm was obviously larger than that of the frontal rainstorm, while the spatial scale of the error was smaller. Similarly, both types of the rainstorm were limited by practical predictability and inherent predictability, while the nonlinear increase characteristics occurred to be more distinct in the warm-sector rainstorm, resulting in the lower inherent predictability.The comparison between the warm-sector rainstorm and the frontal rainstorm revealed that the forecast field was closer to the real situation derived from more accurate initial errors, but only the increase rate in the frontal rainstorm was restrained evidently.展开更多
This article addresses the predictability of Bitcoin’s price by examining relationships between Bitcoin and financial and emotional variables such as the Fear and Greed Index(FGI),the American Interest Rate(FED),and ...This article addresses the predictability of Bitcoin’s price by examining relationships between Bitcoin and financial and emotional variables such as the Fear and Greed Index(FGI),the American Interest Rate(FED),and the Stock Market Index(NASDAQ).Through the use of statistical techniques such as the Johansen Cointegration Test and Granger Causality,as well as forecasting models,the study reveals that,despite the notorious volatility of the cryptocurrency market,it is possible to identify consistent behavioral patterns that can be successfully used to predict Bitcoin returns.The approach that combines VAR models and neural networks stands out as an effective tool to assist investors and analysts in making informed decisions in an ever-changing market environment.展开更多
The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear o...The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on-off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm (GA) to the CNOP method, named GA-CNOP, to solve the predictability problems involving on-off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on-off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.展开更多
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
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
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
By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Oce...By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Ocean Dipole events were identified. The initial errors cause a significant winter predictability barrier(WPB) or summer predictability barrier(SPB).The WPB is closely related with the initial errors in the tropical Indian Ocean, where two types of WPB-related initial errors display opposite patterns and a west–east dipole. In contrast, the occurrence of the SPB is mainly caused by initial errors in the tropical Pacific Ocean, where two types of SPB-related initial errors exhibit opposite patterns, with one pole in the subsurface western Pacific Ocean and the other in the upper eastern Pacific Ocean. Both of the WPB-related initial errors grow the fastest in winter, because the coupled system is at its weakest, and finally cause a significant WPB. The SPB-related initial errors develop into a La Ni ?na–like mode in the Pacific Ocean. The negative SST errors in the Pacific Ocean induce westerly wind anomalies in the Indian Ocean by modulating the Walker circulation in the tropical oceans. The westerly wind anomalies first cool the sea surface water in the eastern Indian Ocean. When the climatological wind direction reverses in summer, the wind anomalies in turn warm the sea surface water, finally causing a significant SPB. Therefore, in addition to the spatial patterns of the initial errors, the climatological conditions also play an important role in causing a significant predictability barrier.展开更多
Studies have revealed that predictability of the atmospheric general circulation is generally high in thetropics throughout the year and that there is some predictability in the Northern extra-tropical winter atmosphe...Studies have revealed that predictability of the atmospheric general circulation is generally high in thetropics throughout the year and that there is some predictability in the Northern extra-tropical winter atmospheric circulation through some patterns of tele connection. Predictability of the general circulation atthe polar regions has still remained as a’ cold’ topic and little has been known about this question. Based ona preliminary study on the predictability by using the institute of Atmospheric Physics (IAP) general circulation model, it is found that the SST-related predictability of the Southern winter lower atmospheric circulation in Antarctica is reasonably high and that there is some pledictability in the 500 hpa and 200 hpageopotential height fields over Europe and the Okhotsk Sea region during the Northern winter. It is suggested that more researches on this issue based on data analysis and model simulations are needed to obtainbetter understanding.展开更多
In the spring of 2021,southwestern China(SWC)experienced extreme drought,accompanied by the highest seasonal-mean temperature record since 1961.This drought event occurred in the decaying phase of a La Niña event...In the spring of 2021,southwestern China(SWC)experienced extreme drought,accompanied by the highest seasonal-mean temperature record since 1961.This drought event occurred in the decaying phase of a La Niña event with negative geopotential height anomalies over the Philippine Sea,which is distinct from the historical perspective.Historically,spring drought over SWC is often linked to El Niño and strong western North Pacific subtropical high.Here,we show that the extreme drought in the spring of 2021 may be mainly driven by the atmospheric internal variability and amplified by the warming trend.Specifically,the evaporation increase due to the high temperature accounts for about 30%of drought severity,with the contributions of its linear trend portion being nearly 20%and the interannual variability portion being about 10%.Since the sea surface temperature forcing from the tropical central and eastern Pacific played a minor role in the occurrence of drought,it is a challenge for a climate model to capture the 2021 SWC drought beyond one-month lead times.展开更多
