Developing a physics-informed variational autoencoder (VAE) approach to
construct a reduced-order model of the lee vortex in Taiwan
This study develops a physics-informed neural network (PINN) model to
efficiently generate high-fidelity local circulation patterns in Taiwan
while preserving an accurate representation of the physical relationship
between generated local turbulence and upstream synoptic flow regimes.
Large ensemble semi-realistic simulations were conducted using a
high-resolution (2 km) TaiwanVVM model, in which the critical
characteristics of the various synoptic flow regimes are carefully
selected to concentrate on the effects of local circulation variations.
A variational autoencoder (VAE) was constructed to capture essential
representations of local circulation scenarios associated with the lee
vortices by training on the ensemble dataset. The VAE’s latent space
effectively encapsulated synoptic flow regimes as controlling factors,
aligning with the physical understanding of Taiwan’s local circulation.
The critical transition of flow regimes under the southeasterly synoptic
flow regimes is also well represented in the VAE’s latent space,
indicating that the VAE can learn the nonlinear characteristics of the
multiscale interaction of the lee vortex. We further construct a
reduced-order model for local circulation predictions under diverse
synoptic conditions. This physics-informed VAE ensures the accurate
prediction of the nonlinear characteristics of multiscale interactions
between synoptic flows and the turbulence induced by topography, thereby
accelerating the assessment of the local circulation responses under
various climate change scenarios.