期刊文献+
共找到2,253篇文章
< 1 2 113 >
每页显示 20 50 100
Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO 被引量:2
1
作者 Tingyu WANG Ping HUANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第1期141-154,共14页
The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown th... The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO. 展开更多
关键词 ENSO diversity deep learning ENSO prediction dynamical forecast system
下载PDF
Will the Globe Encounter the Warmest Winter after the Hottest Summer in 2023? 被引量:2
2
作者 Fei ZHENG Shuai HU +17 位作者 Jiehua MA Lin WANG Kexin LI Bo WU Qing BAO Jingbei PENG Chaofan LI Haifeng ZONG Yao YAO Baoqiang TIAN Hong CHEN Xianmei LANG Fangxing FAN Xiao DONG Yanling ZHAN Tao ZHU Tianjun ZHOU Jiang ZHU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第4期581-586,共6页
In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how th... In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter.In this report,as shown in the multi-model ensemble mean(MME)prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences,a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2−3 months,which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection,thus serving to modulate the winter climate in East Asia and North America.Despite some uncertainty due to unpredictable internal atmospheric variability,the global mean surface temperature(GMST)in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend.Specifically,the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter,and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991.Moreover,the necessary early warnings are still reliable in the timely updated mediumterm numerical weather forecasts and sub-seasonal-to-seasonal prediction. 展开更多
关键词 winter climate El Niño seasonal forecast GMST
下载PDF
Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts 被引量:1
3
作者 Mengmeng SONG Dazhi YANG +7 位作者 Sebastian LERCH Xiang'ao XIA Gokhan Mert YAGLI Jamie M.BRIGHT Yanbo SHEN Bai LIU Xingli LIU Martin Janos MAYER 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1417-1437,共21页
Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantil... Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks. 展开更多
关键词 ensemble weather forecasting forecast calibration non-crossing quantile regression neural network CORP reliability diagram POST-PROCESSING
下载PDF
A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region 被引量:1
4
作者 Yunqing LIU Lu YANG +3 位作者 Mingxuan CHEN Linye SONG Lei HAN Jingfeng XU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1342-1363,共22页
Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly b... Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. 展开更多
关键词 thunderstorm gusts deep learning weather forecasting convolutional neural network TRANSFORMER
下载PDF
Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models 被引量:1
5
作者 Lu LI Yongjiu DAI +5 位作者 Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1326-1341,共16页
Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient... Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions. 展开更多
关键词 soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism
下载PDF
甲状腺乳头状癌超声图像表现在预测颈部Ⅵ区淋巴结转移危险度的临床价值
6
作者 刘杰 于景超 +3 位作者 王猛 李卫 鲁金乐 陈雅婷 《中国耳鼻咽喉头颈外科》 CSCD 2024年第7期470-473,共4页
目的 分析甲状腺乳头状癌(PTC)超声图像表现在预测颈部Ⅵ区淋巴结转移(lymph node metastasis in the cervicalregion Ⅵ,CLNM-Ⅵ)危险度的临床价值。方法 选取2022年4月~2023年6月在河北省沧州中西医结合医院接受手术治疗并经病理证实... 目的 分析甲状腺乳头状癌(PTC)超声图像表现在预测颈部Ⅵ区淋巴结转移(lymph node metastasis in the cervicalregion Ⅵ,CLNM-Ⅵ)危险度的临床价值。方法 选取2022年4月~2023年6月在河北省沧州中西医结合医院接受手术治疗并经病理证实的350例PTC患者,根据术后病理结果,将患者分为CLNM-Ⅵ组和非CLNM-Ⅵ组。收集并对比两组术前超声图像表现及临床病理特征,应用Logistic回归分析PTC患者CLNM-Ⅵ危险因素,受试者工作特征(ROC)曲线分析PTC超声图像表现对CLNM-Ⅵ的预测价值。结果 单因素分析显示,CLNM-Ⅵ组男性、实性或囊实性、年龄≤45岁、低回声、甲状腺背景正常、点状强回声的构成比均大于非CLNM-Ⅵ组(P均<0.05)。Logistic回归分析显示,男性、实性或囊实性、年龄≤45岁、低回声、甲状腺背景正常、病灶内可见点状强回声是CLNM-Ⅵ的独立危险因素(P均<0.05);进一步经ROC曲线分析显示,以上预测CLNM-Ⅵ的AUC分别为0.565、0.580、0.529、0.585、0.582、0.582,联合预测AUC为0.708。结论PTC超声图像表现在CLNM-Ⅵ风险评估中具有重要意义,可为PTC的预后判断提供一定的参考依据。 展开更多
关键词 甲状腺肿瘤(Thyroid Neoplasms) 超声检查(Ultrasonography) 风险评估(Risk Assessment) 预测(Forecasting) 颈部Ⅵ区(regionⅥof the neck) 淋巴结转移(lymph node metastasis)
下载PDF
Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus 被引量:1
7
作者 Sha-Sha Cai Teng-Ye Zheng +1 位作者 Kang-Yao Wang Hui-Ping Zhu 《World Journal of Diabetes》 SCIE 2024年第1期43-52,共10页
