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
Thunderstorm gusts are a common and hazardous type of severe convective weather,characterized by a small spatial scale,short duration,and significant destructive power.They often lead to severe disasters,highlighting ...Thunderstorm gusts are a common and hazardous type of severe convective weather,characterized by a small spatial scale,short duration,and significant destructive power.They often lead to severe disasters,highlighting the critical importance of their accurate forecasting.Previous studies have explored the environmental factors and spatiotemporal distribution characteristics of thunderstorm gusts,highlighting the need for improved forecasting methods.In recent years,artificial intelligence techniques have shown promise in enhancing the accuracy of thunderstorm gust forecasting,with various machine learning algorithms and models having been developed.This paper proposes a multiscale feature fusion module called Thunderstorm Gusts Block(TG-Block)and a deep learning model named Thunderstorm Gusts net(TG-net)based on the Attention U-net and TG-TransUnet models,and employs interpretable methods such as Integrated Gradient,Deep Learning Importance Features,and Shapley Additive exPlanations to validate the model’s practical relevance and reliability.The analysis of feature importance underscores the model’s ability to capture key thermodynamic and multiscale weather characteristic information for thunderstorm gust nowcasting.It is,however,worth emphasizing that these conclusions are only based on a limited number of thunderstorm gust examples,and the evaluation results may be affected by specific weather types and sample sizes.Nonetheless,TG-net has been put into real-time operation at the Institute of Urban Meteorology,and we will continue to rigorously validate its performance and make any necessary optimizations and enhancements based on feedback to ensure the robustness and stability of the model.展开更多
Owing to the relatively low stiffness of the slide and the considerable deformation under support force,the fluid–structure interaction(FSI)phenomenon in aerostatic slides is generally pronounced.This phenomenon affe...Owing to the relatively low stiffness of the slide and the considerable deformation under support force,the fluid–structure interaction(FSI)phenomenon in aerostatic slides is generally pronounced.This phenomenon affects the performance of static pressure slides,particularly those with high load-carrying capacity and low stiffness.However,most existing methods for analyzing the efect of FSI on the stiffness of static pressure slides are iterative and computationally expensive.To address this issue,a novel direct method is proposed for evaluating the stiffness of static pressure slides while considering FSI.This method can quickly and precisely obtain numerical solutions.Furthermore,the accuracy of the proposed method is validated through experiments.Based on the developed FSI model,the efects of normal force and flm thickness on the normal stiffness of aerostatic slides are also investigated.展开更多
Recently,rapid advances in flexible strain sensors have broadened their application scenario in monitoring of various mechanophysiological signals.Among various strain sensors,the crack-based strain sensors have drawn...Recently,rapid advances in flexible strain sensors have broadened their application scenario in monitoring of various mechanophysiological signals.Among various strain sensors,the crack-based strain sensors have drawn increasing attention in monitoring subtle mechanical deformation due to their high sensitivity.However,early generation and rapid propagation of cracks in the conductive sensing layer result in a narrow working range,limiting their application in monitoring large biomechanical signals.Herein,we developed a stress-deconcentrated ultrasensitive strain(SDUS)sensor with ultrahigh sensitivity(gauge factor up to2.3×10^(6))and a wide working range(0%-50%)via incorporating notch-insensitive elastic substrate and microcrack-tunable conductive layer.Furthermore,the highly elastic amine-based polymer-modified polydimethylsiloxane substrate without obvious hysteresis endows our SDUS sensor with a rapid response time(2.33 ms)to external stimuli.The accurate detection of the radial pulse,joint motion,and vocal cord vibration proves the capability of SDUS sensor for healthcare monitoring and human-machine communications.展开更多
基金supported in part by the Beijing Natural Science Foundation(Grant No.8222051)the National Key R&D Program of China(Grant No.2022YFC3004103)+2 种基金the National Natural Foundation of China(Grant Nos.42275003 and 42275012)the China Meteorological Administration Key Innovation Team(Grant Nos.CMA2022ZD04 and CMA2022ZD07)the Beijing Science and Technology Program(Grant No.Z221100005222012).
文摘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.
基金Supported by the National Key Research and Development Program of China(2022YFC3004103)Beijing Natural Science Foundation(8222051)+1 种基金China Meteorological Administration Key Innovation Team(CMA2022ZD04 and CMA2022ZD07)Nanjing Joint Institute for Atmospheric Sciences Beijige Open Research Fund(BJG202407).
文摘Thunderstorm gusts are a common and hazardous type of severe convective weather,characterized by a small spatial scale,short duration,and significant destructive power.They often lead to severe disasters,highlighting the critical importance of their accurate forecasting.Previous studies have explored the environmental factors and spatiotemporal distribution characteristics of thunderstorm gusts,highlighting the need for improved forecasting methods.In recent years,artificial intelligence techniques have shown promise in enhancing the accuracy of thunderstorm gust forecasting,with various machine learning algorithms and models having been developed.This paper proposes a multiscale feature fusion module called Thunderstorm Gusts Block(TG-Block)and a deep learning model named Thunderstorm Gusts net(TG-net)based on the Attention U-net and TG-TransUnet models,and employs interpretable methods such as Integrated Gradient,Deep Learning Importance Features,and Shapley Additive exPlanations to validate the model’s practical relevance and reliability.The analysis of feature importance underscores the model’s ability to capture key thermodynamic and multiscale weather characteristic information for thunderstorm gust nowcasting.It is,however,worth emphasizing that these conclusions are only based on a limited number of thunderstorm gust examples,and the evaluation results may be affected by specific weather types and sample sizes.Nonetheless,TG-net has been put into real-time operation at the Institute of Urban Meteorology,and we will continue to rigorously validate its performance and make any necessary optimizations and enhancements based on feedback to ensure the robustness and stability of the model.
基金The National Key Research and Development Program of China(2022YFB3402705)The National Natural Science Foundation of China(52105439).
文摘Owing to the relatively low stiffness of the slide and the considerable deformation under support force,the fluid–structure interaction(FSI)phenomenon in aerostatic slides is generally pronounced.This phenomenon affects the performance of static pressure slides,particularly those with high load-carrying capacity and low stiffness.However,most existing methods for analyzing the efect of FSI on the stiffness of static pressure slides are iterative and computationally expensive.To address this issue,a novel direct method is proposed for evaluating the stiffness of static pressure slides while considering FSI.This method can quickly and precisely obtain numerical solutions.Furthermore,the accuracy of the proposed method is validated through experiments.Based on the developed FSI model,the efects of normal force and flm thickness on the normal stiffness of aerostatic slides are also investigated.
基金supported by the National Key Research and Development Program of China(2019YFA0210104)the National Natural Science Foundation of China(81971701)the Natural Science Foundation of Jiangsu Province(BK20201352)。
文摘Recently,rapid advances in flexible strain sensors have broadened their application scenario in monitoring of various mechanophysiological signals.Among various strain sensors,the crack-based strain sensors have drawn increasing attention in monitoring subtle mechanical deformation due to their high sensitivity.However,early generation and rapid propagation of cracks in the conductive sensing layer result in a narrow working range,limiting their application in monitoring large biomechanical signals.Herein,we developed a stress-deconcentrated ultrasensitive strain(SDUS)sensor with ultrahigh sensitivity(gauge factor up to2.3×10^(6))and a wide working range(0%-50%)via incorporating notch-insensitive elastic substrate and microcrack-tunable conductive layer.Furthermore,the highly elastic amine-based polymer-modified polydimethylsiloxane substrate without obvious hysteresis endows our SDUS sensor with a rapid response time(2.33 ms)to external stimuli.The accurate detection of the radial pulse,joint motion,and vocal cord vibration proves the capability of SDUS sensor for healthcare monitoring and human-machine communications.
