Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological foreca...Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.展开更多
Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-ra...Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.展开更多
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on...This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.展开更多
Evaluating whether an ensemble prediction system(EPS)can accurately represent forecast uncertainty is a key aspect of model development and ensemble forecast applications.In this study,a four-dimensional diagnostic an...Evaluating whether an ensemble prediction system(EPS)can accurately represent forecast uncertainty is a key aspect of model development and ensemble forecast applications.In this study,a four-dimensional diagnostic analysis model for assessing ensemble forecast uncertainty is proposed,by analyzing the relationship between the ensemble spread and root-mean-square error(RMSE)of the ensemble mean in terms of their temporal evolution(one-dimensional)and spatial distribution(three-dimensional),together with use of the linear variance calibration(LVC)method.Based on this model and the daily operational forecast data of the China Meteorological Administration(CMA)global EPS(CMA-GEPS)in December 2022–November 2023,characteristics of the CMA-GEPS forecast uncertainty are diagnosed and analyzed,and compared against the state-of-the-art operational global EPS of ECMWF.Generally,there is a deficiency in CMA-GEPS,which underestimates the forecast uncertainty,especially in the tropics.However,at certain initialization times in some seasons and over some locations,the spread appears greater than the RMSE,indicating an overestimation of forecast uncertainty.Moreover,CMA-GEPS performs better in capturing the forecast uncertainty of lower-level variables than upper-level variables;and in comparison with the mass and thermal fields,the forecast uncertainty of the dynamic field is better represented.Diagnostic analysis using the LVC method reveals that the relevance between the ensemble variance and the ensemble mean error variance of CMA-GEPS increases with forecast lead time,and the problem of underestimated forecast uncertainty is continuously alleviated.In addition,ECMWF EPS behaves distinctly better than CMA-GEPS in representing the forecast uncertainty and its growth process,the reasons for which are discussed and elucidated from the perspective of shortcomings in the methods to generate the initial and model perturbations,the ensemble size,and the forecast model adopted by CMA-GEPS.展开更多
For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is th...For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.展开更多
Through daily convection-permitting ensemble simulations conducted over a 3-month period,the forecast uncertainty for the land breeze and associated coastal rainfall during early-summer rainy season over South China i...Through daily convection-permitting ensemble simulations conducted over a 3-month period,the forecast uncertainty for the land breeze and associated coastal rainfall during early-summer rainy season over South China is investigated.The ensemble includes 12 sets of physics parameterization schemes for boundary layer,radiation,surface layer,and land surface processes.Observations from air–sea buoys at sea,coastal weather stations,and radiosondes are employed to evaluate the diurnal variations and vertical structures of the simulated land breezes.Results suggest that the forecast uncertainty of land breeze circulations is closely associated with the model’s representation of the nocturnal near-surface air temperature on land sides.A systematic underestimation of nocturnal air temperature is recognized in most ensemble members,while the diverse errors of daytime air temperature on land can be diminished through the ensemble mean.The cold bias tends to create stronger land breezes,resulting in prolonged and widespread coastal rainfall through more intensive coastal convergence.By comparing the relative contributions of multiple parameterization schemes,it is found that the systematic underestimation for nocturnal air temperature primarily results from the surface layer and land surface parameterization schemes.To improve the nighttime temperature forecast over this rainfall hotspot,it is essential to implement an advanced land surface model that incorporates complex thermodynamic processes tailored to this climate regime.Additionally,improved parameterization schemes for the planetary boundary layer and surface layer are necessary to enhance the nocturnal turbulent intensity under near-neutral conditions.展开更多
Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timel...Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.展开更多
Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to...Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast （BPF）, a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast accord- ing to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting sys- tem in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hy- drology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the de- terministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.展开更多
Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time...Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time based on the end-user's own risk assessment and decision threshold. The presented results of an interview study,conducted with 27 members of German civil protection authorities, show that developments in meteorology and weather forecasting do not necessarily fit the current practices of German emergency services. These practices are mostly carried out based on alarms and ground truth in a superficial reactive manner, rather than on anticipation based on prognoses or forecasts. Emergency managers cope with uncertainty by collecting, comparing, and blending different information about an uncertain event and its uncertain outcomes within the situation assessment to validate the information. Emergency managers struggle most with an increase of emergency calls and missions due to the impacts of severe weather. Because of the additional expenditures, the weather event makes it even harder for them to fulfill their core duties. These findings support the need for impact-based warnings.展开更多
This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging （BMA）, aiming to improve the accuracy of tropical cyclone （TC） intensity forecasts, especially forecasts of minim...This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging （BMA）, aiming to improve the accuracy of tropical cyclone （TC） intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center （Pmin）＇ The multi-model ensemble comprises three operational forecast models： the Global Forecast System （GFS） of NCEP, the Hurricane Weather Research and Forecasting （HWRF） models of NCEP, and the Integrated Forecasting System （IFS） of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution （or probability density function, PDF） of emin was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models var-ied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distri-bution transformation, provided more accurate forecasts than did the best of the ensemble members （HWRF）, with a 5%-7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced ＂predictive variance＂ that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the poten-tial to be applied to routine operational forecasting.展开更多
With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed...With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory(NSWLSTM) network and Gaussian process regression(GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory(LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR(NSWLSTM-GPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFC3700701)National Natural Science Foundation of China(Grant Nos.41775146,42061134009)+1 种基金USTC Research Funds of the Double First-Class Initiative(YD2080002007)Strategic Priority Research Program of Chinese Academy of Sciences(XDB41000000).
文摘Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.
基金supported by the joint funds of the Chinese National Natural Science Foundation(NSFC)(Grant No.U2242213)the funds of the NSFC(Grant No.42341209)+2 种基金the National Key Research and Development(R&D)Program of the Ministry of Science and Technology of China(Grant No.2021YFC3000902)the National Science Foundation for Young Scholars(Grant No.42205166)the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSQ008)。
文摘Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
基金supported by the Israeli Ministry of Infrastructure,Energy and Water Resources.
文摘This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.
基金Supported by the National Natural Science Foundation of China(U2242213 and 41905090)。
文摘Evaluating whether an ensemble prediction system(EPS)can accurately represent forecast uncertainty is a key aspect of model development and ensemble forecast applications.In this study,a four-dimensional diagnostic analysis model for assessing ensemble forecast uncertainty is proposed,by analyzing the relationship between the ensemble spread and root-mean-square error(RMSE)of the ensemble mean in terms of their temporal evolution(one-dimensional)and spatial distribution(three-dimensional),together with use of the linear variance calibration(LVC)method.Based on this model and the daily operational forecast data of the China Meteorological Administration(CMA)global EPS(CMA-GEPS)in December 2022–November 2023,characteristics of the CMA-GEPS forecast uncertainty are diagnosed and analyzed,and compared against the state-of-the-art operational global EPS of ECMWF.Generally,there is a deficiency in CMA-GEPS,which underestimates the forecast uncertainty,especially in the tropics.However,at certain initialization times in some seasons and over some locations,the spread appears greater than the RMSE,indicating an overestimation of forecast uncertainty.Moreover,CMA-GEPS performs better in capturing the forecast uncertainty of lower-level variables than upper-level variables;and in comparison with the mass and thermal fields,the forecast uncertainty of the dynamic field is better represented.Diagnostic analysis using the LVC method reveals that the relevance between the ensemble variance and the ensemble mean error variance of CMA-GEPS increases with forecast lead time,and the problem of underestimated forecast uncertainty is continuously alleviated.In addition,ECMWF EPS behaves distinctly better than CMA-GEPS in representing the forecast uncertainty and its growth process,the reasons for which are discussed and elucidated from the perspective of shortcomings in the methods to generate the initial and model perturbations,the ensemble size,and the forecast model adopted by CMA-GEPS.
基金the National Key R&D Program of China under Grant No.2020YFB0905900in part by the State Grid Corporation of China project“Research on inter-provincial price coupling mechanism of national unified electricity spot market”.
文摘For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.
基金Supported by the National Natural Science Foundation of China(U2242203,42275006,41805035,and 42030610)Guangdong Basic and Applied Basic Research Funds(2024A1515030210 and 2022A1515011288)Key Innovation Team Program of China Meteorological Administration(CMA2023ZD08).