BACKGROUND Among older adults,type 2 diabetes mellitus(T2DM)is widely recognized as one of the most prevalent diseases.Diabetic nephropathy(DN)is a frequent com-plication of DM,mainly characterized by renal microvascu... BACKGROUND Among older adults,type 2 diabetes mellitus(T2DM)is widely recognized as one of the most prevalent diseases.Diabetic nephropathy(DN)is a frequent com-plication of DM,mainly characterized by renal microvascular damage.Early detection,aggressive prevention,and cure of DN are key to improving prognosis.Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.AIM To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.METHODS The clinical data of 210 patients diagnosed with T2DM and admitted to the First People’s Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed.According to whether the patients had DN,they were divided into the DN group(complicated with DN)and the non-DN group(without DN).Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM.The data were randomly split into a training set(n=147)and a test set(n=63)in a 7:3 ratio using a random function.The training set was used to construct the nomogram,decision tree,and random forest models,and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity,specificity,accuracy,recall,precision,and area under the receiver operating characteristic curve.RESULTS Among the 210 patients with T2DM,74(35.34%)had DN.The validation dataset showed that the accuracies of the nomogram,decision tree,and random forest models in predicting DN in patients with T2DM were 0.746,0.714,and 0.730,respectively.The sensitivities were 0.710,0.710,and 0.806,respectively;the specificities were 0.844,0.875,and 0.844,respectively;the area under the receiver operating characteristic curve(AUC)of the patients were 0.811,0.735,and 0.850,respectively.The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models(P<0.05),whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant(P>0.05).CONCLUSION Among the three prediction models,random forest performs best and can help identify patients with T2DM at high risk of DN. 展开更多
关键词 Type 2 diabetes mellitus Diabetic nephropathy Random forest Decision-making tree NOMOGRAM FORECAST
下载PDF
Influence of vapor pressure deficit on vegetation growth in China 被引量:1
8
作者 LI Chuanhua ZHANG Liang +3 位作者 WANG Hongjie PENG Lixiao YIN Peng MIAO Peidong 《Journal of Arid Land》 SCIE CSCD 2024年第6期779-797,共19页
Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric ... Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD. 展开更多
关键词 vapor pressure deficit(VPD) near-infrared reflectance of vegetation(NIRv) leaf area index(LAI) gross primary productivity(GPP) Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06) European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5) climate change
下载PDF
Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region
9
作者 Qiuyan DU Chun ZHAO +6 位作者 Jiawang FENG Zining YANG Jiamin XU Jun GU Mingshuai ZHANG Mingyue XU Shengfu LIN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第5期801-816,共16页
Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological foreca... Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation. 展开更多
关键词 PM_(2.5) forecasting uncertainties forecast lead time meteorological fields Beijing-Tianjin-Hebei region
下载PDF
Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics
10
作者 Guo DENG Xueshun SHEN +23 位作者 Jun DU Jiandong GONG Hua TONG Liantang DENG Zhifang XU Jing CHEN Jian SUN Yong WANG Jiangkai HU Jianjie WANG Mingxuan CHEN Huiling YUAN Yutao ZHANG Hongqi LI Yuanzhe WANG Li GAO Li SHENG Da LI Li LI Hao WANG Ying ZHAO Yinglin LI Zhili LIU Wenhua GUO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第5期767-776,共10页
Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational... Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems. 展开更多
关键词 Beijing Winter Olympic Games CMA national forecasting system data assimilation ensemble forecast bias correction and downscaling machine learning-based fusion methods
下载PDF
The Collaboratory for the Study of Earthquake Predictability in China:Experiment Design and Preliminary Results of CSEP2.0
11
作者 ZHANG Shengfeng ZHANG Yongxian +3 位作者 Maximilian J.WERNER Kenny G.RAHAM David A.RHOADES JoséA.BAYONA 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2024年第S01期94-97,共4页
Since the inaugural international collaboration under the framework of the Collaboratory for the Study of Earthquake Predictability(CSEP)in 2007,numerous forecast models have been developed and operated for earthquake... Since the inaugural international collaboration under the framework of the Collaboratory for the Study of Earthquake Predictability(CSEP)in 2007,numerous forecast models have been developed and operated for earthquake forecasting experiments across CSEP testing centers(Schorlemmer et al.,2018).Over more than a decade,efforts to compare forecasts with observed earthquakes using numerous statistical test methods and insights into earthquake predictability,which have become a highlight of the CSEP platform. 展开更多
