Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasona...Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.展开更多
With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated b...With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.展开更多
An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of ...An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.展开更多
Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eigh...Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the “Climate of the 20th Century Experiment” （20C3M） for the Fourth IPCC Assessment Report. Climate Research Unit （CRU） data were chosen for the observation analysis field. Root mean square （RMS） error and correlation coeffi-cients （R） are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression （MLR） performs best. However, for bias-corrected data, the superensemble based on singular value decomposition （SVD） produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.展开更多
Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and the possible causes of the extreme weather events with low temperature and icing condition...Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and the possible causes of the extreme weather events with low temperature and icing conditions,which occurred in the southern part of China during early 2008, are investigated in this study. In addition, multimodel consensus forecasting experiments are conducted by using the ensemble forecasts of ECMWF, JMA, NCEP and CMA taken from the TIGGE archives. Results show that more than a third of the stations in the southern part of China were covered by the extremely abundant precipitation with a 50-a return period, and extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan province as well. For the 24- to 216-h surface temperature forecasts, the bias-removed multimodel ensemble mean with running training period(R-BREM) has the highest forecast skill of all individual models and multimodel consensus techniques. Taking the RMSEs of the ECMWF 96-h forecasts as the criterion, the forecast time of the surface temperature may be prolonged to 192 h over the southeastern coast of China by using the R-BREM technique. For the sprinkle forecasts over central and southern China, the R-BREM technique has the best performance in terms of threat scores(TS) for the 24- to 192-h forecasts except for the 72-h forecasts among all individual models and multimodel consensus techniques. For the moderate rain, the forecast skill of the R-BREM technique is superior to those of individual models and multimodel ensemble mean.展开更多
The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for opt...The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method.展开更多
A novel intelligent adaptive fuzzy PHD controller based on multimodel control approach is presented in this paper.It can improve the system performance of the dynamic time- varying system at various operating conditio...A novel intelligent adaptive fuzzy PHD controller based on multimodel control approach is presented in this paper.It can improve the system performance of the dynamic time- varying system at various operating conditions.The fuzzy PHD controller is implemented by combining a fuzzy PI with a fuzzy PD controller in a parallel structure. The parameters of the fuzzy PHD controller are linked, via analytical derivation, to the gains of the linear PID controller. The sum of error square is used as performance criterion to locate the model that best reresents the process among the multiple models, The desired control output to drive the process along the desired path is generated only by modifying the output scale factots GU_I and GU_D of the fuzzy PID controller, Among the prescribed models, the control signal of the nearestmmodel to the system is applied. The system can be driven to its original trajectory because of the robustness of the fuzzy PID controller, Computer simulation results show that the展开更多
Improvements that can be attained in seasonal climate predictions in various parts of Africa using the multimodel supersensemble scheme are presented in this study. The synthetic superensemble (SSE) used follows the a...Improvements that can be attained in seasonal climate predictions in various parts of Africa using the multimodel supersensemble scheme are presented in this study. The synthetic superensemble (SSE) used follows the approach originally developed at Florida State University (FSU). The technique takes more advantage of the skill in the climate forecast data sets from atmosphere-ocean general circulation models running at many centres worldwide including the WMO global producing centers (GPCs). The module used in this work drew data sets from the Four versions of FSU coupled model system, seven models from the DEMETER project which is the forerun to the current European Ensembles Forecast System, the NCAR Model, and the Predictive Ocean Atmosphere Model for Australia (POAMA), all making a set of 13 individual models. An archive consisting of monthly simulations of precipitation was available over all the 5 regions of Africa, namely Eastern, Central, Northern, Southern, and Western Africa. The results showed that the SSE forecast for precipitation carries a higher skill