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Super Resolution for the NECCTON project. A Neural Network allows to go from a low resolution field to a high resolution field to do data assimilation in the HR space, to then go back in the LR dimension to run a LR model. To do so, the Neural Network computes the residuals it needs to add to a bilinear upsampling of the LR field to get the true HR field.
To run the super resolution algorithm and compare the result to a bsaline, open Test_ResUnet.ipynb and run all the cells
The data required to run the Notebook are:
- High resolution fields (for validation)
- Low resolution fields upsampled to the the HR grid as an input of the neural network
- The High resolution land mask
- The weights of the NN
The HR and LR fields result from a run from the Topaz 5 and Topaz 2 data assimilation system respectively, which relies on a coupled ocean / sea-ice model (HYCOM / CICE) over the Arctic and the North Atlantic sea. They are available through FTP (details provided in the code). For now, only the Sea Surface Temperature variable is available.
The superresolution with the residual U-Net is compared to bilinear interpolation.
The performance of the super-resolution model is evaluated using the following metrics:
Bias indicates the systematic error in predictions, showing whether the model tends to overestimate or underestimate the true values. It is computed as the mean difference between predicted and true values. A bias close to zero suggests balanced predictions, while a significant bias indicates a systematic tendency in the model's predictions.
RMSE quantifies the difference between predicted and true values. It measures the average magnitude of the errors without considering their direction. Lower RMSE values indicate better model performance.
PSNR measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. It is simply a scaled log transformation of the MSE, but is more commonly used in Super Resolution to quantify the quality of the results. The higher the PSNR value, the better the quality of the reconstruction.
SSIM measures the similarity between two images. It considers three components: luminance, contrast, and structure. SSIM values range from -1 to 1, with 1 indicating perfect similarity. Higher SSIM values suggest better preservation of image quality.
- Python version: 3.10.5
- TensorFlow version: 2.8.0
- abfile module (available in this repo)
- netCDF4 version: 1.5.8
(To be filled)