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Sensitivities of Numerical Model Forecasts of Extreme Cyclone Events 被引量:5
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作者 A. C. Yih J. E. Walsh 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 1991年第1期51-66,共16页
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
关键词 Sensitivities of Numerical model forecasts of Extreme Cyclone Events SST
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Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts 被引量:1
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作者 Chaoqun MA Tijian WANG +1 位作者 Zengliang ZANG Zhijin LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2018年第7期813-825,共13页
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
关键词 data assimilation model output statistics WRF-Chem operational forecast
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Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models 被引量:1
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作者 Lin WANG Hong-Li REN +2 位作者 Fang ZHOU Nick DUNSTONE Xiangde XU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2023年第11期1998-2012,I0002,I0003,共17页
The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using... The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services. 展开更多
关键词 Asian-Australian monsoon(AAM) leading interannual variability modes El Niño seasonal forecasting models multimodel ensemble(MME)
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Hurricane Forecasts in the FSU Models
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作者 T. N. Krishnamurti H. S. Bedi +1 位作者 K. S. Yap D. Oosterhof 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 1993年第1期121-132,共12页
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
关键词 Hurricane forecasts in the FSU models
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Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models
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作者 Lei Wang 《Open Journal of Statistics》 2023年第2期222-232,共11页
Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urg... Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches. 展开更多
关键词 Dynamic Harmonic Regression with ARIMA Errors COVID-19 Pandemic Forecasting models Time Series Analysis Weekly Seasonality
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SeisGuard: A Software Platform to Establish Automatically an Earthquake Forecasting Model
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作者 Xiliang Liu Yajing Gao Mei Li 《Open Journal of Earthquake Research》 2023年第4期177-197,共21页
SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an ... SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations. 展开更多
关键词 SeisGuard Platform Geophysical Observing Data Electromagnetic Emission Time Series Database Spatiotemporal Fusion Earthquake Forecasting model
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Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System
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作者 BILAL Ahmed Khan HASEEB ur Rehman +5 位作者 QAISAR Nadeem MUHAMMAD Ahmad Naveed Qureshi JAWARIA Ahad MUHAMMAD Naveed Akhtar AMJAD Farooq MASROOR Ahmad 《原子能科学技术》 EI CAS CSCD 北大核心 2024年第10期2068-2076,共9页
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat... This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies. 展开更多
关键词 prediction of meteorological parameters weather research and forecasting model artificial neural networks nuclear emergency support system
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Improving the Forecasts of Coastal Wind Speeds in Tianjin,China Based on the WRF Model with Machine Learning Algorithms
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作者 Weihang ZHANG Meng TIAN +5 位作者 Shangfei HAI Fei WANG Xiadong AN Wanju LI Xiaodong LI Lifang SHENG 《Journal of Meteorological Research》 SCIE CSCD 2024年第3期570-585,共16页
Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the... Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind forecasting.In this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for Tianjin.The results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing time.The back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE.In regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)winds.In addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed.This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting. 展开更多
关键词 machine learning Weather Research and Forecast(WRF)model wind speed forecasting coastal region
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Effect of Distributional Assumption on GARCH Model into Shenzhen Stock Market: a Forecasting Evaluation
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作者 Md. Mostafizur Rahman Jianping Zhu 《Chinese Business Review》 2006年第3期40-49,共10页
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
关键词 GARCH model forecasts student-t generalized error density stock market indices
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The Water-Bearing Numerical Model and Its Operational Forecasting Experiments PartII: The Operational Forecasting Experiments 被引量:19
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作者 徐幼平 夏大庆 钱越英 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 1998年第3期39-54,共16页
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
关键词 Water-bearing Numerical forecasting model Operational forecasting experiment
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Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model 被引量:19
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作者 Naiming Xie Sifeng Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第1期96-102,共7页
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
关键词 grey number grey system theory INTERVAL discrete grey forecasting model non-homogeneous exponential sequence
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Numerical Simulation of the Heavy Rainfall in the Yangtze-Huai River Basin during Summer 2003 Using the WRF Model 被引量:13
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作者 LIU Hong-Bo 《Atmospheric and Oceanic Science Letters》 2012年第1期20-25,共6页
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
关键词 heavy rainfall Yangtze-Huai River Basin the Weather Research and Forecast model low-level jet
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Expansion modelling of discrete grey model based on multi-factor information aggregation 被引量:7
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作者 Naiming Xie Chaoyu Zhu Jing Zheng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第5期833-839,共7页
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
关键词 multi-variable system Solow residual method dis crete grey forecasting model grey system theory (GST).
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A hybrid model for numerical wave forecasting and its implementation-Ⅰ.The wind wave model 被引量:14
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作者 Wen Shengchang (S.C. Wen)1, Zhang Dacuo, Chen Bohai and Guo Peifang Institute of Physical Oceanography, Ocean University of Qingdao (Formerly, Shandong College of Oceanography), Qingdao, China 《Acta Oceanologica Sinica》 SCIE CAS CSCD 1989年第1期1-14,共14页
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
关键词 WAVE A hybrid model for numerical wave forecasting and its implementation The wind wave model
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The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations 被引量:12
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作者 DONG Sheng CHI Kun +1 位作者 ZHANG Qiyi ZHANG Xiangdong 《Journal of Ocean University of China》 SCIE CAS 2012年第1期13-17,共5页
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
关键词 Grey Markov model forecasting estuary disaster prevention maximum water level
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The Water-Bearing Numerical Model and Its Operational Forecasting Experiments Part I: The Water-Bearing Numerical Model 被引量:3
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作者 夏大庆 徐幼平 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 1998年第2期88-90,92-99,共11页
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
关键词 Water-Bearing Numerical Forecasting model Cloud Physical Framework Calculating Scheme
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Validation of WRF model on simulating forcing data for Heihe River Basin 被引量:10
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作者 XiaoDuo Pan Xin Li 《Research in Cold and Arid Regions》 2011年第4期344-357,共14页
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
关键词 forcing data weather research and forecasting model watershed airborne telemetry experimental research Heihe River Basin
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Forecast correct model of overpressure attenuation during gas explosion in excavation roadway 被引量:3
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作者 YANG Ying-di ZHANG Guo-shu CHEN Cai-yun 《Journal of Coal Science & Engineering(China)》 2010年第3期267-271,共5页
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
关键词 excavation roadway gas explosion overpressure amount forecast model
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STUDY ON GREY FORECASTING MODEL OF COPPER EXTRACTION RATE WITH BIOLEACHING OF PRIMARY SULFIDE ORE 被引量:2
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作者 A.X. Wu Y. Xi +2 位作者 B.H. Yang X.S. Chen H.C. Jiang 《Acta Metallurgica Sinica(English Letters)》 SCIE EI CAS CSCD 2007年第2期117-128,共12页
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
关键词 primary copper sulfide ore BIOLEACHING extraction rate grey theory forecasting model
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A new grey forecasting model based on BP neural network and Markov chain 被引量:6
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作者 李存斌 王恪铖 《Journal of Central South University of Technology》 EI 2007年第5期713-718,共6页
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
关键词 grey forecasting model neural network Markov chain electricity demand forecasting
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