compared to each of the member models and the ensemble mean. Relative to the ensemble mean (EM), the SSE provides an improvement of 18% in simulation of season cycle of precipitation climatology. In Eastern Africa, during December-February season, a north-south gradient of precipitation prevails between Tropical East Africa and the sector of the region towards Southern Africa. This regional scale climate pattern is a direct influence of the Intertropical Convergence Zone (ITZC) across the African continent during this time of the year. The SSE emerges with superior skill scores such as lowest root mean square error above the EM and the member models, for example in the prediction of spatial location and precipitation magnitudes that characterize the see-saw precipitation pattern in Eastern Africa. In all parts of Africa, and especially Eastern Africa where seasonal precipitation variability is a frequent cause huge human suffering due to droughts and famine, the multimodel superensemble and its subsequent improvements will always provide a forecast that outweighs the best Atmosphere-Ocean Climate Model. This approach and results herein imply that climate services centres worldwide and Africa in particular can make more objective use of model forecast data sets provided by global producing centres (GPCs) for consensus climate outlooks.展开更多
Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automat...Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.展开更多
The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local mod...The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local models valid in a given region or domain. Different strategies exist in the literature and are generally based on a partitioning of the non-linear system’s full range of operation into multiple smaller operating regimes each of which is associated with a locally valid model or controller. However, most of these strategies, which suppose the determination of these local models as well as their validity domain, remain arbitrary and are generally fixed thanks to a certain a priori knowledge of the system whatever its order. Recently, we have proposed a new approach to derive a multimodel basis which allows us to limit the number of models in the basis to almost four models. Meanwhile, the transition problem between the different models, which may use either a simple commutation or a fusion technique, remains still arise. In this plenary talk, a fuzzy fusion technique is presented and has the following main advantages: （1） use of a fuzzy partitioning in order to determine the validity of each model which enhances the robustness of the solution; 2 introduction, besides the four extreme models, of another model, called average model, determined as an average of the boundary models.展开更多
In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical m...In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.展开更多
Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF （European Centre for Medium-Range Weather Forecasts）, JMA （Japan Meteorological Agency）, NCEP （National Centers for Environmental Predic...Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF （European Centre for Medium-Range Weather Forecasts）, JMA （Japan Meteorological Agency）, NCEP （National Centers for Environmental Prediction）, and UKMO （United Kingdom Met Office） in THORPEX （The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble （TIGGE） datasets, for the Northern Hemisphere （10~ 87.5~N, 0~ 360~） from i June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean （BREM）, the multiple linear regression based superensemble （LRSUP）, and the neural network based superensemble （NNSUP） techniques for the forecast period from 8 to 31 August 2007. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.展开更多
By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combinat...By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combination of empirical orthogonal function and canonical correlation analysis） to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1% 7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multi- model ensemble （SDMME）, the relative error is improved from -15.8% to -1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble （MME） and the downscaling of most individual models. The projections of SDMME reveal that under the RCP （Representative Concentration Pathway） 4.5 scenario, the projected domain-averaged precipitation changes for the early （2016-2035）, middle （2046 2065）, and late （2081-2100） 21st century are 1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of l15°E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods~ with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5°N for both periods.展开更多
The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed b...The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed by seminal monographs(see Buckland et al.(1997)and Burnham and Anderson(2003)),which are termed as smoothed Akaike information criterion(SAIC)and smoothed Bayesian information criterion(SBIC)methods.Due to their simplicity and applicability,these methods are very widely used in many fields.By using an illustrative example and deriving limiting properties for the weights in the linear regression,we find that the existing variance estimation for SAIC is not applicable because of a restrictive condition,but for SBIC it is applicable.Especially,we propose a simulation-based inference for SAIC based on the limiting properties.Both the simulation study and the real data example show the promising performance of the proposed simulationbased inference.展开更多
