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| license: mit |
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| # Model Card: GPROF-NN 3D |
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| ## 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) |
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| ### Outputs |
| - Surface precipitation estimates |
| - Hydrometeor profiles |
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| ## 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 |
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| ## 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 |
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| ## Ethical Considerations |
| - **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 |
| 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. |
