This article describes a new and low-cost microwave passive sensor for hail prediction (forecasting) and detection developed in Armenia, which can be used to implement fully autonomous and automatically functioning ha...This article describes a new and low-cost microwave passive sensor for hail prediction (forecasting) and detection developed in Armenia, which can be used to implement fully autonomous and automatically functioning hail protection of locally limited or large agricultural and urban areas in order to prevent, suppress or catch hail in traps. The article also presents the results of measurements of the intrinsic emission characteristics of water and ice, rain and hail clouds, carried out in laboratory and field conditions in the Ku-band of radio frequencies. The results obtained showed that the intrinsic emission of a hail cloud in the Ku-band of radio frequencies differs significantly from the intrinsic emission of a rain cloud. The presented results show that indeed the radar is not very suitable for the timely detection and determination of hail with a high probability, which is very important for the timely starting up of anti-hail protection means. On the contrary, radiometers (passive microwave sensors) can become an effective sensing tool for timely detection and recognition of hail with a high probability of long-range approaches up to ~12 - 15 km.展开更多
In order to understand the current and potential use of ensemble forecasts in operational tropical cyclone(TC)forecasting,a questionnaire on the use of dynamic ensembles was conducted at operational TC forecast center...In order to understand the current and potential use of ensemble forecasts in operational tropical cyclone(TC)forecasting,a questionnaire on the use of dynamic ensembles was conducted at operational TC forecast centers across the world,in association with the World Meteorological Organisation(WMO)High-Impact Weather Project(HIWeather).The results of the survey are presented,and show that ensemble forecasts are used by nearly all respondents,particularly in TC track and genesis forecasting,with several examples of where ensemble forecasts have been pulled through successfully into the operational TC forecasting process.There is still however,a notable difference between the high proportion of operational TC forecasters who use and value ensemble forecast information,and the slower pull-through into operational forecast warnings and products of the probabilistic guidance and uncertainty information that ensembles can provide.Those areas of research and development that would help TC forecasters to make increased use of ensemble forecast information in the future include improved access to ensemble forecast data,verification and visualizations,the development of hazard and impact-based products,an improvement in the skill of the ensembles(particularly for intensity and structure),and improved guidance on how to use ensembles and optimally combine forecasts from all available models.A change in operational working practices towards using probabilistic information,and providing and communicating dynamic uncertainty information in operational forecasts and warnings,is also recommended.展开更多
In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The fea...In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The features of this methodology are as follows： （1） A Bayesian Ying-Yang Learning （BYYL） algorithm is used to construct a compact but high-performance system automatically. （2） A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. （3） The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.展开更多
This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administratio...This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones(TC)ASANI and MOCHA.The analysis of various Numerical Weather Prediction(NWP)model forecasts[ECMWF(European Centre fo...This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones(TC)ASANI and MOCHA.The analysis of various Numerical Weather Prediction(NWP)model forecasts[ECMWF(European Centre for Medium range Weather Forecast),NCEP(National Centers for Environmental Prediction),NCUM(National Centre for Medium Range Weather Forecast-Unified Model),IMD(India Meteorological Department),HWRF(Hurricane Weather Research and Forecasting)],MME(Multi-model Ensemble),SCIP(Statistical Cyclone Intensity Prediction)model,and OFCL(Official)forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good,but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA.Among all model forecasts,the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively.Also,the landfall point forecast errors of IMD were least for ASANI,and the MME and OFCL forecast errors were least for MOCHA.No model is found to be consistently better for landfall time forecast for ASANI,and the errors of ECMWF,IMD and HWRF were least and of same order for MOCHA.The intensity forecast errors of OFCL and SCIP were least for ASANI,and the forecast errors of HWRF,IMD,NCEP,SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general.The ECMWF model forecast errors for intensity were found to be highest for both the TCs.The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are revi...In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.展开更多
Accurate prediction of water level changes in reservoirs is crucial for optimizing the operation of reservoir projects and ensuring their safety.This study proposed a method for reservoir water level prediction based ...Accurate prediction of water level changes in reservoirs is crucial for optimizing the operation of reservoir projects and ensuring their safety.This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms.By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method and fuzzy entropy(FE)with the new and highly efficient Runge–Kuta optimizer(RUN),adaptive parameter optimization for the support vector machine(SVM)and radial basis function neural network(RBFNN)algorithms was achieved.Regression prediction was conducted on the two reconstructed sequences using SVM and RBFNN according to their respective features.This approach improved the accuracy and stability of predictions.In terms of accuracy,the combined model outperformed single models,with the determination coefficient,root mean square error,and mean absolute error values of 0.9975,0.2418 m,and 0.1616 m,respectively.In terms of stability,the model predicted more consistently in training and testing periods,with stable overall prediction accuracy and a better adaptive ability to complex datasets.The case study demonstrated that the combined prediction model effectively addressed the environmental factors affecting reservoir water levels,leveraged the strength of each predictive method,compensated for their limitations,and clarified the impacts of environmental factors on reservoir water levels.展开更多
Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by enviro...Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by environmental sensitivity,dynamic ocean conditions,and high maintenance demands.Artificial Intelligence(AI)offers promising solutions to these challenges by enabling intelligent,adaptive,and resilient energy systems.This review explores AI applications in ORE,focusing on three critical domains:optimization,forecasting,and control.Optimization techniques,including Genetic Algorithms(GA)and Swarm Intelligence(SI),are employed to enhance device efficiency,improve energy capture,optimize farm layouts,reduce environmental impacts,and lower installation costs.Forecasting uses Machine Learning(ML)and Deep Learning(DL)models to predict wave height,tidal flow,and energy output,aiding in grid integration and energy scheduling.In control systems,AI approaches like Reinforcement Learning(RL)and Fuzzy Logic ensure real-time responsiveness and predictive maintenance,improving system reliability in dynamic marine environments.Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making,while Digital Twin frameworks simulate and predict system performance before deployment.Explainable AI(XAI)is also discussed to ensure transparent and trustworthy decision-making.Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings.Overall this review offers a comprehensive synthesis of how AI enhances the performance,efficiency,and scalability of ORE systems.It serves as a valuable resource for researchers,policymakers,and industry professionals seeking to advance clean,smart,and sustainable ocean energy solutions.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel...Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.展开更多
Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many ...Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study em...A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.展开更多
文摘This article describes a new and low-cost microwave passive sensor for hail prediction (forecasting) and detection developed in Armenia, which can be used to implement fully autonomous and automatically functioning hail protection of locally limited or large agricultural and urban areas in order to prevent, suppress or catch hail in traps. The article also presents the results of measurements of the intrinsic emission characteristics of water and ice, rain and hail clouds, carried out in laboratory and field conditions in the Ku-band of radio frequencies. The results obtained showed that the intrinsic emission of a hail cloud in the Ku-band of radio frequencies differs significantly from the intrinsic emission of a rain cloud. The presented results show that indeed the radar is not very suitable for the timely detection and determination of hail with a high probability, which is very important for the timely starting up of anti-hail protection means. On the contrary, radiometers (passive microwave sensors) can become an effective sensing tool for timely detection and recognition of hail with a high probability of long-range approaches up to ~12 - 15 km.
文摘In order to understand the current and potential use of ensemble forecasts in operational tropical cyclone(TC)forecasting,a questionnaire on the use of dynamic ensembles was conducted at operational TC forecast centers across the world,in association with the World Meteorological Organisation(WMO)High-Impact Weather Project(HIWeather).The results of the survey are presented,and show that ensemble forecasts are used by nearly all respondents,particularly in TC track and genesis forecasting,with several examples of where ensemble forecasts have been pulled through successfully into the operational TC forecasting process.There is still however,a notable difference between the high proportion of operational TC forecasters who use and value ensemble forecast information,and the slower pull-through into operational forecast warnings and products of the probabilistic guidance and uncertainty information that ensembles can provide.Those areas of research and development that would help TC forecasters to make increased use of ensemble forecast information in the future include improved access to ensemble forecast data,verification and visualizations,the development of hazard and impact-based products,an improvement in the skill of the ensembles(particularly for intensity and structure),and improved guidance on how to use ensembles and optimally combine forecasts from all available models.A change in operational working practices towards using probabilistic information,and providing and communicating dynamic uncertainty information in operational forecasts and warnings,is also recommended.