This paper presents the development of a new nonlinear representation by exploiting the multimodel approach and the new linear representation ARX-Laguerre for each operating region. The resulting multimodel, entitled ...This paper presents the development of a new nonlinear representation by exploiting the multimodel approach and the new linear representation ARX-Laguerre for each operating region. The resulting multimodel, entitled ARX-Laguerre multimodel, is characterized by the parameter number reduction with a recursive representation. However, a significant reduction of this multimodel is subject to an optimal choice of Laguerre poles characterizing each local linear model ARX-Laguerre. Therefore, the authors propose an optimization algorithm to estimate, from input/output measurements, the optimal values of Laguerre poles. The ARX-Laguerre multimodel as well as the proposed optimization algorithm are tested on a continuous stirred tank reactor system （CSTR）. Moreover, the authors take into account a practical validation on an experimental communicating two tank system （CTTS）.展开更多
地震灾害观测数据多源异构、蕴含知识分散且关联程度低,导致难以高效利用数据进行信息整合和查询,进而提供风险评估、救援决策辅助支持。知识图谱是一种有效的数据关联和融合的手段。首先,基于自顶向下方法梳理地震灾害领域概念,构建地...地震灾害观测数据多源异构、蕴含知识分散且关联程度低,导致难以高效利用数据进行信息整合和查询,进而提供风险评估、救援决策辅助支持。知识图谱是一种有效的数据关联和融合的手段。首先,基于自顶向下方法梳理地震灾害领域概念,构建地震灾害数据、地质/地理环境、地震灾害事件、地震灾害应急任务、地震灾害模型本体,形成地震灾害本体层;结合自底向上方法构建高质量数据层,通过卷积神经网络对遥感影像进行灾害前后变化识别,实现从影像信息到文本知识的智能结构化转换;融合微调后通用信息抽取框架(universal information extraction,简称UIE)预训练模型对文本数据进行命名实体及关系属性知识抽取,精确率分别为82.04%和70.66%。通过计算词向量语义相似度实现数据融合与统一表达。以2023年12月18日甘肃省临夏州积石山县地震为例,通过本体构建、数据抽取、统一表达形成高质量地震灾害知识图谱,实现地震灾害多源异构地震数据到统一知识表达的转化。基于所构建的地震灾害知识图谱实现了灾害损失、应急链决策支持的查询展示,及结合相关地质数据推理和查询潜在次生灾害。该方法结合深度学习与预训练技术,融合多模态数据,构建了地震灾害知识图谱构建,为快速准确的地震灾害信息查询与次生灾害发生提供辅助支撑。展开更多
基金sponsored by the National Natural Science Foundation of China(Grant Nos.42175052 and U2442206)the Joint Research Project for Meteorological Capacity Improvement(Grant No.23NLTSQ007,23NLTSZ003)+2 种基金the Innovative Development Special Project of the China Meteorological Administration(Grant No.CXFZ2023J002)the National Key R&D Program of China(Grant No.2023YFC3007700,2024YFC3013100)the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN06)。
文摘Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.
基金supported by the National Natural Science Foundation of China (Grant Nos 40775065 and 41075074)the National Special Fund for Meteorology (Grant No GYHY200806029)
文摘With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.
基金supported by the National Key Technology Research and Development Program(Grant No.2006BAC02B04)the Major State Basic Research Development Program of China(Grant No.2006CB400503)
文摘An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.
文摘Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the “Climate of the 20th Century Experiment” （20C3M） for the Fourth IPCC Assessment Report. Climate Research Unit （CRU） data were chosen for the observation analysis field. Root mean square （RMS） error and correlation coeffi-cients （R） are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression （MLR） performs best. However, for bias-corrected data, the superensemble based on singular value decomposition （SVD） produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.
基金Special Scientific Research Fund of Meteorological Public Welfare Industries of China(GYHY(QX)2007-6-1)National Nature Science Foundation of China(41305081)
文摘Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and the possible causes of the extreme weather events with low temperature and icing conditions,which occurred in the southern part of China during early 2008, are investigated in this study. In addition, multimodel consensus forecasting experiments are conducted by using the ensemble forecasts of ECMWF, JMA, NCEP and CMA taken from the TIGGE archives. Results show that more than a third of the stations in the southern part of China were covered by the extremely abundant precipitation with a 50-a return period, and extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan province as well. For the 24- to 216-h surface temperature forecasts, the bias-removed multimodel ensemble mean with running training period(R-BREM) has the highest forecast skill of all individual models and multimodel consensus techniques. Taking the RMSEs of the ECMWF 96-h forecasts as the criterion, the forecast time of the surface temperature may be prolonged to 192 h over the southeastern coast of China by using the R-BREM technique. For the sprinkle forecasts over central and southern China, the R-BREM technique has the best performance in terms of threat scores(TS) for the 24- to 192-h forecasts except for the 72-h forecasts among all individual models and multimodel consensus techniques. For the moderate rain, the forecast skill of the R-BREM technique is superior to those of individual models and multimodel ensemble mean.