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
In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively est...In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.展开更多
The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Ph...The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.展开更多
A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employ...A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.展开更多
The complex behavior and predictability of the Dry Spell Lengths (DSL) series obtained in Benin synoptic stations, from 1951 to 2010 are analyzed in this paper using a fractal approach. The synoptic stations are locat...The complex behavior and predictability of the Dry Spell Lengths (DSL) series obtained in Benin synoptic stations, from 1951 to 2010 are analyzed in this paper using a fractal approach. The synoptic stations are located in Cotonou, Bohicon, Save (subequatorial climate), and Parakou, Natitingou, Kandi (Sudanian climate). The DSLs are computed by considering four thresholds level, R0 = {1.0, 1.5, 2.0 and 5.0} mm/day. The fractal trace is estimated for dry spell density by the mean of the “Dry Spell Spell” (DSS) n-index. The rescaled range method is used to determine the predictability of DSL. By analyzing the DSS, results show that low DSS n-index values (n-index < 0.4) are more favored in the northern part of Benin than in the southern region, whereas, high values of DSS n-index (n-index > 0.4) occur preferentially in the southern part. Therefore, during 1951-2010, the Sudanian region presents frequent wet spells, alternated with short dry spells than in the subequatorial one. However, a high degree of long dry spell persistence, followed by short dry events is observed in the subequatorial region than in the Sudanian one. The longest DSL is observed in the subequatorial region, especially in Cotonou. Except for the Kandi station, the DSLs series obtained at synoptic stations are characterized by persistence. Therefore, autoregressive processes could be applied to the DSL series. Generally, the physical process governing dry spells observed at Save, Natitingou, and Bohicon are consistently predictable than the process governing the stations of the Cotonou and Parakou. However, at Kandi station, the DSL process approximates the usual Brownian motion, and it is, therefore, unpredictable or difficult to predict.展开更多
Fog has recently become a frequent high-impact weather phenomenon along the coastal regions of North China. Accurate fog forecasting remains challenging due to limited understanding of the predictability and mechanism...Fog has recently become a frequent high-impact weather phenomenon along the coastal regions of North China. Accurate fog forecasting remains challenging due to limited understanding of the predictability and mechanism of fog formation associated with synoptic-scale circulation. One frequent synoptic pattern of fog formation in this area is associated with cold front passage(cold-front synoptic pattern, CFSP). This paper explored the predictability of a typical CFSP fog event from the perspective of analyzing key characteristics of synoptic-scale circulation determining fog forecasting performance and the possible mechanism. The event was ensemble forecasted with the Weather Research and Forecasting model. Two groups of ensemble members with good and bad forecasting performance were selected and composited. Results showed that the predictability of this case was largely determined by the simulated strengths of the cold-front circulation(i.e., trough and ridge and the associated surface high). The bad-performing members tended to have a weaker ridge behind a stronger trough, and associated higher pressure over land and a weaker surface high over the sea, leading to an adverse impact on strength and direction of steering flows that inhibit warm moist advection and enhance cold dry advection transported to the focus region. Associated with this cold dry advection, adverse synoptic conditions of stratification and moisture for fog formation were produced, consequently causing failure of fog forecasting in the focus region. This study highlights the importance of accurate synoptic-scale information for improved CFSP fog forecasting, and enhances understanding of fog predictability from perspective of synoptic-scale circulation.展开更多
Analysis is done of monthly and seasonal variations as climatic features of the tracks from 1196 tropical cyclones originating in the western North Pacific over the period 1949 to 1980, followed by the investigation o...Analysis is done of monthly and seasonal variations as climatic features of the tracks from 1196 tropical cyclones originating in the western North Pacific over the period 1949 to 1980, followed by the investigation of 301 onland cyclone tracks over China mainland in terms of methodology for nonlinear system. Obtained by computing the accumulated distance distribution function of the tracks Cm (l) is the characteristic chaos quantity for the related dynamic systems and then the fractual dimensionality d = 4.86 and Kolmogorov entropy approximation K2 = 0.0164, thereby leading to the predictability time scale = 2.54 days. It is found that the reference path among the onland typhoon No.23 of 1971, or Bess in the international nomenclature. Our results could be of operational use as a kind of reference.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42105061 and 42030604).
