A challenge on the mapping of real nadirs and KaRin satellite altimeter sea surface height data organised by Datlas and CLS.
Check out the data challenge website for more infos
-
Ocean Data Challenges [https://github.com/ocean-data-challenges]
- Artificial SWOT data calibration (Gulf Stream): 2022c_SWOT_error_calibration_GS
- Artificial SWOT data denoising (North Atlantic): 2022a_SWOT_karin_error_filtering
- Artificial SWOT data mapping (Gulf Stream): 2020a_SSH_mapping_NATL60
- Real SWOT data denoising (multiple regions) -- under construction: 2024b_DC_SWOTdenoising
- Real SWOT data mapping (global) -- under construction: 2024_DC_SSH_mapping_SWOT_OSE
-
SWOT Swell [https://github.com/ardhuin/swellSWOT/]
This repository contains codes and sample notebooks for downloading and processing the 2024 SWOT SSH mapping data challenge. Note that this data challenge is a somewhat extended version of the data challenge 2023a_SSH_mapping_OSE on a global scale. Here, we are using nadirs altimeter data and the new Level-3 Karin data available since August 2023.
Real-time observation of ocean surface topography is essential for various oceanographic applications. Historically, these observations relied mainly on satellite nadir altimetry data, which were limited to observe scales greater than approximately 60 km. However, the recent launch of the wide-swath SWOT mission in December 2022 marks a significant advancement, enabling the two-dimensional observation of finer oceanic scales (~15 km). While the direct analysis of the two-dimensional content of these swaths can provide valuable insights into ocean surface dynamics, integrating such data into mapping systems presents several challenges. This data-challenge focuses on integrating the SWOT mission into multi-mission mapping systems. Specifically, it examines the contribution of the SWOT mission to both the current nadir altimetry constellation (seven nadirs) and a reduced nadir altimetry constellation (three nadirs).
Several mapping techniques, such as statistical interpolation methods or ocean model assimilation methods, are currently proposed to provide operational maps of ocean surface heights and currents. New mapping techniques (e.g. data-driven methods) are emerging and being tested in a research and development context. It is therefore becoming important to inform users and developers about the accuracy of scale represented by each mapping system. A sensitivity study of different mapping methods in a SWOT context is also proposed.
Figure: Example of geostrophic current reconstruction on 2023-08-31 with MIOST at a) global scale, b) view from Karin L3 products over the Agulhas region, c) from MIOST reconstruction integration 1SWOT and 6 nadirs, d) from MIOST reconstruction integration 6 nadirs and e) the difference in MIOST reconstructions between integration 1SWOT and 6 nadirs vs 6 nadirs only
The goal of the present data-challenge is:
- to investigate the contribution of SWOT KaRin data in global & regional mapping systems
- to investigate how to best reconstruct sequences of Sea Surface Height (SSH) from partial nadir and KaRin satellite altimetry observations and in using various mapping method (dynamical, data-driven approach...)
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 contribution of SWOT KaRin data and the best mapping method according to scores described below and in Jupyter notebooks.
To produce the gridded sea level maps, we used the global ocean sea level anomaly observations from the Near-Real-Time (NRT) Level-3 altimeter satellite along-track data distributed by the EU Copernicus Marine Service (product reference SEALEVEL_GLO_PHY_L3_NRT_008_044), specifically for the Jason-3, Sentinel-3A, Sentinel-3B, Sentinel-6A, SARAL-Altika, Cryosat-2, Haiyang-2B, missions. This dataset covers the global ocean and is available at a sampling rate of 1 Hz (approximately 7 km spatial spacing).
In addition to the nadir altimetry constellation previously mentioned, we conducted experiments involving the integration of SWOT Level-3 Ocean product (specifically referencing SWOT_L3_SSH) during the 21-day phase of the mission. The SWOT_L3_SSH product combines ocean topography measurements collected from both the SWOT KaRIn and nadir altimeter instruments, consolidating them into a unified variable on a 2 km spatial grid spacing. For our investigation, we used version 0.3 & version 1.0 of the product accessible through the AVISO+ portal (AVISO/DUACS, 2023). These data were derived from the Level-2 "Beta Pre-validated" KaRIn Low Rate (Ocean) product (NASA/JPL and CNES).
Figure: Spatial sampling of a) 3 nadirs altimeters, b) 7 nadirs altimeters and c) 1 SWOT
The SSH reconstructions are assessed at global scale and over the period from 2023-08-01 to 2024-05-01. The SSH reconstructions can also be assessed at regional scale and over the same period from 2023-08-01 to 2024-05-01.
For reconstruction methods that need a spin-up, the observations from other period can be used.
The altimeter data from Saral/AltiKa data mentioned above should never be used so that any reconstruction can be considered uncorrelated to the evaluation period.
💻 How to get started ?
Clone the data challenge repo:
git clone https://github.com/ocean-data-challenges/2024_DC_SSH_mapping_SWOT_OSE.git
or using SSH:
git clone git@github.com:ocean-data-challenges/2024_DC_SSH_mapping_SWOT_OSE.git
create the data challenge conda environment, named env-dc-global-ose, by running the following command:
conda env create --file=dc_environment.yml
and activate it with:
conda activate env-dc-global-ose
then add it to the available kernels for jupyter to see:
ipython kernel install --name "env-dc-global-ose" --user
finally, select the "env-dc-global-ose" kernel in your notebook with Kernel > Change Kernel.
You're now good to go !
If you are only interested in regional data, a notebook is available to read online the global data and download only regional data:
The dataset is presented with the following directory structure:
.
|-- alongtrack
.
|-- independant_alongtrack
| |-- al % DT Altika Drifting Phase Global Ocean Along track SSALTO/DUACS Sea Surface Height L3 product
| | |-- 2023
| | | |-- nrt_global_al_phy_l3_1hz_2023*.nc
| | |-- 2024
| | | |-- nrt_global_al_phy_l3_1hz_2024*.nc
.
|-- sad
| |-- distance_to_nearest_coastline_60.nc
| |-- land_water_mask_60.nc
| |-- variance_cmems_dt_allsat.nc
.
|-- maps
| |-- mapping_miost_s3a_s3b_s6a-hr % MIOST reconstruction 3 nadirs
| |-- mapping_miost_s3a_s3b_s6a-hr_swot % MIOST reconstruction 3 nadirs + 1 SWOT
| |-- mapping_miost_c2n_h2b_j3n_s3a_s3b_s6a-hr % MIOST reconstruction 6 nadirs
| |-- mapping_miost_c2n_h2b_j3n_s3a_s3b_s6a-hr % MIOST reconstruction 6 nadirs + 1 SWOT
The data can be downloaded locally using the wget command. We recommand that the data be stored in the data/ repository.
For example, to download and unzip the experiment alongtrack data:
CHANGE: TODO
cd data/
wget https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/fileServer/meomopendap/extract/MEOM/OCEAN_DATA_CHALLENGES/2023a_SSH_mapping_OSE/alongtrack/*
tar -xvf alongtrack.tar.gz
rm -f alongtrack.tar.gz
The mapping methods are evaluated against independent data using two independant datasets:
Independant nadir SSH data: Check example 1
The ocean surface topography reconstruction is compared with independant data from Saral/AltiKa altimeter. The metrics available using this independant dataset are:
- Grid boxes statistics (maps)
- Statistics by regimes (scalar scores)
- Spectral effective resolution (maps)
Cross-functional modules are gathered in the src directory. They include tools for regridding, plots, evaluation, writing and reading NetCDF files.