In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predic...In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.展开更多
Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict ...Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.展开更多
High-frequency resistance(HFR)is a critical quantity strongly related to a fuel cell system’s performance.It is beneficial to estimate the fuel cell system’s HFR from the measurable operating conditions without reso...High-frequency resistance(HFR)is a critical quantity strongly related to a fuel cell system’s performance.It is beneficial to estimate the fuel cell system’s HFR from the measurable operating conditions without resorting to costly HFR measurement devices.In this study,we propose a data-driven approach for a real-time prediction of HFR.Specifically,we use a long short-term memory(LSTM)based machine learning model that takes into account both the current and past states of the fuel cell,as characterized through a set of sensors.These sensor signals form the input to the LSTM.The data is experimentally collected from a vehicle lab that operates a 100 kW automotive fuel cell stack running on an automotive-scale test station.Our current results indicate that our prediction model achieves high accuracy HFR predictions and outperforms other frequently used regression models.We also study the effect of the extracted features generated by our LSTM model.Our study finds that only very few dimensions of the extracted feature are influential in HFR prediction.The study highlights the potential to monitor HFR condition accurately and timely on a car.展开更多
基金supported by the National Natural Science Foundation of China [grant number 42088101]。
文摘In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.
基金supported by the National Key Research and Development Program of China (Grant No. 2017YFA0505500)Japan Society for the Promotion of Science KAKENHI Program (Grant No. JP15H05707)National Natural Science Foundation of China (Grant Nos. 11771010,31771476,91530320, 91529303,91439103 and 81471047)
文摘Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.
文摘High-frequency resistance(HFR)is a critical quantity strongly related to a fuel cell system’s performance.It is beneficial to estimate the fuel cell system’s HFR from the measurable operating conditions without resorting to costly HFR measurement devices.In this study,we propose a data-driven approach for a real-time prediction of HFR.Specifically,we use a long short-term memory(LSTM)based machine learning model that takes into account both the current and past states of the fuel cell,as characterized through a set of sensors.These sensor signals form the input to the LSTM.The data is experimentally collected from a vehicle lab that operates a 100 kW automotive fuel cell stack running on an automotive-scale test station.Our current results indicate that our prediction model achieves high accuracy HFR predictions and outperforms other frequently used regression models.We also study the effect of the extracted features generated by our LSTM model.Our study finds that only very few dimensions of the extracted feature are influential in HFR prediction.The study highlights the potential to monitor HFR condition accurately and timely on a car.