关键词 earthquake forecasting seismicity modeling CSEP2.0 Pattern Informatics(PI)algorithm long-to-intermediate-term forecast Relative Intensity(RI)algorithm Completeness Magnitude S test N test
下载PDF
Longitudinal dependence of the forecast accuracy of the ionospheric total electron content in the equatorial zone
12
作者 Artem Kharakhashyan Olga Maltseva 《Geodesy and Geodynamics》 EI CSCD 2024年第5期528-541,共14页
The longitudinal dependence of the behavior of ionospheric parameters has been the subject of a number of works where significant variations are discovered.This also applies to the prediction of the ionospheric total ... The longitudinal dependence of the behavior of ionospheric parameters has been the subject of a number of works where significant variations are discovered.This also applies to the prediction of the ionospheric total electron content(TEC),which neural network methods have recently been widely used.However,the results are mainly presented for a limited set of meridians.This paper examines the longitudinal dependence of the TEC forecast accuracy in the equatorial zone.In this case,the methods are used that provided the best accuracy on three meridians:European(30°E),Southeastern(110°E)and American(75°W).Results for the stations considered are analyzed as a function of longitude using the Jet Propulsion Laboratory Global Ionosphere Map(JPL GIM)for 2015.These results are for 2 h ahead and 24 h ahead forecast.It was found that in this case,based on the metric values,three groups of architectures can be distinguished.The first group included long short-term memory(LSTM),gated recurrent unit(GRU),and temporal convolutional networks(TCN)models as a part of unidirectional deep learning models;the second group is based on the recurrent models from the first group,which were supplemented with a bidirectional algorithm,increasing the TEC forecasting accuracy by 2-3 times.The third group,which includes the bidirectional TCN architecture(BiTCN),provided the highest accuracy.For this architecture,according to data obtained for 9 equatorial stations,practical independence of the TEC prediction accuracy from longitude was observed under the following metrics(Mean Absolute Error MAE,Root Mean Square Error RMSE,Mean Absolute Percentage Error MAPE):MAE(2 h)is 0.2 TECU approximately;MAE(24 h)is 0.4 TECU approximately;RMSE(2 h)is less than 0.5 TECU except Niue station(RMSE(2 h)is 1 TECU approximately);RMSE(24 h)is in the range of 1.0-1.7 TECU;MAPE(2 h)<1%except Darwin station,MAPE(24 h)<2%.This result was confirmed by data from additional 5 stations that formed latitudinal chains in the equatorial part of the three meridians.The complete correspondence of the observational and predicted TEC values is illustrated using several stations for disturbed conditions on December 19-22,2015,which included the strongest magnetic storm in the second half of the year(min Dst=-155 nT). 展开更多
关键词 IONOSPHERE Total electron content Forecasting BiGRU BiLSTM BiTCN Temporal convolution
下载PDF
Preface to the Special Issue:AI Applications in Atmospheric and Oceanic Science:Pioneering the Future(Part I)
13
作者 Zhemin TAN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1279-1280,共2页
As AI continues to establish itself as a cornerstone technology across various industries and scientific disciplines,its profound impact on atmospheric and oceanic science is becoming increasingly apparent.The advanta... As AI continues to establish itself as a cornerstone technology across various industries and scientific disciplines,its profound impact on atmospheric and oceanic science is becoming increasingly apparent.The advantages of AI in surmounting obstacles within our field are undeniable,as evidenced by breakthroughs in weather forecasting(e.g.,Bi et al.,2023),climate prediction(e.g.,Ham et al.,2019),AI-based parameterization schemes(e.g.,Rasp et al.,2018;Wang and Tan,2023),and beyond.Recognizing the transformative potential of AI in atmospheric and oceanic science,this special issue endeavors to explore the extensive applications of AI in our domain. 展开更多
关键词 WEATHER forecasting PREDICTION
下载PDF
Promising Results Predict Role for Artificial Intelligence in Weather Forecasting
14
作者 Mitch Leslie 《Engineering》 SCIE EI CAS CSCD 2024年第8期10-12,共3页
