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
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
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.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
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using...It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.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 study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hour...This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.展开更多
Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyz...Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.展开更多
According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation dat...According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation data of the competition fields,a dynamic optimal correction method was proposed to improve the accuracy rate of temperature and wind speed prediction.Through techniques such as deviation correction and univariate linear regression,mathematical models applicable to different competition regions were constructed,and the effective correction of objective forecast products within 0-120 h were realized.The results show that this method significantly improved the accuracy rate of the prediction of temperature,wind speed and extreme wind speed,and the effect was more obvious especially when the model performance was unstable.Meanwhile,terrain and climate background had a significant impact on the correction effect.This study provides new technical support for mountain meteorological forecast.展开更多
Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on th...Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on the Nanjing University of Information Science&Technology Climate Forecast System version 1.1(NUIST CFS1.1)and analyze the related physical processes.Our results show that the model can accurately forecast the occurrence of MHWs on a global scale out to a lead time of 25 days.Notably,even at lead times of 51–55 days,the forecast skill in most tropical regions,as well as in the northeastern and southeastern Pacific,is superior to both random forecasts and persistence forecasts.Accurate predictions of sea level pressure,zonal currents,and mixed-layer depth are important for MHW forecasting.Furthermore,we also conduct forecast skill assessments for two well-documented MHW events.Due to its ability to correctly forecast the changes in heat flux anomalies at a lead time of 25 days,the model can accurately forecast the strong MHW event that occurred in the South China Sea in May–October 2020.However,the forecasting results were less than optimal for the strong MHW event that occurred along the Australian west coast in January–April 2011.Although the model accurately forecasts its occurrence,the forecast of its intensity is poor.Additionally,when the lead time exceeds 10 days,forecasts of the relevant physical processes of this MHW event are also inaccurate.展开更多
The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
The advantage distillation(AD)technology has been proven to effectively improve the secret key rate and the communication distance of quantum key distribution(QKD).The mode-pairing quantum key distribution(MP-QKD)prot...The advantage distillation(AD)technology has been proven to effectively improve the secret key rate and the communication distance of quantum key distribution(QKD).The mode-pairing quantum key distribution(MP-QKD)protocol can overcome a fundamental physical limit,known as the Pirandola-Laurenza-Ottaviani-Banchi bound,without requiring global phase-locking.In this work,we propose a method based on multi-step AD to further enhance the performance of MP-QKD.The simulation results show that,compared to one-step AD,multi-step AD achieves better performance in long-distance scenarios and can tolerate a higher quantum bit error rate.Specifically,when the difference between the communication distances from Alice and Bob to Charlie is 25 km,50 km and 75 km,and the corresponding transmission distance exceeds 523 km,512 km and 496 km,respectively,the secret key rate achieved by multi-step AD surpasses that of one-step AD.Our findings indicate that the proposed method can effectively promote the application of MP-QKD in scenarios with high loss and high error rate.展开更多
The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this...The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.展开更多
The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate r...The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate relationship between variations in ecological parameters during spring algal bloom incidents and the associated changes in temperature and wind fields in this study.A long short-term memory recurrent neural network was employed,and a predictive model for spring algal bloom in this region was developed.This model integrated various inputs,including temperature,wind speed,and other pertinent variables,and chlorophyll concentration served as the primary output indicator.The model training used chlorophyll concentration data,which were supplemented by reanalysis and forecast temperature and wind field data.The model demonstrated proficiency in forecasting next-day chlorophyll concentrations and assessing the likelihood of spring algal bloom occurrences using a defined chlorophyll concentration threshold.The historical validation from 2016 to 2019 corroborated the model's accuracy with an 81.71%probability of correct prediction,which was further proven by its precise prediction of two spring algal bloom incidents in late April 2023 and early May 2023.An interpretable machine learning-based model for spring algal bloom prediction,displaying effective forecasting with limited data,was established through the detailed analysis of the spring algal bloom mechanism and the careful selection of input variables.The insights gained from this study offer valuable contributions to the development of early warning systems for spring algal bloom in the Wenzhou coastal area of Zhejiang Province.展开更多
Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products,thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity.T...Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products,thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity.The traditional method that relies on forecasters'subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting.To address this problem,this paper proposes a Transformer-enhanced UNet(TransUNet)model for wave forecast AI correction,which fuses wind and wave information.The Transformer structure is integrated into the encoder of the UNet model,and instead of using the traditional upsampling method,the dual-sampling module is employed in the decoder to enhance the feature extraction capability.This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data,wave height forecast data,and significant wave height reanalysis data provided by ECMWF.The experimental results indicate that the TransUNet model yields smaller root-meansquare errors,mean errors,and standard deviations of the corrected results for the next 24-h forecasts than does the UNet model.Specifically,the root-mean-square error decreased by more than 21.55%compared to its precorrection value.According to the statistical analysis,87.81%of the corrected wave height errors for the next 24-h forecast were within±0.2m,with only 4.56%falling beyond±0.3 m.This model effectively limits the error range and enhances the ability to forecast wave heights.展开更多
Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we prop...Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.展开更多
A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal ...A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal prediction skill of this system via a 21-year hindcast experiment for the period 2000–20 with eight ensemble members.Results showed moderate-to-high skill for the primary atmospheric variables. The most accurate predictions emerged in the cold season but were largely confined within tropical bands as the forecast lead time was increased. Compared with the NCEP S2S FS, the CAMS-CSM S2S FS showed comparable subseasonal skill for 500-h Pa geopotential height, but slightly higher(lower) skill for precipitation(2-m temperature). The skillful lead time in the CAMS-CSM S2S FS for the Madden–Julian Oscillation and North Atlantic Oscillation reached 20 and 10 days, respectively, consistent with the NCEP S2S FS. Consequently, these findings guide future research on subseasonal predictability based on the CAMS-CSM S2S FS, and where efforts should be focused to improve the prediction system.展开更多
Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the au...Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.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
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.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.42375062 and 42275158)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)the Natural Science Foundation of Gansu Province(Grant No.22JR5RF1080)。
文摘It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
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 by the National Natural Science Foundation of China(Grant Nos.42030610 and 42205006)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.
基金supported by the National Key Research and Development Program[grant number 2022YFC3004203]the S&T Development Fund of CAMS(Chinese Academy of Meteorological Sciences)[grant numbers 2023KJ040 and 2024KJ013].
文摘Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.
文摘According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation data of the competition fields,a dynamic optimal correction method was proposed to improve the accuracy rate of temperature and wind speed prediction.Through techniques such as deviation correction and univariate linear regression,mathematical models applicable to different competition regions were constructed,and the effective correction of objective forecast products within 0-120 h were realized.The results show that this method significantly improved the accuracy rate of the prediction of temperature,wind speed and extreme wind speed,and the effect was more obvious especially when the model performance was unstable.Meanwhile,terrain and climate background had a significant impact on the correction effect.This study provides new technical support for mountain meteorological forecast.
基金supported by National Natural Science Foundation of China(Grant Nos.42030605 and 42088101)National Key R&D Program of China(Grant No.2020YFA0608004)High Performance Computing of Nanjing University of Information Science&Technology for their support of this work。
文摘Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on the Nanjing University of Information Science&Technology Climate Forecast System version 1.1(NUIST CFS1.1)and analyze the related physical processes.Our results show that the model can accurately forecast the occurrence of MHWs on a global scale out to a lead time of 25 days.Notably,even at lead times of 51–55 days,the forecast skill in most tropical regions,as well as in the northeastern and southeastern Pacific,is superior to both random forecasts and persistence forecasts.Accurate predictions of sea level pressure,zonal currents,and mixed-layer depth are important for MHW forecasting.Furthermore,we also conduct forecast skill assessments for two well-documented MHW events.Due to its ability to correctly forecast the changes in heat flux anomalies at a lead time of 25 days,the model can accurately forecast the strong MHW event that occurred in the South China Sea in May–October 2020.However,the forecasting results were less than optimal for the strong MHW event that occurred along the Australian west coast in January–April 2011.Although the model accurately forecasts its occurrence,the forecast of its intensity is poor.Additionally,when the lead time exceeds 10 days,forecasts of the relevant physical processes of this MHW event are also inaccurate.
文摘The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
基金supported by the National Natural Science Foundation of China(Grant Nos.62171144and 62031024)Guangxi Science Foundation(Grant Nos.2025GXNSFAA069137 and GXR-1BGQ2424005)Innovation Project of Guangxi Graduate Education(Grant No.YCBZ2025064)。
文摘The advantage distillation(AD)technology has been proven to effectively improve the secret key rate and the communication distance of quantum key distribution(QKD).The mode-pairing quantum key distribution(MP-QKD)protocol can overcome a fundamental physical limit,known as the Pirandola-Laurenza-Ottaviani-Banchi bound,without requiring global phase-locking.In this work,we propose a method based on multi-step AD to further enhance the performance of MP-QKD.The simulation results show that,compared to one-step AD,multi-step AD achieves better performance in long-distance scenarios and can tolerate a higher quantum bit error rate.Specifically,when the difference between the communication distances from Alice and Bob to Charlie is 25 km,50 km and 75 km,and the corresponding transmission distance exceeds 523 km,512 km and 496 km,respectively,the secret key rate achieved by multi-step AD surpasses that of one-step AD.Our findings indicate that the proposed method can effectively promote the application of MP-QKD in scenarios with high loss and high error rate.
