Developing an Explainable Variational Autoencoder (VAE) Framework for Accurate Representation of Local Circulation in Taiwan

This study develops an explainable variational autoencoder (VAE) framework to efficiently generate high‐fidelity local circulation patterns in Taiwan, ensuring an accurate representation of the physical relationship between generated local circulation and upstream synoptic flow regimes. Large ensemb...

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Vydáno v:Journal of geophysical research. Atmospheres Ročník 129; číslo 12
Hlavní autoři: Hsieh, Min‐Ken, Wu, Chien‐Ming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Washington Blackwell Publishing Ltd 28.06.2024
Témata:
Accuracy
Circulation
Circulation patterns
Climate and weather
Climate change
Climate change scenarios
Climate prediction
Climatic conditions
deep generative model
deep learning
explainable artificial intelligence
Flow distribution
Flow pattern
Fluid flow
large eddy simulation
local circulation
Local flow
Neural networks
Representations
Simulation
Synoptic weather conditions
Training
Uncertainty
Vortices
Weather
Weather conditions
Weather patterns
Wind speed
Taiwan
ISSN:2169-897X, 2169-8996
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Shrnutí:This study develops an explainable variational autoencoder (VAE) framework to efficiently generate high‐fidelity local circulation patterns in Taiwan, ensuring an accurate representation of the physical relationship between generated local circulation and upstream synoptic flow regimes. Large ensemble semi‐realistic simulations were conducted using a high‐resolution (2 km) model, TaiwanVVM, where critical characteristics of various synoptic flow regimes were carefully selected to focus on the effects of local circulation variations. The VAE was constructed to capture essential representations of local circulation scenarios associated with the lee vortices by training on the ensemble data set. The VAE's latent space effectively captures the synoptic flow regimes as controlling factors, aligning with the physical understanding of Taiwan's local circulation dynamics. The critical transition of flow regimes under the influence of southeasterly synoptic flow regimes is also well represented in the VAE's latent space. This indicates that the VAE can learn the nonlinear characteristics of the multiscale interactions involving the lee vortex. The latent space within VAE can serve as a reduced‐order model for predicting local circulation using synoptic wind speed and direction. This explainable VAE binds the physical reasoning to the predictions of the local circulation that ensures the physical examination of the uncertainty in accelerating the local weather assessments under various climate change scenarios. Plain Language Summary This research introduces an advanced neural network framework for generating high‐fidelity local flow patterns in Taiwan. This framework, known as an explainable variational autoencoder, can accurately simulate how wind patterns of synoptic weather conditions interact in this region. We used detailed simulations to train the variational autoencoder, ensuring it captures the complex relationships between local flow and larger‐scale weather patterns. By training on the detailed simulations, the variational autoencoder learned and represented these large‐scale weather patterns in a way that helps maintain the physical relationship between local flow prediction and the large‐scale weather patterns. One of the key outcomes of this study is the development of a reduced‐order model. This simplified model takes advantage of what we have learned about complex weather interactions and can quickly predict local weather under different conditions. This approach provides opportunities for physical examination of uncertainty in local circulation predictions using a neural network model under complex situations involving changing climate conditions. Key Points An explainable variational autoencoder is constructed to capture Taiwan's local circulation using TaiwanVVM ensemble simulations The representation of local circulation in the latent space of the VAE can be formulated as synoptic wind speed and direction This framework can effectively generate accurate local circulation in Taiwan for fast climate response assessment
Bibliografie:ObjectType-Article-1
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ISSN:2169-897X
2169-8996
DOI:10.1029/2024JD041167