文摘Through daily convection-permitting ensemble simulations conducted over a 3-month period,the forecast uncertainty for the land breeze and associated coastal rainfall during early-summer rainy season over South China is investigated.The ensemble includes 12 sets of physics parameterization schemes for boundary layer,radiation,surface layer,and land surface processes.Observations from air–sea buoys at sea,coastal weather stations,and radiosondes are employed to evaluate the diurnal variations and vertical structures of the simulated land breezes.Results suggest that the forecast uncertainty of land breeze circulations is closely associated with the model’s representation of the nocturnal near-surface air temperature on land sides.A systematic underestimation of nocturnal air temperature is recognized in most ensemble members,while the diverse errors of daytime air temperature on land can be diminished through the ensemble mean.The cold bias tends to create stronger land breezes,resulting in prolonged and widespread coastal rainfall through more intensive coastal convergence.By comparing the relative contributions of multiple parameterization schemes,it is found that the systematic underestimation for nocturnal air temperature primarily results from the surface layer and land surface parameterization schemes.To improve the nighttime temperature forecast over this rainfall hotspot,it is essential to implement an advanced land surface model that incorporates complex thermodynamic processes tailored to this climate regime.Additionally,improved parameterization schemes for the planetary boundary layer and surface layer are necessary to enhance the nocturnal turbulent intensity under near-neutral conditions.
文摘Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.
基金Supported by the National Natural Science Foundation of China (41075035)National Science and Technology Support Program of China (2009BAC51B00)+1 种基金National (Key) Basic Research and Development (973) Program of China (2012CB417204)China Meteorological Administration Special Public Welfare Research Fund (GYHY200906007)
文摘Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast （BPF）, a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast accord- ing to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting sys- tem in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hy- drology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the de- terministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.
基金funded by the BMVI (Federal Ministry of Transport and Digital Infrastructures)
文摘Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time based on the end-user's own risk assessment and decision threshold. The presented results of an interview study,conducted with 27 members of German civil protection authorities, show that developments in meteorology and weather forecasting do not necessarily fit the current practices of German emergency services. These practices are mostly carried out based on alarms and ground truth in a superficial reactive manner, rather than on anticipation based on prognoses or forecasts. Emergency managers cope with uncertainty by collecting, comparing, and blending different information about an uncertain event and its uncertain outcomes within the situation assessment to validate the information. Emergency managers struggle most with an increase of emergency calls and missions due to the impacts of severe weather. Because of the additional expenditures, the weather event makes it even harder for them to fulfill their core duties. These findings support the need for impact-based warnings.
基金Supported by the National Natural Science Foundation of China(40830957)China Meteorological Administration Special Public Welfare Research Fund(GYHY201106018)+2 种基金National Basic Research and Development(973)Program of China(2011CB421504)National Science and Technology Support Program of China(2010BAC51B05)Knowledge Innovation Project of the Chinese Academy of Sciences(KZCX2-YW-Q1-02)
文摘This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging （BMA）, aiming to improve the accuracy of tropical cyclone （TC） intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center （Pmin）＇ The multi-model ensemble comprises three operational forecast models： the Global Forecast System （GFS） of NCEP, the Hurricane Weather Research and Forecasting （HWRF） models of NCEP, and the Integrated Forecasting System （IFS） of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution （or probability density function, PDF） of emin was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models var-ied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distri-bution transformation, provided more accurate forecasts than did the best of the ensemble members （HWRF）, with a 5%-7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced ＂predictive variance＂ that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the poten-tial to be applied to routine operational forecasting.
基金supported by the National Key Research and Development Programe of China (2016YFB0901900)the National Natural Science Foundation of China (U1908213)+2 种基金the Fundamental the Research Funds for the Central Universities(N182303037)Colleges and Universities in Hebei Province Science Research Program (QN2020504)the Foundation of Northeastern University at Qinhuangdao (XNB201803)。
文摘With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory(NSWLSTM) network and Gaussian process regression(GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory(LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR(NSWLSTM-GPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.
