This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multi...With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.展开更多
Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center(SEPC) affiliated with the National Space Science Center(NSSC) has been providing space weath...Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center(SEPC) affiliated with the National Space Science Center(NSSC) has been providing space weather services and helps secure space missions. Presently, SEPC is capable to offer a variety of space weather services covering many phases of space science missions including planning, design, launch,and orbital operation. The service packages consist of space weather forecasts, warnings, and effect analysis that can be utilized to avoid potential space weather hazard or reduce the damage caused by space storms,space radiation exposure for example. Extensive solar storms that occurred over Chinese Ghost Festival(CGF)in September 2017 led to a large enhancement of the solar energetic particle flux at 1 AU, which affected the near Earth radiation environment and brought great threat to orbiting satellites. Based on the space weather service by SEPC, satellite ground support groups collaborating with the space Tracking, Telemetering and Command system(TT&C) team were able to take immediate measures to react to the CGF solar storm event.展开更多
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
基金Supported by National Natural Science Foundation of China(41574181)。
文摘With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.
文摘Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center(SEPC) affiliated with the National Space Science Center(NSSC) has been providing space weather services and helps secure space missions. Presently, SEPC is capable to offer a variety of space weather services covering many phases of space science missions including planning, design, launch,and orbital operation. The service packages consist of space weather forecasts, warnings, and effect analysis that can be utilized to avoid potential space weather hazard or reduce the damage caused by space storms,space radiation exposure for example. Extensive solar storms that occurred over Chinese Ghost Festival(CGF)in September 2017 led to a large enhancement of the solar energetic particle flux at 1 AU, which affected the near Earth radiation environment and brought great threat to orbiting satellites. Based on the space weather service by SEPC, satellite ground support groups collaborating with the space Tracking, Telemetering and Command system(TT&C) team were able to take immediate measures to react to the CGF solar storm event.
