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20232026
most citedSuper-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques

1 citations · 3 across the 7 of their papers we have counts for

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physics.ao-ph2026

Quantifying internal variability in large-ensemble climate data with the Wasserstein distance

Yuki Yasuda, Shoichiro Kido

Quantifying forced and internal variability in climate data is fundamental to detecting the climate change signal and assessing uncertainty in climate projections. We propose a met…

physics.ao-ph2025

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 bridge…

physics.ao-ph2025

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.…

physics.ao-ph2024★ 1 cited

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…

physics.ao-ph2023★ 1 cited

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…

physics.ao-ph2023★ 1 cited

Super-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques

Yuki Yasuda, Ryo Onishi, Keigo Matsuda

Atmospheric simulations for urban cities can be computationally intensive because of the need for high spatial resolution, such as a few meters, to accurately represent buildings a…