---
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.