HIR Nowcasting using ConvLSTM
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
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| _version_ | 1866901062649118720 |
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| author | Kim, Gyuyeon |
| author_facet | Kim, Gyuyeon |
| contents | <div> <div> <div> <div dir="auto"> <div> <div> <p>This repository presents a deep learning–based framework for short-term prediction of High-Intensity Rainfall (HIR) using GEO-KOMPSAT-2A (GK2A) and Global Precipitation Measurement (GPM) IMERG satellite data. Three ConvLSTM2D (Convolutional Long Short-Term Memory 2D) models were developed utilizing brightness temperature (BT), cloud analysis, and precipitation data. The models achieve up to 50% Critical Success Index (CSI) and over 70% F1 score, demonstrating strong potential for global HIR prediction, particularly in regions where ground-based radar observations are unavailable.</p> </div> </div> </div> </div> </div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19016825 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HIR Nowcasting using ConvLSTM Kim, Gyuyeon <div> <div> <div> <div dir="auto"> <div> <div> <p>This repository presents a deep learning–based framework for short-term prediction of High-Intensity Rainfall (HIR) using GEO-KOMPSAT-2A (GK2A) and Global Precipitation Measurement (GPM) IMERG satellite data. Three ConvLSTM2D (Convolutional Long Short-Term Memory 2D) models were developed utilizing brightness temperature (BT), cloud analysis, and precipitation data. The models achieve up to 50% Critical Success Index (CSI) and over 70% F1 score, demonstrating strong potential for global HIR prediction, particularly in regions where ground-based radar observations are unavailable.</p> </div> </div> </div> </div> </div> </div> |
| title | HIR Nowcasting using ConvLSTM |
| url | https://doi.org/10.5281/zenodo.19016825 |