Artificial intelligence(AI)has already demonstrated its proficiency at difficult scientific tasks like predicting how proteins will fold and identifying new astronomical objects in masses of observational data[1].Now,... Artificial intelligence(AI)has already demonstrated its proficiency at difficult scientific tasks like predicting how proteins will fold and identifying new astronomical objects in masses of observational data[1].Now,recent results suggest that AI also excels at weather forecasting.For global predictions,GraphCast,an AI system developed by Google subsidiary DeepMind(London,UK),outperforms the state-of-the-art model from the European Centre for Medium-Range Weather Forecasts(ECMWF),providing more accurate projections of variables such as temperature and humidity 90%of the time[2,3].Other AI systems,including Pangu-Weather from the Chinese tech company Huawei(Shenzhen,China)[4],can also match or beat traditional global forecasting models. 展开更多
关键词 forecasting humidity WEATHER
下载PDF
Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method
15
作者 Hao-Chen Wang Kai Zhang +7 位作者 Nancy Chen Wen-Sheng Zhou Chen Liu Ji-Fu Wang Li-Ming Zhang Zhi-Gang Yu Shi-Ti Cui Mei-Chun Yang 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期716-728,共13页
To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studie... To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods. 展开更多
关键词 Production forecasting Multiple patterns Few-shot learning Transfer learning
下载PDF
The Forecast Skills and Predictability Sources of Marine Heatwaves in the NUIST-CFS1.0 Hindcasts
16
作者 Jing MA Haiming XU +1 位作者 Changming DONG Jing-Jia LUO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第8期1589-1600,共12页
Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast s... Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer. 展开更多
关键词 marine heatwaves NUIST-CFS1.0 hindcasts forecast skill predictability source ENSO
下载PDF
A NOVEL STOCHASTIC HEPATITIS B VIRUS EPIDEMIC MODEL WITH SECOND-ORDER MULTIPLICATIVE α-STABLE NOISE AND REAL DATA
17
作者 Anwarud DIN Yassine SABBAR 吴鹏 《Acta Mathematica Scientia》 SCIE CSCD 2024年第2期752-788,共37页
This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and stochas-ticity.Based on some biological features of the vi... This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and stochas-ticity.Based on some biological features of the virus and the assumptions,the corresponding deterministic model is formulated,which takes into consideration the effect of vaccination.This deterministic model is extended to a stochastic framework by considering a new form of disturbance which makes it possible to simulate strong and significant fluctuations.The long-term behaviors of the virus are predicted by using stochastic differential equations with second-order multiplicative α-stable jumps.By developing the assumptions and employing the novel theoretical tools,the threshold parameter responsible for ergodicity(persistence)and extinction is provided.The theoretical results of the current study are validated by numerical simulations and parameters estimation is also performed.Moreover,we obtain the following new interesting findings:(a)in each class,the average time depends on the value ofα;(b)the second-order noise has an inverse effect on the spread of the virus;(c)the shapes of population densities at stationary level quickly changes at certain values of α.The last three conclusions can provide a solid research base for further investigation in the field of biological and ecological modeling. 展开更多
关键词 HBV model nonlinear perturbation probabilistic bifurcation long-run forecast numerical simulation
下载PDF
Generalized load graphical forecasting method based on modal decomposition
18
作者 Lizhen Wu Peixin Chang +1 位作者 Wei Chen Tingting Pei 《Global Energy Interconnection》 EI CSCD 2024年第2期166-178,共13页
In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power su... In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method. 展开更多
关键词 Load forecasting Generalized load Image processing DenseNet Modal decomposition
下载PDF
Probabilistic modeling of multifunction radars with autoregressive kernel mixture network
19
作者 Hancong Feng Kaili.Jiang +4 位作者 Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第5期275-288,共14页
The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrai... The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection. 展开更多
关键词 Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection Outlier detection
下载PDF
Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting
20
作者 Xiaojun Pu Jiaqi Zhu +3 位作者 Yunkun Wu Chang Leng Zitong Bo Hongan Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第3期769-786,共18页
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode... Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting. 展开更多
关键词 ADAPTIVE COVID-19 forecasting dynamic INTERVENTION spatio-temporal graph neural networks
下载PDF
上一页 1 2 113 下一页 到第
使用帮助 返回顶部