Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,t...Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.展开更多
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradi...Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.展开更多
This paper studies the renewable power forecasting task with a more advanced formulation,the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the pri...This paper studies the renewable power forecasting task with a more advanced formulation,the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the privacy of data in each plant.To realize such a task,an advanced domain-invariant feature learning embedded federated learning(DIFL)framework is proposed to coordinate the development of a system of deep networkbased models serving as multiple clients and one server.In DIFL,each client,which serves each local renew-able power plant,maps its raw data input into latent features via a local feature extractor and generates power output sequence probabilistic forecasts via a locally hosted forecasting model.The cloud-hosted server first aggregates the knowledge from models of clients and next dispatches the aggregated model back to each client for facilitating each local feature extractor to identify domain-invariant features via interacting with a server-side discriminator.Therefore,only desensitized data,such as parameters of the models,are allowed to be transmitted among end users for preserving local data privacy of power plants.To verify the advantages of the DIFL,a preliminary exploration of its theoretical property is first conducted.Next,computational studies are performed to benchmark the DIFL against famous baselines based on datasets collected from commercial renewable power plants.Results further confirm that,in terms of the averaged performance,the DIFL consistently realizes im-provements against all benchmarks based on both real wind farm and solar power plant datasets.展开更多
The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrai...The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.展开更多
A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which fu...A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.展开更多
Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters.It acts as a basis for arranging distributed energy resources,implementing demand re...Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters.It acts as a basis for arranging distributed energy resources,implementing demand response,etc.Compared to aggre-gated-level load,the electric load of an individual building is more stochastic and thus spawns many probabilistic forecasting meth-ods.Many of them resort to artificial neural networks(ANN)to build forecasting models.However,a well-designed forecasting model for one building may not be suitable for others,and manually designing and tuning optimal forecasting models for various buildings are tedious and time-consuming.This paper proposes an adaptive probabilistic load forecasting model to automatically generate high-performance NN structures for different buildings and produce quantile forecasts for future loads.Specifically,we cascade the long short term memory(LSTM)layer with the adjusted Differential ArchiTecture Search(DARTS)cell and use the pinball loss function to guide the model during the improved model fitting process.A case study on an open dataset shows that our proposed model has superior performance and adaptivity over the state-of-the-art static neural network model.Besides,the improved fitting process of DARTS is proved to be more time-efficient than the original one.展开更多
This study explores the initiation mechanisms of convective wind events,emphasizing their variability across different atmospheric circulation patterns.Historically,the inadequate feature categorization within multi-f...This study explores the initiation mechanisms of convective wind events,emphasizing their variability across different atmospheric circulation patterns.Historically,the inadequate feature categorization within multi-faceted forecast models has led to suboptimal forecast efficacy,particularly for events in dynamically weak forcing conditions during the warm season.To improve the prediction accuracy of convective wind events,this research introduces a novel approach that combines machine learning techniques to identify varying meteorological flow regimes.Convective winds(CWs)are defined as wind speeds reaching or exceeding 17.2 m s^(-1)and severe convective winds(SCWs)as speeds surpassing 24.5 m s^(-1).This study examines the spatial and temporal distribution of CW and SCW events from 2013 to 2021 and their circulation dynamics associated with three primary flow regimes:cold air advection,warm air advection,and quasibarotropic conditions.Key circulation features are used as input variables to construct an effective weather system pattern recognition model.This model employs an Adaptive Boosting(AdaBoost)algorithm combined with Random Under-Sampling(RUS)to address the class imbalance issue,achieving a recognition accuracy of 90.9%.Furthermore,utilizing factor analysis and Support Vector Machine(SVM)techniques,three specialized and independent probabilistic prediction models are developed based on the variance in predictor distributions across different flow regimes.By integrating the type of identification model with these prediction models,an enhanced comprehensive model is constructed.This advanced model autonomously identifies flow types and accordingly selects the most appropriate prediction model.Over a three-year validation period,this improved model outperformed the initially unclassified model in terms of prediction accuracy.Notably,for CWs and SCWs,the maximum Peirce Skill Score(PSS)increased from 0.530 and 0.702 to 0.628 and 0.726,respectively,and the corresponding maximum Threat Score(TS)improved from 0.087 and 0.024 to 0.120 and 0.026.These improvements were significant across all samples,with the cold air advection type showing the greatest enhancement due to the significant spatial variability of each factor.Additionally,the model improved forecast precision by prioritizing thermal factors,which played a key role in modulating false alarm rates in warm air advection and quasi-barotropic flow regimes.The results confirm the critical contribution of circulation feature recognition and segmented modeling to enhancing the adaptability and predictive accuracy of weather forecast models.展开更多
