SWOTmodule

Documentation

The complete documentation of this module can be found : here [April 2023: not updated]

Description

This module is a collection of tools used to read data from the SWOT simulator, filter it and save it in an output file.

• Codes:

  • SWOTdenoise.cfg: Configuration file called by SWOTdenoise.py. Used to specify the name of the variables of the input file to be filtered.

  • SWOTdenoise.py: Module to read SWOT data, filter it and save it in a new output file or obtain the SSH de-noised variable.

• Example data: The two examples below are SWOT simulated passes from the NATL60 model, generated for the fast-sampling phase in the western Mediterranean Sea.

  • MED_fastPhase_1km_swotFAST_c01_p009.nc: example SWOT dataset directly out of SWOT simulator (version 2.21)

  • MED_1km_nogap_JAS12_swotFastPhase_BOX_c01_p009_v2.nc: example SWOT dataset subregion (box_dataset) used in paper Gomez-Navarro et al. (in review).

• Example notebooks:

  • discover-SWOTmodule.ipynb : Example of using SWOTdenoise module with the SWOT simulator output netcdfs

  • discover-SWOTmodule_box_dataset.ipynb : Example of using SWOTdenoise module with modified SWOT simulator output netcdfs. In this case the dataset used in the study Gomez-Navarro et al. (2020). [April 2023: this notebook has not been updated, contrary to the first one.]

Denoising methods

• Convolution filters: boxcar and Gaussian kernels
• Bilateral filter (Milanfar, 2013)
• Variational regularization filter (Gomez Navarro et al, 2020)

How to install the module :

• Clone this repository :

git clone https://github.com/meom-group/SWOTmodule.git

• Create and activate the swotmod conda environment :

conda env create -f env_swotmod.yml
conda activate swotmod

• Add the swotmod kernel to jupyter :

python -m ipykernel install --user --name swotmod --display swotmod

How to use the module :

• Launch the demonstration notebook
• Modify the SWOTdenoise.cfg for your own use