In weather forecasting,generating atmospheric variables for regions with complex topography,such as the Andean regions with peaks reaching 6500 m above sea level,poses significant challenges.Traditional regional clima...In weather forecasting,generating atmospheric variables for regions with complex topography,such as the Andean regions with peaks reaching 6500 m above sea level,poses significant challenges.Traditional regional climate models often struggle to accurately represent the atmospheric behavior in such areas.Furthermore,the capability to produce high spatio-temporal resolution data(less than 27 km and hourly)is limited to a few institutions globally due to the substantial computational resources required.This study presents the results of atmospheric data generated using a new type of artificial intelligence(AI)models,aimed to reduce the computational cost of generating downscaled climate data using climate regional models like the Weather Research and Forecasting(WRF)model over the Andes.The WRF model was selected for this comparison due to its frequent use in simulating atmospheric variables in the Andes.Our results demonstrate a higher downscaling performance for the four target weather variables studied(temperature,relative humidity,zonal and meridional wind)over coastal,mountain,and jungle regions.Moreover,this AI model offers several advantages,including lower computational costs compared to dynamic models like WRF and continuous improvement potential with additional training data.展开更多
针对船舶轨迹预测精确性与实时性的需求,从数据层面探究影响船舶航行轨迹的特征,通过相关性分析确定网络的输入,提出结合循环神经网络-长短期记忆(Recurrent Neural Networks-Long Short Term Memory,RNN-LSTM)的船舶航行轨迹预测模型...针对船舶轨迹预测精确性与实时性的需求,从数据层面探究影响船舶航行轨迹的特征,通过相关性分析确定网络的输入,提出结合循环神经网络-长短期记忆(Recurrent Neural Networks-Long Short Term Memory,RNN-LSTM)的船舶航行轨迹预测模型。通过船舶Z形试验相关数据与实船实际航行数据对网络模型进行训练,并对未来船舶航行轨迹进行预测。对未来轨迹的预测值与实际值进行对比。结果表明,模型预测误差小,验证该方案在船舶轨迹预测中的实用性和有效性。展开更多
文摘In weather forecasting,generating atmospheric variables for regions with complex topography,such as the Andean regions with peaks reaching 6500 m above sea level,poses significant challenges.Traditional regional climate models often struggle to accurately represent the atmospheric behavior in such areas.Furthermore,the capability to produce high spatio-temporal resolution data(less than 27 km and hourly)is limited to a few institutions globally due to the substantial computational resources required.This study presents the results of atmospheric data generated using a new type of artificial intelligence(AI)models,aimed to reduce the computational cost of generating downscaled climate data using climate regional models like the Weather Research and Forecasting(WRF)model over the Andes.The WRF model was selected for this comparison due to its frequent use in simulating atmospheric variables in the Andes.Our results demonstrate a higher downscaling performance for the four target weather variables studied(temperature,relative humidity,zonal and meridional wind)over coastal,mountain,and jungle regions.Moreover,this AI model offers several advantages,including lower computational costs compared to dynamic models like WRF and continuous improvement potential with additional training data.
文摘针对船舶轨迹预测精确性与实时性的需求,从数据层面探究影响船舶航行轨迹的特征,通过相关性分析确定网络的输入,提出结合循环神经网络-长短期记忆(Recurrent Neural Networks-Long Short Term Memory,RNN-LSTM)的船舶航行轨迹预测模型。通过船舶Z形试验相关数据与实船实际航行数据对网络模型进行训练,并对未来船舶航行轨迹进行预测。对未来轨迹的预测值与实际值进行对比。结果表明,模型预测误差小,验证该方案在船舶轨迹预测中的实用性和有效性。