Probabilistic forecasting provides insights in estimating the uncertainty of photovoltaic(PV)power forecasts.In this study,an innovative probabilistic ultra-short-term PV power forecasting framework that integrates na...Probabilistic forecasting provides insights in estimating the uncertainty of photovoltaic(PV)power forecasts.In this study,an innovative probabilistic ultra-short-term PV power forecasting framework that integrates natural gradient boosting(NGBoost)and deep neural networks is developed.Specifically,an attention-enhanced neural network combining convolutional neural networks(CNN)and bidirectional long short-term memory(BiLSTM)networks is employed for feature engineering to extract abstract features from time-series data.The extracted features are then fed into an optimized NGBoost model to yield probabilistic forecasts.In comparison to the benchmark models,i.e.,the recently reported quantile regression(QR)-based deep learning methods and NGBoost,the proposed model demonstrates an enhanced ability to capture variation patterns in PV power output,further improving the forecast skill score by approximately 15–60%in deterministic forecasting.In terms of probabilistic forecasting,the proposed model shows superior forecast reliability and sharpness compared to all benchmark methods.Its continuous ranked probability score(CRPS)ranges from 0.0710 kW to 0.0898 kW,achieving reductions of approximately 21–43%over QR-based models and 29–40%over NGBoost.Furthermore,within confidence intervals of 10–90%,the proposed model consistently maintains higher coverage probabilities along with narrower average forecast intervals,as evidenced by a lower Winkler score(WS)than the benchmark models.The findings of this study provide insightful references for probabilistic PV power forecasting research,contributing to efficient solar power management and dispatch.展开更多
Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly a...Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems.展开更多
Lately,the power demand of consumers is increasing in distribution networks,while renewable power generation keeps penetrating into the distribution networks.Insufficient data make it hard to accurately predict the ne...Lately,the power demand of consumers is increasing in distribution networks,while renewable power generation keeps penetrating into the distribution networks.Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude.Hence,this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques.First,we formulate the short-term probabilistic residential load forecasting problem.Then,we propose a sequence-to-sequence(Seq2Seq)adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain(with massive consumption records of regular loads)to the target domain(with limited observations of new residential loads)and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem.For implementation,the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network,including the Seq2Seq recurrent neural networks(RNNs)composed of a long short-term memory(LSTM)encoder and an LSTM decoder,and quantile loss.Finally,this study conducts the case studies via multiple evaluation indices,comparative methods of classic machine learning and advanced deep learning,and various available data of the new residentical loads and other regular loads.The experimental results validate the effectiveness and stability of the proposed scheme.展开更多
The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations.Accurate and reliable electricity price forecasting is cru...The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations.Accurate and reliable electricity price forecasting is crucial for effective market participation,where price dynamics can be significantly more challenging to predict.Probabilistic forecasting,through prediction intervals,efficiently quantifies the inherent uncertainties in electricity prices,supporting better decision-making for market participants.This study explores the enhancement of probabilistic price prediction using Conformal Prediction(CP)techniques,specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference.These methods provide precise and reliable prediction intervals,outperforming traditional models in validity metrics.We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques.This ensemble delivers both narrow prediction intervals and high coverage,leading to more reliable and accurate forecasts.We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system.The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets,highlighting its practical benefits for market participants.展开更多
Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting...Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.展开更多