文摘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.
文摘The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.
基金National Key Research and Development Program of China(2017YFC1502000)。
文摘In south China, warm-sector rainstorms are significantly different from the traditional frontal rainstorms due to complex mechanism, which brings great challenges to their forecast. In this study, based on ensemble forecasting, the high-resolution mesoscale numerical forecast model WRF was used to investigate the effect of initial errors on a warmsector rainstorm and a frontal rainstorm under the same circulation in south China, respectively. We analyzed the sensitivity of forecast errors to the initial errors and their evolution characteristics for the warm-sector and the frontal rainstorm. Additionally, the difference of the predictability was compared via adjusting the initial values of the GOOD member and the BAD member. Compared with the frontal rainstorm, the warm-sector rainstorm was more sensitive to initial error, which increased faster in the warm-sector. Furthermore, the magnitude of error in the warm-sector rainstorm was obviously larger than that of the frontal rainstorm, while the spatial scale of the error was smaller. Similarly, both types of the rainstorm were limited by practical predictability and inherent predictability, while the nonlinear increase characteristics occurred to be more distinct in the warm-sector rainstorm, resulting in the lower inherent predictability.The comparison between the warm-sector rainstorm and the frontal rainstorm revealed that the forecast field was closer to the real situation derived from more accurate initial errors, but only the increase rate in the frontal rainstorm was restrained evidently.
文摘This article addresses the predictability of Bitcoin’s price by examining relationships between Bitcoin and financial and emotional variables such as the Fear and Greed Index(FGI),the American Interest Rate(FED),and the Stock Market Index(NASDAQ).Through the use of statistical techniques such as the Johansen Cointegration Test and Granger Causality,as well as forecasting models,the study reveals that,despite the notorious volatility of the cryptocurrency market,it is possible to identify consistent behavioral patterns that can be successfully used to predict Bitcoin returns.The approach that combines VAR models and neural networks stands out as an effective tool to assist investors and analysts in making informed decisions in an ever-changing market environment.
基金funded by the National Key Research and Development Program of China[grant number 2022YFE0106800]the National Natural Science Foundation of China[grant number 41730964]the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311021001].
基金supported bythe National Natural Science Foundation of China(Grant Nos40975063 and 40830955)
文摘The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on-off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm (GA) to the CNOP method, named GA-CNOP, to solve the predictability problems involving on-off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on-off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.
基金support from the National Natural Science Foundation of China(Grant Nos:41790475,42005046,and 41790473)。
文摘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.
基金supported by the National Key Research&Development Program of China(Grant Nos.2017YFC1404100 and 2017YFC1404104)the National Natural Science Foundation of China(Grant Nos.41775100 and 41830964)。
文摘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.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC1502103)the National Natural Science Foundation of China(Grant Nos.41430427 and 41705035)+1 种基金the China Scholarship Councilthe Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX17_0876)。
文摘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.
基金jointly sponsored by the National Natural Science Foundation of China (Grant Nos. 41506032 and 41530961)the National Programme on Global Change and Air–Sea Interaction (Grant No. GASI-IPOVAI-06)
文摘By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Ocean Dipole events were identified. The initial errors cause a significant winter predictability barrier(WPB) or summer predictability barrier(SPB).The WPB is closely related with the initial errors in the tropical Indian Ocean, where two types of WPB-related initial errors display opposite patterns and a west–east dipole. In contrast, the occurrence of the SPB is mainly caused by initial errors in the tropical Pacific Ocean, where two types of SPB-related initial errors exhibit opposite patterns, with one pole in the subsurface western Pacific Ocean and the other in the upper eastern Pacific Ocean. Both of the WPB-related initial errors grow the fastest in winter, because the coupled system is at its weakest, and finally cause a significant WPB. The SPB-related initial errors develop into a La Ni ?na–like mode in the Pacific Ocean. The negative SST errors in the Pacific Ocean induce westerly wind anomalies in the Indian Ocean by modulating the Walker circulation in the tropical oceans. The westerly wind anomalies first cool the sea surface water in the eastern Indian Ocean. When the climatological wind direction reverses in summer, the wind anomalies in turn warm the sea surface water, finally causing a significant SPB. Therefore, in addition to the spatial patterns of the initial errors, the climatological conditions also play an important role in causing a significant predictability barrier.
文摘Studies have revealed that predictability of the atmospheric general circulation is generally high in thetropics throughout the year and that there is some predictability in the Northern extra-tropical winter atmospheric circulation through some patterns of tele connection. Predictability of the general circulation atthe polar regions has still remained as a’ cold’ topic and little has been known about this question. Based ona preliminary study on the predictability by using the institute of Atmospheric Physics (IAP) general circulation model, it is found that the SST-related predictability of the Southern winter lower atmospheric circulation in Antarctica is reasonably high and that there is some pledictability in the 500 hpa and 200 hpageopotential height fields over Europe and the Okhotsk Sea region during the Northern winter. It is suggested that more researches on this issue based on data analysis and model simulations are needed to obtainbetter understanding.
基金supported by the National Key R&D Program of China(Grant No.2017YFA0605004)Guangdong Major Project of Basic and Applied Basic Research(Grant No.2020B0301030004)+2 种基金National Natural Science Foundations of China(Grant No.42175056)the China Meteorological Administration Innovation and Development Project(CXFZ2022J031)the Joint Open Project of KLME&CIC-FEMD,NUIST(Grant No.KLME202102).
文摘In the spring of 2021,southwestern China(SWC)experienced extreme drought,accompanied by the highest seasonal-mean temperature record since 1961.This drought event occurred in the decaying phase of a La Niña event with negative geopotential height anomalies over the Philippine Sea,which is distinct from the historical perspective.Historically,spring drought over SWC is often linked to El Niño and strong western North Pacific subtropical high.Here,we show that the extreme drought in the spring of 2021 may be mainly driven by the atmospheric internal variability and amplified by the warming trend.Specifically,the evaporation increase due to the high temperature accounts for about 30%of drought severity,with the contributions of its linear trend portion being nearly 20%and the interannual variability portion being about 10%.Since the sea surface temperature forcing from the tropical central and eastern Pacific played a minor role in the occurrence of drought,it is a challenge for a climate model to capture the 2021 SWC drought beyond one-month lead times.
基金supported by the Key Research Program of Frontier Sciences,CAS (Grant No. ZDBS-LY-DQC010)the National Natural Science Foundation of China (Grant Nos. 41876012 and 41861144015,42175045)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDB42000000).
文摘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.
基金jointly supported by the National Natural Science Foundation of China for Excellent Young Scholars (Grant No. 41522502)the National Program on Global Change and Air–Sea Interaction (Grant Nos. GASI-IPOVAI06 and GASI-IPOVAI-03)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2015BAC03B07)
文摘In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.
基金jointly supported by the National Basic Research Program of China (Grant No.2009CB421407)the National Key Technologies R&D Program of China (Grant Nos.2007BAC29B03 and 2006BAC02B04)the National Natural Science Foundation of China (Grant No.40605023)
文摘The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.
