46 citations · 76 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
Spatio-Temporal Super-Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks with Domain Generalization
Yuki Yasuda, Ryo Onishi
Deep learning has recently gained attention in the atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations or to reduce computational co…
Super-Resolution of Near-Surface Temperature Utilizing Physical Quantities for Real-Time Prediction of Urban Micrometeorology
Yuki Yasuda, Ryo Onishi, Yuichi Hirokawa +2
The present paper proposes a super-resolution (SR) model based on a convolutional neural network and applies it to the near-surface temperature in urban areas. The SR model incorpo…
Storm modulation is feasible through a strategic use of air conditioners
Daisuke Hiruma, Ryo Onishi, Keiko Takahashi +1
Storm trainings, consisting of line-shaped aggregates of cumulonimbi, bring persistent local heavy rains, often causing devastating floods and landslides. Weather control technique…
Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology
Ryo Onishi, Daisuke Sugiyama, Keigo Matsuda
We propose a super-resolution (SR) simulation system that consists of a physics-based meteorological simulation and an SR method based on a deep convolutional neural network (CNN).…