https://github.com/sarwanshah/asu_2024_precipitation-nowcasting-using-deep-learning
Using deep learning methodologies to address the problem of precipitation nowcasting.
https://github.com/sarwanshah/asu_2024_precipitation-nowcasting-using-deep-learning
computer-vision deep-learning gru lstm machine-learning meteorology precipitation-nowcasting remote-sensing
Last synced: 8 months ago
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Using deep learning methodologies to address the problem of precipitation nowcasting.
- Host: GitHub
- URL: https://github.com/sarwanshah/asu_2024_precipitation-nowcasting-using-deep-learning
- Owner: SarwanShah
- License: cc0-1.0
- Created: 2025-02-09T23:53:09.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-10T00:05:22.000Z (8 months ago)
- Last Synced: 2025-02-10T00:25:21.906Z (8 months ago)
- Topics: computer-vision, deep-learning, gru, lstm, machine-learning, meteorology, precipitation-nowcasting, remote-sensing
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Abstract
Precipitation nowcasting for short-term
storm forecasting (0–6 hours) is essential for timely
severe weather warnings. Traditional methods such as
numerical weather prediction (NWP) and radar extrapolation, often lack accuracy at short scales and are
computationally intensive. Recent deep learning models,
such as ConvLSTM and TrajGRU have offered promising
advances by capturing complex spatiotemporal dynamics.
This paper aims to evaluate these models on satellite data,
addressing the limitation posed radar’s limited global
coverage, while focusing on the region of Sindh, Pakistan
— a region with minimal meteorological infrastructure.
Thus, by contributing towards the improvement of global
nowcasting capabilities this work addresses critical forecasting needs heightened by climate change.
**REPORT**: [Final_Report.pdf](Paper/EEE598_Final_Paper.pdf)
**Sample Test Result: Target (Left) vs Prediction (Right)**
# Poster
