Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+# Model Card: GPROF-NN 3D
+
+## Model Details
+**Model Name:** GPROF-NN 3D  
+**Developer:** Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow
+**License:** MIT 
+**Model Type:** Neural Network for Precipitation Retrieval  
+**Language:** Not applicable
+**Framework:** PyTorch
+**Repository:** github.com/simonpf/gprof_nn
+
+## Model Description
+GPROF-NN 3D a precipitation retrieval algorithm for passive microwave (PMW) observations for the sensors of the GPM constellation. It is based on a convolutional neural network leveraging both spatial (2D) and spectral (+1D) information. The version provided here is an early prototype of the model that will become GPROF V8.
+### Inputs
+- Brightness temperatures from passive microwave sensors
+- Earth incidence angles
+- Ancillary atmospheric and surface state information (e.g., surface temperature, humidity)   
+
+### Outputs
+- Surface precipitation estimates
+- Hydrometeor profiles
+
+## Training Data
+- **Training Data Source:** Satellite-based observations and collocated ground truth precipitation estimates (e.g., GPM DPR, rain gauges, reanalysis data)  
+- **Data Preprocessing:** Normalization, quality control, and augmentation techniques applied to enhance generalization  
+
+## Training Procedure
+- **Optimizer:** AdamW 
+- **Loss Function:** Quantile regression
+- **Training Hardware:** 1 A100 GPU 
+- **Hyperparameters:** Not exhaustively tuned 
+
+## Performance
+- **Evaluation Metrics:** Bias, Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient, Symmetric Mean Absolute Percentage Error (SMAPE)
+- **Benchmark Comparisons:** Compared against conventional GPROF algorithm.
+- **Strengths:** Lower errors, higher correlation, higher effective resolution
+- **Limitations:** Sensitivity to sensor-specific biases  
+
+## 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  
+
+
+## How to Use
+
+See the external model implementation available from the [IPWG ML working group model repository](github.com/ipwgml/ipwgml_models).
+
+## Citation
+If you use GPROF-NN 3D in your research, please cite:
+```bibtex
+@Article{amt-17-515-2024,
+AUTHOR = {Pfreundschuh, S. and Guilloteau, C. and Brown, P. J. and Kummerow, C. D. and Eriksson, P.},
+TITLE = {GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean},
+JOURNAL = {Atmospheric Measurement Techniques},
+VOLUME = {17},
+YEAR = {2024},
+NUMBER = {2},
+PAGES = {515--538},
+URL = {https://amt.copernicus.org/articles/17/515/2024/},
+DOI = {10.5194/amt-17-515-2024}
+}
+```
+
+## Contact
+For questions see corresponding author in reference.