The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs.Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing ...The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs.Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing forecast accuracy.Within the context of meteorological services for alpine skiing at the 2022 Beijing Winter Olympics,The ECMWF ensemble products were used to evaluate six post-processing methods.These methods include ensemble model output statistics(EMOS),backpropagation neural networks(BP),particle swarm optimization algorithms with backpropagation neural networks(PSO),truncated normal distributions,truncated logarithmic distributions,and generalized extreme value(GEV) distributions.The performance of these methods in predicting gust probabilities at five observation points along a ski track was compared.All six methods exhibited a substantial reduction in forecast errors compared to the original ECMWF products;however,the ability to correct the model forecast results varied significantly across different wind speed ranges.Specifically,the EMOS,truncated normal distribution,truncated logarithmic distribution,and GEV distribution demonstrated advantages in low wind-speed ranges,whereas the BP and PSO methods exhibit lower forecast errors for high wind-speed events.Furthermore,this study affirms the rationality of utilizing the statistical characteristics derived from ensemble forecasts as probabilistic forecast factors.The application of probability integral transform(PIT) and quantile–quantile(QQ) plots demonstrates that gust variations at the majority of observation sites conform to the GEV distribution,thereby indicating the potential for further enhanced forecast accuracy.The results also underscore the significant utility of the PSO hybrid model,which amalgamates particle swarm optimization with a BP neural network,in the probabilistic forecasting of strong winds within the field of meteorology.展开更多
Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and...Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.展开更多
Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to be...Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to better represent forecast uncertainties with limited computational resources？ （2） What is tile relative impact on forecast skill of increasing model resolution and ensemble size？ Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500- hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs--were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more （less） beneficial than increasing the ensemble size for a short （long） forecast range.展开更多
In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer ...In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts （QPF） and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method （proposed with the single case study in Part I） was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.展开更多
A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation mode...A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.展开更多
A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple...A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System （NCEP GEFS） forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts （PQPF）. However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.展开更多
The predictability of the position,spatial coverage and intensity of the East Asian subtropical westerly jet （EASWJ） in the summers of 2010 to 2012 was examined for ensemble prediction systems （EPSs） from four rep...The predictability of the position,spatial coverage and intensity of the East Asian subtropical westerly jet （EASWJ） in the summers of 2010 to 2012 was examined for ensemble prediction systems （EPSs） from four representative TIGGE centers,including the ECMWF,the NCEP,the CMA,and the JMA.Results showed that each EPS predicted all EASWJ properties well,while the levels of skill of all EPSs declined as the lead time extended.Overall,improvements from the control to the ensemble mean forecasts for predicting the EASWJ were apparent.For the deterministic forecasts of all EPSs,the prediction of the average axis was better than the prediction of the spatial coverage and intensity of the EASWJ.ECMWF performed best,with a lead of approximately 0.5-1 day in predictability over the second-best EPS for all EASWJ properties throughout the forecast range.For probabilistic forecasts,differences in skills among the different EPSs were more evident in the earlier part of the forecast for the EASWJ axis and spatial coverage,while they departed obviously throughout the forecast range for the intensity.ECMWF led JMA by about 0.5-1 day for the EASWJ axis,and by about 1-2 days for the spatial coverage and intensity at almost all lead times.The largest lead of ECMWF over the relatively worse EPSs,such as NCEP and CMA,was approximately 3-4 days for all EASWJ properties.In summary,ECMWF showed the highest level of skill for predicting the EASWJ,followed by JMA.展开更多
To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts（SREFs）, a fivemember SREF experiment with perturbed initial soil moisture（ISM） was performed over a northern Chin...To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts（SREFs）, a fivemember SREF experiment with perturbed initial soil moisture（ISM） was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 h Pa, and produced skillful ensemble-mean 24-h accumulated precipitation（APCP24） forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.展开更多
基金funded by the National Natural Science Foundation of China under Grant 62273022.
文摘Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.
基金the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftungfunding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。
文摘Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.
基金supported in part by the Hong Kong RGC General Research Fund Project with No.11213124in part by Hong Kong ITC Innovation and Technology Fund Project with No.ITS/034/22MS+2 种基金in part by in part by Guangdong Provincial Basic and Applied Basic Research-Offshore Wind Power Joint Fund Project under Grant 2022A1515240066in part by Guangdong Province Technological Project with No.2023A0505030014in part by the Shenzhen-Hong Kong-Macao Science&Technology Category C Project with No.SGDX20220530111205037.
