Papers › Two-Stage Super-Resolution Simulation Method of Three-Dimensional Street-Scale...
Two-Stage Super-Resolution Simulation Method of Three-Dimensional Street-Scale Atmospheric Flows for Real-Time Urban Micrometeorology Prediction
Yuki Yasuda, Ryo Onishi
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
A two-stage super-resolution simulation method is proposed for street-scale air temperature and wind velocity, which considerably reduces computation time while maintaining accuracy. The first stage employs a convolutional neural network (CNN) to correct large-scale flows above buildings in the input low-resolution simulation results. The second stage uses another CNN to reconstruct small-scale flows between buildings from the output of the first stage, resulting in high-resolution inferences. The CNNs are trained using high-resolution simulation data for the second stage and their coarse-grained version for the first stage as the ground truth, where the high-resolution simulations are conducted independently of the low-resolution simulations used as input. This learning approach separates the spatial scales of inference in each stage. The effectiveness of the proposed method was evaluated using micrometeorological simulations in an actual urban area around Tokyo Station in Japan. The super-resolution simulation successfully inferred high-resolution atmospheric flows, reducing errors by approximately 50% compared to the low-resolution simulations. Furthermore, the two-stage approach enabled localized high-resolution inferences, reducing GPU memory usage to as low as 12% during training. The total wall-clock time for 60-min predictions was reduced to 6.83 min, which was 3.32% of the high-resolution simulation time.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections