The observation of friction anisotropy on graphene by friction measurement at atomic scale has been reported in this paper.Atomic-scale friction measurement revealed friction anisotropy with a periodicity of 60°,...The observation of friction anisotropy on graphene by friction measurement at atomic scale has been reported in this paper.Atomic-scale friction measurement revealed friction anisotropy with a periodicity of 60°,which is consistent with the hexagonal periodicity of the graphene.Both experiments and theory show that the value of the friction force is related to the graphene lattice orientation,and the friction force along armchair orientation is also larger than the one along zigzag orientation.These results will play a critical role in the use of graphene to manufacture nanoscale devices.展开更多
Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately...Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter(En KF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with En KF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from En KF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of En KF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.展开更多
基金supported by the National High Technology Research and Development Program of China(Grant No.2009AA03Z316)the National Natural Science Foundation of China(Grant Nos.60904095,51050110445 and 61175103)the CAS FEA International Partnership Program for Creative Research Teams
文摘The observation of friction anisotropy on graphene by friction measurement at atomic scale has been reported in this paper.Atomic-scale friction measurement revealed friction anisotropy with a periodicity of 60°,which is consistent with the hexagonal periodicity of the graphene.Both experiments and theory show that the value of the friction force is related to the graphene lattice orientation,and the friction force along armchair orientation is also larger than the one along zigzag orientation.These results will play a critical role in the use of graphene to manufacture nanoscale devices.
文摘Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter(En KF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with En KF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from En KF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of En KF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.
