sanakel/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 (OSE) 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 the Cryosat-2 altimeter data are not taken in the mapping to perform 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 reconstruction 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(RMSEs)	σ(RMSEs)	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
BFN	0.88	0.06	122	BFN mapping	example_eval_bfn.ipynb

µ(RMSE_s): average RMSE score (best score := 1, worse score := 0).
σ(RMSE_s): 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.

To work with code and demo notebooks using conda, execute the following command: conda env create -f environment.yml

Download the data

The data are hosted on the AVISO+ website and tagged with DOI: XXXXXXXXXXXXXXXXXX. The website also provides a data handbook. This wiki can help you create an AVISO account to access the data. The demo notebook example_data_access_aviso.ipynb shows how to access and manipulate the data with python libraries.

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.