CFD-quality nowcasting for urban air mobility with a deep learning-based emulator
Bibliographic Data
| ID | 15548654 |
|---|---|
| Authors | J H Bae (0009-0008-6278-7298, Pukyong National University, corresponding author), Yelim Lee (0000-0002-9943-1796, Pukyong National University, corresponding author), Yunhyeong Lee, Ju-Hwan Rho (0009-0001-4635-837X, Pukyong National University, corresponding author), Geon Kang (0000-0002-0460-8916, Pukyong National University, corresponding author), Jae-Jin Kim (0000-0002-1395-4562, Pukyong National University, corresponding author), Rackhun Son (0000-0002-3366-495X, Pukyong National University, corresponding author) |
| Year | 2025 |
| Volume | 20 |
| Issue | 7 |
| Pages | 074001-074001 |
| Publication date | 2025-05-23 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/addc84 |
| OpenAlex | W4410632910 |
| Language | EN |
| Citations received | 1 |
| References cited | 36 |
Urban air mobility (UAM) aircraft typically operate at altitudes of 1000–2000 feet over complex urban terrains, where airflow interactions with structures create dynamic conditions requiring precise, real-time meteorological forecasts. Traditional large-scale forecasting systems often struggle to adequately capture turbulent boundary-layer characteristics due to coarse grid resolutions, while high-resolution computational fluid dynamics (CFD) simulations remain too computationally expensive for real-time operations. To overcome these challenges, this study introduces a deep learning-based emulator that rapidly generates CFD-like results using data from the local data assimilation and prediction system operated by the Korean Meteorological Administration. By integrating residual dense blocks with a wind direction classification system, the emulator significantly enhances predictive accuracy and computational efficiency. This approach enables urban-scale high-resolution weather forecasts, which are critical for UAM operations and broader urban meteorological applications, establishing a new standard for the safe and effective integration of UAM
Air quality index · Computational fluid dynamics · Geography · Meteorology · Nowcasting · Quality (philosophy · Air Quality Monitoring and Forecasting · Computer Science · Engineering · Environmental Science · Evacuation and Crowd Dynamics · Wind and Air Flow Studies · Aerospace Engineering
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |