A5038978697- Climate variability and models
- Meteorological Phenomena and Simulations
- Atmospheric aerosols and clouds
- Atmospheric chemistry and aerosols
- Cryospheric studies and observations
- Traffic Prediction and Management Techniques
- Energy Load and Power Forecasting
- Image and Signal Denoising Methods
National Taiwan University
2022-2024
We applied tracer transport simulations using the Taiwan vector vorticity equation cloud-resolving model (TaiwanVVM) to evaluate effects of local circulation associated with lee vortex and planetary boundary layer development on accumulation pollutants a diurnal time scale in central Taiwan. The wind directions crucial synoptic northeast monsoon are idealized as initial conditions examine impact pollutant transport. primary nontraffic emission sources taken sites so that experiment results...
Abstract This study quantifies the potential effect of lee vortex on fine particulate matter (PM 2.5 ) pollution deterioration under complex topography in Taiwan using observational data. We select lee-vortex days that favor development vortices northwestern southeasterly synoptic winds. then define enhancement index discerns areas with high occurrence frequencies PM flow regime relative to seasonal background concentrations. Under days, center western exhibits indices higher than 0.65. In...
Abstract This study introduces a recent field experiment investigating multiscale terrain–circulation–precipitation interactions. When synoptic‐scale northeasterly wind prevails under the active East Asian winter monsoon, stratocumulus cloud decks with severe rainfall exceeding 100 mm·day −1 frequently occur in northeastern plain area and adjacent mountains Yilan, Taiwan. The Yilan Experiment of Severe Rainfall (YESR2020) is campaign from November 20, 2020, to 24, survey physical processes...
Abstract This study develops an explainable variational autoencoder (VAE) framework to efficiently generate high‐fidelity local circulation patterns in Taiwan, ensuring accurate representation of the physical relationship between generated and upstream synoptic flow regimes. Large ensemble semi‐realistic simulations were conducted using a high‐resolution (2 km) model, TaiwanVVM, where critical characteristics various regimes carefully selected focus on effects variations. The VAE was...
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 turbulence and upstream synoptic flow regimes. Large ensemble semi-realistic simulations were conducted using high-resolution (2 km) TaiwanVVM model, which critical characteristics various regimes are carefully selected concentrate on effects variations. A...
This study develops an explainable variational autoencoder (VAE) framework to efficiently generate high-fidelity local circulation patterns in Taiwan, ensuring accurate representation of the physical relationship between generated and upstream synoptic flow regimes. Large ensemble semi-realistic simulations were conducted using a high-resolution (2 km) model, TaiwanVVM, where critical characteristics various regimes carefully selected focus on effects variations. The VAE was constructed...