TY - GEN
T1 - Real-Time Validation of Operational Ocean Models Via Eddy-Decting Deep Neural Networks
AU - Moschos, Evangelos
AU - Stegner, Alexandre
AU - Le Vu, Briac
AU - Schwander, Olivier
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Surface currents provided, in real time, by operational ocean models often differ from each other but also from satellite altimetry observations, especially in terms of mesoscale dynamics. Eddies, which play a dominant role on circulation at the regional scale, have a signature on both altimetry maps and satellite imagery, such as sea surface temperature. Combining these independent signatures allows for a highly reliable detection of reference eddies. To this end, we build a convolutional neural network capable of detecting the contours of mesoscale eddies on SST maps in real time. Combined with a standard eddy detection algorithm applied to altimetry maps, we were able to locate and identify with high accuracy more than 900 eddies, in the Mediterranean Sea, over a period of 6 months, and use them as a reference for numerical model validation. We compare as a case study the performance of two operational models: MERCATOR and MFS.
AB - Surface currents provided, in real time, by operational ocean models often differ from each other but also from satellite altimetry observations, especially in terms of mesoscale dynamics. Eddies, which play a dominant role on circulation at the regional scale, have a signature on both altimetry maps and satellite imagery, such as sea surface temperature. Combining these independent signatures allows for a highly reliable detection of reference eddies. To this end, we build a convolutional neural network capable of detecting the contours of mesoscale eddies on SST maps in real time. Combined with a standard eddy detection algorithm applied to altimetry maps, we were able to locate and identify with high accuracy more than 900 eddies, in the Mediterranean Sea, over a period of 6 months, and use them as a reference for numerical model validation. We compare as a case study the performance of two operational models: MERCATOR and MFS.
KW - Deep Learning
KW - Mesoscale Eddies
KW - Model Validation
KW - Remote Sensing
U2 - 10.1109/IGARSS46834.2022.9883253
DO - 10.1109/IGARSS46834.2022.9883253
M3 - Conference contribution
AN - SCOPUS:85140389485
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 8008
EP - 8011
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -