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README.md

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