---
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.