文摘This paper studies the renewable power forecasting task with a more advanced formulation,the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the privacy of data in each plant.To realize such a task,an advanced domain-invariant feature learning embedded federated learning(DIFL)framework is proposed to coordinate the development of a system of deep networkbased models serving as multiple clients and one server.In DIFL,each client,which serves each local renew-able power plant,maps its raw data input into latent features via a local feature extractor and generates power output sequence probabilistic forecasts via a locally hosted forecasting model.The cloud-hosted server first aggregates the knowledge from models of clients and next dispatches the aggregated model back to each client for facilitating each local feature extractor to identify domain-invariant features via interacting with a server-side discriminator.Therefore,only desensitized data,such as parameters of the models,are allowed to be transmitted among end users for preserving local data privacy of power plants.To verify the advantages of the DIFL,a preliminary exploration of its theoretical property is first conducted.Next,computational studies are performed to benchmark the DIFL against famous baselines based on datasets collected from commercial renewable power plants.Results further confirm that,in terms of the averaged performance,the DIFL consistently realizes im-provements against all benchmarks based on both real wind farm and solar power plant datasets.
基金supported by the National Natural Science Foundation of China under Grant 62301119。
文摘The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.
基金Under the auspices of National Natural Science Foundation of China (No. 50609005)Chinese Postdoctoral Science Foundation (No. 2009451116)+1 种基金Postdoctoral Foundation of Heilongjiang Province (No. LBH-Z08255)Foundation of Heilongjiang Province Educational Committee (No. 11451022)
文摘A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.
基金supported in part by the Seed Fund for Basic Research for New Staff of The University of Hong Kong(202107185032)and in part by the Alibaba Innovative Research programme.
文摘Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters.It acts as a basis for arranging distributed energy resources,implementing demand response,etc.Compared to aggre-gated-level load,the electric load of an individual building is more stochastic and thus spawns many probabilistic forecasting meth-ods.Many of them resort to artificial neural networks(ANN)to build forecasting models.However,a well-designed forecasting model for one building may not be suitable for others,and manually designing and tuning optimal forecasting models for various buildings are tedious and time-consuming.This paper proposes an adaptive probabilistic load forecasting model to automatically generate high-performance NN structures for different buildings and produce quantile forecasts for future loads.Specifically,we cascade the long short term memory(LSTM)layer with the adjusted Differential ArchiTecture Search(DARTS)cell and use the pinball loss function to guide the model during the improved model fitting process.A case study on an open dataset shows that our proposed model has superior performance and adaptivity over the state-of-the-art static neural network model.Besides,the improved fitting process of DARTS is proved to be more time-efficient than the original one.
基金Guangdong S&T Program(2024A1111120024)CMA Innovation and Development Fund(CXFZ2024J014)+3 种基金CMA Youth Innovation Team(CMA2024QN01)PRB Meteorological Open Research Fund(ZJLY202425-GD02)GBA Meteorological S&T Program(GHMA2024Y04)Guangzhou Meteorological Research Project(Z202401)。
文摘This study explores the initiation mechanisms of convective wind events,emphasizing their variability across different atmospheric circulation patterns.Historically,the inadequate feature categorization within multi-faceted forecast models has led to suboptimal forecast efficacy,particularly for events in dynamically weak forcing conditions during the warm season.To improve the prediction accuracy of convective wind events,this research introduces a novel approach that combines machine learning techniques to identify varying meteorological flow regimes.Convective winds(CWs)are defined as wind speeds reaching or exceeding 17.2 m s^(-1)and severe convective winds(SCWs)as speeds surpassing 24.5 m s^(-1).This study examines the spatial and temporal distribution of CW and SCW events from 2013 to 2021 and their circulation dynamics associated with three primary flow regimes:cold air advection,warm air advection,and quasibarotropic conditions.Key circulation features are used as input variables to construct an effective weather system pattern recognition model.This model employs an Adaptive Boosting(AdaBoost)algorithm combined with Random Under-Sampling(RUS)to address the class imbalance issue,achieving a recognition accuracy of 90.9%.Furthermore,utilizing factor analysis and Support Vector Machine(SVM)techniques,three specialized and independent probabilistic prediction models are developed based on the variance in predictor distributions across different flow regimes.By integrating the type of identification model with these prediction models,an enhanced comprehensive model is constructed.This advanced model autonomously identifies flow types and accordingly selects the most appropriate prediction model.Over a three-year validation period,this improved model outperformed the initially unclassified model in terms of prediction accuracy.Notably,for CWs and SCWs,the maximum Peirce Skill Score(PSS)increased from 0.530 and 0.702 to 0.628 and 0.726,respectively,and the corresponding maximum Threat Score(TS)improved from 0.087 and 0.024 to 0.120 and 0.026.These improvements were significant across all samples,with the cold air advection type showing the greatest enhancement due to the significant spatial variability of each factor.Additionally,the model improved forecast precision by prioritizing thermal factors,which played a key role in modulating false alarm rates in warm air advection and quasi-barotropic flow regimes.The results confirm the critical contribution of circulation feature recognition and segmented modeling to enhancing the adaptability and predictive accuracy of weather forecast models.
