Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an eff...Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.展开更多
We introduced the two-parameter stratiform cloud model of Hu and Yan (1986) into the mesoscale model ofAnthes et al. (1987), and reprogramed the latter, then constructed a three-dimensional stratiform cloud system mod...We introduced the two-parameter stratiform cloud model of Hu and Yan (1986) into the mesoscale model ofAnthes et al. (1987), and reprogramed the latter, then constructed a three-dimensional stratiform cloud system modelwhich includes three phases of water and detailed cloud physical processes. For the stability and accuracy of calculationin a larger time step, we accepted a set of hybrid-schemes for all and the time split scheme for some of the cloud physicalprocesses, and proposed a parameterized method which calculates different types of phase change processessimultaneously, and designed the falling schemes of particles following the Lagrangian method.We used a dry model, a cumulus parameterization model, a two-phase explicit scheme model, and the model pres-ented here to simulate two low-level mesoscale vortices, compared and analysed the simulating capability of these mod-els. The results show that in simulation of the circulation structure of meso-vortex, the structure of cloud system, andsurface precipitation, the model presented here is more reasonable and closer to the observations than other models.展开更多
基金the National Key R&D Program of China(2017YFB1002702).
文摘Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.
文摘We introduced the two-parameter stratiform cloud model of Hu and Yan (1986) into the mesoscale model ofAnthes et al. (1987), and reprogramed the latter, then constructed a three-dimensional stratiform cloud system modelwhich includes three phases of water and detailed cloud physical processes. For the stability and accuracy of calculationin a larger time step, we accepted a set of hybrid-schemes for all and the time split scheme for some of the cloud physicalprocesses, and proposed a parameterized method which calculates different types of phase change processessimultaneously, and designed the falling schemes of particles following the Lagrangian method.We used a dry model, a cumulus parameterization model, a two-phase explicit scheme model, and the model pres-ented here to simulate two low-level mesoscale vortices, compared and analysed the simulating capability of these mod-els. The results show that in simulation of the circulation structure of meso-vortex, the structure of cloud system, andsurface precipitation, the model presented here is more reasonable and closer to the observations than other models.
