In spite of the impending flattening of Moore’s law,the complexity and size of the systems we are interested in keep on increasing.This challenges the computer simulation tools due to the expensive computational cost...In spite of the impending flattening of Moore’s law,the complexity and size of the systems we are interested in keep on increasing.This challenges the computer simulation tools due to the expensive computational cost.Fortunately,advanced theoretical methods can be considered as alternatives to accurately and efficiently capture the structural and thermodynamic properties of complex inhomogeneous fluids.In the last decades,classical density functional theory(cDFT)has proven to be a sophisticated,robust,and efficient approach for studying complex inhomogeneous fluids.In this work,we present a pedagogical introduction to a broadly accessible open-source density functional theory software package named"an advanced theoretical tool for inhomogeneous fluids"(Atif)and of the underlying theory.To demonstrate Atif,we take three cases as examples using a typical laptop computer:(i)electric double-layer of asymmetric electrolytes;(ii)adsorptions of sequencedefined semiflexible polyelectrolytes on an oppositely charged surface;and(iii)interactions between surfaces mediated by polyelectrolytes.We believe that this pedagogical introduction will lower the barrier to entry to the use of Atif by experimental as well as theoretical groups.A companion website,which provides all of the relevant sources including codes and examples,is attached.展开更多
Accurate nowcasting provides key information for disaster weather warnings.Nowcasting is mostly based on radar echo extrapolation,where the echo evolution results from complex interactions among cloud systems and vari...Accurate nowcasting provides key information for disaster weather warnings.Nowcasting is mostly based on radar echo extrapolation,where the echo evolution results from complex interactions among cloud systems and various thermal–dynamic features of the weather background.However,existing research has not integrated high spatiotemporal resolution weather background information.In this study,multiple data fusion units(Radarcells)integrating the radar mosaic and rapid-refresh data are constructed as input for the radar echo extrapolation network architecture designed according to Attention-Resnet Unet with Radarcells(ARUR).In addition,a self-defined loss function combining weighted mean square error and structural similarity index is further proposed to improve the extrapolation effect.The rapid-refresh data includes relative humidity,zonal wind,meridional wind,and vertical velocity within the range from 115.48 to 117.48°E,38.81 to 40.81°N during June–August from 2018 to 2021.Four ARUR-based models with different Radarcells as input and an ARU-based model(Attention-Resnet Unet,without fusing physical data)are trained for 120 min of extrapolation,respectively.The models are evaluated with the indicators,including critical success index(CSI),probability of detection(POD),and false alarm rate(FAR),by the test dataset.The results show that the performance of echo prediction and timeliness by ARUR-based models is better than the ARU-based model,especially for strong echo prediction.Under the reflectivity thresholds of 25 and 35 d BZ,the average values of CSI,FAR,and POD calculated by ARUR-based models are improved by 8.42%,7.76%,8.52%and 10.36%,7.36%,9.10%than those by ARU-based,respectively.The study suggests that integrating weather background information can significantly enhance the effect of extrapolation by means of improving the issues of echo blurriness,formation,and dissipation compared with the previous deep learning-based models.展开更多
基金financially supported by the National Natural Science Foundation of China(No.21973104)。
文摘In spite of the impending flattening of Moore’s law,the complexity and size of the systems we are interested in keep on increasing.This challenges the computer simulation tools due to the expensive computational cost.Fortunately,advanced theoretical methods can be considered as alternatives to accurately and efficiently capture the structural and thermodynamic properties of complex inhomogeneous fluids.In the last decades,classical density functional theory(cDFT)has proven to be a sophisticated,robust,and efficient approach for studying complex inhomogeneous fluids.In this work,we present a pedagogical introduction to a broadly accessible open-source density functional theory software package named"an advanced theoretical tool for inhomogeneous fluids"(Atif)and of the underlying theory.To demonstrate Atif,we take three cases as examples using a typical laptop computer:(i)electric double-layer of asymmetric electrolytes;(ii)adsorptions of sequencedefined semiflexible polyelectrolytes on an oppositely charged surface;and(iii)interactions between surfaces mediated by polyelectrolytes.We believe that this pedagogical introduction will lower the barrier to entry to the use of Atif by experimental as well as theoretical groups.A companion website,which provides all of the relevant sources including codes and examples,is attached.
基金Supported by the Key Laboratory of High Impact Weather(special)China Meteorological Administration(2024-K-02)+3 种基金Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(SCSF202301)Natural Science Foundation of Hebei Province of China(D2024304002)Open Founds of China Meteorological Administration Hydro–Meteorology Key Laboratory(23SWQXM007)Innovation and Development Project of China Meteorological Administration(CXFZ2025J106)。
文摘Accurate nowcasting provides key information for disaster weather warnings.Nowcasting is mostly based on radar echo extrapolation,where the echo evolution results from complex interactions among cloud systems and various thermal–dynamic features of the weather background.However,existing research has not integrated high spatiotemporal resolution weather background information.In this study,multiple data fusion units(Radarcells)integrating the radar mosaic and rapid-refresh data are constructed as input for the radar echo extrapolation network architecture designed according to Attention-Resnet Unet with Radarcells(ARUR).In addition,a self-defined loss function combining weighted mean square error and structural similarity index is further proposed to improve the extrapolation effect.The rapid-refresh data includes relative humidity,zonal wind,meridional wind,and vertical velocity within the range from 115.48 to 117.48°E,38.81 to 40.81°N during June–August from 2018 to 2021.Four ARUR-based models with different Radarcells as input and an ARU-based model(Attention-Resnet Unet,without fusing physical data)are trained for 120 min of extrapolation,respectively.The models are evaluated with the indicators,including critical success index(CSI),probability of detection(POD),and false alarm rate(FAR),by the test dataset.The results show that the performance of echo prediction and timeliness by ARUR-based models is better than the ARU-based model,especially for strong echo prediction.Under the reflectivity thresholds of 25 and 35 d BZ,the average values of CSI,FAR,and POD calculated by ARUR-based models are improved by 8.42%,7.76%,8.52%and 10.36%,7.36%,9.10%than those by ARU-based,respectively.The study suggests that integrating weather background information can significantly enhance the effect of extrapolation by means of improving the issues of echo blurriness,formation,and dissipation compared with the previous deep learning-based models.
