Stormscapes

Abstract

The complex interplay of a number of physical and meteorological phenomena makes simulating clouds a challenging and open research problem. We explore a physically accurate model for simulating clouds and the dynamics of their transitions. We propose first-principle formulations for computing buoyancy and air pressure that allow us to simulate the variations of atmospheric density and varying temperature gradients. Our simulation allows us to model various cloud types, such as cumulus, stratus, and stratoscumulus, and their realistic formations caused by changes in the atmosphere. Moreover, we are able to simulate large-scale cloud super cells - clusters of cumulonimbus formations - that are commonly present during thunderstorms. To enable the efficient exploration of these stormscapes, we propose a lightweight set of high-level parameters that allow us to intuitively explore cloud formations and dynamics. Our method allows us to simulate cloud formations of up to about 20 km × 20 km extents at interactive rates. We explore the capabilities of physically accurate and yet interactive cloud simulations by showing numerous examples and by coupling our model with atmosphere measurements of real-time weather services to simulate cloud formations in the now. Finally, we quantitatively assess our model with cloud fraction profiles, a common measure for comparing cloud types.

Interactive landscape–scale cloud animation using DCGAN

“…However, the animation pattern of such generated clouds is not entirely realistic. Hädrich et al (2020) have recently simulated a variety of clouds using a set of lightweight higher level primitives. Vimont et al (2020) have employed a similar concept in Eulerian settings instead of the Lagrangian framework.…”

SkyGAN: Realistic Cloud Imagery for Image‐based Lighting

“…However, path tracing of atmospheres is a lengthy process, and moreover, the models do not directly support the rendering of clouds and overcast skies. For that, one would render fully volumetric clouds from a simulation [HMP*20, HHP*21] with expensive volumetric path tracing, or use machine learning [KMM*17] for improved efficiency.…”

Deep Synthesis of Cloud Lighting

“…Simulating clouds is also widely investigated in the literature, for example, Dobashi et al [21], Harris and Lastra [22], and Dobashi et al [23]. Recently, the complex nature of cloud formation was investigated by Hädrich et al [24] who developed a novel framework to simulate physically accurate clouds. This led to plausible cloud dynamics in 3D, but it is computationally expensive and furthermore needs to be coupled with expensive light transport simulation to produce realistic imagery.…”

“…This faces the challenge that changes to cloud shape and position have to be smooth and coherent over hundreds of frames, while simultaneously predicting correct illumination. We also have to predict clouds moving at different speeds, due to factors such as different wind strengths for clouds at different altitudes [24]. While cloud evolution at these timescales can be predicted by physics-based models such as simulating fluid dynamics in the atmosphere, we operate in image space as this removes the need for complicated and expensive cloud, terrain, and atmospheric models.…”