基金supported by the Research Committee of University of Macao with Grant No. MYRG2014-00060FSTthe Science and Technology Development Fund (FDCT) of Macao S.A.R with Grant No. 016/2012/A1
文摘In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The features of this methodology are as follows： （1） A Bayesian Ying-Yang Learning （BYYL） algorithm is used to construct a compact but high-performance system automatically. （2） A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. （3） The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.
基金supported by the National Key R&D Program of China [grant number 2023YFC3008004]。
文摘This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones(TC)ASANI and MOCHA.The analysis of various Numerical Weather Prediction(NWP)model forecasts[ECMWF(European Centre for Medium range Weather Forecast),NCEP(National Centers for Environmental Prediction),NCUM(National Centre for Medium Range Weather Forecast-Unified Model),IMD(India Meteorological Department),HWRF(Hurricane Weather Research and Forecasting)],MME(Multi-model Ensemble),SCIP(Statistical Cyclone Intensity Prediction)model,and OFCL(Official)forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good,but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA.Among all model forecasts,the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively.Also,the landfall point forecast errors of IMD were least for ASANI,and the MME and OFCL forecast errors were least for MOCHA.No model is found to be consistently better for landfall time forecast for ASANI,and the errors of ECMWF,IMD and HWRF were least and of same order for MOCHA.The intensity forecast errors of OFCL and SCIP were least for ASANI,and the forecast errors of HWRF,IMD,NCEP,SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general.The ECMWF model forecast errors for intensity were found to be highest for both the TCs.The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金sponsored by the National Natural Science Foun-dation of China(Grant No.42330111).
文摘In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3005401)the National Natural Science Foundation of China(Grant No.52239009)。
文摘Accurate prediction of water level changes in reservoirs is crucial for optimizing the operation of reservoir projects and ensuring their safety.This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms.By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method and fuzzy entropy(FE)with the new and highly efficient Runge–Kuta optimizer(RUN),adaptive parameter optimization for the support vector machine(SVM)and radial basis function neural network(RBFNN)algorithms was achieved.Regression prediction was conducted on the two reconstructed sequences using SVM and RBFNN according to their respective features.This approach improved the accuracy and stability of predictions.In terms of accuracy,the combined model outperformed single models,with the determination coefficient,root mean square error,and mean absolute error values of 0.9975,0.2418 m,and 0.1616 m,respectively.In terms of stability,the model predicted more consistently in training and testing periods,with stable overall prediction accuracy and a better adaptive ability to complex datasets.The case study demonstrated that the combined prediction model effectively addressed the environmental factors affecting reservoir water levels,leveraged the strength of each predictive method,compensated for their limitations,and clarified the impacts of environmental factors on reservoir water levels.
文摘Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by environmental sensitivity,dynamic ocean conditions,and high maintenance demands.Artificial Intelligence(AI)offers promising solutions to these challenges by enabling intelligent,adaptive,and resilient energy systems.This review explores AI applications in ORE,focusing on three critical domains:optimization,forecasting,and control.Optimization techniques,including Genetic Algorithms(GA)and Swarm Intelligence(SI),are employed to enhance device efficiency,improve energy capture,optimize farm layouts,reduce environmental impacts,and lower installation costs.Forecasting uses Machine Learning(ML)and Deep Learning(DL)models to predict wave height,tidal flow,and energy output,aiding in grid integration and energy scheduling.In control systems,AI approaches like Reinforcement Learning(RL)and Fuzzy Logic ensure real-time responsiveness and predictive maintenance,improving system reliability in dynamic marine environments.Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making,while Digital Twin frameworks simulate and predict system performance before deployment.Explainable AI(XAI)is also discussed to ensure transparent and trustworthy decision-making.Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings.Overall this review offers a comprehensive synthesis of how AI enhances the performance,efficiency,and scalability of ORE systems.It serves as a valuable resource for researchers,policymakers,and industry professionals seeking to advance clean,smart,and sustainable ocean energy solutions.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
文摘Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.
基金supported by the National Natural Science Foundation of China(Grant No.42076214)Natural Science Foundation of Shandong Province(Grant No.ZR2024QD057).
文摘Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金supported by the National Natural Science Foundation of China [grant number 42030605]the National Key R&D Program of China [grant number 2020YFA0608004]。
文摘A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.
