A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of ...A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of January 1978, the New England blizzard of February 1978, and the Mid-Atlantic cyclone of February 1979. While medium-resolution simulations capture much of the intensification, the forecasts of the precise timing and intensity levels suffer from various degrees of error. The coastal cyclones show a 5-10 hPa dependence on the western North Atlantic sea surface temperature, which is varied within a range (± 2.5℃) compatible with interannual fluctuations. The associated vertical velocities and precipitation rates show proportionately stronger dependences on the ocean temperature perturbations. The Ohio Valley blizzard, which intensified along a track 700-800 km from the coast, shows little sensitivity to ocean temperature. The effect of a shift of - 10?latitude in the position of the snow boundary is negligible in each case. The forecasts depend strongly on the model resolution, and the coarse-resolution forecasts are consistently inferior to the medium-resolution forecasts. Studies of the corresponding sensitivities of extreme cyclonic events over eastern Asia are encouraged in order to identify characteristics that are common to numerical forecasts for the two regions.展开更多
Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimila...Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.展开更多
A brief account of our studies on the hurricane forecast problem is presented here. This covers recent prediction results from the Florida State University (FSU) regional and global numerical weather prediction models...A brief account of our studies on the hurricane forecast problem is presented here. This covers recent prediction results from the Florida State University (FSU) regional and global numerical weather prediction models. The regions covered are the Indian and the Pacific Oceans. The life cycle of the onset vortex (a hurricane) of the summer monsoon, typhoons over the western Pacific Ocean and tropical cyclones over the Bay of Bengal (Andhra Pradesh and the Bangladesh storms) are covered here. The essential elements in the storm formaton are the strong horizontal shear in the cyclogenetic areas, a lack of vertical shear and warn sea surface temperatures. The storm motion has a steering component largely described by the advection of vorticity by a vertically averaged layer mean wind, the recurvature of a storm appears to invoke physical processes via the advection of divergence by the divergent part of the wind especially in the outflow layers of the storm. Very high resolution global models seem to be able to handle the motion and structure during the entire life of typhoons quite reasonably. The scope for better diagnosis of the storms life cycle appears very promising in view of the realistic simulation of the life cycle.展开更多
Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricac...Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.展开更多
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using...It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.展开更多
Cash flow is a core element for enterprises to maintain operations and development.Cash flow forecasting models,through systematic analysis of an enterprise’s historical cash flow data,trends in operating activities,...Cash flow is a core element for enterprises to maintain operations and development.Cash flow forecasting models,through systematic analysis of an enterprise’s historical cash flow data,trends in operating activities,and external environmental factors,scientifically predict the scale,direction,and fluctuation of cash flow within a certain period in the future.This article focuses on the application of cash flow forecasting models in enterprise investment and financing decisions,sorts out the types and core functions of the models,analyzes their specific roles in investment project screening,financing plan formulation,risk prevention and control,and fund allocation,points out the existing problems in current applications,and proposes optimization paths.Research shows that the scientific application of cash flow forecasting models can enhance the accuracy and rationality of enterprises’investment and financing decisions,and help enterprises achieve sustainable development.展开更多
Modeling and forecasting of the geomagnetic variation are important research topics concerning geomagnetic navigation and space environment monitoring.We propose a combined forecasting model using a dynamic recursive ...Modeling and forecasting of the geomagnetic variation are important research topics concerning geomagnetic navigation and space environment monitoring.We propose a combined forecasting model using a dynamic recursive neural network called echo state network(ESN),the method of complementary ensemble empirical mode decomposition(EEMD)and the complexity theory of sample entropy(SampEn).Firstly,we use EEMD-SampEn to decompose the geomagnetic variation time series into many series of geomagnetic variation subsequences whose complexity degrees are transparently different.Then,we use ESN to build a forecasting model for each subsequence,selecting the optimal model parameters.Finally,we use the real data collected from the geomagnetic observatory to conduct simulations.The results show that the forecasting value of the combined model can closely conform to the tendency of geomagnetic variation field,and is superior to the least square support vector machine(LSSVM)model.The mean absolute error of the model for three-hour forecasting is less than 1.40nT when Kp index is less than 3.展开更多
