ocean-data-challenges/2021a_ssh_mapping_ose

A challenge on the mapping of real satellite altimeter sea surface height data organised by MEOM@IGE, Ocean-Next and CLS.

SSH Mapping Data Challenge 2021a

This repository contains codes and sample notebooks for downloading and processing the SSH mapping data challenge.

The quickstart can be run online by clicking here:

Motivation

The goal is to investigate how to best reconstruct sequences of Sea Surface Height (SSH) maps from partial satellite altimetry observations. This data challenge follows an Observation System Experiment framework: Satellite observations are from real sea surface height data from altimeter. The practical goal of the challenge is to investigate the best mapping method according to scores described below and in Jupyter notebooks.

Observations

The SSH observations include SARAL/Altika, Jason 2, Jason 3, Sentinel 3A, Haiyang-2A and Cryosat-2 altimeter data. This nadir altimeters constellation was operating during the 20170101-20171231 period. Note that for the mapping the Cryosat-2 altimeter data are not taken in the mapping to perfor the independent assessment of the various reconstructions.

Data sequence and use

The SSH reconstructions are assessed over the period from 2017-01-01 to 2017-12-31.

For reconstruction methods that need a spin-up, the observations can be used from 2016-12-01 until the beginning of the evaluation period (31 days). This spin-up period is not included in the evaluation. For reconstruction methods that need learning from full fields, the baseline reconstruction or duacs reconstrcution can be used from 2017-01-01 to 2017-12-31. The altimeter data from Cryosat-2 should never be used so that any reconstruction can be considered uncorrelated to the evaluation period.

Leaderboard

Method	µ(RMSE)	σ(RMSE)	λx (km)	Notes	Reference
BASELINE	0.85	0.09	140	Covariances BASELINE OI	example_eval_baseline.ipynb
DUACS	0.88	0.07	152	Covariances DUACS DT2018	example_eval_duacs.ipynb
MIOST	0.89	0.08	139	Multiscale mapping	example_eval_miost.ipynb
DYMOST	0.89	0.06	129	Dynamic mapping	example_eval_dymost.ipynb
BNF	0.88	0.06	122	BFN mapping	example_eval_bfn.ipynb
4DVarNet (v2021)	0.89	0.06	122	4DVarNet mapping	example_eval_4DVarnet.ipynb
4DVarNet (v2022)	0.89	0.09	109	4DVarNet mapping	example_eval_4DVarnet_v2022.ipynb
4DVarQG	0.90	0.06	106	4DVarQG mapping	example_eval_4dvarqg.ipynb

µ(RMSE): average RMSE score.
σ(RMSE): standard deviation of the RMSE score.
λx: minimum spatial scale resolved.

Quick start

You can follow the quickstart guide in this notebook or launch it directly from binder.

Download the data

The data are hosted on the AVISO+ website and tagged with DOI: 10.24400/527896/a01-2021.005. The website also provides a data handbook. This is the recommended access. This wiki can help you create an AVISO account to access the data. The data are also temporarily available here. They are presented with the following directory structure:

. 
|-- dc_obs
|   |-- dt_global_alg_phy_l3_20161201-20180131_285-315_23-53.nc
|   |-- dt_global_c2_phy_l3_20161201-20180131_285-315_23-53.nc 
|   |-- dt_global_h2g_phy_l3_20161201-20180131_285-315_23-53.nc
|   |-- dt_global_j2g_phy_l3_20161201-20180131_285-315_23-53.nc
|   |-- dt_global_j2n_phy_l3_20161201-20180131_285-315_23-53.nc
|   |-- dt_global_j3_phy_l3_20161201-20180131_285-315_23-53.nc
|   |-- dt_global_s3a_phy_l3_20161201-20180131_285-315_23-53.nc

|-- dc_maps
|   |-- OSE_ssh_mapping_BASELINE.nc
|   |-- OSE_ssh_mapping_BFN.nc
|   |-- OSE_ssh_mapping_DUACS.nc
|   |-- OSE_ssh_mapping_DYMOST.nc
|   |-- OSE_ssh_mapping_MIOST.nc
|   |-- OSE_ssh_mapping_4dvarNet.nc
|   |-- OSE_ssh_mapping_4dvarNet_2022.nc (NOT on AVISO+ yet !!!!)
|   |-- mdt.nc

Baseline and evaluation

Baseline

The baseline mapping method is optimal interpolation (OI), in the spirit of the present-day standard for DUACS products provided by AVISO. OI is implemented in the baseline_oi Jupyter notebook. The SSH reconstructions are saved as a NetCDF file in the results directory. The content of this directory is git-ignored.

Evaluation

The evaluation of the mapping methods is based on the comparison of the SSH reconstructions with the independent Cryosat-2 along-track dataset. It includes two scores, one based on the Root-Mean-Square Error (RMSE), the other based on Fourier wavenumber spectra. The evaluation notebook example_data_eval implements the computation of these two scores as they could appear in the leaderboard. The notebook also provides additional, graphical diagnostics based on RMSE and spectra.

Data processing

Cross-functional modules are gathered in the src directory. They include tools for regridding, plots, evaluation, writing and reading NetCDF files. The directory also contains a module that implements the baseline method.

Acknowledgement

The structure of this data challenge was to a large extent inspired by WeatherBench.

2021a_ssh_mapping_ose's Issues

UnicodeDecodeError

hello,i download the code about 2021a_SSH_mapping_OSE-master,but when i run it showed "UnicodeDecodeError: 'ascii' codec can't decode byte 0x8b in position 1: ordinal not in range(128)" . I dont know where to change decode. Thanks

Pb url cryosat2

Bonjour, merci pour ces données et ce challenge très intéressant,
Je reporte juste une petite faute de frappe aux conséquences potentiellement importantes. Dans le Notebook d'exemple d'accès aux données d'aviso l'url des données cryosat est incorrecte.
Merci beaucoup pour ce travail dans tout les cas.

Théo Archambault
Doctorant Sorbonne Université LIP6

TODO

• Put the along-track data & the various gridded SSH reconstructions on AVISO+ server
• Update notebooks with correct file links
• Update README
• Make DEMO Quickstart with: reading some along-track, baseline mapping over few month and validation with independent dataset
• Add test case with DUACS-DT2021 OI parameters
• Update requirements.txt
• Put correct DOI
• Add archive to zenodo + get DOI
• Add binder demo
• Handbook AVISO+
• Test AVISO+ data accessibility when not connected to CLS network
• webpage AVISO+
• Add info for managing env: conda env create -f environment.yml
• Overall review