6 papers
Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models
Aditya Sai Pranith Ayapilla, Kazuya Miyashita, Yuki Yasuda +1
Data assimilation (DA) improves prediction of chaotic systems by combining model forecasts with sparse, noisy observations. Many DA methods are inherently probabilistic, but accura…
Predictor-Driven Diffusion for Spatiotemporal Generation
Yuki Yasuda, Tobias Bischoff
Multiscale spatial structure complicates temporal prediction because small-scale spatial fluctuations influence large-scale evolution, yet resolving all scales is often intractable…
Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge
Yuki Yasuda, Ryo Onishi
This study employs a neural network that represents the solution to a Schrödinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schrödinger brid…
Zero-Shot Super-Resolution from Unstructured Data Using a Transformer-Based Neural Operator for Urban Micrometeorology
Yuki Yasuda, Ryo Onishi
This study demonstrates that a transformer-based neural operator (TNO) can perform zero-shot super-resolution of two-dimensional temperature fields near the ground in urban areas.…
Unsupervised Super-Resolution Data Assimilation Using Conditional Variational Autoencoders with Estimating Background Covariances via Super-Resolution
Yuki Yasuda, Ryo Onishi
This study proposes a theory of unsupervised super-resolution data assimilation (SRDA) using conditional variational autoencoders (CVAEs). We derive an evidence lower bound for uns…
Two-Stage Super-Resolution Simulation Method of Three-Dimensional Street-Scale Atmospheric Flows for Real-Time Urban Micrometeorology Prediction
Yuki Yasuda, Ryo Onishi
A two-stage super-resolution simulation method is proposed for street-scale air temperature and wind velocity, which considerably reduces computation time while maintaining accurac…