基金supported by the National Key R&D Program of China(2021YFE0107400)Innovation Fund Denmark in relation to SEM4Cities(IFD 0143–0004).
文摘Probabilistic forecasting provides insights in estimating the uncertainty of photovoltaic(PV)power forecasts.In this study,an innovative probabilistic ultra-short-term PV power forecasting framework that integrates natural gradient boosting(NGBoost)and deep neural networks is developed.Specifically,an attention-enhanced neural network combining convolutional neural networks(CNN)and bidirectional long short-term memory(BiLSTM)networks is employed for feature engineering to extract abstract features from time-series data.The extracted features are then fed into an optimized NGBoost model to yield probabilistic forecasts.In comparison to the benchmark models,i.e.,the recently reported quantile regression(QR)-based deep learning methods and NGBoost,the proposed model demonstrates an enhanced ability to capture variation patterns in PV power output,further improving the forecast skill score by approximately 15–60%in deterministic forecasting.In terms of probabilistic forecasting,the proposed model shows superior forecast reliability and sharpness compared to all benchmark methods.Its continuous ranked probability score(CRPS)ranges from 0.0710 kW to 0.0898 kW,achieving reductions of approximately 21–43%over QR-based models and 29–40%over NGBoost.Furthermore,within confidence intervals of 10–90%,the proposed model consistently maintains higher coverage probabilities along with narrower average forecast intervals,as evidenced by a lower Winkler score(WS)than the benchmark models.The findings of this study provide insightful references for probabilistic PV power forecasting research,contributing to efficient solar power management and dispatch.
基金The first author thanks the Brazilian National Council for Scientific and Technological Development for the Post-Doc scholarship(155814/2018-4).
文摘Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems.
基金supported by the National Natural Science Foundation of China(No.52177087)。
文摘Lately,the power demand of consumers is increasing in distribution networks,while renewable power generation keeps penetrating into the distribution networks.Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude.Hence,this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques.First,we formulate the short-term probabilistic residential load forecasting problem.Then,we propose a sequence-to-sequence(Seq2Seq)adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain(with massive consumption records of regular loads)to the target domain(with limited observations of new residential loads)and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem.For implementation,the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network,including the Seq2Seq recurrent neural networks(RNNs)composed of a long short-term memory(LSTM)encoder and an LSTM decoder,and quantile loss.Finally,this study conducts the case studies via multiple evaluation indices,comparative methods of classic machine learning and advanced deep learning,and various available data of the new residentical loads and other regular loads.The experimental results validate the effectiveness and stability of the proposed scheme.
基金financial support of Science Foundation Ireland,Ireland under Grant Nos.18/CRT/6223 and 12/RC/2289-P2which are co-funded under the European Regional Development Fund.
文摘The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations.Accurate and reliable electricity price forecasting is crucial for effective market participation,where price dynamics can be significantly more challenging to predict.Probabilistic forecasting,through prediction intervals,efficiently quantifies the inherent uncertainties in electricity prices,supporting better decision-making for market participants.This study explores the enhancement of probabilistic price prediction using Conformal Prediction(CP)techniques,specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference.These methods provide precise and reliable prediction intervals,outperforming traditional models in validity metrics.We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques.This ensemble delivers both narrow prediction intervals and high coverage,leading to more reliable and accurate forecasts.We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system.The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets,highlighting its practical benefits for market participants.
基金supported by the Swiss Federal Office of Energy(SFOE)and by the Italian Ministry of Education,University and Research(MIUR),through the ERA-NET Smart Energy Systems RegSys joint call 2018 project“DiGRiFlex-Real time Distribution GRid control and Flexibility provision under uncertainties.”。
文摘Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.
