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license: mit |
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--- |
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# Model Card: GPROF-NN 3D |
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## Model Details |
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- **Model Name:** GPROF-NN 3D |
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- **Developer:** Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow |
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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_nn |
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## Model Description |
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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. |
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### Inputs |
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- Brightness temperatures from passive microwave sensors |
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- Earth incidence angles |
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- Ancillary atmospheric and surface state information (e.g., surface temperature, humidity) |
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### Outputs |
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- Surface precipitation estimates |
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- Hydrometeor profiles |
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## Training Data |
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- **Training Data Source:** Satellite-based observations and collocated ground truth precipitation estimates (e.g., GPM DPR, rain gauges, reanalysis data) |
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- **Data Preprocessing:** Normalization, quality control, and augmentation techniques applied to enhance generalization |
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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:** 1 A100 GPU |
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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, Symmetric Mean Absolute Percentage Error (SMAPE) |
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- **Benchmark Comparisons:** Compared against conventional GPROF algorithm. |
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- **Strengths:** Lower errors, higher correlation, higher effective resolution |
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- **Limitations:** Sensitivity to sensor-specific biases |
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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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## How to Use |
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See the external model implementation available from the [IPWG ML working group model repository](github.com/ipwgml/ipwgml_models). |
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## Citation |
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If you use GPROF-NN 3D in your research, please cite: |
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```bibtex |
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@Article{amt-17-515-2024, |
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AUTHOR = {Pfreundschuh, S. and Guilloteau, C. and Brown, P. J. and Kummerow, C. D. and Eriksson, P.}, |
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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}, |
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JOURNAL = {Atmospheric Measurement Techniques}, |
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VOLUME = {17}, |
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YEAR = {2024}, |
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NUMBER = {2}, |
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PAGES = {515--538}, |
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URL = {https://amt.copernicus.org/articles/17/515/2024/}, |
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DOI = {10.5194/amt-17-515-2024} |
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} |
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``` |
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## Contact |
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For questions see corresponding author in reference. |
