paper
2022
Extremes & hazards
Applications & impacts New model or method
CNN / U-Net6 h–3 daysNanjing University of Information Science & Technology (NUIST) Journal
2022-01-01
018
Jan 2022
ヨシアキ ミヤモト, Yoshiaki Miyamoto, 佳明 宮本
Korean Society of Hazard Mitigation
Nanjing University of Information Science & Technology (NUIST)
A deep learning model trained on image-like wind fields around PIREP turbulence reports predicts Japanese airspace turbulence with accuracy comparable to existing grid-point indices.
画像学習を用いた日本空域における乱気流の予測システムの構築
In one sentence. A deep learning model trained on image-like wind fields around PIREP turbulence reports predicts Japanese airspace turbulence with accuracy comparable to existing grid-point indices.
Labels
- Topic
- Extremes & hazards, Applications & impacts
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- ERA5 / reanalysis, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.9798/kosham.2018.18.2.483 OpenAlex:W7139865401
- Added
- 2026-10-22
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