In today’s rapidly evolving business environment,enterprises face unprecedented competitive pressures and complexities,necessitating efficient and precise strategic decision-making capabilities.Management accounting,...In today’s rapidly evolving business environment,enterprises face unprecedented competitive pressures and complexities,necessitating efficient and precise strategic decision-making capabilities.Management accounting,as the core of internal corporate management,plays a critical role in optimizing resource allocation,long-term planning,and formulating market competition strategies.This paper explores the application of Artificial Intelligence(AI)in management accounting,aiming to analyze the current state of AI in management accounting,examine its role in supporting external strategic decisions,and develop an AI-driven strategic forecasting and analysis model.The findings indicate that AI technology,through its advanced data processing and analytical capabilities,significantly enhances the efficiency and accuracy of management accounting,optimizes internal resource allocation,and strengthens enterprises’market competitiveness.展开更多
This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect ...This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.展开更多
This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on th...This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.展开更多
おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successf...おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successfully resolved in these experiments through developing and using a series of technical measures. The operational forecasting running of the water-bearing numerical model is realized stably and reliably, and satisfactory forecasts are obtained.展开更多
In this study, a 47-day regional climate simulation of the heavy rainfall in the Yangtze-Huai River Basin during the summer of 2003 was conducted using the Weather Research and Forecast （WRY） model. The simulation r...In this study, a 47-day regional climate simulation of the heavy rainfall in the Yangtze-Huai River Basin during the summer of 2003 was conducted using the Weather Research and Forecast （WRY） model. The simulation reproduces reasonably well the evolution of the rainfall during the study period＇s three successive rainy phases, especially the frequent heavy rainfall events occurring in the Huai River Basin. The model captures the major rainfall peak observed by the monitoring stations in the morning. Another peak appears later than that shown by the observations. In addition, the simulation realistically captures not only the evolution of the low-level winds but also the characteristics of their diurnal variation. The strong southwesterly （low-level jet, LLJ） wind speed increases beginning in the early evening and reaches a peak in the morning; it then gradually decreases until the afternoon. The intense LLJ forms a strong convergent circulation pattern in the early morning along the Yangtze-Huai River Basin. This pattern partly explains the rainfall peak observed at this time. This study furnishes a basis for the further analysis of the mechanisms of evolution of the LLJ and for the further study of the interactions between the LLJ and rainfall.展开更多
This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h ...This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.展开更多
The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This p...The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.展开更多
A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is eq...A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.展开更多
Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Marko...Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.展开更多
In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in ...In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in which the dynamic framework of hydrostatic equilibrium is taken. The main contributions are: the mixing ratios of all species of hydrometeors are added as the prognostic variables of model, the prognostic equations of these hydrometeors are introduced, the cloud physical framework is specially designed, some technical measures are used to resolve a series of physical, mathematical and computational problems arising from water-bearing; and so on. The various problems (in such aspects as the designs of physical and calculating schemes and the composition of computational programme) which are exposed in feasibility test, in sensibility test, and especially in operational forecasting experiments are successfully resolved using a lot of technical measures having been developed from researches and tests. Finally, the operational forecasting running of the water-bearing numerical model and its forecasting system is realized stably and reliably, and the fine forecasts are obtained. All of these mentioned above will be described in second paper.展开更多
The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model...The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA （China Meteorological Administration） and hourly meteorological observation data from seven sites of WATER （Watershed Airborne Telemetry Experimental Research） are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE （Mean Bias Error） of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were -0.19 ℃, -4.49 hPa, 4.08% and 0.92 m/s, the average RMSE （Root Mean Square Error） of them were 2.11 ℃, 5.37 hPa, 9.55% and 1.73 m/s, and the average R （correlation coefficient） of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were-0.16 ℃,-6.62 hPa,-5.14%, 0.26 m/s, 33.0 W/m^2 and-6.44 W/m^2, the average RMSE of them were 2.62 ℃, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m^2 and 53.5 W/m^2, and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained： （1） regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; （2） the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; （3） both wind speed and rainfall simulated from WRF model did not agree well with observation data.展开更多
Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square r...Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square root of the accumulated volume of gas,the square root of the propagation distance multiplicative inverse.Also, attenuation speedof the forecast model calculation is faster than that of experimental data.Based on theoriginal forecast models and experimental data, deduced the relation of factors by introducinga correlation coefficient with concrete volume and distance, which had been verifiedby the roadway experiment data.The results show that it is closer to the roadway experimentaldata and the overpressure amount increases first then decreases with thepropagation distance.展开更多
A model GM （grey model） （1,1） for forecasting the rate of copper extraction during the bioleaching of primary sulphide ore was established on the basis of the mathematical theory and the modeling process of grey s...A model GM （grey model） （1,1） for forecasting the rate of copper extraction during the bioleaching of primary sulphide ore was established on the basis of the mathematical theory and the modeling process of grey system theory. It was used for forecasting the rate of copper extraction from the primary sulfide ore during a laboratory microbial column leaching experiment. The precision of the forecasted results were examined and modified via ＂posterior variance examination＂. The results show that the forecasted values coincide with the experimental values. GM （1,1） model has high forecast accuracy; and it is suitable for simulation control and prediction analysis of the original data series of the processes that have grey characteristics, such as mining, metallurgical and mineral processing, etc. The leaching rate of such copper sulphide ore is low. The grey forecasting result indicates that the rate of copper extraction is approximately 20% even after leaching for six months.展开更多
文摘A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of January 1978, the New England blizzard of February 1978, and the Mid-Atlantic cyclone of February 1979. While medium-resolution simulations capture much of the intensification, the forecasts of the precise timing and intensity levels suffer from various degrees of error. The coastal cyclones show a 5-10 hPa dependence on the western North Atlantic sea surface temperature, which is varied within a range (± 2.5℃) compatible with interannual fluctuations. The associated vertical velocities and precipitation rates show proportionately stronger dependences on the ocean temperature perturbations. The Ohio Valley blizzard, which intensified along a track 700-800 km from the coast, shows little sensitivity to ocean temperature. The effect of a shift of - 10?latitude in the position of the snow boundary is negligible in each case. The forecasts depend strongly on the model resolution, and the coarse-resolution forecasts are consistently inferior to the medium-resolution forecasts. Studies of the corresponding sensitivities of extreme cyclonic events over eastern Asia are encouraged in order to identify characteristics that are common to numerical forecasts for the two regions.
基金supported by the State Key Research and Development Program (Grant Nos. 2017YFC0209803, 2016YFC0208504, 2016YFC0203303 and 2017YFC0210106)the National Natural Science Foundation of China (Grant Nos. 91544230, 41575145, 41621005 and 41275128)
文摘Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.
基金The research reported here was supported by NSF grant no. ATM-8812053LAWS grant no. NAG 8-761+1 种基金LAWS contract no. NAS5-30932Computations were performed on the CRAY / YMP at NCAR
文摘A brief account of our studies on the hurricane forecast problem is presented here. This covers recent prediction results from the Florida State University (FSU) regional and global numerical weather prediction models. The regions covered are the Indian and the Pacific Oceans. The life cycle of the onset vortex (a hurricane) of the summer monsoon, typhoons over the western Pacific Ocean and tropical cyclones over the Bay of Bengal (Andhra Pradesh and the Bangladesh storms) are covered here. The essential elements in the storm formaton are the strong horizontal shear in the cyclogenetic areas, a lack of vertical shear and warn sea surface temperatures. The storm motion has a steering component largely described by the advection of vorticity by a vertically averaged layer mean wind, the recurvature of a storm appears to invoke physical processes via the advection of divergence by the divergent part of the wind especially in the outflow layers of the storm. Very high resolution global models seem to be able to handle the motion and structure during the entire life of typhoons quite reasonably. The scope for better diagnosis of the storms life cycle appears very promising in view of the realistic simulation of the life cycle.
文摘Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.
基金supported by the National Natural Science Foundation of China(Grant Nos.42375062 and 42275158)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)the Natural Science Foundation of Gansu Province(Grant No.22JR5RF1080)。
文摘It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.
文摘Cash flow is a core element for enterprises to maintain operations and development.Cash flow forecasting models,through systematic analysis of an enterprise’s historical cash flow data,trends in operating activities,and external environmental factors,scientifically predict the scale,direction,and fluctuation of cash flow within a certain period in the future.This article focuses on the application of cash flow forecasting models in enterprise investment and financing decisions,sorts out the types and core functions of the models,analyzes their specific roles in investment project screening,financing plan formulation,risk prevention and control,and fund allocation,points out the existing problems in current applications,and proposes optimization paths.Research shows that the scientific application of cash flow forecasting models can enhance the accuracy and rationality of enterprises’investment and financing decisions,and help enterprises achieve sustainable development.
基金supported by the Natural Science Foundation of Shaanxi Province(Grant No.2023-JC-YB-221)。
文摘Modeling and forecasting of the geomagnetic variation are important research topics concerning geomagnetic navigation and space environment monitoring.We propose a combined forecasting model using a dynamic recursive neural network called echo state network(ESN),the method of complementary ensemble empirical mode decomposition(EEMD)and the complexity theory of sample entropy(SampEn).Firstly,we use EEMD-SampEn to decompose the geomagnetic variation time series into many series of geomagnetic variation subsequences whose complexity degrees are transparently different.Then,we use ESN to build a forecasting model for each subsequence,selecting the optimal model parameters.Finally,we use the real data collected from the geomagnetic observatory to conduct simulations.The results show that the forecasting value of the combined model can closely conform to the tendency of geomagnetic variation field,and is superior to the least square support vector machine(LSSVM)model.The mean absolute error of the model for three-hour forecasting is less than 1.40nT when Kp index is less than 3.
文摘In today’s rapidly evolving business environment,enterprises face unprecedented competitive pressures and complexities,necessitating efficient and precise strategic decision-making capabilities.Management accounting,as the core of internal corporate management,plays a critical role in optimizing resource allocation,long-term planning,and formulating market competition strategies.This paper explores the application of Artificial Intelligence(AI)in management accounting,aiming to analyze the current state of AI in management accounting,examine its role in supporting external strategic decisions,and develop an AI-driven strategic forecasting and analysis model.The findings indicate that AI technology,through its advanced data processing and analytical capabilities,significantly enhances the efficiency and accuracy of management accounting,optimizes internal resource allocation,and strengthens enterprises’market competitiveness.
文摘This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.
基金supported by the National Natural Science Foundation of China(7090104171171113)the Aeronautical Science Foundation of China(2014ZG52077)
文摘This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.
文摘おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successfully resolved in these experiments through developing and using a series of technical measures. The operational forecasting running of the water-bearing numerical model is realized stably and reliably, and satisfactory forecasts are obtained.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-Q11-04)the National High Technology Research and Development Program of China (863 Program, Grant No. 2010AA012304)+2 种基金the National Natural Science Foundation of China (Grant No. 40905049)the LASG State Key Laboratory special fundthe LASG free exploration fund
文摘In this study, a 47-day regional climate simulation of the heavy rainfall in the Yangtze-Huai River Basin during the summer of 2003 was conducted using the Weather Research and Forecast （WRY） model. The simulation reproduces reasonably well the evolution of the rainfall during the study period＇s three successive rainy phases, especially the frequent heavy rainfall events occurring in the Huai River Basin. The model captures the major rainfall peak observed by the monitoring stations in the morning. Another peak appears later than that shown by the observations. In addition, the simulation realistically captures not only the evolution of the low-level winds but also the characteristics of their diurnal variation. The strong southwesterly （low-level jet, LLJ） wind speed increases beginning in the early evening and reaches a peak in the morning; it then gradually decreases until the afternoon. The intense LLJ forms a strong convergent circulation pattern in the early morning along the Yangtze-Huai River Basin. This pattern partly explains the rainfall peak observed at this time. This study furnishes a basis for the further analysis of the mechanisms of evolution of the LLJ and for the further study of the interactions between the LLJ and rainfall.
基金supported by the National Natural Science Foundation of China(7117111370901041)
文摘This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.
文摘The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.
基金supported by the National Natural Science Foundation of China (50879085)the Program for New Century Excellent Talents in University(NCET-07-0778)the Key Technology Research Project of Dynamic Environmental Flume for Ocean Monitoring Facilities (201005027-4)
文摘Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.
文摘In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in which the dynamic framework of hydrostatic equilibrium is taken. The main contributions are: the mixing ratios of all species of hydrometeors are added as the prognostic variables of model, the prognostic equations of these hydrometeors are introduced, the cloud physical framework is specially designed, some technical measures are used to resolve a series of physical, mathematical and computational problems arising from water-bearing; and so on. The various problems (in such aspects as the designs of physical and calculating schemes and the composition of computational programme) which are exposed in feasibility test, in sensibility test, and especially in operational forecasting experiments are successfully resolved using a lot of technical measures having been developed from researches and tests. Finally, the operational forecasting running of the water-bearing numerical model and its forecasting system is realized stably and reliably, and the fine forecasts are obtained. All of these mentioned above will be described in second paper.
基金supported by grant from the National High Technology Research and Development Program (863) of China (Grant No.2009AA122104)grants from the National Natural Science Foundation of China (No.40901202, No.40925004)+1 种基金supported by the CAS Action Plan for West Development Program (Grant No.KZCX2-XB2-09)Chinese State Key Basic Research Project (Grant No.2007CB714400)
文摘The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA （China Meteorological Administration） and hourly meteorological observation data from seven sites of WATER （Watershed Airborne Telemetry Experimental Research） are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE （Mean Bias Error） of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were -0.19 ℃, -4.49 hPa, 4.08% and 0.92 m/s, the average RMSE （Root Mean Square Error） of them were 2.11 ℃, 5.37 hPa, 9.55% and 1.73 m/s, and the average R （correlation coefficient） of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were-0.16 ℃,-6.62 hPa,-5.14%, 0.26 m/s, 33.0 W/m^2 and-6.44 W/m^2, the average RMSE of them were 2.62 ℃, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m^2 and 53.5 W/m^2, and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained： （1） regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; （2） the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; （3） both wind speed and rainfall simulated from WRF model did not agree well with observation data.
基金Supported by the National Natural Science Foundation of China(50874005)Anhui Province College Young Teachers Scientific Research"Allotment Planning"Key Project(2009SQRZ067)
文摘Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square root of the accumulated volume of gas,the square root of the propagation distance multiplicative inverse.Also, attenuation speedof the forecast model calculation is faster than that of experimental data.Based on theoriginal forecast models and experimental data, deduced the relation of factors by introducinga correlation coefficient with concrete volume and distance, which had been verifiedby the roadway experiment data.The results show that it is closer to the roadway experimentaldata and the overpressure amount increases first then decreases with thepropagation distance.
基金supported by the National Key Basic Research and Development Programme of China(No.2004CB619200)the National Science Foundation for Distinguished Young Scholars of China(No.50325415)the National Natural Science Foundation of China(No.50321402).
文摘A model GM （grey model） （1,1） for forecasting the rate of copper extraction during the bioleaching of primary sulphide ore was established on the basis of the mathematical theory and the modeling process of grey system theory. It was used for forecasting the rate of copper extraction from the primary sulfide ore during a laboratory microbial column leaching experiment. The precision of the forecasted results were examined and modified via ＂posterior variance examination＂. The results show that the forecasted values coincide with the experimental values. GM （1,1） model has high forecast accuracy; and it is suitable for simulation control and prediction analysis of the original data series of the processes that have grey characteristics, such as mining, metallurgical and mineral processing, etc. The leaching rate of such copper sulphide ore is low. The grey forecasting result indicates that the rate of copper extraction is approximately 20% even after leaching for six months.
