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license: mit |
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# Model Card: GPROF IR |
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## Model Details |
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- **Model Name:** GPROF IR |
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- **Developer:** Simon Pfreundschuh |
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- **License:** MIT |
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- **Model Type:** Neural Network for Precipitation Retrieval |
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- **Language:** Not applicable |
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- **Framework:** PyTorch |
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- **Repository:** github.com/simonpf/gprof_ir |
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## Model Description |
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GPROF IR is a satellite precipitation retrieval for geostationary IR observations. |
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### Inputs |
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- 11 um brightness temperatures from geostationary sensors |
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### Outputs |
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- Surface precipitation estimates |
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## Training Data |
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- **Training Data Source:** Satellite-based observations and collocated ground truth precipitation estimates derived from GPM 2BCMB. |
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- **Data Preprocessing:** Normalization |
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## Training Procedure |
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- **Optimizer:** AdamW |
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- **Loss Function:** Quantile regression |
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- **Training Hardware:** 2 NVIDIA RTX 6000 |
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- **Hyperparameters:** Not exhaustively tuned |
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## Performance |
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- **Evaluation Metrics:** Bias, Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient |
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- **Benchmark Comparisons:** Compared against ground-based radar. |
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## Intended Use |
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- **Primary Use Case:** Satellite-based precipitation retrieval for weather and climate applications |
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- **Potential Applications:** Hydrology, extreme weather forecasting, climate research |
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- **Usage Recommendations:** Performance may vary across different climate regimes |
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## Ethical Considerations |
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- **Bias Mitigation:** Extensive validation against independent datasets |
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## Contact |
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For questions see corresponding author in reference. |