文摘The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method.
文摘A novel intelligent adaptive fuzzy PHD controller based on multimodel control approach is presented in this paper.It can improve the system performance of the dynamic time- varying system at various operating conditions.The fuzzy PHD controller is implemented by combining a fuzzy PI with a fuzzy PD controller in a parallel structure. The parameters of the fuzzy PHD controller are linked, via analytical derivation, to the gains of the linear PID controller. The sum of error square is used as performance criterion to locate the model that best reresents the process among the multiple models, The desired control output to drive the process along the desired path is generated only by modifying the output scale factots GU_I and GU_D of the fuzzy PID controller, Among the prescribed models, the control signal of the nearestmmodel to the system is applied. The system can be driven to its original trajectory because of the robustness of the fuzzy PID controller, Computer simulation results show that the
文摘Improvements that can be attained in seasonal climate predictions in various parts of Africa using the multimodel supersensemble scheme are presented in this study. The synthetic superensemble (SSE) used follows the approach originally developed at Florida State University (FSU). The technique takes more advantage of the skill in the climate forecast data sets from atmosphere-ocean general circulation models running at many centres worldwide including the WMO global producing centers (GPCs). The module used in this work drew data sets from the Four versions of FSU coupled model system, seven models from the DEMETER project which is the forerun to the current European Ensembles Forecast System, the NCAR Model, and the Predictive Ocean Atmosphere Model for Australia (POAMA), all making a set of 13 individual models. An archive consisting of monthly simulations of precipitation was available over all the 5 regions of Africa, namely Eastern, Central, Northern, Southern, and Western Africa. The results showed that the SSE forecast for precipitation carries a higher skill compared to each of the member models and the ensemble mean. Relative to the ensemble mean (EM), the SSE provides an improvement of 18% in simulation of season cycle of precipitation climatology. In Eastern Africa, during December-February season, a north-south gradient of precipitation prevails between Tropical East Africa and the sector of the region towards Southern Africa. This regional scale climate pattern is a direct influence of the Intertropical Convergence Zone (ITZC) across the African continent during this time of the year. The SSE emerges with superior skill scores such as lowest root mean square error above the EM and the member models, for example in the prediction of spatial location and precipitation magnitudes that characterize the see-saw precipitation pattern in Eastern Africa. In all parts of Africa, and especially Eastern Africa where seasonal precipitation variability is a frequent cause huge human suffering due to droughts and famine, the multimodel superensemble and its subsequent improvements will always provide a forecast that outweighs the best Atmosphere-Ocean Climate Model. This approach and results herein imply that climate services centres worldwide and Africa in particular can make more objective use of model forecast data sets provided by global producing centres (GPCs) for consensus climate outlooks.
文摘Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.
文摘The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local models valid in a given region or domain. Different strategies exist in the literature and are generally based on a partitioning of the non-linear system’s full range of operation into multiple smaller operating regimes each of which is associated with a locally valid model or controller. However, most of these strategies, which suppose the determination of these local models as well as their validity domain, remain arbitrary and are generally fixed thanks to a certain a priori knowledge of the system whatever its order. Recently, we have proposed a new approach to derive a multimodel basis which allows us to limit the number of models in the basis to almost four models. Meanwhile, the transition problem between the different models, which may use either a simple commutation or a fusion technique, remains still arise. In this plenary talk, a fuzzy fusion technique is presented and has the following main advantages: （1） use of a fuzzy partitioning in order to determine the validity of each model which enhances the robustness of the solution; 2 introduction, besides the four extreme models, of another model, called average model, determined as an average of the boundary models.
基金Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)Innovation Foundation of CMA Public Meteorological Service Center(K2023002)+1 种基金“Tianchi Talents”Introduction Plan(2023)Key Innovation Team for Energy and Meteorology of China Meteorological Administration。
文摘In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY(QX)2007-6-1)National Key Basic Research and Development (973) Program of China (2012CB955204)
文摘Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF （European Centre for Medium-Range Weather Forecasts）, JMA （Japan Meteorological Agency）, NCEP （National Centers for Environmental Prediction）, and UKMO （United Kingdom Met Office） in THORPEX （The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble （TIGGE） datasets, for the Northern Hemisphere （10~ 87.5~N, 0~ 360~） from i June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean （BREM）, the multiple linear regression based superensemble （LRSUP）, and the neural network based superensemble （NNSUP） techniques for the forecast period from 8 to 31 August 2007. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.