基金This work is supported by the National Natural Science Foundation of China(71320107003 and 71532009).
文摘A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.
文摘The complex behavior and predictability of the Dry Spell Lengths (DSL) series obtained in Benin synoptic stations, from 1951 to 2010 are analyzed in this paper using a fractal approach. The synoptic stations are located in Cotonou, Bohicon, Save (subequatorial climate), and Parakou, Natitingou, Kandi (Sudanian climate). The DSLs are computed by considering four thresholds level, R0 = {1.0, 1.5, 2.0 and 5.0} mm/day. The fractal trace is estimated for dry spell density by the mean of the “Dry Spell Spell” (DSS) n-index. The rescaled range method is used to determine the predictability of DSL. By analyzing the DSS, results show that low DSS n-index values (n-index < 0.4) are more favored in the northern part of Benin than in the southern region, whereas, high values of DSS n-index (n-index > 0.4) occur preferentially in the southern part. Therefore, during 1951-2010, the Sudanian region presents frequent wet spells, alternated with short dry spells than in the subequatorial one. However, a high degree of long dry spell persistence, followed by short dry events is observed in the subequatorial region than in the Sudanian one. The longest DSL is observed in the subequatorial region, especially in Cotonou. Except for the Kandi station, the DSLs series obtained at synoptic stations are characterized by persistence. Therefore, autoregressive processes could be applied to the DSL series. Generally, the physical process governing dry spells observed at Save, Natitingou, and Bohicon are consistently predictable than the process governing the stations of the Cotonou and Parakou. However, at Kandi station, the DSL process approximates the usual Brownian motion, and it is, therefore, unpredictable or difficult to predict.
基金supported by the National Key R&D Program of China (Nos. 2017YFC1404100 and 2017YFC1404104)the National Natural Science Foundation of China (Nos. 41705081 and 41575067)the Global Change Research Program of China (No. 2015CB953904)
文摘Fog has recently become a frequent high-impact weather phenomenon along the coastal regions of North China. Accurate fog forecasting remains challenging due to limited understanding of the predictability and mechanism of fog formation associated with synoptic-scale circulation. One frequent synoptic pattern of fog formation in this area is associated with cold front passage(cold-front synoptic pattern, CFSP). This paper explored the predictability of a typical CFSP fog event from the perspective of analyzing key characteristics of synoptic-scale circulation determining fog forecasting performance and the possible mechanism. The event was ensemble forecasted with the Weather Research and Forecasting model. Two groups of ensemble members with good and bad forecasting performance were selected and composited. Results showed that the predictability of this case was largely determined by the simulated strengths of the cold-front circulation(i.e., trough and ridge and the associated surface high). The bad-performing members tended to have a weaker ridge behind a stronger trough, and associated higher pressure over land and a weaker surface high over the sea, leading to an adverse impact on strength and direction of steering flows that inhibit warm moist advection and enhance cold dry advection transported to the focus region. Associated with this cold dry advection, adverse synoptic conditions of stratification and moisture for fog formation were produced, consequently causing failure of fog forecasting in the focus region. This study highlights the importance of accurate synoptic-scale information for improved CFSP fog forecasting, and enhances understanding of fog predictability from perspective of synoptic-scale circulation.
基金This work is funded by the National Natural Science Foundation of China.
文摘Analysis is done of monthly and seasonal variations as climatic features of the tracks from 1196 tropical cyclones originating in the western North Pacific over the period 1949 to 1980, followed by the investigation of 301 onland cyclone tracks over China mainland in terms of methodology for nonlinear system. Obtained by computing the accumulated distance distribution function of the tracks Cm (l) is the characteristic chaos quantity for the related dynamic systems and then the fractual dimensionality d = 4.86 and Kolmogorov entropy approximation K2 = 0.0164, thereby leading to the predictability time scale = 2.54 days. It is found that the reference path among the onland typhoon No.23 of 1971, or Bess in the international nomenclature. Our results could be of operational use as a kind of reference.
