Join us for this exciting talk!

Abstract

https://www.ghrsst.org/ghrsst-talks/27-july-2023-ghrsst-talk-with-marouan-bouali-orbty-work-on-sst/

Time averaged (3 days, weekly, monthly) composite maps of satellite-based geophysical parameters may display artifacts that result from the temporal variability of the geophysical variable under consideration, sensor calibration errors or uncertainties in Level 2 retrieval algorithms. The presence of such spatial discontinuities can affect downstream applications including the spatio-temporal analysis of gradients. Using computer vision principles, ORBTY has recently developed the Metocean Multi-Dimentional Dynamic Data Fusion System (M3DFS), a new methodology that seamlessly ingests multitemporal Level 2 datasets from a single or multiple instruments to produce Level 3 maps of Metocean parameters with significantly improved spatio-temporal representation.

In this presentation, a brief overview of the M3DFS technical foundation is provided.

Results from the application of the M3DFS for the generation of Level 3C (collated) and Level 3S (super-collated) Sea Surface Temperature (SST) from microwave and Infrared instruments are illustrated along with the impact on the spatial distribution and temporal variability of SST gradients.

Our speaker

Dr. Bouali is member of the GHRSST international science team. Dr. Bouali is Chief Scientific Officer (CSO) and Senior Remote Sensing scientist at Orbty Ltda., São José dos Campos, Brazil. Within the GHRSST framework, his research interests include satellite SST gradients (intercomparison and validation with in situ data), cloud detection algorithms, sensor noise correction and computer vision/machine learning methods for the fusion of multi-mission SST datasets. He is currently working on new techniques for the generation of Metocean Level 3C/3S/4 products from microwave and infrared satellite instruments.

ORBTY work on Sea Surface Temperature from the satellite constellation