基金Supported by the National Natural Science Foundation of China(41230528)Priority Academic Program Development(PAPD)of Jiangsu Higher Education InstitutionsNational Key Pesearch and Development Program of China(2016YFA0600402)
文摘By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combination of empirical orthogonal function and canonical correlation analysis） to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1% 7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multi- model ensemble （SDMME）, the relative error is improved from -15.8% to -1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble （MME） and the downscaling of most individual models. The projections of SDMME reveal that under the RCP （Representative Concentration Pathway） 4.5 scenario, the projected domain-averaged precipitation changes for the early （2016-2035）, middle （2046 2065）, and late （2081-2100） 21st century are 1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of l15°E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods~ with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5°N for both periods.
基金supported by National Key R&D Program of China(Grant No.2020AAA 0105200)National Natural Science Foundation of China(Grant Nos.12001559,71925007,71988101 and 72042019)+3 种基金Ministry of Education of China(Grant No.17YJC910011)the Youth Innovation Promotion Association of the Chinese Academy of Sciencesthe Beijing Academy of Artificial IntelligenceAcademy for Multidisciplinary Studies,Capital Normal University。
文摘The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed by seminal monographs(see Buckland et al.(1997)and Burnham and Anderson(2003)),which are termed as smoothed Akaike information criterion(SAIC)and smoothed Bayesian information criterion(SBIC)methods.Due to their simplicity and applicability,these methods are very widely used in many fields.By using an illustrative example and deriving limiting properties for the weights in the linear regression,we find that the existing variance estimation for SAIC is not applicable because of a restrictive condition,but for SBIC it is applicable.Especially,we propose a simulation-based inference for SAIC based on the limiting properties.Both the simulation study and the real data example show the promising performance of the proposed simulationbased inference.
文摘This paper presents the development of a new nonlinear representation by exploiting the multimodel approach and the new linear representation ARX-Laguerre for each operating region. The resulting multimodel, entitled ARX-Laguerre multimodel, is characterized by the parameter number reduction with a recursive representation. However, a significant reduction of this multimodel is subject to an optimal choice of Laguerre poles characterizing each local linear model ARX-Laguerre. Therefore, the authors propose an optimization algorithm to estimate, from input/output measurements, the optimal values of Laguerre poles. The ARX-Laguerre multimodel as well as the proposed optimization algorithm are tested on a continuous stirred tank reactor system （CSTR）. Moreover, the authors take into account a practical validation on an experimental communicating two tank system （CTTS）.
文摘地震灾害观测数据多源异构、蕴含知识分散且关联程度低,导致难以高效利用数据进行信息整合和查询,进而提供风险评估、救援决策辅助支持。知识图谱是一种有效的数据关联和融合的手段。首先,基于自顶向下方法梳理地震灾害领域概念,构建地震灾害数据、地质/地理环境、地震灾害事件、地震灾害应急任务、地震灾害模型本体,形成地震灾害本体层;结合自底向上方法构建高质量数据层,通过卷积神经网络对遥感影像进行灾害前后变化识别,实现从影像信息到文本知识的智能结构化转换;融合微调后通用信息抽取框架(universal information extraction,简称UIE)预训练模型对文本数据进行命名实体及关系属性知识抽取,精确率分别为82.04%和70.66%。通过计算词向量语义相似度实现数据融合与统一表达。以2023年12月18日甘肃省临夏州积石山县地震为例,通过本体构建、数据抽取、统一表达形成高质量地震灾害知识图谱,实现地震灾害多源异构地震数据到统一知识表达的转化。基于所构建的地震灾害知识图谱实现了灾害损失、应急链决策支持的查询展示,及结合相关地质数据推理和查询潜在次生灾害。该方法结合深度学习与预训练技术,融合多模态数据,构建了地震灾害知识图谱构建,为快速准确的地震灾害信息查询与次生灾害发生提供辅助支撑。