基金Supported by the National Meteorological Centre’s Special Project for Meteorological Modernization Construction in 2022(QXXDH202230)。
文摘The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs.Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing forecast accuracy.Within the context of meteorological services for alpine skiing at the 2022 Beijing Winter Olympics,The ECMWF ensemble products were used to evaluate six post-processing methods.These methods include ensemble model output statistics(EMOS),backpropagation neural networks(BP),particle swarm optimization algorithms with backpropagation neural networks(PSO),truncated normal distributions,truncated logarithmic distributions,and generalized extreme value(GEV) distributions.The performance of these methods in predicting gust probabilities at five observation points along a ski track was compared.All six methods exhibited a substantial reduction in forecast errors compared to the original ECMWF products;however,the ability to correct the model forecast results varied significantly across different wind speed ranges.Specifically,the EMOS,truncated normal distribution,truncated logarithmic distribution,and GEV distribution demonstrated advantages in low wind-speed ranges,whereas the BP and PSO methods exhibit lower forecast errors for high wind-speed events.Furthermore,this study affirms the rationality of utilizing the statistical characteristics derived from ensemble forecasts as probabilistic forecast factors.The application of probability integral transform(PIT) and quantile–quantile(QQ) plots demonstrates that gust variations at the majority of observation sites conform to the GEV distribution,thereby indicating the potential for further enhanced forecast accuracy.The results also underscore the significant utility of the PSO hybrid model,which amalgamates particle swarm optimization with a BP neural network,in the probabilistic forecasting of strong winds within the field of meteorology.
基金National Natural Science Foundation of China(No.51205043)the Special Fundamental Research Funds for Central Universities of China(No.DUT14QY21)
文摘Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.
文摘Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to better represent forecast uncertainties with limited computational resources？ （2） What is tile relative impact on forecast skill of increasing model resolution and ensemble size？ Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500- hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs--were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more （less） beneficial than increasing the ensemble size for a short （long） forecast range.
基金supported by a project of the National Natural Science Foundation of China (Grant No. 40875079)
文摘In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts （QPF） and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method （proposed with the single case study in Part I） was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.
基金Supported by the National Basic Research and Development (973) Program of China (2010CB428402)China Meteorological Administration Special Public Welfare Research Fund (GYHY200706001)
文摘A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.
基金supported by the project of the NSFC (Grants No. 40875079)
文摘A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System （NCEP GEFS） forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts （PQPF）. However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.
基金supported by the National (Key) Basic Research and Development Program of China (Grant No. 2012CB17204)
文摘The predictability of the position,spatial coverage and intensity of the East Asian subtropical westerly jet （EASWJ） in the summers of 2010 to 2012 was examined for ensemble prediction systems （EPSs） from four representative TIGGE centers,including the ECMWF,the NCEP,the CMA,and the JMA.Results showed that each EPS predicted all EASWJ properties well,while the levels of skill of all EPSs declined as the lead time extended.Overall,improvements from the control to the ensemble mean forecasts for predicting the EASWJ were apparent.For the deterministic forecasts of all EPSs,the prediction of the average axis was better than the prediction of the spatial coverage and intensity of the EASWJ.ECMWF performed best,with a lead of approximately 0.5-1 day in predictability over the second-best EPS for all EASWJ properties throughout the forecast range.For probabilistic forecasts,differences in skills among the different EPSs were more evident in the earlier part of the forecast for the EASWJ axis and spatial coverage,while they departed obviously throughout the forecast range for the intensity.ECMWF led JMA by about 0.5-1 day for the EASWJ axis,and by about 1-2 days for the spatial coverage and intensity at almost all lead times.The largest lead of ECMWF over the relatively worse EPSs,such as NCEP and CMA,was approximately 3-4 days for all EASWJ properties.In summary,ECMWF showed the highest level of skill for predicting the EASWJ,followed by JMA.
基金supported by the National Key R&D Program on Monitoring, Early Warning and Prevention of Major Natural Disaster (2017YFC1502103)the National Natural Science Foundation of China (Grant Nos. 41305099 and 41305053)
文摘To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts（SREFs）, a fivemember SREF experiment with perturbed initial soil moisture（ISM） was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 h Pa, and produced skillful ensemble-mean 24-h accumulated precipitation（APCP24） forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.
