Type of dataset: SWOT Sea Surface Height Data Products (SWOT_L3_LR_SSH)

Digital Object Identifiers (DOIs): 

• SWOT_L3_LR_SSH_Basic: 10.24400/527896/A01-2023.017 - more metadata 
• SWOT_L3_LR_SSH_Expert: 10.24400/527896/A01-2023.018  - more metadata
• SWOT_L3_LR_SSH_Unsmoothed: 10.24400/527896/A01-2024.003  - more metadata

Condition of access: SWOT_L3_LR_SSH is distributed by AVISO under standard AVISO+ license agreement.

Contents: The SWOT_L3_LR_SSH product provides ocean topography measurements obtained from the SWOT KaRIn and Nadir altimeter instruments, merged into a single variable. The dataset includes measurements from KaRIn swaths on both sides of the image, while the measurements from the Nadir altimeter are located in the central columns. In the areas between the Nadir track and the two KaRIn swaths, as well as on the outer edges of each swath (restricted to cross-track distances ranging from 10 to 60 km), default values are expected.

Use: SWOT_L3_LR_SSH is a cross-calibrated product from multiple missions that contains only the ocean topography content necessary for thematic research (e.g., oceanography, geodesy) and related applications. This product is designed to be simple and ready-to-use, and can be combined with other altimetry missions. The SWOT_L3_LR_SSH product is a research-orientated extension of the L2_LR_SSH product, distributed by the SWOT project (NASA/JPL and CNES). SWOT_L3_LR_SSH is managed by the SWOT Science Team project DESMOS.

Description: The SWOT_L3_LR_SSH product is organized in 3 subproducts:

• Basic L3_LR_SSH (or lightweight), which includes only SSHA (Sea Surface Height Anomaly) and mean dynamic topography.
• Expert L3_LR_SSH (or extended), which includes the backscatter coefficient (sigma0), the mean sea surface (MSS) and geostrophic currents (absolute and anomalies) in addition to SSHA and mean dynamic topography (MDT). It also integrates a quality flag, corrections and external models as separate layers.
• Unsmoothed L3_LR_SSH: which includes the MSS, MDT and geostrophic currents (absolute and anomalies) in addition to the SSHA and MDT on the 250 m KaRIn native grid. Like the Expert subproduct, it also integrates a quality flag, corrections and external models as separate layers.

Geographic coverage: global.

Citation Basic L3_LR_SSH: "The SWOT_L3_LR_SSH product, derived from the L2 SWOT KaRIn low rate ocean data products (NASA/JPL and CNES), is produced and made freely available by AVISO and DUACS teams as part of the DESMOS Science Team project". AVISO/DUACS, 2024. SWOT Level-3 KaRIn Low Rate SSH Basic (v1.0.2) [Data set]. CNES. https://doi.org/10.24400/527896/A01-2023.017 

Citation Expert L3_LR_SSH: "The SWOT_L3_LR_SSH product, derived from the L2 SWOT KaRIn low rate ocean data products (NASA/JPL and CNES), is produced and made freely available by AVISO and DUACS teams as part of the DESMOS Science Team project". AVISO/DUACS, 2024. SWOT Level-3 KaRIn Low Rate SSH Expert (v1.0.2) [Data set]. CNES. https://doi.org/10.24400/527896/A01-2023.018 

Citation Unsmoothed L3_LR_SSH: "The SWOT_L3_LR_SSH product, derived from the L2 SWOT KaRIn low rate ocean data products (NASA/JPL and CNES), is produced and made freely available by AVISO and DUACS teams as part of the DESMOS Science Team project". AVISO/DUACS, 2024. SWOT Level-3 KaRIn Low Rate SSH Unsmoothed (v1.0.2) [Data set]. CNES. https://doi.org/10.24400/527896/A01-2024.003 

Resources:

• SWOT_L3_LR_SSH User Handbook
• SWOT Level-3 L3_LR_SSH Product Frequently Asked Questions
• Dibarboure, G., Anadon, C., Briol, F., Cadier, E., Chevrier, R., Delepoulle, A., Faugère, Y., Laloue, A., Morrow, R., Picot, N., Prandi, P., Pujol, M.-I., Raynal, M., Treboutte, A., and Ubelmann, C.: Blending 2D topography images from SWOT into the altimeter constellation with the Level-3 multi-mission DUACS system, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1501 , 2024.
• Ubelmann, C.; Dibarboure, G.; Flamant, B.; Delepoulle, A.; Vayre, M.; Faugère, Y.; Prandi, P.; Raynal, M.; Briol, F.; Bracher, G.; et al. Data-Driven Calibration of SWOT’s Systematic Errors: First In-Flight Assessment. Remote Sens. 2024, 16, 3558. https://doi.org/10.3390/rs16193558 
• Dibarboure, G.; Ubelmann, C.; Flamant, B.; Briol, F.; Peral, E.; Bracher, G.; Vergara, O.; Faugère, Y.; Soulat, F.; Picot, N. Data-Driven Calibration Algorithm and Pre-Launch Performance Simulations for the SWOT Mission. Remote Sens. 2022, 14, 6070. https://doi.org/10.3390/rs14236070 
• Tréboutte, A.; Carli, E.; Ballarotta, M.; Carpentier, B.; Faugère, Y.; Dibarboure, G. KaRIn Noise Reduction Using a Convolutional Neural Network for the SWOT Ocean Products. Remote Sens. 2023, 15, 2183. https://doi.org/10.3390/rs15082183 
• Dibarboure et al., Poster at SWOT Science Team 2023, SWOT Level-3 Overview algorithms and examples
• Dibarboure et al., Presentation at SWOT Science Team 2023, SWOT Level-3 Overview algorithms and examples

Tutorials:

• GitHub SWOT community (see https://github.com/SWOT-community/CNES-AVISO)