基金supported by the open foundation of State Key Laboratory of Chemical Engineering(SKL-ChE-22B01)the Natural Science Foundation of China(22008169).
文摘The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.
基金the Zhejiang Provincial Natural Science Foundation of China(No.LY21D 060003)the Project of State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Ocean-ography,MNR(No.SOEDZZ2103)+1 种基金the National Natural Science Foundation of China(No.42076216)the Open Research Fund of the Key Laboratory of Marine Ecological Monitoring and Restoration Technologies,MNR(No.MEMRT202210)。
文摘The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate relationship between variations in ecological parameters during spring algal bloom incidents and the associated changes in temperature and wind fields in this study.A long short-term memory recurrent neural network was employed,and a predictive model for spring algal bloom in this region was developed.This model integrated various inputs,including temperature,wind speed,and other pertinent variables,and chlorophyll concentration served as the primary output indicator.The model training used chlorophyll concentration data,which were supplemented by reanalysis and forecast temperature and wind field data.The model demonstrated proficiency in forecasting next-day chlorophyll concentrations and assessing the likelihood of spring algal bloom occurrences using a defined chlorophyll concentration threshold.The historical validation from 2016 to 2019 corroborated the model's accuracy with an 81.71%probability of correct prediction,which was further proven by its precise prediction of two spring algal bloom incidents in late April 2023 and early May 2023.An interpretable machine learning-based model for spring algal bloom prediction,displaying effective forecasting with limited data,was established through the detailed analysis of the spring algal bloom mechanism and the careful selection of input variables.The insights gained from this study offer valuable contributions to the development of early warning systems for spring algal bloom in the Wenzhou coastal area of Zhejiang Province.
基金supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.SML2023SP214)the National Natural Science Foundation of China(Grant Nos.62071279 and 42206029)the National Key R&D Program of China(Grant No.2020YFA0608804)。
文摘Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products,thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity.The traditional method that relies on forecasters'subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting.To address this problem,this paper proposes a Transformer-enhanced UNet(TransUNet)model for wave forecast AI correction,which fuses wind and wave information.The Transformer structure is integrated into the encoder of the UNet model,and instead of using the traditional upsampling method,the dual-sampling module is employed in the decoder to enhance the feature extraction capability.This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data,wave height forecast data,and significant wave height reanalysis data provided by ECMWF.The experimental results indicate that the TransUNet model yields smaller root-meansquare errors,mean errors,and standard deviations of the corrected results for the next 24-h forecasts than does the UNet model.Specifically,the root-mean-square error decreased by more than 21.55%compared to its precorrection value.According to the statistical analysis,87.81%of the corrected wave height errors for the next 24-h forecast were within±0.2m,with only 4.56%falling beyond±0.3 m.This model effectively limits the error range and enhances the ability to forecast wave heights.
文摘Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.
基金jointly funded by the National Key Research and Development Program (Grant No.2022YFC3004203)the National Natural Science Foundation of China (Grant No.42375033)the Basic Scientific Research and Operation Foundation of the Chinese Academy of Meteorological Sciences (Grant Nos.2023Z018, 2024KJ013, and 2023KJ036)。
文摘A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal prediction skill of this system via a 21-year hindcast experiment for the period 2000–20 with eight ensemble members.Results showed moderate-to-high skill for the primary atmospheric variables. The most accurate predictions emerged in the cold season but were largely confined within tropical bands as the forecast lead time was increased. Compared with the NCEP S2S FS, the CAMS-CSM S2S FS showed comparable subseasonal skill for 500-h Pa geopotential height, but slightly higher(lower) skill for precipitation(2-m temperature). The skillful lead time in the CAMS-CSM S2S FS for the Madden–Julian Oscillation and North Atlantic Oscillation reached 20 and 10 days, respectively, consistent with the NCEP S2S FS. Consequently, these findings guide future research on subseasonal predictability based on the CAMS-CSM S2S FS, and where efforts should be focused to improve the prediction system.
基金jointly supported by the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 316323005]the Guangdong Basic and Applied Basic Research Foundation[grant numbers 2023A1515010741 and 2024B1515020035]the Science and Technology Planning Project of Guangdong Province[grant number 2023B1212060019]。
文摘Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.
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.
