Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events
Problem
Deep learning precipitation nowcasting models underperform on extreme rainfall rates due to prediction smoothing, weakening short-term flash-flood and thunderstorm forecasts.
Why it matters
Short-term forecasts of extreme rainfall are crucial for flash flood and severe thunderstorm warning.
What the AI did
A stacking method trained a convolutional neural network to combine an ensemble of deep learning nowcasting models with orographic features.
Results
Doubled prediction skill versus ensemble members and their average on extreme rain rates; outperformed them on all rain regimes (TAASRAD19 dataset)
Impact
Evidence supports a concrete skill improvement for extreme-rainfall nowcasting on the studied radar dataset.
Record
| Venue | Atmosphere |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Gabriele Franch, Daniele Nerini, Marta Pendesini, Luca Coviello, Giuseppe Jurman, Cesare Furlanello |
| Citations | 97 (OpenAlex, at import time) |
| References | 49 |
| Model or method | Stacked CNN combining deep learning ensemble with orographic features |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/atmos11030267 |
| Open access | gold |
| Keywords | precipitation nowcasting, deep learning, stacked generalization, extreme rainfall, radar, orographic features |
Importance rating
significant — thousands to millions affected
Abstract calls short-term extreme-rainfall forecasting one of the most crucial nowcasting applications, supporting flash flood and severe thunderstorm warnings that protect lives and property.
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